diff --git a/archived/pyg_construction_demo.ipynb b/archived/pyg_construction_demo.ipynb
index b0cdb2d521e223aeaf5e7fcece8f6a177acf6c19..d326ac998ceeb2253bc4dc8365145bc3d53acaf7 100644
--- a/archived/pyg_construction_demo.ipynb
+++ b/archived/pyg_construction_demo.ipynb
@@ -245,10 +245,10 @@
}
],
"source": [
- "import torch_geometric.transforms as T\n",
- "graph_out = graph_constructor.construct(questions_df, answers_df, comments_df)\n",
- "#graph_out['module'].x = torch.empty(0, 110, dtype=torch.long)\n",
- "graph_out"
+ " import torch_geometric.transforms as T\n",
+ " graph_out = graph_constructor.construct(questions_df, answers_df, comments_df)\n",
+ " #graph_out['module'].x = torch.empty(0, 110, dtype=torch.long)\n",
+ " graph_out"
],
"metadata": {
"collapsed": false
diff --git a/embeddings/.nfs00000002b780ee7100000052 b/embeddings/.nfs00000002b780ee7100000052
new file mode 100644
index 0000000000000000000000000000000000000000..eab483f72e62335e658f2e597e8c3c06b8c1ae9e
--- /dev/null
+++ b/embeddings/.nfs00000002b780ee7100000052
@@ -0,0 +1,576 @@
+import json
+import logging
+import os
+import string
+import time
+
+import networkx as nx
+import pandas as pd
+import plotly
+import torch
+from sklearn.metrics import f1_score, accuracy_score
+from torch_geometric.loader import DataLoader
+from torch_geometric.nn import HeteroConv, GATv2Conv, GATConv, Linear, global_mean_pool, GCNConv, SAGEConv, GraphConv
+from helper_functions import calculate_class_weights, split_test_train_pytorch
+import wandb
+from torch_geometric.utils import to_networkx
+import torch.nn.functional as F
+from sklearn.model_selection import KFold
+from torch.optim.lr_scheduler import ExponentialLR
+import pickle
+
+from custom_logger import setup_custom_logger
+from dataset import UserGraphDataset
+from dataset_in_memory import UserGraphDatasetInMemory
+from Visualize import GraphVisualization
+import helper_functions
+from hetero_GAT_constants import OS_NAME, TRAIN_BATCH_SIZE, TEST_BATCH_SIZE, IN_MEMORY_DATASET, INCLUDE_ANSWER, USE_WANDB, WANDB_PROJECT_NAME, NUM_WORKERS, EPOCHS, NUM_LAYERS, HIDDEN_CHANNELS, FINAL_MODEL_OUT_PATH, SAVE_CHECKPOINTS, WANDB_RUN_NAME, CROSS_VALIDATE, FOLD_FILES, USE_CLASS_WEIGHTS_SAMPLER, USE_CLASS_WEIGHTS_LOSS, DROPOUT, GAMMA, START_LR, PICKLE_PATH_KF, ROOT, TRAIN_DATA_PATH, TEST_DATA_PATH, WARM_START_FILE, MODEL, REL_SUBSET
+
+log = setup_custom_logger("heterogenous_GAT_model", logging.INFO)
+
+if OS_NAME == "linux":
+ torch.multiprocessing.set_sharing_strategy('file_system')
+ import resource
+ rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
+ resource.setrlimit(resource.RLIMIT_NOFILE, (2048, rlimit[1]))
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+log.info(f"Proceeding with {device} . .")
+print("HIII")
+
+
+"""
+G
+A
+T
+"""
+
+class HeteroGAT(torch.nn.Module):
+ """
+ Heterogeneous Graph Attentional Network (GAT) model.
+ """
+ def __init__(self, hidden_channels, out_channels, num_layers):
+ super().__init__()
+ log.info("MODEL: GAT")
+
+ self.convs = torch.nn.ModuleList()
+
+ # Create Graph Attentional layers
+ for _ in range(num_layers):
+ conv = HeteroConv({
+ ('tag', 'describes', 'question'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('tag', 'describes', 'answer'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('tag', 'describes', 'comment'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('module', 'imported_in', 'question'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('module', 'imported_in', 'answer'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('question', 'rev_describes', 'tag'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('answer', 'rev_describes', 'tag'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('comment', 'rev_describes', 'tag'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('question', 'rev_imported_in', 'module'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('answer', 'rev_imported_in', 'module'): GATConv((-1, -1), hidden_channels, add_self_loops=False),
+ }, aggr='sum')
+ self.convs.append(conv)
+
+ self.lin1 = Linear(-1, hidden_channels)
+ self.lin2 = Linear(hidden_channels, out_channels)
+ self.softmax = torch.nn.Softmax(dim=-1)
+
+ def forward(self, x_dict, edge_index_dict, batch_dict, post_emb):
+<<<<<<< HEAD
+ x_dict = {key: x_dict[key] for key in x_dict.keys() if key in ["question", "answer", "comment", "tag", "module"]}
+
+
+=======
+ # Ablation study, keep only questions
+
+>>>>>>> 4f4ade9725679214f5f9a544a583641646835e7e
+ for conv in self.convs:
+ break
+ x_dict = conv(x_dict, edge_index_dict)
+ x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()}
+ x_dict = {key: F.dropout(x, p=DROPOUT, training=self.training) for key, x in x_dict.items()}
+
+ outs = []
+
+ for x, batch in zip(x_dict.values(), batch_dict.values()):
+ if len(x):
+ outs.append(global_mean_pool(x, batch=batch, size=len(post_emb)).to(device))
+ else:
+ outs.append(torch.zeros(1, x.size(-1)).to(device))
+
+
+ out = torch.cat(outs, dim=1).to(device)
+
+ out = torch.cat([out, post_emb], dim=1).to(device)
+
+ out = F.dropout(out, p=DROPOUT, training=self.training)
+
+
+ out = self.lin1(out)
+ out = F.leaky_relu(out)
+
+ out = self.lin2(out)
+ out = F.leaky_relu(out)
+
+ out = self.softmax(out)
+ return out
+
+
+
+"""
+G
+R
+A
+P
+H
+S
+A
+G
+E
+"""
+
+class HeteroGraphSAGE(torch.nn.Module):
+ """
+ Heterogeneous GraphSAGE model.
+ """
+ def __init__(self, hidden_channels, out_channels, num_layers):
+ super().__init__()
+ log.info("MODEL: GraphSAGE")
+ self.convs = torch.nn.ModuleList()
+
+ # Create Graph Attentional layers
+ for _ in range(num_layers):
+ conv = HeteroConv({
+ ('tag', 'describes', 'question'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('tag', 'describes', 'answer'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('tag', 'describes', 'comment'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('module', 'imported_in', 'question'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('module', 'imported_in', 'answer'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('question', 'rev_describes', 'tag'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('answer', 'rev_describes', 'tag'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('comment', 'rev_describes', 'tag'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('question', 'rev_imported_in', 'module'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ ('answer', 'rev_imported_in', 'module'): SAGEConv((-1, -1), hidden_channels, add_self_loops=False),
+ }, aggr='sum')
+ self.convs.append(conv)
+
+ self.lin1 = Linear(-1, hidden_channels)
+ self.lin2 = Linear(hidden_channels, out_channels)
+ self.softmax = torch.nn.Softmax(dim=-1)
+
+ def forward(self, x_dict, edge_index_dict, batch_dict, post_emb):
+ for conv in self.convs:
+ x_dict = conv(x_dict, edge_index_dict)
+ x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()}
+ x_dict = {key: F.dropout(x, p=DROPOUT, training=self.training) for key, x in x_dict.items()}
+
+ outs = []
+ for x, batch in zip(x_dict.values(), batch_dict.values()):
+ if len(x):
+ outs.append(global_mean_pool(x, batch=batch, size=len(post_emb)).to(device))
+ else:
+ outs.append(torch.zeros(1, x.size(-1)).to(device))
+
+
+ out = torch.cat(outs, dim=1).to(device)
+
+ out = torch.cat([out, post_emb], dim=1).to(device)
+
+ out = F.dropout(out, p=DROPOUT, training=self.training)
+
+
+ out = self.lin1(out)
+ out = F.leaky_relu(out)
+
+ out = self.lin2(out)
+ out = F.leaky_relu(out)
+
+ out = self.softmax(out)
+ return out
+
+"""
+G
+R
+A
+P
+H
+C
+O
+N
+V
+"""
+"""
+G
+A
+T
+"""
+
+class HeteroGraphConv(torch.nn.Module):
+ """
+ Heterogeneous GraphConv model.
+ """
+ def __init__(self, hidden_channels, out_channels, num_layers):
+ super().__init__()
+ log.info("MODEL: GraphConv")
+
+ self.convs = torch.nn.ModuleList()
+
+ # Create Graph Attentional layers
+ for _ in range(num_layers):
+ conv = HeteroConv({
+ ('tag', 'describes', 'question'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('tag', 'describes', 'answer'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('tag', 'describes', 'comment'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('module', 'imported_in', 'question'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('module', 'imported_in', 'answer'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('question', 'rev_describes', 'tag'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('answer', 'rev_describes', 'tag'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('comment', 'rev_describes', 'tag'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('question', 'rev_imported_in', 'module'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ ('answer', 'rev_imported_in', 'module'): GraphConv((-1, -1), hidden_channels, add_self_loops=False, heads=6),
+ }, aggr='sum')
+ self.convs.append(conv)
+
+ self.lin1 = Linear(-1, hidden_channels)
+ self.lin2 = Linear(hidden_channels, out_channels)
+ self.softmax = torch.nn.Softmax(dim=-1)
+
+ def forward(self, x_dict, edge_index_dict, batch_dict, post_emb):
+ x_dict = {key: x_dict[key] for key in x_dict.keys() if key in ["question", "answer", "comment", "tag", "module"]}
+
+
+ for conv in self.convs:
+ break
+ x_dict = conv(x_dict, edge_index_dict)
+ x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()}
+ x_dict = {key: F.dropout(x, p=DROPOUT, training=self.training) for key, x in x_dict.items()}
+
+ outs = []
+
+ for x, batch in zip(x_dict.values(), batch_dict.values()):
+ if len(x):
+ outs.append(global_mean_pool(x, batch=batch, size=len(post_emb)).to(device))
+ else:
+ outs.append(torch.zeros(1, x.size(-1)).to(device))
+
+
+ out = torch.cat(outs, dim=1).to(device)
+
+ out = torch.cat([out, post_emb], dim=1).to(device)
+
+ out = F.dropout(out, p=DROPOUT, training=self.training)
+
+
+ out = self.lin1(out)
+ out = F.leaky_relu(out)
+
+ out = self.lin2(out)
+ out = F.leaky_relu(out)
+
+ out = self.softmax(out)
+ return out
+
+"""
+T
+R
+A
+I
+N
+"""
+def train(model, train_loader):
+ running_loss = 0.0
+ model.train()
+
+ for i, data in enumerate(train_loader): # Iterate in batches over the training dataset.
+ data.to(device)
+
+ optimizer.zero_grad() # Clear gradients.
+
+ if INCLUDE_ANSWER:
+ # Concatenate question and answer embeddings to form post embeddings
+ post_emb = torch.cat([data.question_emb, data.answer_emb], dim=1).to(device)
+ else:
+ # Use only question embeddings as post embedding
+ post_emb = data.question_emb.to(device)
+ # post_emb.requires_grad = True
+
+ out = model(data.x_dict, data.edge_index_dict, data.batch_dict, post_emb) # Perform a single forward pass.
+
+ #y = torch.tensor([1 if x > 0 else 0 for x in data.score]).to(device)
+ loss = criterion(out, torch.squeeze(data.label, -1)) # Compute the loss.
+ loss.backward() # Derive gradients.
+ optimizer.step() # Update parameters based on gradients.
+
+ running_loss += loss.item()
+ if i % 5 == 0:
+ log.info(f"[{i + 1}] Loss: {running_loss / 5}")
+ running_loss = 0.0
+
+"""
+T
+E
+S
+T
+"""
+def test(loader):
+ table = wandb.Table(columns=["ground_truth", "prediction"]) if USE_WANDB else None
+ model.eval()
+
+ predictions = []
+ true_labels = []
+
+ cumulative_loss = 0
+
+ for data in loader: # Iterate in batches over the training/test dataset.
+ data.to(device)
+
+ if INCLUDE_ANSWER:
+ post_emb = torch.cat([data.question_emb, data.answer_emb], dim=1).to(device)
+ else:
+ post_emb = data.question_emb.to(device)
+
+ out = model(data.x_dict, data.edge_index_dict, data.batch_dict, post_emb) # Perform a single forward pass.
+
+ #y = torch.tensor([1 if x > 0 else 0 for x in data.score]).to(device)
+ loss = criterion(out, torch.squeeze(data.label, -1)) # Compute the loss.
+ cumulative_loss += loss.item()
+
+ # Use the class with highest probability.
+ pred = out.argmax(dim=1)
+
+ # Cache the predictions for calculating metrics
+ predictions += list([x.item() for x in pred])
+ true_labels += list([x.item() for x in data.label])
+
+ # Log table of predictions to WandB
+ if USE_WANDB:
+ #graph_html = wandb.Html(plotly.io.to_html(create_graph_vis(data)))
+
+ for pred, label in zip(pred, torch.squeeze(data.label, -1)):
+ table.add_data(label, pred)
+
+ # Collate results into a single dictionary
+ test_results = {
+ "accuracy": accuracy_score(true_labels, predictions),
+ "f1-score-weighted": f1_score(true_labels, predictions, average='weighted'),
+ "f1-score-macro": f1_score(true_labels, predictions, average='macro'),
+ "loss": cumulative_loss / len(loader),
+ "table": table,
+ "preds": predictions,
+ "trues": true_labels
+ }
+ return test_results
+
+
+"""
+M
+A
+I
+N
+"""
+if __name__ == '__main__':
+
+ if USE_WANDB:
+ log.info(f"Connecting to Weights & Biases . .")
+ if WANDB_RUN_NAME is None:
+ WANDB_RUN_NAME = f"run@{time.strftime('%Y%m%d-%H%M%S')}"
+ config = helper_functions.start_wandb_for_training(WANDB_PROJECT_NAME, WANDB_RUN_NAME)
+ config.hidden_channels = HIDDEN_CHANNELS
+ config.dropout = DROPOUT
+ config.epoch = EPOCHS
+ config.resampling = USE_CLASS_WEIGHTS_SAMPLER
+ config.class_weights = USE_CLASS_WEIGHTS_LOSS
+ config.include_answer = INCLUDE_ANSWER
+ config.scheduler = "EXP"
+ config.initial_lr = START_LR
+ config.gamma = GAMMA
+ config.batch_size = TRAIN_BATCH_SIZE
+
+
+ # Datasets
+ if IN_MEMORY_DATASET:
+ train_dataset = UserGraphDatasetInMemory(root=ROOT, file_name_out=TRAIN_DATA_PATH, question_ids=[])
+ test_dataset = UserGraphDatasetInMemory(root=ROOT, file_name_out=TEST_DATA_PATH, question_ids=[])
+ else:
+ dataset = UserGraphDataset(root=ROOT, skip_processing=True)
+ train_dataset, test_dataset = split_test_train_pytorch(dataset, 0.7)
+
+ if CROSS_VALIDATE:
+ print(FOLD_FILES)
+ folds = [UserGraphDatasetInMemory(root="../data", file_name_out=fold_path, question_ids=[]) for fold_path in FOLD_FILES]
+ kfold_results = []
+ for i in range(len(folds)):
+ test_fold = folds[i]
+ train_fold = torch.utils.data.ConcatDataset([fold for j, fold in enumerate(folds) if j != i])
+
+ log.info(f"Fold {i + 1} of {len(folds)}\n-------------------")
+
+ sampler = None
+ class_weights = calculate_class_weights(train_fold).to(device)
+
+ # Sample by class weight
+ if USE_CLASS_WEIGHTS_SAMPLER:
+ train_labels = [x.label for x in train_fold]
+ sampler = torch.utils.data.WeightedRandomSampler([class_weights[x] for x in train_labels], len(train_labels))
+
+ # Define data loaders for training and testing data in this fold
+ train_fold_loader = DataLoader(
+ train_fold,
+ batch_size=TRAIN_BATCH_SIZE,
+ sampler=sampler
+ )
+ test_fold_loader = DataLoader(
+ test_fold,
+ batch_size=TEST_BATCH_SIZE
+ )
+ # Model
+ model = HeteroGAT(hidden_channels=HIDDEN_CHANNELS, out_channels=2, num_layers=NUM_LAYERS)
+ model.to(device) # To GPU if available
+
+ # Optimizers & Loss function
+ optimizer = torch.optim.Adam(model.parameters(), lr=START_LR)
+ scheduler = ExponentialLR(optimizer, gamma=GAMMA, verbose=True)
+
+ # Optional class weights on the criterion
+ fold_class_weights = calculate_class_weights(train_fold).to(device)
+ criterion = torch.nn.CrossEntropyLoss(weight=fold_class_weights if USE_CLASS_WEIGHTS_LOSS else None)
+ for epoch in range(1, EPOCHS):
+ log.info(f"Epoch: {epoch:03d} > > >")
+
+ # train model . .
+ train(model, train_fold_loader)
+
+ # evaluate on test fold . .
+ test_info = test(test_fold_loader)
+
+ # log for current epoch
+ log.info(f'Epoch: {epoch:03d}, Test F1 (weighted): {test_info["f1-score-weighted"]:.4f}, Test F1 (macro): {test_info["f1-score-macro"]:.4f}')
+
+ # step scheduler
+ scheduler.step()
+
+ # print confusion matrix
+ df = pd.DataFrame({'actual': test_info["trues"], 'prediction': test_info["preds"]})
+ confusion_matrix = pd.crosstab(df['actual'], df['prediction'], rownames=['Actual'], colnames=['Predicted'])
+ print(confusion_matrix)
+
+
+ log.info(f'Fold {i+1}, Test F1: {test_info["f1-score"]:.4f}')
+ kfold_results.append(test_info)
+
+ print(f"K-Fold Results: {kfold_results}")
+ # Pickle results
+ if PICKLE_PATH_KF is not None:
+ with open(PICKLE_PATH_KF, "wb") as f:
+ pickle.dump(kfold_results, f)
+
+
+
+ log.info(f"Sample graph:\n{train_dataset[0]}")
+ log.info(f"Train Dataset Size: {len(train_dataset)}")
+ log.info(f"Test Dataset Size: {len(test_dataset)}")
+
+ # Weights&Biases dashboard
+ # data_details = {
+ # "num_node_features": train_dataset.num_node_features,
+ # "num_classes": 2
+ # }
+ # log.info(f"Data Details:\n{data_details}")
+ # if USE_WANDB:
+ # wandb.log(data_details)
+
+ # Take subset for EXP3
+ if REL_SUBSET is not None:
+ indices = list(range(int(len(train_dataset)*REL_SUBSET)))
+ train_dataset = torch.utils.data.Subset(train_dataset, indices)
+ log.info(f"Subset contains {len(train_dataset)}")
+
+
+ sampler = None
+ class_weights = calculate_class_weights(train_dataset).to(device)
+
+ # Sample by class weight
+ if USE_CLASS_WEIGHTS_SAMPLER:
+ train_labels = [x.label for x in train_dataset]
+ sampler = torch.utils.data.WeightedRandomSampler([class_weights[x] for x in train_labels], len(train_labels))
+
+
+ # Dataloaders
+ log.info(f"Train DataLoader batch size is set to {TRAIN_BATCH_SIZE}")
+ train_loader = DataLoader(train_dataset, sampler=sampler, batch_size=TRAIN_BATCH_SIZE, num_workers=NUM_WORKERS)
+ test_loader = DataLoader(test_dataset, batch_size=TEST_BATCH_SIZE, num_workers=NUM_WORKERS)
+
+ # Model
+ if MODEL == "GAT":
+ model = HeteroGAT(hidden_channels=HIDDEN_CHANNELS, out_channels=2, num_layers=NUM_LAYERS)
+ model.to(device) # To GPU if available
+ elif MODEL == "SAGE":
+ model = HeteroGraphSAGE(hidden_channels=HIDDEN_CHANNELS, out_channels=2, num_layers=NUM_LAYERS)
+<<<<<<< HEAD
+ elif MODEL == "GC":
+ model = HeteroGraphConv(hidden_channels=HIDDEN_CHANNELS, out_channels=2, num_layers=NUM_LAYERS)
+ else:
+ log.error(f"Model does not exist! ({MODEL})")
+ exit(1)
+
+ model.to(device) # To GPU if available
+=======
+ model.to(device) # To GPU if available
+ else:
+ log.error(f"Model does not exist! ({MODEL})")
+ exit(1)
+
+
+>>>>>>> 4f4ade9725679214f5f9a544a583641646835e7e
+
+ if WARM_START_FILE is not None:
+ model.load_state_dict(torch.load(WARM_START_FILE, map_location=device), strict=False)
+
+ # Optimizers & Loss function
+ optimizer = torch.optim.Adam(model.parameters(), lr=START_LR)
+ scheduler = ExponentialLR(optimizer, gamma=GAMMA, verbose=True)
+
+ # Cross Entropy Loss (with optional class weights)
+ criterion = torch.nn.CrossEntropyLoss(weight=class_weights if USE_CLASS_WEIGHTS_LOSS else None)
+
+
+ for epoch in range(1, EPOCHS+1):
+ log.info(f"Epoch: {epoch:03d} > > >")
+
+ # train model . .
+ train(model, train_loader)
+
+ # evaluate on training set . .
+ train_info = test(train_loader)
+
+ # evaluate on test set . .
+ test_info = test(test_loader)
+
+ # log for current epoch
+ log.info(f'Epoch: {epoch:03d}, Test F1 (weighted): {test_info["f1-score-weighted"]:.4f}, Test F1 (macro): {test_info["f1-score-macro"]:.4f}')
+
+ # step scheduler
+ scheduler.step()
+
+ # print confusion matrix
+ df = pd.DataFrame({'actual': test_info["trues"], 'prediction': test_info["preds"]})
+ confusion_matrix = pd.crosstab(df['actual'], df['prediction'], rownames=['Actual'], colnames=['Predicted'])
+ print(confusion_matrix)
+
+ if SAVE_CHECKPOINTS:
+ torch.save(model.state_dict(), f"../models/model-{epoch}.pt")
+
+ # log evaluation results to wandb
+ if USE_WANDB:
+ helper_functions.log_results_to_wandb(train_info, "train")
+ helper_functions.log_results_to_wandb(test_info, "test")
+
+ log.info(f'Test F1: {test_info["f1-score-weighted"]:.4f}')
+
+ helper_functions.save_model(model, FINAL_MODEL_OUT_PATH)
+ # Plot confusion matrix
+ if USE_WANDB:
+ helper_functions.add_cm_to_wandb(test_info)
+ wandb.finish()
+
diff --git a/embeddings/GAT.py b/embeddings/GAT.py
index 41e9b97b03be95db27b7dfb06a407375a7275755..77c89cf4ae1becefb5c4071d9995ce63546912b4 100644
--- a/embeddings/GAT.py
+++ b/embeddings/GAT.py
@@ -4,7 +4,30 @@ from torch_geometric.nn import GATConv, Linear, MeanAggregation, to_hetero
from dataset import UserGraphDataset
+metadata = (['question', 'answer', 'comment', 'tag', 'module'],
+ [('tag', 'describes', 'question'), ('tag', 'describes', 'answer'), ('tag', 'describes', 'comment'), ('module', 'imported_in', 'question'), ('module', 'imported_in', 'answer'), ('question', 'rev_describes', 'tag'), ('answer', 'rev_describes', 'tag'), ('comment', 'rev_describes', 'tag'), ('question', 'rev_imported_in', 'module'), ('answer', 'rev_imported_in', 'module')])
+
+class Model(torch.nn.Module):
+ def __init__(self, hidden_channels):
+ super(Model, self).__init__()
+ self.lin = Linear(hidden_channels, 2)
+ self.softmax = torch.nn.Softmax(dim=1)
+ self.gat = to_hetero(GAT(hidden_channels), metadata)
+ self.pool = MeanAggregation()
+
+ def forward(self, data, post_emb):
+ convolved = self.gat(data.x_dict, data.edge_index_dict, data.batch_dict)
+ pooled = self.pool(convolved, data.batch_dict['question'])
+
+ x = torch.cat([convolved, post_emb], dim=1)
+
+ # 3. Concatenate with post embedding
+ # x = torch.cat((x, post_emb))
+ # 4. Apply a final classifier.
+ x = self.lin(x)
+ x = self.softmax(x)
+ return x
class GAT(torch.nn.Module):
def __init__(self, hidden_channels):
super(GAT, self).__init__()
@@ -12,38 +35,30 @@ class GAT(torch.nn.Module):
self.conv1 = GATConv((-1, -1), hidden_channels, add_self_loops=False)
self.conv2 = GATConv((-1, -1), hidden_channels, add_self_loops=False)
self.conv3 = GATConv((-1, -1), hidden_channels, add_self_loops=False)
- self.lin = Linear(hidden_channels, 2)
- self.softmax = torch.nn.Softmax(dim=1)
- self.pool = MeanAggregation()
- def forward(self, x, edge_index, batch, post_emb):
+
+
+ def forward(self, x, edge_index, batch):
# 1. Obtain node embeddings
x = self.conv1(x, edge_index)
x = x.relu()
x = self.conv2(x, edge_index)
x = x.relu()
x = self.conv3(x, edge_index)
-
+ x = x.relu()
# 2. Readout layer
x = self.pool(x, batch) # [batch_size, hidden_channels]
-
- x = torch.cat([x, post_emb], dim=1)
-
- # 3. Concatenate with post embedding
- #x = torch.cat((x, post_emb))
- # 4. Apply a final classifier.
- x = self.lin(x)
- x = self.softmax(x)
return x
+
def train(model, train_loader):
model.train()
for data in train_loader: # Iterate in batches over the training dataset.
print(data)
- out = model(data.x_dict, data.edge_index_dict, data.batch_dict, torch.cat([data.question_emb, data.answer_emb], dim=1)) # Perform a single forward pass.
+ out = model(data, torch.cat([data.question_emb, data.answer_emb], dim=1)) # Perform a single forward pass.
print(out, data.label)
loss = criterion(out, torch.squeeze(data.label, -1)) # Compute the loss.
loss.backward() # Derive gradients.
@@ -72,10 +87,9 @@ if __name__ == '__main__':
val_loader = DataLoader(val_dataset, batch_size=64)
test_loader = DataLoader(test_dataset, batch_size=64)
- model = GAT(hidden_channels=64)
+ model = Model(hidden_channels=64)
sample = train_dataset[0]
- metadata = (['question', 'answer', 'comment', 'tag', 'module'], [('tag', 'describes', 'question'), ('tag', 'describes', 'answer'), ('tag', 'describes', 'comment'), ('module', 'imported_in', 'question'), ('module', 'imported_in', 'answer'), ('question', 'rev_describes', 'tag'), ('answer', 'rev_describes', 'tag'), ('comment', 'rev_describes', 'tag'), ('question', 'rev_imported_in', 'module'), ('answer', 'rev_imported_in', 'module')])
- model = to_hetero(model, metadata)
+ #model = to_hetero(model, metadata)
#print(model(sample.x_dict, sample.edge_index_dict, sample.batch_dict))
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
diff --git a/embeddings/UG1.pdf b/embeddings/UG1.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..e6ca373650b78f002662f245e49f0885d14093c8
Binary files /dev/null and b/embeddings/UG1.pdf differ
diff --git a/embeddings/__pycache__/dataset.cpython-38.pyc b/embeddings/__pycache__/dataset.cpython-38.pyc
index 8617005a538eb4e7446cac82072e127819a24142..bf295e1e98f4d636c01bf75d54e609b548dd3ac8 100644
Binary files a/embeddings/__pycache__/dataset.cpython-38.pyc and b/embeddings/__pycache__/dataset.cpython-38.pyc differ
diff --git a/embeddings/__pycache__/hetero_GAT.cpython-38.pyc b/embeddings/__pycache__/hetero_GAT.cpython-38.pyc
index f6d1f13cb8992133708e5fb7d3969e2cd1635ee5..e8f12288acd2e9f7fce9c5d34ccefea2899badd8 100644
Binary files a/embeddings/__pycache__/hetero_GAT.cpython-38.pyc and b/embeddings/__pycache__/hetero_GAT.cpython-38.pyc differ
diff --git a/embeddings/__pycache__/hetero_GAT_constants.cpython-38.pyc b/embeddings/__pycache__/hetero_GAT_constants.cpython-38.pyc
index 3a60fc0b5348db9f10a2db81acd9346b48bd8da8..c7af401255e7f95924ef9856aa909f71823cf3a8 100644
Binary files a/embeddings/__pycache__/hetero_GAT_constants.cpython-38.pyc and b/embeddings/__pycache__/hetero_GAT_constants.cpython-38.pyc differ
diff --git a/embeddings/__pycache__/static_graph_construction.cpython-38.pyc b/embeddings/__pycache__/static_graph_construction.cpython-38.pyc
index 4950fb6e8f9383745d3cf3ab80eeb4278c33fde5..35d8fd198cf898502656208fd502e2c0d89cd4cd 100644
Binary files a/embeddings/__pycache__/static_graph_construction.cpython-38.pyc and b/embeddings/__pycache__/static_graph_construction.cpython-38.pyc differ
diff --git a/embeddings/dataset.py b/embeddings/dataset.py
index 1de7930fe1e02dd6260ef652c1bcc58e624f48d7..3b9c0af05cff3610732d8b439fe214a23db56a1b 100644
--- a/embeddings/dataset.py
+++ b/embeddings/dataset.py
@@ -23,6 +23,8 @@ from static_graph_construction import StaticGraphConstruction
log = setup_custom_logger('dataset', logging.INFO)
+TIMES = []
+
class UserGraphDataset(Dataset):
def __init__(self, root, transform=None, pre_transform=None, pre_filter=None, db_address:str=None, skip_processing=False):
self._skip_processing = skip_processing
@@ -102,7 +104,12 @@ class UserGraphDataset(Dataset):
answer_emb = torch.concat((answer_word_embs[0], answer_code_embs[0]))
# Build graph
+ start = time.time()
graph: HeteroData = self.construct_graph(answer["OwnerUserId"])
+ end = time.time()
+ log.info(f"Graph construction took {end-start} seconds.")
+ TIMES.append(end-start)
+ log.info(f"Average graph construction time: {sum(TIMES)/len(TIMES)} seconds. {TIMES}")
# pytorch geometric data object
graph.__setattr__('question_emb', question_emb)
diff --git a/embeddings/explainer.ipynb b/embeddings/explainer.ipynb
index fa05a808b3ff1617a4ec8a4cc246c3e1e2a207ce..5c85d3f925dd950a2bfbf52c834e090863f94453 100644
--- a/embeddings/explainer.ipynb
+++ b/embeddings/explainer.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 142,
"metadata": {
"collapsed": true
},
@@ -13,11 +13,21 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 143,
"outputs": [],
"source": [
"import torch\n",
- "from hetero_GAT import HeteroGAT"
+ "from hetero_GAT import HeteroGAT\n",
+ "from dataset_in_memory import fetch_question_ids"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Load model"
],
"metadata": {
"collapsed": false
@@ -25,26 +35,21 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 144,
"outputs": [
{
- "ename": "RuntimeError",
- "evalue": "Error(s) in loading state_dict for HeteroGAT:\n\tsize mismatch for convs.0.convs.tag__describes__question.lin_l.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.tag__describes__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__question.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.lin_l.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.lin_l.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.lin_r.weight: copying a param with shape torch.Size([64, 768]) from checkpoint, the shape in current model is torch.Size([384, 768]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.lin_l.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.lin_l.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 768]) from checkpoint, the shape in current model is torch.Size([384, 768]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.1.convs.tag__describes__question.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__question.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).",
- "output_type": "error",
- "traceback": [
- "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[1;32mIn [15], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# Load model (EXP 1)\u001B[39;00m\n\u001B[0;32m 2\u001B[0m model \u001B[38;5;241m=\u001B[39m HeteroGAT(out_channels\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m, num_layers\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m, hidden_channels\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m64\u001B[39m)\n\u001B[1;32m----> 3\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mload_state_dict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mload\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m../models/gat_qa_10e_64h_3l.pt\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmap_location\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdevice\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mcpu\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstrict\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m model\u001B[38;5;241m.\u001B[39meval()\n",
- "File \u001B[1;32m~\\Documents\\graph4stackoverflow\\venv\\lib\\site-packages\\torch\\nn\\modules\\module.py:1667\u001B[0m, in \u001B[0;36mModule.load_state_dict\u001B[1;34m(self, state_dict, strict)\u001B[0m\n\u001B[0;32m 1662\u001B[0m error_msgs\u001B[38;5;241m.\u001B[39minsert(\n\u001B[0;32m 1663\u001B[0m \u001B[38;5;241m0\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mMissing key(s) in state_dict: \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m. \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\n\u001B[0;32m 1664\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(k) \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m missing_keys)))\n\u001B[0;32m 1666\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(error_msgs) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m-> 1667\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mError(s) in loading state_dict for \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m:\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;130;01m\\t\u001B[39;00m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\n\u001B[0;32m 1668\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;130;01m\\t\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(error_msgs)))\n\u001B[0;32m 1669\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",
- "\u001B[1;31mRuntimeError\u001B[0m: Error(s) in loading state_dict for HeteroGAT:\n\tsize mismatch for convs.0.convs.tag__describes__question.lin_l.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.tag__describes__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__question.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.lin_l.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.lin_l.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.lin_r.weight: copying a param with shape torch.Size([64, 768]) from checkpoint, the shape in current model is torch.Size([384, 768]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.lin_l.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.lin_l.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.lin_r.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 768]) from checkpoint, the shape in current model is torch.Size([384, 768]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 50]) from checkpoint, the shape in current model is torch.Size([384, 50]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 1536]) from checkpoint, the shape in current model is torch.Size([384, 1536]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 30]) from checkpoint, the shape in current model is torch.Size([384, 30]).\n\tsize mismatch for convs.1.convs.tag__describes__question.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__question.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.lin_l.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.lin_l.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.lin_r.weight: copying a param with shape torch.Size([64, 64]) from checkpoint, the shape in current model is torch.Size([384, 64])."
- ]
+ "data": {
+ "text/plain": "HeteroGAT(\n (convs): ModuleList(\n (0): HeteroConv(num_relations=10)\n (1): HeteroConv(num_relations=10)\n )\n (lin1): Linear(3392, 64, bias=True)\n (lin2): Linear(64, 2, bias=True)\n (softmax): Softmax(dim=-1)\n)"
+ },
+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "\n",
"# Load model (EXP 1)\n",
"model = HeteroGAT(out_channels=2, num_layers=2, hidden_channels=64)\n",
- "model.load_state_dict(torch.load('../models/gat_qa_10e_64h_3l.pt', map_location=torch.device('cpu')), strict=False)\n",
+ "model.load_state_dict(torch.load('../models/gat.pt', map_location=torch.device('cpu')), strict=False)\n",
"model.eval()"
],
"metadata": {
@@ -53,14 +58,100 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 145,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "HeteroGraphSAGE(\n (convs): ModuleList(\n (0): HeteroConv(num_relations=10)\n (1): HeteroConv(num_relations=10)\n )\n (lin1): Linear(3392, 64, bias=True)\n (lin2): Linear(64, 2, bias=True)\n (softmax): Softmax(dim=-1)\n)"
+ },
+ "execution_count": 145,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from hetero_GAT import HeteroGraphSAGE\n",
+ "\n",
+ "model2 = HeteroGraphSAGE(out_channels=2, num_layers=2, hidden_channels=64)\n",
+ "model2.load_state_dict(torch.load('../models/SAGE_3l_60e_64h.pt', map_location=torch.device('cpu')), strict=False)\n",
+ "model2.eval()"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Load data"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
"outputs": [],
"source": [
- "from dataset_in_memory import UserGraphDatasetInMemory\n",
+ "import pickle\n",
+ "with open('../data/raw/tag_question_ids.pickle', 'rb') as f:\n",
+ " tag_question_ids = pickle.load(f)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "outputs": [],
+ "source": [
+ "good_topic_ids = tag_question_ids['output']\n",
+ "bad_topic_ids = tag_question_ids['primes']"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-04-21 15:05:50 INFO PostEmbedding instantiated!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias']\n",
+ "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from dataset import UserGraphDataset\n",
+ "from torch_geometric.explain import HeteroExplanation, CaptumExplainer, Explainer\n",
+ "\n",
+ "from tqdm import tqdm\n",
+ "from torch_geometric.loader import DataLoader\n",
"\n",
- "train_dataset = UserGraphDatasetInMemory(root=\"../data\", file_name_out='train-4175-qs.pt', question_ids=[])\n",
+ "# Instantiate the dataset class for access to database\n",
+ "ugd = UserGraphDataset(root='../data/', db_address='../stackoverflow.db', skip_processing=True)\n",
"\n",
- "test_dataset = UserGraphDatasetInMemory(root=\"../data\", file_name_out='test-1790-qs.pt', question_ids=[])"
+ "def fetch_answer_files(question_id: int):\n",
+ " for f in os.listdir('../data/processed/'):\n",
+ " question_id_search = re.search(r\"id_(\\d+)\", f)\n",
+ " if question_id_search:\n",
+ " if int(question_id_search.group(1)) == question_id:\n",
+ " yield torch.load(os.path.join('../data/processed/', f))"
],
"metadata": {
"collapsed": false
@@ -68,22 +159,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 149,
"outputs": [],
"source": [
- "from torch_geometric.explain import HeteroExplanation\n",
+ "def explain_preds(q_id):\n",
"\n",
- "explainer = Explainer(\n",
- " model=model,\n",
- " algorithm=GNNExplainer(epochs=200),\n",
- " explanation_type='model',\n",
- " node_mask_type='attributes',\n",
- " model_config=dict(\n",
- " mode='binary_classification',\n",
- " task_level='graph',\n",
- " return_type='probs',\n",
- " )\n",
- ")"
+ " graph_files = list(fetch_answer_files(q_id))\n",
+ " dataloader = DataLoader(graph_files, batch_size=1, shuffle=False)\n",
+ "\n",
+ " for answer, graph in zip(ugd.fetch_answers_for_question(q_id).itertuples(), dataloader):\n",
+ " print(answer)\n",
+ "\n",
+ " constructed_graph = ugd.construct_graph(answer.OwnerUserId)\n",
+ "\n",
+ " explainer = Explainer(\n",
+ " model=model2,\n",
+ " algorithm=CaptumExplainer('Saliency'),\n",
+ " explanation_type='model',\n",
+ " model_config=dict(\n",
+ " mode='binary_classification',\n",
+ " task_level='graph',\n",
+ " return_type='probs',\n",
+ " ),\n",
+ " edge_mask_type='object'\n",
+ " )\n",
+ " print(graph.label)\n",
+ " post_emb = torch.cat([graph.question_emb, graph.answer_emb], dim=1)\n",
+ " yield explainer(graph.x_dict, graph.edge_index_dict, batch_dict=graph.batch_dict, post_emb=post_emb, index=0)#, graph"
],
"metadata": {
"collapsed": false
@@ -91,13 +193,77 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 150,
"outputs": [],
"source": [
- "from torch_geometric.loader import DataLoader\n",
+ "from collections import defaultdict\n",
+ "from matplotlib import pyplot as plt\n",
+ "import networkx as nx\n",
+ "from torch_geometric.utils import to_networkx\n",
+ "\n",
+ "def aggregate_edge_directions(edge_mask, data):\n",
+ " edge_mask_dict = defaultdict(float)\n",
+ " for val, u, v in list(zip(edge_mask, *data.edge_index)):\n",
+ " u, v = u.item(), v.item()\n",
+ " if u > v:\n",
+ " u, v = v, u\n",
+ " edge_mask_dict[(u, v)] += val\n",
+ " return edge_mask_dict\n",
+ "\n",
+ "\n",
+ "def draw_molecule(g, k, iterations, edge_mask=None, draw_edge_labels=False, node_colors=None):\n",
+ " g = g.copy().to_undirected()\n",
+ "\n",
+ " # Layout, make nodes more spread out\n",
+ " pos = nx.planar_layout(g)\n",
+ " pos = nx.spring_layout(g, pos=pos, k=k, iterations=iterations)\n",
+ "\n",
+ " # Color edges\n",
+ " if edge_mask is None:\n",
+ " edge_color = 'black'\n",
+ " widths = None\n",
+ " else:\n",
+ " edge_color = [edge_mask[(u, v)] for u, v in g.edges()]\n",
+ " widths = [x * 10 for x in edge_color]\n",
+ "\n",
+ " edge_color = ['#82D173' if x > 0.5 else 'gray' for x in edge_color]\n",
+ "\n",
+ " if node_colors is None:\n",
+ " node_colors = 'black'\n",
+ "\n",
+ " nx.draw(g, with_labels=True, width=widths,\n",
+ " edge_color=edge_color,\n",
+ " node_color=node_colors, pos=pos)\n",
"\n",
- "dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False)\n",
- "data = next(iter(dataloader))"
+ " if draw_edge_labels and edge_mask is not None:\n",
+ " edge_labels = {k: ('%.2f' % v) for k, v in edge_mask.items()}\n",
+ " nx.draw_networkx_edge_labels(g, pos, edge_labels=edge_labels,\n",
+ " font_color='red')\n",
+ " return g\n",
+ "\n",
+ "\n",
+ "def visualize_explanation (explanation, k=2.5, iterations=250):\n",
+ " explanation_homog = explanation.to_homogeneous()\n",
+ "\n",
+ " # detatch the edge mask\n",
+ " explanation_homog.edge_mask = explanation_homog.edge_mask.detach()\n",
+ " mask = torch.abs(explanation_homog.edge_mask) / torch.max(torch.abs(explanation_homog.edge_mask))\n",
+ " mask = torch.clip(mask, 0.2, 1)\n",
+ " mask = mask.detach().numpy()\n",
+ "\n",
+ " # Color nodes by type\n",
+ " node_color = []\n",
+ " cmap = {'question': '#377EB8', 'answer': '#4DAF4A', 'comment': '#FF7F00', 'tag': '#F781BF', 'module': '#A65628'}\n",
+ " for node_type in explanation.x_dict.keys():\n",
+ " node_color.extend([cmap[node_type]] * explanation.x_dict[node_type].shape[0])\n",
+ "\n",
+ " edge_mask_dict = aggregate_edge_directions(mask, explanation_homog)\n",
+ "\n",
+ " f = plt.figure(figsize=(8, 7.5))\n",
+ " # set background to white\n",
+ " f.set_facecolor(\"#00000F\")\n",
+ " draw_molecule(to_networkx(explanation_homog), k, iterations, edge_mask=edge_mask_dict, node_colors=node_color)\n",
+ " #print(explanation.target, graph.label)"
],
"metadata": {
"collapsed": false
@@ -105,11 +271,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 151,
"outputs": [],
"source": [
- "post_emb = torch.cat([data.question_emb, data.answer_emb], dim=0)\n",
- "post_emb.shape"
+ "from ModuleEmbeddings import ModuleEmbeddingTrainer\n",
+ "from NextTagEmbedding import NextTagEmbeddingTrainer\n",
+ "\n",
+ "# Import tag embedding model\n",
+ "\n",
+ "tag_embedding_model = NextTagEmbeddingTrainer.load_model(\"../models/tag-emb-7_5mil-50d-63653-3.pt\", embedding_dim=50, vocab_size=63654, context_length=3)\n",
+ "\n",
+ "# unpickle tag_to_ix\n",
+ "with open('../models/tag_to_ix_tag-emb-7_5mil-50d-63653-3.pt', 'rb') as f:\n",
+ " tag_to_ix = pickle.load(f)\n",
+ "ix_to_tag = {v: k for k, v in tag_to_ix.items()}\n",
+ "\n",
+ "def reverse_search_tag(emb):\n",
+ " distance = torch.norm(tag_embedding_model.embedding.weight.data - emb, dim=1)\n",
+ " nearest = torch.argmin(distance)\n",
+ " return ix_to_tag[nearest.item()]"
],
"metadata": {
"collapsed": false
@@ -117,27 +297,372 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 152,
"outputs": [],
"source": [
- "explanation = explainer(data.x_dict, data.edge_index_dict, batch_dict=data.batch_dict, post_emb=post_emb)\n",
- "print(f'Generated explanations in {explanation.available_explanations}')\n",
+ "# Import module embedding mode\n",
+ "module_embedding_model = ModuleEmbeddingTrainer.load_model(\"../models/module-emb-1milx5-30d-49911.pt\", embedding_dim=30, vocab_size=49911)\n",
+ "\n",
+ "# unpickle module_to_ix\n",
+ "with open('../models/mod_to_ix_module-emb-1milx5-30d-49911.pt', 'rb') as f:\n",
+ " module_to_ix = pickle.load(f)\n",
+ "ix_to_module = {v: k for k, v in module_to_ix.items()}\n",
+ "\n",
+ "def reverse_search_module(emb):\n",
+ " distance = torch.norm(module_embedding_model.embedding.weight.data - emb, dim=1)\n",
+ " nearest = torch.argmin(distance)\n",
+ " return ix_to_module[nearest.item()]"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Good Topic"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "explaniner_gen = explain_preds(good_topic_ids[0])"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pandas(Index=0, PostId=287896, PostTypeId=2, AcceptedAnswerId=None, CreationDate='2008-11-13T19:06:23.037', Score=27, ViewCount=None, Body='<p>For Windows you cannot print to console with colors unless you\\'re using the <a href=\"https://en.wikipedia.org/wiki/Windows_API\" rel=\"noreferrer\">Win32</a> API.</p>\\n<p>For Linux it\\'s as simple as using print, with the escape sequences outlined here:</p>\\n<p><a href=\"http://www.linuxhowtos.org/Tips%20and%20Tricks/ansi_escape_sequences.htm\" rel=\"noreferrer\">Colors</a></p>\\n<p>For the character to print like a box, it really depends on what font you are using for the console window. The pound symbol works well, but it depends on the font:</p>\\n<pre><code>#\\n</code></pre>\\n', OwnerUserId=34395, LastEditorUserId=63550.0, LastEditorDisplayName=None, LastEditDate='2021-01-09T16:32:13.100', Title=None, Tags=None, AnswerCount=None, CommentCount=1, FavoriteCount=None, CommunityOwnedDate=None, ContentLicense='CC BY-SA 4.0', ParentId=287871)\n",
+ "20 6 20 26 1 73\n",
+ "TAGS {46: 'lambda', 47: 'ftp', 48: 'ftplib', 49: 'regex', 50: 'package', 51: 'winapi', 52: 'wxpython', 53: 'list', 54: 'iterator', 55: 'debugging', 56: 'subprocess', 57: 'persistence', 58: 'pywin32', 59: 'xml', 60: 'logging', 61: 'split', 62: 'windows', 63: 'terminal', 64: 'output', 65: 'ansi-colors', 66: 'attributeerror', 67: 'wxwidgets', 68: 'import', 69: 'python-2.5', 70: 'list-comprehension', 71: 'exception'} MODULES {72: 're'}\n",
+ "65\n",
+ "tensor([[1]])\n",
+ "Pandas(Index=1, PostId=287919, PostTypeId=2, AcceptedAnswerId=None, CreationDate='2008-11-13T19:13:59.683', Score=19, ViewCount=None, Body='<p>You can use the Python implementation of the <a href=\"https://en.wikipedia.org/wiki/Curses_%28programming_library%29\" rel=\"nofollow noreferrer\">curses</a> library:\\n<em><a href=\"http://docs.python.org/library/curses.html\" rel=\"nofollow noreferrer\">curses — Terminal handling for character-cell displays</a></em></p>\\n<p>Also, run this and you\\'ll find your box:</p>\\n<pre><code>for i in range(255):\\n print i, chr(i)\\n</code></pre>\\n', OwnerUserId=23597, LastEditorUserId=63550.0, LastEditorDisplayName=None, LastEditDate='2021-01-09T16:37:09.613', Title=None, Tags=None, AnswerCount=None, CommentCount=1, FavoriteCount=None, CommunityOwnedDate=None, ContentLicense='CC BY-SA 4.0', ParentId=287871)\n",
+ "2 13 11 30 1 57\n",
+ "TAGS {26: 'tornado', 27: 'pika', 28: 'fabric', 29: 'paramiko', 30: 'python-multithreading', 31: 'hardware', 32: 'extract', 33: 'post', 34: 'web', 35: 'routing', 36: 'loops', 37: 'timer', 38: 'polling', 39: 'terminal', 40: 'output', 41: 'ansi-colors', 42: 'file', 43: 'asynchronous', 44: 'oauth', 45: 'twitter', 46: 'json', 47: 'io', 48: 'udp', 49: 'websocket', 50: 'google-app-engine', 51: 'templates', 52: 'django-templates', 53: 'linux', 54: 'nginx', 55: 'daemon'} MODULES {56: 'tornado'}\n",
+ "57\n",
+ "tensor([[1]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "explanation = next(explaniner_gen)\n",
+ "explanation = next(explaniner_gen)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[(22, 'tornado'),\n (23, 'pika'),\n (24, 'fabric'),\n (25, 'paramiko'),\n (26, 'python-multithreading'),\n (27, 'hardware'),\n (28, 'extract'),\n (29, 'post'),\n (30, 'web'),\n (31, 'routing'),\n (32, 'loops'),\n (33, 'timer'),\n (34, 'terminal'),\n (35, 'output'),\n (36, 'ansi-colors'),\n (37, 'file'),\n (38, 'asynchronous'),\n (39, 'oauth'),\n (40, 'twitter'),\n (41, 'json'),\n (42, 'io'),\n (43, 'udp'),\n (44, 'google-app-engine'),\n (45, 'templates'),\n (46, 'django-templates'),\n (47, 'websocket'),\n (48, 'linux'),\n (49, 'nginx')]"
+ },
+ "execution_count": 155,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tags = []\n",
"\n",
- "path = 'feature_importance.png'\n",
- "explanation.visualize_feature_importance(path, top_k=10)\n",
- "print(f\"Feature importance plot has been saved to '{path}'\")\n",
+ "offset = len(explanation.x_dict['question']) + len(explanation.x_dict['answer']) + len(explanation.x_dict['comment'])\n",
+ "# Convert tag embeddings back to tags\n",
+ "for i in range(len(explanation.x_dict['tag'])):\n",
+ " tags.append(reverse_search_tag(explanation.x_dict['tag'][i]))\n",
"\n",
- "path = 'subgraph.pdf'\n",
- "explanation.visualize_graph(path)\n",
- "print(f\"Subgraph visualization plot has been saved to '{path}'\")"
+ "# enumerate the tags including offset\n",
+ "tags = list(enumerate(tags, offset))\n",
+ "tags"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[(50, 't')]"
+ },
+ "execution_count": 156,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
],
+ "source": [
+ "modules = []\n",
+ "\n",
+ "offset = len(explanation.x_dict['question']) + len(explanation.x_dict['answer']) + len(explanation.x_dict['comment']) + len(explanation.x_dict['tag'])\n",
+ "# Convert module embeddings back to modules\n",
+ "for i in range(len(explanation.x_dict['module'])):\n",
+ " modules.append(reverse_search_module(explanation.x_dict['module'][i]))\n",
+ "\n",
+ "# enumerate the modules including offset\n",
+ "modules = list(enumerate(modules, offset))\n",
+ "modules"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "<Figure size 800x750 with 1 Axes>",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.patches as mpatches\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "red_patch = mpatches.Patch(color='#377EB8', label='Question')\n",
+ "green_patch = mpatches.Patch(color='#4DAF4A', label='Answer')\n",
+ "blue_patch = mpatches.Patch(color='#FF7F00', label='Comment')\n",
+ "pink_patch = mpatches.Patch(color='#F781BF', label='Tag')\n",
+ "brown_patch = mpatches.Patch(color='#A65628', label='Module')\n",
+ "\n",
+ "\n",
+ "g = visualize_explanation(explanation)\n",
+ "plt.legend(handles=[red_patch, green_patch, blue_patch, pink_patch, brown_patch], loc='lower right')\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "tensor([[0.0194, 0.9806]])"
+ },
+ "execution_count": 158,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "explanation.prediction"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Bad Topic"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "outputs": [],
+ "source": [
+ "bad_explainer_gen = explain_preds(bad_topic_ids[0])"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pandas(Index=0, PostId=1628962, PostTypeId=2, AcceptedAnswerId=None, CreationDate='2009-10-27T05:49:26.093', Score=7, ViewCount=None, Body='<p>The line <code>k = k-1</code> does not do what you think. It has no effect. Changing <code>k</code> does not affect the loop. At each iteration, <code>k</code> is assigned to the next element of the range, so any changes you have made to <code>k</code> inside the loop will be overwritten.</p>\\n', OwnerUserId=86989.0, LastEditorUserId=nan, LastEditorDisplayName=None, LastEditDate=None, Title=None, Tags=None, AnswerCount=None, CommentCount=1, FavoriteCount=None, CommunityOwnedDate=None, ContentLicense='CC BY-SA 2.5', ParentId=1628949)\n",
+ "5 20 20 54 2 101\n",
+ "TAGS {45: 'django', 46: 'forms', 47: 'class', 48: 'self', 49: 'lambda', 50: 'variadic-functions', 51: 'methods', 52: 'super', 53: 'pass-by-reference', 54: 'pass-by-value', 55: 'parameter-passing', 56: 'java', 57: 'php', 58: 'associative-array', 59: 'ocaml', 60: 'reference', 61: 'go', 62: 'instance', 63: 'function-pointers', 64: 'static-methods', 65: 'function', 66: 'parameters', 67: 'global', 68: 'padding', 69: 'strftime', 70: 'lexical-closures', 71: 'heap', 72: 'list-comprehension', 73: 'floating-point', 74: 'integer', 75: 'division', 76: 'python-2.x', 77: 'pointers', 78: 'repr', 79: 'set', 80: 'powerset', 81: 'c++', 82: 'list', 83: 'time', 84: 'execution-time', 85: 'hash', 86: 'python-2.5', 87: 'recursion', 88: 'functional-programming', 89: 'closures', 90: 'complexity-theory', 91: 'tuples', 92: 'arrays', 93: 'scheme', 94: 'multithreading', 95: 'threadpool', 96: 'arguments', 97: 'copy', 98: 'descriptor'} MODULES {99: 'os', 100: 'time'}\n",
+ "99\n",
+ "tensor([[1]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "explanation = next(bad_explainer_gen)\n",
+ "#\n",
+ "# pred = torch.argmax(explanation.prediction)\n",
+ "# label = graph.label\n",
+ "# f\"Prediction: {pred}, Label: {label.item()}\""
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "<Figure size 800x750 with 1 Axes>",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "red_patch = mpatches.Patch(color='#377EB8', label='Question')\n",
+ "green_patch = mpatches.Patch(color='#4DAF4A', label='Answer')\n",
+ "blue_patch = mpatches.Patch(color='#FF7F00', label='Comment')\n",
+ "pink_patch = mpatches.Patch(color='#F781BF', label='Tag')\n",
+ "brown_patch = mpatches.Patch(color='#A65628', label='Module')\n",
+ "\n",
+ "\n",
+ "visualize_explanation(explanation, k=0.55, iterations=40)\n",
+ "plt.legend(handles=[red_patch, green_patch, blue_patch, pink_patch, brown_patch], loc='lower right')\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "tensor([[0.9784, 0.0216]])"
+ },
+ "execution_count": 162,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "explanation.prediction"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "<Figure size 800x750 with 1 Axes>",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "explanation_homog = explanation.to_homogeneous()\n",
+ "\n",
+ "# detatch the edge mask\n",
+ "explanation_homog.edge_mask = explanation_homog.edge_mask.detach()\n",
+ "mask = torch.abs(explanation_homog.edge_mask) / torch.max(torch.abs(explanation_homog.edge_mask))\n",
+ "mask = torch.clip(mask, 0.2, 1)\n",
+ "mask = mask.detach().numpy()\n",
+ "\n",
+ "# Color nodes by type\n",
+ "node_color = []\n",
+ "cmap = {'question': '#377EB8', 'answer': '#4DAF4A', 'comment': '#FF7F00', 'tag': '#F781BF', 'module': '#A65628'}\n",
+ "for node_type in explanation.x_dict.keys():\n",
+ " node_color.extend([cmap[node_type]] * explanation.x_dict[node_type].shape[0])\n",
+ "\n",
+ "edge_mask_dict = aggregate_edge_directions(mask, explanation_homog)\n",
+ "\n",
+ "f = plt.figure(figsize=(8, 7.5))\n",
+ "# set background to white\n",
+ "f.set_facecolor(\"#00000F\")\n",
+ "\n",
+ "\n",
+ "g = to_networkx(explanation_homog).copy().to_undirected()\n",
+ "\n",
+ "# Layout, make nodes more spread out\n",
+ "pos = nx.planar_layout(g)\n",
+ "pos = nx.spring_layout(g, pos=pos, k=0.55, iterations=38)\n",
+ "\n",
+ "\n",
+ "nx.draw(g, with_labels=True,\n",
+ " node_color=node_color, pos=pos)\n",
+ "\n",
+ "\n",
+ "red_patch = mpatches.Patch(color='#377EB8', label='Question')\n",
+ "green_patch = mpatches.Patch(color='#4DAF4A', label='Answer')\n",
+ "blue_patch = mpatches.Patch(color='#FF7F00', label='Comment')\n",
+ "pink_patch = mpatches.Patch(color='#F781BF', label='Tag')\n",
+ "brown_patch = mpatches.Patch(color='#A65628', label='Module')\n",
+ "plt.legend(handles=[red_patch, green_patch, blue_patch, pink_patch, brown_patch], loc='lower right')\n",
+ "\n",
+ "f.savefig(\"UG1.pdf\", bbox_inches='tight')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "outputs": [],
+ "source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 163,
"outputs": [],
"source": [],
"metadata": {
diff --git a/embeddings/fault-analysis.ipynb b/embeddings/fault-analysis.ipynb
index 9f118b429d0420e6ef1b7426faa42abe96524497..234ae2b68189ea876077cf3a45af49167d4c8ace 100644
--- a/embeddings/fault-analysis.ipynb
+++ b/embeddings/fault-analysis.ipynb
@@ -11,17 +11,25 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-03-31 10:06:42 INFO PostEmbedding instantiated!\n"
+ "2023-04-16 16:49:22 INFO PostEmbedding instantiated!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias']\n",
+ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-04-16 16:49:33 INFO Proceeding with cpu . .\n",
+ "HIII\n"
+ ]
}
],
"source": [
@@ -40,22 +48,18 @@
"execution_count": 2,
"outputs": [
{
- "ename": "RuntimeError",
- "evalue": "Error(s) in loading state_dict for HeteroGAT:\n\tMissing key(s) in state_dict: \"convs.0.convs.tag__describes__question.att\", \"convs.0.convs.tag__describes__question.lin_l.weight\", \"convs.0.convs.tag__describes__question.lin_l.bias\", \"convs.0.convs.tag__describes__question.lin_r.weight\", \"convs.0.convs.tag__describes__question.lin_r.bias\", \"convs.0.convs.tag__describes__answer.att\", \"convs.0.convs.tag__describes__answer.lin_l.weight\", \"convs.0.convs.tag__describes__answer.lin_l.bias\", \"convs.0.convs.tag__describes__answer.lin_r.weight\", \"convs.0.convs.tag__describes__answer.lin_r.bias\", \"convs.0.convs.tag__describes__comment.att\", \"convs.0.convs.tag__describes__comment.lin_l.weight\", \"convs.0.convs.tag__describes__comment.lin_l.bias\", \"convs.0.convs.tag__describes__comment.lin_r.weight\", \"convs.0.convs.tag__describes__comment.lin_r.bias\", \"convs.0.convs.module__imported_in__question.att\", \"convs.0.convs.module__imported_in__question.lin_l.weight\", \"convs.0.convs.module__imported_in__question.lin_l.bias\", \"convs.0.convs.module__imported_in__question.lin_r.weight\", \"convs.0.convs.module__imported_in__question.lin_r.bias\", \"convs.0.convs.module__imported_in__answer.att\", \"convs.0.convs.module__imported_in__answer.lin_l.weight\", \"convs.0.convs.module__imported_in__answer.lin_l.bias\", \"convs.0.convs.module__imported_in__answer.lin_r.weight\", \"convs.0.convs.module__imported_in__answer.lin_r.bias\", \"convs.0.convs.question__rev_describes__tag.att\", \"convs.0.convs.question__rev_describes__tag.lin_l.weight\", \"convs.0.convs.question__rev_describes__tag.lin_l.bias\", \"convs.0.convs.question__rev_describes__tag.lin_r.weight\", \"convs.0.convs.question__rev_describes__tag.lin_r.bias\", \"convs.0.convs.answer__rev_describes__tag.att\", \"convs.0.convs.answer__rev_describes__tag.lin_l.weight\", \"convs.0.convs.answer__rev_describes__tag.lin_l.bias\", \"convs.0.convs.answer__rev_describes__tag.lin_r.weight\", \"convs.0.convs.answer__rev_describes__tag.lin_r.bias\", \"convs.0.convs.comment__rev_describes__tag.att\", \"convs.0.convs.comment__rev_describes__tag.lin_l.weight\", \"convs.0.convs.comment__rev_describes__tag.lin_l.bias\", \"convs.0.convs.comment__rev_describes__tag.lin_r.weight\", \"convs.0.convs.comment__rev_describes__tag.lin_r.bias\", \"convs.0.convs.question__rev_imported_in__module.att\", \"convs.0.convs.question__rev_imported_in__module.lin_l.weight\", \"convs.0.convs.question__rev_imported_in__module.lin_l.bias\", \"convs.0.convs.question__rev_imported_in__module.lin_r.weight\", \"convs.0.convs.question__rev_imported_in__module.lin_r.bias\", \"convs.0.convs.answer__rev_imported_in__module.att\", \"convs.0.convs.answer__rev_imported_in__module.lin_l.weight\", \"convs.0.convs.answer__rev_imported_in__module.lin_l.bias\", \"convs.0.convs.answer__rev_imported_in__module.lin_r.weight\", \"convs.0.convs.answer__rev_imported_in__module.lin_r.bias\", \"convs.1.convs.tag__describes__question.att\", \"convs.1.convs.tag__describes__question.lin_l.weight\", \"convs.1.convs.tag__describes__question.lin_l.bias\", \"convs.1.convs.tag__describes__question.lin_r.weight\", \"convs.1.convs.tag__describes__question.lin_r.bias\", \"convs.1.convs.tag__describes__answer.att\", \"convs.1.convs.tag__describes__answer.lin_l.weight\", \"convs.1.convs.tag__describes__answer.lin_l.bias\", \"convs.1.convs.tag__describes__answer.lin_r.weight\", \"convs.1.convs.tag__describes__answer.lin_r.bias\", \"convs.1.convs.tag__describes__comment.att\", \"convs.1.convs.tag__describes__comment.lin_l.weight\", \"convs.1.convs.tag__describes__comment.lin_l.bias\", \"convs.1.convs.tag__describes__comment.lin_r.weight\", \"convs.1.convs.tag__describes__comment.lin_r.bias\", \"convs.1.convs.module__imported_in__question.att\", \"convs.1.convs.module__imported_in__question.lin_l.weight\", \"convs.1.convs.module__imported_in__question.lin_l.bias\", \"convs.1.convs.module__imported_in__question.lin_r.weight\", \"convs.1.convs.module__imported_in__question.lin_r.bias\", \"convs.1.convs.module__imported_in__answer.att\", \"convs.1.convs.module__imported_in__answer.lin_l.weight\", \"convs.1.convs.module__imported_in__answer.lin_l.bias\", \"convs.1.convs.module__imported_in__answer.lin_r.weight\", \"convs.1.convs.module__imported_in__answer.lin_r.bias\", \"convs.1.convs.question__rev_describes__tag.att\", \"convs.1.convs.question__rev_describes__tag.lin_l.weight\", \"convs.1.convs.question__rev_describes__tag.lin_l.bias\", \"convs.1.convs.question__rev_describes__tag.lin_r.weight\", \"convs.1.convs.question__rev_describes__tag.lin_r.bias\", \"convs.1.convs.answer__rev_describes__tag.att\", \"convs.1.convs.answer__rev_describes__tag.lin_l.weight\", \"convs.1.convs.answer__rev_describes__tag.lin_l.bias\", \"convs.1.convs.answer__rev_describes__tag.lin_r.weight\", \"convs.1.convs.answer__rev_describes__tag.lin_r.bias\", \"convs.1.convs.comment__rev_describes__tag.att\", \"convs.1.convs.comment__rev_describes__tag.lin_l.weight\", \"convs.1.convs.comment__rev_describes__tag.lin_l.bias\", \"convs.1.convs.comment__rev_describes__tag.lin_r.weight\", \"convs.1.convs.comment__rev_describes__tag.lin_r.bias\", \"convs.1.convs.question__rev_imported_in__module.att\", \"convs.1.convs.question__rev_imported_in__module.lin_l.weight\", \"convs.1.convs.question__rev_imported_in__module.lin_l.bias\", \"convs.1.convs.question__rev_imported_in__module.lin_r.weight\", \"convs.1.convs.question__rev_imported_in__module.lin_r.bias\", \"convs.1.convs.answer__rev_imported_in__module.att\", \"convs.1.convs.answer__rev_imported_in__module.lin_l.weight\", \"convs.1.convs.answer__rev_imported_in__module.lin_l.bias\", \"convs.1.convs.answer__rev_imported_in__module.lin_r.weight\", \"convs.1.convs.answer__rev_imported_in__module.lin_r.bias\", \"convs.2.convs.tag__describes__question.att\", \"convs.2.convs.tag__describes__question.lin_l.weight\", \"convs.2.convs.tag__describes__question.lin_l.bias\", \"convs.2.convs.tag__describes__question.lin_r.weight\", \"convs.2.convs.tag__describes__question.lin_r.bias\", \"convs.2.convs.tag__describes__answer.att\", \"convs.2.convs.tag__describes__answer.lin_l.weight\", \"convs.2.convs.tag__describes__answer.lin_l.bias\", \"convs.2.convs.tag__describes__answer.lin_r.weight\", \"convs.2.convs.tag__describes__answer.lin_r.bias\", \"convs.2.convs.tag__describes__comment.att\", \"convs.2.convs.tag__describes__comment.lin_l.weight\", \"convs.2.convs.tag__describes__comment.lin_l.bias\", \"convs.2.convs.tag__describes__comment.lin_r.weight\", \"convs.2.convs.tag__describes__comment.lin_r.bias\", \"convs.2.convs.module__imported_in__question.att\", \"convs.2.convs.module__imported_in__question.lin_l.weight\", \"convs.2.convs.module__imported_in__question.lin_l.bias\", \"convs.2.convs.module__imported_in__question.lin_r.weight\", \"convs.2.convs.module__imported_in__question.lin_r.bias\", \"convs.2.convs.module__imported_in__answer.att\", \"convs.2.convs.module__imported_in__answer.lin_l.weight\", \"convs.2.convs.module__imported_in__answer.lin_l.bias\", \"convs.2.convs.module__imported_in__answer.lin_r.weight\", \"convs.2.convs.module__imported_in__answer.lin_r.bias\", \"convs.2.convs.question__rev_describes__tag.att\", \"convs.2.convs.question__rev_describes__tag.lin_l.weight\", \"convs.2.convs.question__rev_describes__tag.lin_l.bias\", \"convs.2.convs.question__rev_describes__tag.lin_r.weight\", \"convs.2.convs.question__rev_describes__tag.lin_r.bias\", \"convs.2.convs.answer__rev_describes__tag.att\", \"convs.2.convs.answer__rev_describes__tag.lin_l.weight\", \"convs.2.convs.answer__rev_describes__tag.lin_l.bias\", \"convs.2.convs.answer__rev_describes__tag.lin_r.weight\", \"convs.2.convs.answer__rev_describes__tag.lin_r.bias\", \"convs.2.convs.comment__rev_describes__tag.att\", \"convs.2.convs.comment__rev_describes__tag.lin_l.weight\", \"convs.2.convs.comment__rev_describes__tag.lin_l.bias\", \"convs.2.convs.comment__rev_describes__tag.lin_r.weight\", \"convs.2.convs.comment__rev_describes__tag.lin_r.bias\", \"convs.2.convs.question__rev_imported_in__module.att\", \"convs.2.convs.question__rev_imported_in__module.lin_l.weight\", \"convs.2.convs.question__rev_imported_in__module.lin_l.bias\", \"convs.2.convs.question__rev_imported_in__module.lin_r.weight\", \"convs.2.convs.question__rev_imported_in__module.lin_r.bias\", \"convs.2.convs.answer__rev_imported_in__module.att\", \"convs.2.convs.answer__rev_imported_in__module.lin_l.weight\", \"convs.2.convs.answer__rev_imported_in__module.lin_l.bias\", \"convs.2.convs.answer__rev_imported_in__module.lin_r.weight\", \"convs.2.convs.answer__rev_imported_in__module.lin_r.bias\". \n\tUnexpected key(s) in state_dict: \"convs.0.convs.tag__describes__question.att_src\", \"convs.0.convs.tag__describes__question.att_dst\", \"convs.0.convs.tag__describes__question.lin_src.weight\", \"convs.0.convs.tag__describes__question.lin_dst.weight\", \"convs.0.convs.tag__describes__answer.att_src\", \"convs.0.convs.tag__describes__answer.att_dst\", \"convs.0.convs.tag__describes__answer.lin_src.weight\", \"convs.0.convs.tag__describes__answer.lin_dst.weight\", \"convs.0.convs.tag__describes__comment.att_src\", \"convs.0.convs.tag__describes__comment.att_dst\", \"convs.0.convs.tag__describes__comment.lin_src.weight\", \"convs.0.convs.tag__describes__comment.lin_dst.weight\", \"convs.0.convs.module__imported_in__question.att_src\", \"convs.0.convs.module__imported_in__question.att_dst\", \"convs.0.convs.module__imported_in__question.lin_src.weight\", \"convs.0.convs.module__imported_in__question.lin_dst.weight\", \"convs.0.convs.module__imported_in__answer.att_src\", \"convs.0.convs.module__imported_in__answer.att_dst\", \"convs.0.convs.module__imported_in__answer.lin_src.weight\", \"convs.0.convs.module__imported_in__answer.lin_dst.weight\", \"convs.0.convs.question__rev_describes__tag.att_src\", \"convs.0.convs.question__rev_describes__tag.att_dst\", \"convs.0.convs.question__rev_describes__tag.lin_src.weight\", \"convs.0.convs.question__rev_describes__tag.lin_dst.weight\", \"convs.0.convs.answer__rev_describes__tag.att_src\", \"convs.0.convs.answer__rev_describes__tag.att_dst\", \"convs.0.convs.answer__rev_describes__tag.lin_src.weight\", \"convs.0.convs.answer__rev_describes__tag.lin_dst.weight\", \"convs.0.convs.comment__rev_describes__tag.att_src\", \"convs.0.convs.comment__rev_describes__tag.att_dst\", \"convs.0.convs.comment__rev_describes__tag.lin_src.weight\", \"convs.0.convs.comment__rev_describes__tag.lin_dst.weight\", \"convs.0.convs.question__rev_imported_in__module.att_src\", \"convs.0.convs.question__rev_imported_in__module.att_dst\", \"convs.0.convs.question__rev_imported_in__module.lin_src.weight\", \"convs.0.convs.question__rev_imported_in__module.lin_dst.weight\", \"convs.0.convs.answer__rev_imported_in__module.att_src\", \"convs.0.convs.answer__rev_imported_in__module.att_dst\", \"convs.0.convs.answer__rev_imported_in__module.lin_src.weight\", \"convs.0.convs.answer__rev_imported_in__module.lin_dst.weight\", \"convs.1.convs.tag__describes__question.att_src\", \"convs.1.convs.tag__describes__question.att_dst\", \"convs.1.convs.tag__describes__question.lin_src.weight\", \"convs.1.convs.tag__describes__question.lin_dst.weight\", \"convs.1.convs.tag__describes__answer.att_src\", \"convs.1.convs.tag__describes__answer.att_dst\", \"convs.1.convs.tag__describes__answer.lin_src.weight\", \"convs.1.convs.tag__describes__answer.lin_dst.weight\", \"convs.1.convs.tag__describes__comment.att_src\", \"convs.1.convs.tag__describes__comment.att_dst\", \"convs.1.convs.tag__describes__comment.lin_src.weight\", \"convs.1.convs.tag__describes__comment.lin_dst.weight\", \"convs.1.convs.module__imported_in__question.att_src\", \"convs.1.convs.module__imported_in__question.att_dst\", \"convs.1.convs.module__imported_in__question.lin_src.weight\", \"convs.1.convs.module__imported_in__question.lin_dst.weight\", \"convs.1.convs.module__imported_in__answer.att_src\", \"convs.1.convs.module__imported_in__answer.att_dst\", \"convs.1.convs.module__imported_in__answer.lin_src.weight\", \"convs.1.convs.module__imported_in__answer.lin_dst.weight\", \"convs.1.convs.question__rev_describes__tag.att_src\", \"convs.1.convs.question__rev_describes__tag.att_dst\", \"convs.1.convs.question__rev_describes__tag.lin_src.weight\", \"convs.1.convs.question__rev_describes__tag.lin_dst.weight\", \"convs.1.convs.answer__rev_describes__tag.att_src\", \"convs.1.convs.answer__rev_describes__tag.att_dst\", \"convs.1.convs.answer__rev_describes__tag.lin_src.weight\", \"convs.1.convs.answer__rev_describes__tag.lin_dst.weight\", \"convs.1.convs.comment__rev_describes__tag.att_src\", \"convs.1.convs.comment__rev_describes__tag.att_dst\", \"convs.1.convs.comment__rev_describes__tag.lin_src.weight\", \"convs.1.convs.comment__rev_describes__tag.lin_dst.weight\", \"convs.1.convs.question__rev_imported_in__module.att_src\", \"convs.1.convs.question__rev_imported_in__module.att_dst\", \"convs.1.convs.question__rev_imported_in__module.lin_src.weight\", \"convs.1.convs.question__rev_imported_in__module.lin_dst.weight\", \"convs.1.convs.answer__rev_imported_in__module.att_src\", \"convs.1.convs.answer__rev_imported_in__module.att_dst\", \"convs.1.convs.answer__rev_imported_in__module.lin_src.weight\", \"convs.1.convs.answer__rev_imported_in__module.lin_dst.weight\", \"convs.2.convs.tag__describes__question.att_src\", \"convs.2.convs.tag__describes__question.att_dst\", \"convs.2.convs.tag__describes__question.lin_src.weight\", \"convs.2.convs.tag__describes__question.lin_dst.weight\", \"convs.2.convs.tag__describes__answer.att_src\", \"convs.2.convs.tag__describes__answer.att_dst\", \"convs.2.convs.tag__describes__answer.lin_src.weight\", \"convs.2.convs.tag__describes__answer.lin_dst.weight\", \"convs.2.convs.tag__describes__comment.att_src\", \"convs.2.convs.tag__describes__comment.att_dst\", \"convs.2.convs.tag__describes__comment.lin_src.weight\", \"convs.2.convs.tag__describes__comment.lin_dst.weight\", \"convs.2.convs.module__imported_in__question.att_src\", \"convs.2.convs.module__imported_in__question.att_dst\", \"convs.2.convs.module__imported_in__question.lin_src.weight\", \"convs.2.convs.module__imported_in__question.lin_dst.weight\", \"convs.2.convs.module__imported_in__answer.att_src\", \"convs.2.convs.module__imported_in__answer.att_dst\", \"convs.2.convs.module__imported_in__answer.lin_src.weight\", \"convs.2.convs.module__imported_in__answer.lin_dst.weight\", \"convs.2.convs.question__rev_describes__tag.att_src\", \"convs.2.convs.question__rev_describes__tag.att_dst\", \"convs.2.convs.question__rev_describes__tag.lin_src.weight\", \"convs.2.convs.question__rev_describes__tag.lin_dst.weight\", \"convs.2.convs.answer__rev_describes__tag.att_src\", \"convs.2.convs.answer__rev_describes__tag.att_dst\", \"convs.2.convs.answer__rev_describes__tag.lin_src.weight\", \"convs.2.convs.answer__rev_describes__tag.lin_dst.weight\", \"convs.2.convs.comment__rev_describes__tag.att_src\", \"convs.2.convs.comment__rev_describes__tag.att_dst\", \"convs.2.convs.comment__rev_describes__tag.lin_src.weight\", \"convs.2.convs.comment__rev_describes__tag.lin_dst.weight\", \"convs.2.convs.question__rev_imported_in__module.att_src\", \"convs.2.convs.question__rev_imported_in__module.att_dst\", \"convs.2.convs.question__rev_imported_in__module.lin_src.weight\", \"convs.2.convs.question__rev_imported_in__module.lin_dst.weight\", \"convs.2.convs.answer__rev_imported_in__module.att_src\", \"convs.2.convs.answer__rev_imported_in__module.att_dst\", \"convs.2.convs.answer__rev_imported_in__module.lin_src.weight\", \"convs.2.convs.answer__rev_imported_in__module.lin_dst.weight\". \n\tsize mismatch for convs.0.convs.tag__describes__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.tag__describes__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.tag__describes__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.tag__describes__comment.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.module__imported_in__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.module__imported_in__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.question__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.answer__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.comment__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.question__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.answer__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for lin1.weight: copying a param with shape torch.Size([2, 3392]) from checkpoint, the shape in current model is torch.Size([64, 3392]).\n\tsize mismatch for lin1.bias: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([64]).",
- "output_type": "error",
- "traceback": [
- "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[1;32mIn [2], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# Load model (EXP 1)\u001B[39;00m\n\u001B[0;32m 2\u001B[0m model \u001B[38;5;241m=\u001B[39m HeteroGAT(\u001B[38;5;241m64\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m3\u001B[39m)\n\u001B[1;32m----> 3\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mload_state_dict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mload\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m../models/qa_40e_64h_3l.pt\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmap_location\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdevice\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mcpu\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m model\u001B[38;5;241m.\u001B[39meval()\n",
- "File \u001B[1;32m~\\Documents\\graph4stackoverflow\\venv\\lib\\site-packages\\torch\\nn\\modules\\module.py:1667\u001B[0m, in \u001B[0;36mModule.load_state_dict\u001B[1;34m(self, state_dict, strict)\u001B[0m\n\u001B[0;32m 1662\u001B[0m error_msgs\u001B[38;5;241m.\u001B[39minsert(\n\u001B[0;32m 1663\u001B[0m \u001B[38;5;241m0\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mMissing key(s) in state_dict: \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m. \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\n\u001B[0;32m 1664\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(k) \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m missing_keys)))\n\u001B[0;32m 1666\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(error_msgs) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m-> 1667\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mError(s) in loading state_dict for \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m:\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;130;01m\\t\u001B[39;00m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\n\u001B[0;32m 1668\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;130;01m\\t\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(error_msgs)))\n\u001B[0;32m 1669\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",
- "\u001B[1;31mRuntimeError\u001B[0m: Error(s) in loading state_dict for HeteroGAT:\n\tMissing key(s) in state_dict: \"convs.0.convs.tag__describes__question.att\", \"convs.0.convs.tag__describes__question.lin_l.weight\", \"convs.0.convs.tag__describes__question.lin_l.bias\", \"convs.0.convs.tag__describes__question.lin_r.weight\", \"convs.0.convs.tag__describes__question.lin_r.bias\", \"convs.0.convs.tag__describes__answer.att\", \"convs.0.convs.tag__describes__answer.lin_l.weight\", \"convs.0.convs.tag__describes__answer.lin_l.bias\", \"convs.0.convs.tag__describes__answer.lin_r.weight\", \"convs.0.convs.tag__describes__answer.lin_r.bias\", \"convs.0.convs.tag__describes__comment.att\", \"convs.0.convs.tag__describes__comment.lin_l.weight\", \"convs.0.convs.tag__describes__comment.lin_l.bias\", \"convs.0.convs.tag__describes__comment.lin_r.weight\", \"convs.0.convs.tag__describes__comment.lin_r.bias\", \"convs.0.convs.module__imported_in__question.att\", \"convs.0.convs.module__imported_in__question.lin_l.weight\", \"convs.0.convs.module__imported_in__question.lin_l.bias\", \"convs.0.convs.module__imported_in__question.lin_r.weight\", \"convs.0.convs.module__imported_in__question.lin_r.bias\", \"convs.0.convs.module__imported_in__answer.att\", \"convs.0.convs.module__imported_in__answer.lin_l.weight\", \"convs.0.convs.module__imported_in__answer.lin_l.bias\", \"convs.0.convs.module__imported_in__answer.lin_r.weight\", \"convs.0.convs.module__imported_in__answer.lin_r.bias\", \"convs.0.convs.question__rev_describes__tag.att\", \"convs.0.convs.question__rev_describes__tag.lin_l.weight\", \"convs.0.convs.question__rev_describes__tag.lin_l.bias\", \"convs.0.convs.question__rev_describes__tag.lin_r.weight\", \"convs.0.convs.question__rev_describes__tag.lin_r.bias\", \"convs.0.convs.answer__rev_describes__tag.att\", \"convs.0.convs.answer__rev_describes__tag.lin_l.weight\", \"convs.0.convs.answer__rev_describes__tag.lin_l.bias\", \"convs.0.convs.answer__rev_describes__tag.lin_r.weight\", \"convs.0.convs.answer__rev_describes__tag.lin_r.bias\", \"convs.0.convs.comment__rev_describes__tag.att\", \"convs.0.convs.comment__rev_describes__tag.lin_l.weight\", \"convs.0.convs.comment__rev_describes__tag.lin_l.bias\", \"convs.0.convs.comment__rev_describes__tag.lin_r.weight\", \"convs.0.convs.comment__rev_describes__tag.lin_r.bias\", \"convs.0.convs.question__rev_imported_in__module.att\", \"convs.0.convs.question__rev_imported_in__module.lin_l.weight\", \"convs.0.convs.question__rev_imported_in__module.lin_l.bias\", \"convs.0.convs.question__rev_imported_in__module.lin_r.weight\", \"convs.0.convs.question__rev_imported_in__module.lin_r.bias\", \"convs.0.convs.answer__rev_imported_in__module.att\", \"convs.0.convs.answer__rev_imported_in__module.lin_l.weight\", \"convs.0.convs.answer__rev_imported_in__module.lin_l.bias\", \"convs.0.convs.answer__rev_imported_in__module.lin_r.weight\", \"convs.0.convs.answer__rev_imported_in__module.lin_r.bias\", \"convs.1.convs.tag__describes__question.att\", \"convs.1.convs.tag__describes__question.lin_l.weight\", \"convs.1.convs.tag__describes__question.lin_l.bias\", \"convs.1.convs.tag__describes__question.lin_r.weight\", \"convs.1.convs.tag__describes__question.lin_r.bias\", \"convs.1.convs.tag__describes__answer.att\", \"convs.1.convs.tag__describes__answer.lin_l.weight\", \"convs.1.convs.tag__describes__answer.lin_l.bias\", \"convs.1.convs.tag__describes__answer.lin_r.weight\", \"convs.1.convs.tag__describes__answer.lin_r.bias\", \"convs.1.convs.tag__describes__comment.att\", \"convs.1.convs.tag__describes__comment.lin_l.weight\", \"convs.1.convs.tag__describes__comment.lin_l.bias\", \"convs.1.convs.tag__describes__comment.lin_r.weight\", \"convs.1.convs.tag__describes__comment.lin_r.bias\", \"convs.1.convs.module__imported_in__question.att\", \"convs.1.convs.module__imported_in__question.lin_l.weight\", \"convs.1.convs.module__imported_in__question.lin_l.bias\", \"convs.1.convs.module__imported_in__question.lin_r.weight\", \"convs.1.convs.module__imported_in__question.lin_r.bias\", \"convs.1.convs.module__imported_in__answer.att\", \"convs.1.convs.module__imported_in__answer.lin_l.weight\", \"convs.1.convs.module__imported_in__answer.lin_l.bias\", \"convs.1.convs.module__imported_in__answer.lin_r.weight\", \"convs.1.convs.module__imported_in__answer.lin_r.bias\", \"convs.1.convs.question__rev_describes__tag.att\", \"convs.1.convs.question__rev_describes__tag.lin_l.weight\", \"convs.1.convs.question__rev_describes__tag.lin_l.bias\", \"convs.1.convs.question__rev_describes__tag.lin_r.weight\", \"convs.1.convs.question__rev_describes__tag.lin_r.bias\", \"convs.1.convs.answer__rev_describes__tag.att\", \"convs.1.convs.answer__rev_describes__tag.lin_l.weight\", \"convs.1.convs.answer__rev_describes__tag.lin_l.bias\", \"convs.1.convs.answer__rev_describes__tag.lin_r.weight\", \"convs.1.convs.answer__rev_describes__tag.lin_r.bias\", \"convs.1.convs.comment__rev_describes__tag.att\", \"convs.1.convs.comment__rev_describes__tag.lin_l.weight\", \"convs.1.convs.comment__rev_describes__tag.lin_l.bias\", \"convs.1.convs.comment__rev_describes__tag.lin_r.weight\", \"convs.1.convs.comment__rev_describes__tag.lin_r.bias\", \"convs.1.convs.question__rev_imported_in__module.att\", \"convs.1.convs.question__rev_imported_in__module.lin_l.weight\", \"convs.1.convs.question__rev_imported_in__module.lin_l.bias\", \"convs.1.convs.question__rev_imported_in__module.lin_r.weight\", \"convs.1.convs.question__rev_imported_in__module.lin_r.bias\", \"convs.1.convs.answer__rev_imported_in__module.att\", \"convs.1.convs.answer__rev_imported_in__module.lin_l.weight\", \"convs.1.convs.answer__rev_imported_in__module.lin_l.bias\", \"convs.1.convs.answer__rev_imported_in__module.lin_r.weight\", \"convs.1.convs.answer__rev_imported_in__module.lin_r.bias\", \"convs.2.convs.tag__describes__question.att\", \"convs.2.convs.tag__describes__question.lin_l.weight\", \"convs.2.convs.tag__describes__question.lin_l.bias\", \"convs.2.convs.tag__describes__question.lin_r.weight\", \"convs.2.convs.tag__describes__question.lin_r.bias\", \"convs.2.convs.tag__describes__answer.att\", \"convs.2.convs.tag__describes__answer.lin_l.weight\", \"convs.2.convs.tag__describes__answer.lin_l.bias\", \"convs.2.convs.tag__describes__answer.lin_r.weight\", \"convs.2.convs.tag__describes__answer.lin_r.bias\", \"convs.2.convs.tag__describes__comment.att\", \"convs.2.convs.tag__describes__comment.lin_l.weight\", \"convs.2.convs.tag__describes__comment.lin_l.bias\", \"convs.2.convs.tag__describes__comment.lin_r.weight\", \"convs.2.convs.tag__describes__comment.lin_r.bias\", \"convs.2.convs.module__imported_in__question.att\", \"convs.2.convs.module__imported_in__question.lin_l.weight\", \"convs.2.convs.module__imported_in__question.lin_l.bias\", \"convs.2.convs.module__imported_in__question.lin_r.weight\", \"convs.2.convs.module__imported_in__question.lin_r.bias\", \"convs.2.convs.module__imported_in__answer.att\", \"convs.2.convs.module__imported_in__answer.lin_l.weight\", \"convs.2.convs.module__imported_in__answer.lin_l.bias\", \"convs.2.convs.module__imported_in__answer.lin_r.weight\", \"convs.2.convs.module__imported_in__answer.lin_r.bias\", \"convs.2.convs.question__rev_describes__tag.att\", \"convs.2.convs.question__rev_describes__tag.lin_l.weight\", \"convs.2.convs.question__rev_describes__tag.lin_l.bias\", \"convs.2.convs.question__rev_describes__tag.lin_r.weight\", \"convs.2.convs.question__rev_describes__tag.lin_r.bias\", \"convs.2.convs.answer__rev_describes__tag.att\", \"convs.2.convs.answer__rev_describes__tag.lin_l.weight\", \"convs.2.convs.answer__rev_describes__tag.lin_l.bias\", \"convs.2.convs.answer__rev_describes__tag.lin_r.weight\", \"convs.2.convs.answer__rev_describes__tag.lin_r.bias\", \"convs.2.convs.comment__rev_describes__tag.att\", \"convs.2.convs.comment__rev_describes__tag.lin_l.weight\", \"convs.2.convs.comment__rev_describes__tag.lin_l.bias\", \"convs.2.convs.comment__rev_describes__tag.lin_r.weight\", \"convs.2.convs.comment__rev_describes__tag.lin_r.bias\", \"convs.2.convs.question__rev_imported_in__module.att\", \"convs.2.convs.question__rev_imported_in__module.lin_l.weight\", \"convs.2.convs.question__rev_imported_in__module.lin_l.bias\", \"convs.2.convs.question__rev_imported_in__module.lin_r.weight\", \"convs.2.convs.question__rev_imported_in__module.lin_r.bias\", \"convs.2.convs.answer__rev_imported_in__module.att\", \"convs.2.convs.answer__rev_imported_in__module.lin_l.weight\", \"convs.2.convs.answer__rev_imported_in__module.lin_l.bias\", \"convs.2.convs.answer__rev_imported_in__module.lin_r.weight\", \"convs.2.convs.answer__rev_imported_in__module.lin_r.bias\". \n\tUnexpected key(s) in state_dict: \"convs.0.convs.tag__describes__question.att_src\", \"convs.0.convs.tag__describes__question.att_dst\", \"convs.0.convs.tag__describes__question.lin_src.weight\", \"convs.0.convs.tag__describes__question.lin_dst.weight\", \"convs.0.convs.tag__describes__answer.att_src\", \"convs.0.convs.tag__describes__answer.att_dst\", \"convs.0.convs.tag__describes__answer.lin_src.weight\", \"convs.0.convs.tag__describes__answer.lin_dst.weight\", \"convs.0.convs.tag__describes__comment.att_src\", \"convs.0.convs.tag__describes__comment.att_dst\", \"convs.0.convs.tag__describes__comment.lin_src.weight\", \"convs.0.convs.tag__describes__comment.lin_dst.weight\", \"convs.0.convs.module__imported_in__question.att_src\", \"convs.0.convs.module__imported_in__question.att_dst\", \"convs.0.convs.module__imported_in__question.lin_src.weight\", \"convs.0.convs.module__imported_in__question.lin_dst.weight\", \"convs.0.convs.module__imported_in__answer.att_src\", \"convs.0.convs.module__imported_in__answer.att_dst\", \"convs.0.convs.module__imported_in__answer.lin_src.weight\", \"convs.0.convs.module__imported_in__answer.lin_dst.weight\", \"convs.0.convs.question__rev_describes__tag.att_src\", \"convs.0.convs.question__rev_describes__tag.att_dst\", \"convs.0.convs.question__rev_describes__tag.lin_src.weight\", \"convs.0.convs.question__rev_describes__tag.lin_dst.weight\", \"convs.0.convs.answer__rev_describes__tag.att_src\", \"convs.0.convs.answer__rev_describes__tag.att_dst\", \"convs.0.convs.answer__rev_describes__tag.lin_src.weight\", \"convs.0.convs.answer__rev_describes__tag.lin_dst.weight\", \"convs.0.convs.comment__rev_describes__tag.att_src\", \"convs.0.convs.comment__rev_describes__tag.att_dst\", \"convs.0.convs.comment__rev_describes__tag.lin_src.weight\", \"convs.0.convs.comment__rev_describes__tag.lin_dst.weight\", \"convs.0.convs.question__rev_imported_in__module.att_src\", \"convs.0.convs.question__rev_imported_in__module.att_dst\", 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\"convs.1.convs.answer__rev_describes__tag.lin_dst.weight\", \"convs.1.convs.comment__rev_describes__tag.att_src\", \"convs.1.convs.comment__rev_describes__tag.att_dst\", \"convs.1.convs.comment__rev_describes__tag.lin_src.weight\", \"convs.1.convs.comment__rev_describes__tag.lin_dst.weight\", \"convs.1.convs.question__rev_imported_in__module.att_src\", \"convs.1.convs.question__rev_imported_in__module.att_dst\", \"convs.1.convs.question__rev_imported_in__module.lin_src.weight\", \"convs.1.convs.question__rev_imported_in__module.lin_dst.weight\", \"convs.1.convs.answer__rev_imported_in__module.att_src\", \"convs.1.convs.answer__rev_imported_in__module.att_dst\", \"convs.1.convs.answer__rev_imported_in__module.lin_src.weight\", \"convs.1.convs.answer__rev_imported_in__module.lin_dst.weight\", \"convs.2.convs.tag__describes__question.att_src\", \"convs.2.convs.tag__describes__question.att_dst\", \"convs.2.convs.tag__describes__question.lin_src.weight\", \"convs.2.convs.tag__describes__question.lin_dst.weight\", \"convs.2.convs.tag__describes__answer.att_src\", \"convs.2.convs.tag__describes__answer.att_dst\", \"convs.2.convs.tag__describes__answer.lin_src.weight\", \"convs.2.convs.tag__describes__answer.lin_dst.weight\", \"convs.2.convs.tag__describes__comment.att_src\", \"convs.2.convs.tag__describes__comment.att_dst\", \"convs.2.convs.tag__describes__comment.lin_src.weight\", \"convs.2.convs.tag__describes__comment.lin_dst.weight\", \"convs.2.convs.module__imported_in__question.att_src\", \"convs.2.convs.module__imported_in__question.att_dst\", \"convs.2.convs.module__imported_in__question.lin_src.weight\", \"convs.2.convs.module__imported_in__question.lin_dst.weight\", \"convs.2.convs.module__imported_in__answer.att_src\", \"convs.2.convs.module__imported_in__answer.att_dst\", \"convs.2.convs.module__imported_in__answer.lin_src.weight\", \"convs.2.convs.module__imported_in__answer.lin_dst.weight\", \"convs.2.convs.question__rev_describes__tag.att_src\", \"convs.2.convs.question__rev_describes__tag.att_dst\", \"convs.2.convs.question__rev_describes__tag.lin_src.weight\", \"convs.2.convs.question__rev_describes__tag.lin_dst.weight\", \"convs.2.convs.answer__rev_describes__tag.att_src\", \"convs.2.convs.answer__rev_describes__tag.att_dst\", \"convs.2.convs.answer__rev_describes__tag.lin_src.weight\", \"convs.2.convs.answer__rev_describes__tag.lin_dst.weight\", \"convs.2.convs.comment__rev_describes__tag.att_src\", \"convs.2.convs.comment__rev_describes__tag.att_dst\", \"convs.2.convs.comment__rev_describes__tag.lin_src.weight\", \"convs.2.convs.comment__rev_describes__tag.lin_dst.weight\", \"convs.2.convs.question__rev_imported_in__module.att_src\", \"convs.2.convs.question__rev_imported_in__module.att_dst\", \"convs.2.convs.question__rev_imported_in__module.lin_src.weight\", \"convs.2.convs.question__rev_imported_in__module.lin_dst.weight\", \"convs.2.convs.answer__rev_imported_in__module.att_src\", \"convs.2.convs.answer__rev_imported_in__module.att_dst\", \"convs.2.convs.answer__rev_imported_in__module.lin_src.weight\", \"convs.2.convs.answer__rev_imported_in__module.lin_dst.weight\". \n\tsize mismatch for convs.0.convs.tag__describes__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.tag__describes__comment.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.module__imported_in__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.comment__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.question__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.0.convs.answer__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.tag__describes__comment.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.module__imported_in__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.comment__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.question__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.1.convs.answer__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.tag__describes__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.tag__describes__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.tag__describes__comment.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.module__imported_in__question.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.module__imported_in__answer.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.question__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.answer__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.comment__rev_describes__tag.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.question__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for convs.2.convs.answer__rev_imported_in__module.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([384]).\n\tsize mismatch for lin1.weight: copying a param with shape torch.Size([2, 3392]) from checkpoint, the shape in current model is torch.Size([64, 3392]).\n\tsize mismatch for lin1.bias: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([64])."
- ]
+ "data": {
+ "text/plain": "HeteroGAT(\n (convs): ModuleList(\n (0): HeteroConv(num_relations=10)\n (1): HeteroConv(num_relations=10)\n (2): HeteroConv(num_relations=10)\n )\n (lin1): Linear(3392, 64, bias=True)\n (lin2): Linear(64, 2, bias=True)\n (softmax): Softmax(dim=-1)\n)"
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
"# Load model (EXP 1)\n",
- "model = HeteroGAT(64, 2, 3)\n",
- "model.load_state_dict(torch.load('../models/qa_40e_64h_3l.pt', map_location=torch.device('cpu')))\n",
+ "model = HeteroGAT(out_channels=2, num_layers=3, hidden_channels=64)\n",
+ "model.load_state_dict(torch.load('../models/SAGE_3l_60e_64h.pt', map_location=torch.device('cpu')), strict=False)\n",
"model.eval()"
],
"metadata": {
@@ -70,14 +74,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-03-30 15:46:34 INFO PostEmbedding instantiated!\n"
+ "2023-04-16 16:49:34 INFO PostEmbedding instantiated!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias']\n",
+ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
@@ -137,16 +141,29 @@
{
"cell_type": "code",
"execution_count": 6,
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 2126/2126 [22:26<00:00, 1.58it/s]\n"
+ ]
+ }
+ ],
"source": [
+ "from tqdm import tqdm\n",
"from torch_geometric.loader import DataLoader\n",
"\n",
"correct_tag_count = {}\n",
"incorrect_tag_count = {}\n",
"\n",
+ "# DEBUG START\n",
+ "tag_question_ids = {}\n",
+ "# DEBUG END\n",
+ "\n",
"tag_predictions = {}\n",
"\n",
- "for i in test_ids[:500]:\n",
+ "for i in tqdm(test_ids):\n",
" graph_files = list(fetch_answer_files(i))\n",
" dataloader = DataLoader(graph_files, batch_size=1, shuffle=False)\n",
"\n",
@@ -164,6 +181,13 @@
" tags = ['python']\n",
"\n",
" for tag in tags:\n",
+ " # DEBUG START\n",
+ " if tag not in tag_question_ids:\n",
+ " tag_question_ids[tag] = [i]\n",
+ " else:\n",
+ " tag_question_ids[tag].append(i)\n",
+ " # DEBUG END\n",
+ "\n",
" if tag not in tag_predictions:\n",
" tag_predictions[tag] = ([pred.item()], [graph.label.item()])\n",
" else:\n",
@@ -189,12 +213,27 @@
{
"cell_type": "code",
"execution_count": 7,
+ "outputs": [],
+ "source": [
+ "# DEBUG START\n",
+ "# Pickle the tag_question_ids dict\n",
+ "import pickle\n",
+ "with open('../data/raw/tag_question_ids.pickle', 'wb') as handle:\n",
+ " pickle.dump(tag_question_ids, handle, protocol=pickle.HIGHEST_PROTOCOL)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
"outputs": [
{
"data": {
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'rapydscript': 2,\n 'scgi': 2,\n 'alias': 4,\n 'matrix': 2,\n 'haml': 4,\n 'geometry': 1,\n 'routing': 2,\n 'kcachegrind': 4,\n 'cprofile': 4,\n 'open-source': 1,\n 'division': 5,\n 'pretty-print': 40,\n 'financial': 6,\n 'reference': 7,\n 'camelcasing': 20,\n 'passwords': 7,\n 'groovy': 6,\n 'standardized': 2,\n 'pythonw': 4,\n 'ownership': 2,\n 'code-organization': 14,\n 'api': 9,\n 'nth-root': 6,\n 'content-type': 11,\n 'string-formatting': 25,\n 'configure': 1,\n 'model-view-controller': 4,\n 'flatten': 25,\n 'nginx': 3,\n 'fastcgi': 1,\n 'client': 7,\n 'python-unittest': 14,\n 'parity': 1,\n 'alpha': 3,\n 'blit': 8,\n 'menu': 2,\n 'splat': 5,\n 'button': 3,\n 'android': 6,\n 'dalvik': 6,\n 'temporary-directory': 2,\n 'macros': 4,\n 'rss': 1,\n 'feedparser': 1,\n 'caching': 12,\n 'memoization': 7,\n 'pyside': 2,\n 'elisp': 1,\n 'concatenation': 2,\n 'rfc822': 2,\n 'refactoring': 7,\n 'fluent-interface': 6,\n 'unix-timestamp': 2,\n 'multiple-return-values': 5,\n 'activepython': 4,\n 'geometry-surface': 5,\n 'wikipedia': 3,\n 'message-queue': 2,\n 'gettext': 3,\n 'architecture': 1,\n 'memcpy': 1,\n 'jenkins': 7,\n 'buildbot': 7,\n 'wave': 10,\n 'generics': 1,\n 'formset': 1,\n 'queue': 7,\n 'thread-safety': 8,\n 'deque': 7,\n 'erlang': 8,\n 'robotics': 8,\n 'terminate': 18,\n 'dom': 2,\n 'next': 2,\n 'language-implementation': 1,\n 'rename': 2,\n 'batch-rename': 2,\n 'timsort': 2,\n 'ntfs': 4,\n 'junction': 4,\n 'jit': 2,\n 'psyco': 2,\n 'autovivification': 4,\n 'mkdir': 1,\n 'coercion': 2,\n 'hpricot': 2,\n 'parallel-processing': 3,\n 'cluster-computing': 3,\n 'directory-structure': 6,\n 'organization': 6,\n 'weak-references': 2,\n 'django-signals': 2,\n 'edi': 6,\n 'reverse-lookup': 5,\n 'levenshtein-distance': 4,\n 'edit-distance': 4,\n 'dialog': 9,\n 'zenity': 9,\n 'partial': 1,\n 'parsec': 2,\n 'combinators': 2,\n 'automated-tests': 2,\n 'pylint': 27,\n 'sql-parametrized-query': 4,\n 'chat': 1,\n 'private-messaging': 1,\n 'doctest': 2,\n 'transparent': 3,\n 'builder': 4,\n 'iso8601': 21,\n 'datetime-parsing': 21,\n 'rfc3339': 21,\n 'debian': 7,\n 'typeof': 13,\n 'interpreter': 2,\n 'pypy': 2,\n 'precision': 17,\n 'python-egg-cache': 6,\n 'visualization': 1,\n 'progress-bar': 1,\n 'httpclient': 1,\n 'variable-variables': 14,\n 'key-value-store': 2,\n 'anonymous-types': 6,\n 'resources': 5,\n 'indentation': 7,\n 'binding': 3,\n 'equality': 10,\n 'equivalence': 3,\n 'lex': 5,\n 'com': 6,\n 'foreign-keys': 2,\n 'gzip': 1,\n 'zlib': 1,\n 'backgrounding': 4,\n 'batch-file': 3,\n 'pwd': 1,\n 'signal-processing': 8,\n 'average': 8,\n 'digital-filter': 8,\n 'django-validation': 4,\n 'keyboard': 6,\n 'blocking': 6,\n 'mako': 3,\n 'pprint': 5,\n 'static-methods': 3,\n 'selenium': 1,\n 'disk-based': 4,\n 'folding': 2,\n 'wildcard': 6,\n 'directory-listing': 6,\n 'cocoa': 1,\n 'lookup-tables': 2,\n 'global-variables': 11,\n 'ms-office': 1,\n 'complexity-theory': 2,\n 'setup.py': 10,\n 'python-packaging': 10,\n 'daemons': 5,\n '32-bit': 5,\n 'multiline': 1,\n 'read-eval-print-loop': 10,\n 'interactive-session': 10,\n 'anonymous-class': 7,\n 'namedtuple': 2,\n 'django-custom-tags': 2,\n 'click': 2,\n 'stringio': 5,\n 'static-typing': 3,\n 'rounding-error': 2,\n 'arbitrary-precision': 2,\n 'multiple-variable-return': 3,\n 'imaging': 3,\n 'quine': 6,\n 'log4j': 1,\n 'os.execl': 4,\n 'syntactic-sugar': 7,\n 'bandwidth-throttling': 1,\n 'google-talk': 2,\n 'linear-programming': 1,\n 'inequality': 1,\n 'digits': 1,\n 'content-management-system': 4,\n 'joomla': 4,\n 'tags': 2,\n 'palindrome': 10,\n 'html-email': 10,\n 'metaclass': 19,\n 'serial-port': 3,\n 'pyserial': 3,\n 'virtual-serial-port': 1,\n 'readprocessmemory': 2,\n 'uml': 2,\n 'unique-id': 5,\n ...}"
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -208,13 +247,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"outputs": [
{
"data": {
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'global-variables': 12,\n 'enumerate': 3,\n 'ms-office': 6,\n 'complexity-theory': 4,\n 'text-manipulation': 2,\n 'daemons': 12,\n '32-bit': 6,\n 'multiline': 6,\n 'read-eval-print-loop': 10,\n 'interactive-session': 10,\n 'anonymous-class': 4,\n 'django-custom-tags': 1,\n 'stringio': 2,\n 'static-typing': 2,\n 'rounding-error': 4,\n 'arbitrary-precision': 4,\n 'imaging': 1,\n 'quine': 4,\n 'mta': 3,\n 'smtpd': 3,\n 'log4j': 2,\n 'os.execl': 2,\n 'ucd': 6,\n 'character-properties': 6,\n 'google-talk': 2,\n 'linear-programming': 4,\n 'inequality': 4,\n 'digits': 10,\n 'content-management-system': 5,\n 'joomla': 5,\n 'tags': 2,\n 'modularity': 5,\n 'software-design': 5,\n 'palindrome': 13,\n 'filesize': 5,\n ...}"
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -228,13 +267,13 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"outputs": [
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'pyqt': ([1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'easy-install': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'pip': ([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'python': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 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1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'django': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'django-models': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'django-managers': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'list': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'multithreading': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'unit-testing': ([1, 0, 0, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 0, 1, 1]),\n 'exception': ([1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'tuples': ([1, 1, 1], [1, 1, 1]),\n 'redirect': ([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1]),\n 'stderr': ([1, 1, 1, 1, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 0, 1, 1]),\n 'sql': ([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1]),\n 'django-queryset': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'django-aggregation': ([1, 1], [1, 1]),\n 'git': ([1, 1, 0], [1, 1, 0]),\n 'githooks': ([1, 1, 0], [1, 1, 0]),\n 'random': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'lisp': ([1, 0, 1, 1, 1, 1, 0, 1], [1, 0, 1, 0, 1, 0, 1, 0]),\n 'clojure': ([1, 0, 1, 1, 1, 1, 0, 1], [1, 0, 1, 0, 1, 0, 1, 0]),\n 'idioms': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'coding-style': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'conventions': ([1, 1, 1, 0], [1, 1, 1, 0]),\n 'algorithm': ([0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'sorting': ([0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'file': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'loops': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'subprocess': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'stdout': ([1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'popen': ([1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'sqlalchemy': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'insert': ([1, 1], [1, 1]),\n 'portability': ([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'java': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'ruby': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'cross-platform': ([1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'string': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'path': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'logging': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'amazon-web-services': ([1, 1, 1, 1, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'boto': ([1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1]),\n 'wxpython': ([1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'distribution': ([1, 0, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 0, 1]),\n 'freeze': ([1, 0, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 0, 1]),\n 'c++': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'swfupload': ([1, 1, 1], [1, 1, 0]),\n 'split': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1]),\n 'text-segmentation': ([1, 1, 1, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'pypi': ([1, 1, 1, 0, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 0, 0, 1, 0, 1, 1]),\n 'garbage-collection': ([0, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]),\n 'attributes': ([1, 1, 0, 1, 1], [1, 1, 1, 1, 0]),\n 'object': ([1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'python-3.x': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'character': ([1, 1, 1, 1, 1, 1], [1, 1, 0, 1, 1, 1]),\n 'numpy': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'scipy': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1]),\n 'statistics': ([1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'posix': ([1, 1, 1, 1, 1], [0, 0, 1, 1, 1]),\n 'django-templates': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'apache': ([0, 1, 0, 0, 0, 1, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0]),\n 'cgi': ([0, 1, 0, 0, 0, 1, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0]),\n 'recursion': ([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'lambda': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'y-combinator': ([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1]),\n 'url': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'uuid': ([1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'tinyurl': ([1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1]),\n 'short-url': ([1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1]),\n 'windows': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'linux': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'preferences': ([1, 0, 1, 1, 1, 1, 0, 1, 1], [1, 0, 1, 0, 0, 0, 0, 1, 0]),\n 'templates': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'code-generation': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'c': ([1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'refcounting': ([1, 1, 0], [1, 1, 1]),\n 'html': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'html-entities': ([1, 1, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1]),\n 'ide': ([0, 1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'matplotlib': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'dictionary': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'django-forms': ([1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'performance': ([1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'compression': ([1, 1], [1, 1]),\n 'zip': ([1, 1], [1, 1]),\n 'extraction': ([1, 1], [1, 1]),\n 'dll': ([1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'floating-point': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'python-sphinx': ([1, 1, 1, 1, 1, 1, 0, 0, 0, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'autodoc': ([1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1]),\n 'range': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'qt4': ([1, 1, 1, 1, 1], [1, 0, 1, 1, 1]),\n 'math': ([0, 1, 1, 1, 1, 1, 0, 1, 0], [1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'probability': ([0, 1, 1, 1], [1, 1, 1, 1]),\n 'spring': ([1, 1, 1], [1, 1, 0]),\n 'google-app-engine': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'xpath': ([0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'beautifulsoup': ([0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]),\n 'mechanize': ([0, 1, 0, 1, 1], [1, 1, 1, 0, 0]),\n 'wamp': ([1, 1, 1], [1, 0, 1]),\n 'mod-python': ([1, 1, 1], [1, 0, 1]),\n 'configuration': ([1, 1, 1, 0, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'serialization': ([1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'integer': ([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'r': ([1, 1], [1, 1]),\n 'data-visualization': ([1, 1], [1, 1]),\n 'web-services': ([0, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'twitter': ([0, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'memory-address': ([0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'repr': ([0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'python-c-api': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'io-redirection': ([1, 1], [1, 1]),\n 'timer': ([0, 1, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'decimal': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'setuptools': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'distutils': ([1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'iterator': ([1, 1, 1, 0, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 1, 1, 1, 0, 1, 1]),\n 'introspection': ([0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'utf-8': ([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'ascii': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'decode': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'arrays': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'perl': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'regex': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'tree-structure': ([1, 0, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'replace': ([1, 0, 1, 1, 0, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'proxy': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'web-crawler': ([1, 1], [1, 1]),\n 'pool': ([1, 1], [1, 1]),\n 'stack-trace': ([1, 0, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'super': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'exit-code': ([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'testing': ([1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'multiprocessing': ([1, 1, 1], [0, 0, 1]),\n 'amazon-s3': ([0, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1]),\n 'memory-management': ([0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n '32bit-64bit': ([0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'reduce': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'orm': ([1, 1, 0, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'persistence': ([1, 1, 0, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'cygwin': ([1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'cmd': ([1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'pipe': ([1, 1, 0, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'append': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'nested-lists': ([1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'io': ([1, 0, 1, 0], [1, 1, 1, 1]),\n 'temporary-files': ([1, 0, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'directory': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1]),\n 'subdirectory': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1]),\n 'scheduled-tasks': ([0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'enterprise': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'quartz-scheduler': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'numbers': ([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'csv': ([1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0]),\n 'database': ([1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1]),\n 'browser': ([1, 1, 0, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'remote-execution': ([1, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'urllib': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0]),\n 'tor': ([1, 1, 1], [1, 1, 1]),\n 'pdf': ([0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'internationalization': ([0, 1], [1, 1]),\n 'pdf-generation': ([0, 1], [1, 1]),\n 'time': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'strftime': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'entity': ([1, 1], [1, 1]),\n 'google-cloud-datastore': ([1, 1], [1, 1]),\n 'centos': ([1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'rpath': ([1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'xml': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'parsing': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'shell': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'zsh': ([1, 1], [1, 1]),\n 'ipython': ([1, 1], [1, 1]),\n 'system-information': ([1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'file-io': ([0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1]),\n 'multidimensional-array': ([0, 1, 1], [1, 1, 1]),\n 'fileparsing': ([0, 1, 1], [1, 1, 1]),\n 'preprocessor': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'equivalent': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'directive': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'scala': ([0, 1, 1, 1, 0, 0], [1, 1, 1, 0, 1, 1]),\n 'oauth': ([0, 1, 1, 1, 0, 0], [1, 1, 1, 0, 1, 1]),\n 'list-comprehension': ([1, 0, 1, 0, 1, 0, 0], [1, 1, 1, 0, 0, 0, 0]),\n 'sockets': ([1, 0, 1, 0], [1, 1, 1, 1]),\n 'wsgi': ([1, 0, 1, 0], [1, 1, 1, 1]),\n 'httplib2': ([1, 0, 1, 0, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'werkzeug': ([1, 0, 1, 0], [1, 1, 1, 1]),\n 'primes': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0]),\n 'memory': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'jvm': ([1, 1, 1], [1, 1, 1]),\n 'data-structures': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1]),\n 'extend': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'python-2.x': ([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'line-endings': ([1, 1, 1], [1, 1, 1]),\n 'mysql': ([1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'documentation': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 1, 1]),\n 'notation': ([1, 1, 1, 1, 1], [1, 1, 1, 0, 0]),\n 'large-files': ([0, 0, 1, 1, 1, 0, 0, 1], [1, 1, 1, 0, 1, 1, 0, 1]),\n 'class': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1]),\n 'instances': ([1, 1, 1, 1], [1, 1, 1, 0]),\n 'unicode': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'python-2.6': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'unicode-literals': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'c#': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'software-quality': ([0, 1, 1, 0, 0], [1, 1, 1, 1, 1]),\n 'code-coverage': ([0, 1, 1, 0, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'coverage.py': ([0, 1, 1, 0, 0], [1, 1, 1, 1, 1]),\n 'gtk': ([1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]),\n 'pygtk': ([1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]),\n 'pango': ([1], [1]),\n 'for-loop': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'map-function': ([1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0]),\n 'yield': ([1, 1, 0], [1, 1, 0]),\n 'virtualenv': ([1, 1, 1, 1, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'version': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'nested-loops': ([1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'oop': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]),\n 'naming-conventions': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'identifier': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'process': ([0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0]),\n 'fork': ([0, 1, 1, 0, 0, 1, 1], [0, 1, 1, 1, 1, 1, 1]),\n 'vi': ([1, 1, 0, 1, 1, 1, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'pep8': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'proxy-classes': ([1, 1, 1, 1, 1], [1, 1, 0, 1, 1]),\n 'readability': ([0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1]),\n 'vim': ([1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'jinja2': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'vim-syntax-highlighting': ([1], [1]),\n 'macos': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 0, 0, 1, 1]),\n 'clipboard': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1]),\n 'python-idle': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'xhtml': ([1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 0, 1, 1, 1, 1]),\n 'fabric': ([1, 1, 1, 0], [1, 1, 1, 1]),\n 'struct': ([1, 0, 1], [1, 1, 1]),\n 'ctypes': ([1, 0, 1], [1, 1, 1]),\n 'python-import': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'circular-dependency': ([1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'cyclic-reference': ([1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'monkeypatching': ([0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1]),\n 'php': ([1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'debugging': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'audio': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'wiki': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'markup': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'unique': ([1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1]),\n 'set': ([0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'compiler-construction': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0]),\n 'static-analysis': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'use-strict': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'reflection': ([1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1]),\n 'timezone': ([0, 1, 0, 1, 1], [1, 1, 1, 1, 1]),\n 'django-views': ([1, 1, 1], [1, 1, 1]),\n 'sizeof': ([1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'mmap': ([0, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'embedded': ([1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0]),\n 'fuzzy-logic': ([1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0]),\n 'hyperlink': ([1, 0, 1, 1, 1], [1, 0, 0, 1, 1]),\n 'scientific-computing': ([0, 1, 1, 0, 1], [1, 1, 1, 1, 0]),\n 'image-processing': ([1, 0, 1, 0, 0, 0, 1], [1, 1, 1, 1, 0, 0, 1]),\n 'colors': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'profile': ([1, 1, 0, 1, 1, 1, 0], [1, 0, 1, 0, 1, 1, 1]),\n 'sql-server': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'pyodbc': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'visual-c++': ([0, 0, 1, 0, 0], [1, 1, 1, 1, 1]),\n 'diff': ([1, 1], [1, 1]),\n 'ip-address': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'tcp': ([1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'erp': ([1, 1], [1, 1]),\n 'dabo': ([1, 1], [1, 1]),\n 'distribute': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'decorator': ([1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'substring': ([1, 1, 1, 1, 0, 1], [1, 1, 1, 0, 0, 1]),\n 'double': ([1, 1, 1], [1, 1, 1]),\n 'django-admin': ([0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'keyword-argument': ([1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0]),\n 'django-urls': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 0, 1, 1, 1, 0]),\n 'terminal': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'command': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'count': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'python-imaging-library': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'streaming': ([1, 1, 1], [1, 1, 1]),\n 'linq': ([1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1]),\n 'functional-programming': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'compilation': ([1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'bytecode': ([1, 1, 1, 0, 1], [1, 1, 1, 1, 1]),\n 'ajax': ([1, 1, 0], [1, 0, 1]),\n 'comet': ([1, 1, 0], [1, 0, 1]),\n 'long-polling': ([1, 1, 0], [1, 0, 1]),\n 'tornado': ([1, 1, 0], [1, 0, 1]),\n 'email': ([0, 1, 1], [1, 1, 1]),\n 'gmail': ([0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'imap': ([0, 1, 1, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'endianness': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'cryptography': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'validation': ([1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'slug': ([1, 0], [1, 1]),\n 'networking': ([1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'mysql-python': ([1, 0, 0, 1, 1, 0], [1, 1, 1, 1, 1, 0]),\n 'unix': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'command-line': ([1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'autocomplete': ([1, 1, 1, 0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'profiling': ([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1]),\n 'mp3': ([1, 1, 1, 1, 1], [1, 0, 1, 1, 1]),\n 'frequency': ([1, 1, 1, 1, 1], [1, 0, 1, 1, 1]),\n 'hash': ([1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'urlencode': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'clean-urls': ([1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'system-tray': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'filesystems': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'glob': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'fnmatch': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'constructor': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 0, 1, 1]),\n 'byte': ([1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0]),\n 'file-attributes': ([0, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 1, 1]),\n 'file-properties': ([0, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 1, 1]),\n 'heap': ([1, 1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'postgresql': ([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'psycopg2': ([1, 1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'forms': ([0, 1, 1, 0, 1], [1, 0, 1, 1, 1]),\n 'widget': ([0, 1, 1, 0, 1], [1, 0, 1, 1, 1]),\n 'admin': ([0, 1, 1, 0, 1], [1, 0, 1, 1, 1]),\n 'project': ([0, 0, 1, 1, 0], [0, 1, 1, 1, 1]),\n 'structure': ([0, 0, 1, 1, 0], [0, 1, 1, 1, 1]),\n 'nose': ([1, 1], [1, 1]),\n 'nosetests': ([1, 1], [1, 1]),\n 'working-directory': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'file-management': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0]),\n 'artificial-intelligence': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'ocr': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'bash': ([1, 1, 1, 0, 1], [1, 1, 1, 1, 1]),\n 'single-instance': ([1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'floating-accuracy': ([1, 1, 1, 0, 1, 0], [1, 1, 1, 0, 1, 1]),\n 'windows-7-x64': ([1, 0, 1, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 1, 1, 0, 1, 0, 1, 0, 0]),\n 'assert': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'assertion': ([1, 1, 1, 1], [1, 1, 1, 1]),\n '.net': ([1, 1, 0, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'dynamic-languages': ([1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'minidom': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'mocking': ([0, 0, 0, 1], [1, 1, 1, 1]),\n 'module': ([1, 1, 1], [1, 1, 1]),\n 'seed': ([1, 1, 1], [1, 1, 1]),\n 'cherrypy': ([1, 1, 0, 1, 1], [1, 1, 1, 1, 1]),\n 'language-agnostic': ([1, 0, 0, 0, 1, 1, 1, 1], [1, 1, 1, 0, 1, 1, 1, 1]),\n 'encoding': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'overriding': ([1, 1, 0, 0], [1, 0, 1, 1]),\n 'class-method': ([1, 1, 0, 0], [1, 0, 1, 1]),\n 'ms-word': ([0, 1, 1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'rounding': ([0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'slice': ([1, 1, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'osx-snow-leopard': ([1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1],\n [1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0]),\n 'smalltalk': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'json': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'function': ([1, 0, 1, 1], [1, 1, 1, 0]),\n 'variables': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'storage': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'photos': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'photo-management': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'pygsl': ([1, 0, 1, 1], [1, 1, 1, 1]),\n 'urllib2': ([1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'socks': ([1, 1, 1], [1, 1, 1]),\n 'time-series': ([1, 1, 1, 0, 1], [1, 1, 1, 1, 0]),\n 'schemaless': ([1, 1, 1, 0, 1], [1, 1, 1, 1, 0]),\n 'ssh': ([0, 1, 1, 1, 0], [1, 1, 1, 1, 0]),\n 'paramiko': ([0, 1, 1, 1, 0], [1, 1, 1, 1, 0]),\n 'uniqueidentifier': ([1, 1, 0, 1, 0], [1, 1, 0, 1, 1]),\n 'integration': ([1, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'numerical-methods': ([1, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'image': ([1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1],\n [1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'ipc': ([1, 1, 0, 1], [1, 1, 1, 1]),\n 'sysv': ([1, 1, 0, 1], [1, 1, 1, 1]),\n 'shared-memory': ([1, 1, 0, 1], [1, 1, 1, 1]),\n 'data-migration': ([1, 1, 1, 1, 0, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'django-south': ([1, 1, 1, 1, 0, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'installation': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'py2exe': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'pyd': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'machine-learning': ([0, 1, 1, 1, 1], [0, 1, 0, 1, 1]),\n 'syntax-rules': ([1, 1], [1, 0]),\n 'screen-scraping': ([0, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 1, 0, 1]),\n 'html-content-extraction': ([0, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 1, 0, 1]),\n 'forwarding': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'asynchronous': ([1, 0, 0, 1], [1, 0, 1, 1]),\n 'aio-write': ([1, 0, 0, 1], [1, 0, 1, 1]),\n 'escaping': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'ansi-colors': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'plot': ([0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n '3d': ([0, 1, 1, 1, 1], [1, 1, 0, 1, 1]),\n 'fsm': ([1, 1, 0, 1], [1, 1, 0, 1]),\n 'video': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'editor': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'data-binding': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'xsd': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'xsd2code': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'profiler': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'stdin': ([1, 1], [1, 1]),\n 'with-statement': ([1, 1, 1, 1, 1], [1, 1, 1, 0, 1]),\n 'numpy-ndarray': ([1, 1, 1, 0, 1, 0, 0, 1], [1, 1, 1, 1, 1, 0, 0, 1]),\n 'awk': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 0]),\n 'continuous': ([1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0]),\n 'numeric-ranges': ([1, 0, 0, 1, 0, 0], [1, 0, 0, 1, 0, 1]),\n 'winapi': ([1, 1, 1, 1, 1], [1, 1, 0, 1, 1]),\n 'min-heap': ([1, 1], [1, 1]),\n 'http': ([1, 1, 1, 0, 1], [1, 1, 1, 1, 1]),\n 'matlab': ([1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'linear-algebra': ([1, 1, 1, 0], [1, 1, 1, 1]),\n 'queue': ([1, 1, 0, 0], [0, 1, 1, 0]),\n 'timeout': ([1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'word-wrap': ([1, 1, 1], [1, 1, 1]),\n 'authentication': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1]),\n 'signals': ([1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1, 1]),\n 'puzzle': ([1, 1, 0, 0], [1, 0, 1, 0]),\n 'buffer': ([1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1]),\n 'character-encoding': ([0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'sqlite': ([1, 1, 1, 1, 0, 0, 1, 1], [1, 1, 1, 1, 0, 0, 0, 1]),\n 'xrange': ([1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'webkit': ([1, 1], [1, 1]),\n 'celery': ([1, 1, 1, 1, 1], [1, 0, 1, 1, 1]),\n 'operating-system': ([1, 1, 1, 1, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'environment-variables': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 0]),\n 'operator-overloading': ([1, 1, 1], [1, 1, 1]),\n 'wget': ([0, 0, 0, 1, 0, 1, 1, 1, 1], [1, 0, 0, 1, 1, 1, 1, 0, 1]),\n 'derived-class': ([0, 1], [1, 1]),\n 'base-class': ([0, 1], [1, 1]),\n 'comparison': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0]),\n 'case-insensitive': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0]),\n 'smtp': ([0, 0, 1, 1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'smtplib': ([0, 0, 1, 1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'import': ([1], [1]),\n 'console': ([1], [1]),\n 'namespaces': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'global': ([1], [1]),\n 'tkinter': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'focus': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'warnings': ([1, 1, 0, 1], [1, 1, 1, 1]),\n 'cmake': ([1, 1], [1, 1]),\n 'shared-libraries': ([1, 1], [1, 1]),\n 'boost-python': ([1, 1], [1, 1]),\n 'read-write': ([1, 1, 1, 0], [1, 1, 1, 1]),\n 'firefox': ([1], [1]),\n 'plugins': ([1], [1]),\n 'firefox-addon': ([1], [1]),\n 'discrete-mathematics': ([0, 1, 1, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'multiplication': ([1, 1], [0, 1]),\n 'shallow-copy': ([1, 1], [0, 1]),\n 'python-datamodel': ([1, 1], [0, 1]),\n 'simplejson': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'database-design': ([1, 1, 0], [1, 1, 0]),\n 'trigonometry': ([1, 0], [1, 1]),\n 'lookup': ([0, 1, 1, 0, 1, 1, 1], [1, 1, 0, 1, 1, 1, 0]),\n 'iteration': ([1, 0, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'continuous-integration': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'hudson': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'cobertura': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'refresh': ([1, 1, 1, 1, 0], [0, 1, 1, 1, 1]),\n 'newline': ([1, 0, 1], [1, 1, 1]),\n 'fencepost': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'date': ([1, 0, 1, 0, 1, 1, 0], [1, 1, 1, 0, 1, 1, 1]),\n 'ruby-on-rails': ([1, 1, 1, 0], [1, 1, 1, 1]),\n 'metaprogramming': ([1, 1, 1, 0], [1, 1, 1, 1]),\n 'code-translation': ([1, 1, 1, 0], [1, 1, 1, 1]),\n 'pickle': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 0, 1]),\n 'reportlab': ([1, 0, 0], [1, 1, 1]),\n 'search': ([0, 1, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'keyboard-input': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'pycurl': ([1, 0, 1, 1], [1, 1, 1, 1]),\n 'text': ([1, 1, 1, 1, 0], [1, 1, 1, 0, 0]),\n 'nlp': ([1, 1, 1, 1, 0], [1, 1, 1, 0, 0]),\n 'words': ([1, 1, 1, 1, 0], [1, 1, 1, 0, 0]),\n 'wordnet': ([1, 1, 1, 1, 0], [1, 1, 1, 0, 0]),\n 'header': ([1, 1, 0], [1, 1, 1]),\n 'aop': ([0, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'figures': ([1, 0, 0], [1, 1, 0]),\n 'jython': ([1, 1, 1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'eoferror': ([1], [1]),\n 'frameworks': ([1, 0, 0, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 0, 1, 0]),\n 'encryption': ([1, 1, 0, 1, 1], [1, 1, 1, 0, 1]),\n 'gnupg': ([1, 1, 0, 1, 1], [1, 1, 1, 0, 1]),\n 'pgp': ([1, 1, 0, 1, 1], [1, 1, 1, 0, 1]),\n 'language-features': ([1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1]),\n 'copy': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0]),\n 'file-copying': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0]),\n 'curl': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'mapping': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'reverse': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'entities': ([1, 0, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'cookies': ([0, 1], [1, 1]),\n 'upload': ([1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 1, 0]),\n 'multipartform-data': ([1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 1, 0]),\n 'static-methods': ([1, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'javascript': ([0, 1, 1, 1, 1], [0, 1, 1, 1, 1]),\n 'lua': ([0, 1, 1, 1, 1], [0, 1, 1, 1, 1]),\n 'pdb': ([1, 1, 1], [1, 0, 1]),\n 'compare': ([1, 0, 1, 0, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'elementtree': ([1, 1, 0, 1], [1, 1, 1, 1]),\n 'parameters': ([1, 1, 1], [1, 1, 1]),\n 'file-upload': ([1, 1], [1, 1]),\n 'linked-list': ([1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'tzinfo': ([1, 1, 1], [0, 1, 1]),\n 'cartesian-product': ([1, 1, 1, 0, 1], [1, 1, 1, 0, 0])}"
+ "text/plain": "{'datetime': ([1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'strptime': ([1, 0, 1, 1, 0, 1, 1, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'user-interface': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0]),\n 'qt': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'pyqt': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'easy-install': ([1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'pip': ([1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'python': ([0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 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1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n ...]),\n 'django-models': ([0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'django-managers': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'list': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 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1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'multithreading': ([1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'unit-testing': ([1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1]),\n 'exception': ([1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'tuples': ([0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'redirect': ([0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'stderr': ([0, 1, 0, 0, 1, 0, 0, 1, 1], [1, 1, 0, 1, 1, 0, 0, 1, 1]),\n 'sql': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'django-queryset': ([1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'django-aggregation': ([1, 0], [1, 1]),\n 'git': ([1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1], [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'githooks': ([1, 1, 0], [1, 1, 0]),\n 'random': ([1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'lisp': ([0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'clojure': ([0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1]),\n 'idioms': ([0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'coding-style': ([1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'conventions': ([1, 1, 1, 0, 0, 0, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'algorithm': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'sorting': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'file': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0]),\n 'loops': ([0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0]),\n 'subprocess': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'stdout': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'popen': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'sqlalchemy': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'insert': ([1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'portability': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1]),\n 'java': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'ruby': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0]),\n 'cross-platform': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'string': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 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0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'path': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'logging': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'amazon-web-services': ([1, 1, 1, 0, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'boto': ([1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1]),\n 'wxpython': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'distribution': ([0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'freeze': ([0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 1, 1, 0, 1]),\n 'c++': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0]),\n 'swfupload': ([1, 1, 1], [1, 1, 0]),\n 'split': ([0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0]),\n 'text-segmentation': ([0, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'pypi': ([1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'garbage-collection': ([0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 0, 1, 1]),\n 'attributes': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'object': ([1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'python-3.x': ([0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'character': ([0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0]),\n 'numpy': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'scipy': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'statistics': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'posix': ([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'django-templates': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'apache': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'cgi': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'recursion': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'lambda': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'y-combinator': ([1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1]),\n 'url': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'uuid': ([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'tinyurl': ([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]),\n 'short-url': ([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1]),\n 'windows': ([1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'linux': ([1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 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0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0]),\n 'html-entities': ([1, 1, 0, 0, 0, 0], [1, 1, 0, 1, 1, 1]),\n 'ide': ([0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0]),\n 'matplotlib': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0]),\n 'dictionary': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 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1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'performance': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'compression': ([1, 1, 0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'zip': ([1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1]),\n 'extraction': ([1, 1], [1, 1]),\n 'dll': ([1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'floating-point': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'python-sphinx': ([0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'autodoc': ([0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'range': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0]),\n 'qt4': ([1, 0, 0, 0, 0, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'math': ([0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'probability': ([0, 0, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'spring': ([1, 1, 0], [1, 1, 0]),\n 'google-app-engine': ([0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'xpath': ([0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'beautifulsoup': ([0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'mechanize': ([0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'wamp': ([1, 1, 1], [1, 0, 1]),\n 'mod-python': ([1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'configuration': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'serialization': ([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'integer': ([0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0]),\n 'r': ([1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1]),\n 'data-visualization': ([1, 1], [1, 1]),\n 'web-services': ([1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'twitter': ([1, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 1, 0, 0, 1, 1]),\n 'memory-address': ([0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'repr': ([0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'python-c-api': ([1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'io-redirection': ([1, 1], [1, 1]),\n 'timer': ([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'decimal': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0]),\n 'setuptools': ([0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'distutils': ([0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0]),\n 'iterator': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'introspection': ([0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'utf-8': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'ascii': ([1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'decode': ([1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'arrays': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'perl': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'regex': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1]),\n 'tree-structure': ([0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'replace': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'proxy': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'web-crawler': ([0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'pool': ([0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'stack-trace': ([1, 0, 0, 1, 1], [1, 1, 1, 1, 0]),\n 'super': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'exit-code': ([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0]),\n 'testing': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'multiprocessing': ([0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'amazon-s3': ([0, 0, 1, 1, 0, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1]),\n 'memory-management': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0]),\n '32bit-64bit': ([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'reduce': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'orm': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'persistence': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'cygwin': ([1, 1, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'cmd': ([1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'pipe': ([1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'append': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'nested-lists': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'io': ([1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'temporary-files': ([1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'directory': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0]),\n 'subdirectory': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1]),\n 'scheduled-tasks': ([0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'enterprise': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'quartz-scheduler': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'numbers': ([1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0]),\n 'csv': ([0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'database': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'browser': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1]),\n 'remote-execution': ([0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'urllib': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'tor': ([1, 1, 0], [1, 1, 1]),\n 'pdf': ([0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'internationalization': ([0, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'pdf-generation': ([0, 1], [1, 1]),\n 'time': ([1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'strftime': ([1, 1, 0, 1, 0], [1, 1, 1, 1, 1]),\n 'entity': ([0, 0], [1, 1]),\n 'google-cloud-datastore': ([0, 0], [1, 1]),\n 'centos': ([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'rpath': ([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'xml': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'parsing': ([1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'shell': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'zsh': ([1, 0], [1, 1]),\n 'ipython': ([1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'system-information': ([1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'file-io': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0]),\n 'multidimensional-array': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'fileparsing': ([0, 0, 0], [1, 1, 1]),\n 'preprocessor': ([1, 1, 0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'equivalent': ([1, 1, 0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'directive': ([1, 1, 0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'scala': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'oauth': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0]),\n 'list-comprehension': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'sockets': ([1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'wsgi': ([1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]),\n 'httplib2': ([1, 0, 0, 0, 0, 0, 0, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'werkzeug': ([1, 0, 0, 0], [1, 1, 1, 1]),\n 'primes': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0]),\n 'memory': ([0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'jvm': ([0, 0, 1], [1, 1, 1]),\n 'data-structures': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1]),\n 'extend': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'python-2.x': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'line-endings': ([0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'mysql': ([0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1]),\n 'documentation': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'notation': ([1, 0, 0, 0, 0], [1, 1, 1, 0, 0]),\n 'large-files': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'class': ([0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'instances': ([0, 1, 1, 0], [1, 1, 1, 0]),\n 'unicode': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'python-2.6': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'unicode-literals': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'c#': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0]),\n 'software-quality': ([0, 0, 1, 0, 1], [1, 1, 1, 1, 1]),\n 'code-coverage': ([0, 0, 1, 0, 1, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'coverage.py': ([0, 0, 1, 0, 1], [1, 1, 1, 1, 1]),\n 'gtk': ([0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1]),\n 'pygtk': ([0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1]),\n 'pango': ([0], [1]),\n 'for-loop': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0]),\n 'map-function': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1]),\n 'yield': ([1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'virtualenv': ([1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1]),\n 'version': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'nested-loops': ([0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'oop': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'naming-conventions': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'identifier': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'process': ([0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'fork': ([0, 1, 1, 1, 0, 1, 1], [0, 1, 1, 1, 1, 1, 1]),\n 'vi': ([0, 0, 0, 0, 1, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'pep8': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'proxy-classes': ([0, 1, 1, 1, 0], [1, 1, 0, 1, 1]),\n 'readability': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'vim': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'jinja2': ([1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1]),\n 'vim-syntax-highlighting': ([1], [1]),\n 'macos': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'clipboard': ([0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1]),\n 'python-idle': ([1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'xhtml': ([1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'fabric': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'struct': ([0, 0, 0], [1, 1, 1]),\n 'ctypes': ([0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'python-import': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'circular-dependency': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0]),\n 'cyclic-reference': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'monkeypatching': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'php': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'debugging': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'audio': ([0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'wiki': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'markup': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'unique': ([0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0]),\n 'set': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'compiler-construction': ([1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'static-analysis': ([1, 1, 1, 0, 1, 1, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'use-strict': ([1, 1, 1, 0, 1, 1, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'reflection': ([0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1],\n [0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'timezone': ([0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'django-views': ([1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'sizeof': ([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'mmap': ([0, 0, 0, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'embedded': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'fuzzy-logic': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0]),\n 'hyperlink': ([1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0],\n [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'scientific-computing': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0]),\n 'image-processing': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'colors': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'profile': ([1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'sql-server': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'pyodbc': ([0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'visual-c++': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'diff': ([0, 0, 0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'ip-address': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'tcp': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]),\n 'erp': ([0, 1], [1, 1]),\n 'dabo': ([0, 1], [1, 1]),\n 'distribute': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'decorator': ([0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'substring': ([0, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 1]),\n 'double': ([1, 1, 1], [1, 1, 1]),\n 'django-admin': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'keyword-argument': ([0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'django-urls': ([1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'terminal': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'command': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'count': ([0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'python-imaging-library': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'streaming': ([0, 0, 0], [1, 1, 1]),\n 'linq': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1]),\n 'functional-programming': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1],\n [0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'compilation': ([0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'bytecode': ([0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'ajax': ([0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'comet': ([0, 0, 0], [1, 0, 1]),\n 'long-polling': ([0, 0, 0], [1, 0, 1]),\n 'tornado': ([0, 0, 0, 0, 1, 0], [1, 0, 1, 1, 1, 1]),\n 'email': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'gmail': ([0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1]),\n 'imap': ([0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'endianness': ([1, 1, 0, 1], [1, 1, 1, 1]),\n 'cryptography': ([1, 0, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'validation': ([1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'slug': ([1, 0], [1, 1]),\n 'networking': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'mysql-python': ([0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'unix': ([1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'command-line': ([1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'autocomplete': ([1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'profiling': ([0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'mp3': ([1, 0, 1, 0, 0], [1, 0, 1, 1, 1]),\n 'frequency': ([1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1]),\n 'hash': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1]),\n 'urlencode': ([1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'clean-urls': ([1, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'system-tray': ([1, 0, 0, 1], [1, 1, 1, 1]),\n 'filesystems': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'glob': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'fnmatch': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'constructor': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0]),\n 'byte': ([1, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0]),\n 'file-attributes': ([0, 1, 0, 0, 1, 0, 0], [1, 1, 1, 1, 0, 1, 1]),\n 'file-properties': ([0, 1, 0, 0, 1, 0, 0], [1, 1, 1, 1, 0, 1, 1]),\n 'heap': ([1, 1, 0, 0, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'postgresql': ([0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'psycopg2': ([0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1]),\n 'forms': ([0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'widget': ([0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'admin': ([0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'project': ([0, 0, 0, 0, 0], [0, 1, 1, 1, 1]),\n 'structure': ([0, 0, 0, 0, 0], [0, 1, 1, 1, 1]),\n 'nose': ([1, 1], [1, 1]),\n 'nosetests': ([1, 1], [1, 1]),\n 'working-directory': ([1, 0, 1, 0], [1, 1, 1, 1]),\n 'file-management': ([1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0]),\n 'artificial-intelligence': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'ocr': ([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 1, 1, 1, 0]),\n 'bash': ([1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'single-instance': ([0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'floating-accuracy': ([0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'windows-7-x64': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 0, 1, 0, 0]),\n 'assert': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'assertion': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0]),\n '.net': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1]),\n 'dynamic-languages': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0]),\n 'minidom': ([1, 0, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'mocking': ([0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'module': ([1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'seed': ([1, 1, 1, 0, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'cherrypy': ([1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'language-agnostic': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1]),\n 'encoding': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'overriding': ([1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'class-method': ([1, 0, 1, 0, 1, 1, 1, 0, 1, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'ms-word': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'rounding': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'slice': ([0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'osx-snow-leopard': ([1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'smalltalk': ([1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),\n 'json': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'function': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'variables': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0]),\n 'storage': ([0, 0, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'photos': ([0, 0, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'photo-management': ([0, 0, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'pygsl': ([1, 0, 1, 1], [1, 1, 1, 1]),\n 'urllib2': ([1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'socks': ([1, 1, 1], [1, 1, 1]),\n 'time-series': ([0, 1, 0, 0, 1], [1, 1, 1, 1, 0]),\n 'schemaless': ([0, 1, 0, 0, 1], [1, 1, 1, 1, 0]),\n 'ssh': ([0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'paramiko': ([0, 0, 1, 0, 1, 0, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1]),\n 'uniqueidentifier': ([0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'integration': ([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'numerical-methods': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'image': ([1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0]),\n 'ipc': ([0, 1, 0, 0], [1, 1, 1, 1]),\n 'sysv': ([0, 1, 0, 0], [1, 1, 1, 1]),\n 'shared-memory': ([0, 1, 0, 0], [1, 1, 1, 1]),\n 'data-migration': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'django-south': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]),\n 'installation': ([0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'py2exe': ([0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'pyd': ([0, 0, 0, 1], [1, 1, 1, 1]),\n 'machine-learning': ([0, 0, 0, 0, 0], [0, 1, 0, 1, 1]),\n 'syntax-rules': ([1, 1], [1, 0]),\n 'screen-scraping': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'html-content-extraction': ([0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 1, 0, 1]),\n 'forwarding': ([1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'asynchronous': ([0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0],\n [1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'aio-write': ([0, 0, 0, 1], [1, 0, 1, 1]),\n 'escaping': ([1, 0, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 0, 1]),\n 'ansi-colors': ([1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'plot': ([0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0]),\n '3d': ([0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'fsm': ([0, 0, 0, 0], [1, 1, 0, 1]),\n 'video': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'editor': ([1, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'data-binding': ([0, 0, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'xsd': ([0, 0, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'xsd2code': ([0, 0, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'profiler': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'stdin': ([1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'with-statement': ([0, 0, 1, 0, 0], [1, 1, 1, 0, 1]),\n 'numpy-ndarray': ([1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0, 0, 1]),\n 'awk': ([1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'continuous': ([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0]),\n 'numeric-ranges': ([0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 1]),\n 'winapi': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'min-heap': ([0, 0], [1, 1]),\n 'http': ([0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'matlab': ([1, 0, 1, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'linear-algebra': ([1, 0, 1, 1], [1, 1, 1, 1]),\n 'queue': ([1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'timeout': ([1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0],\n [0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1]),\n 'word-wrap': ([1, 1, 1], [1, 1, 1]),\n 'authentication': ([1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'signals': ([1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'puzzle': ([0, 0, 0, 0], [1, 0, 1, 0]),\n 'buffer': ([1, 1, 1, 1, 1, 0, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'character-encoding': ([0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'sqlite': ([1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1]),\n 'xrange': ([1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'webkit': ([1, 0], [1, 1]),\n 'celery': ([1, 0, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1]),\n 'operating-system': ([1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0]),\n 'environment-variables': ([1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0]),\n 'operator-overloading': ([1, 1, 1], [1, 1, 1]),\n 'wget': ([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 1, 1, 0, 1]),\n 'derived-class': ([0, 1], [1, 1]),\n 'base-class': ([0, 1], [1, 1]),\n 'comparison': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'case-insensitive': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'smtp': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'smtplib': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]),\n 'import': ([1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'console': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'namespaces': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'global': ([1], [1]),\n 'tkinter': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'focus': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'warnings': ([1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'cmake': ([0, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'shared-libraries': ([0, 1, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0]),\n 'boost-python': ([0, 1], [1, 1]),\n 'read-write': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'firefox': ([0], [1]),\n 'plugins': ([0, 1, 1], [1, 1, 1]),\n 'firefox-addon': ([0], [1]),\n 'discrete-mathematics': ([0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'multiplication': ([0, 0], [0, 1]),\n 'shallow-copy': ([0, 0, 1, 0, 1, 1], [0, 1, 1, 1, 1, 0]),\n 'python-datamodel': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'simplejson': ([1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'database-design': ([0, 1, 1, 1, 1, 0, 1], [1, 1, 0, 1, 1, 1, 1]),\n 'trigonometry': ([1, 0], [1, 1]),\n 'lookup': ([0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 0, 1, 1, 1, 0, 1]),\n 'iteration': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'continuous-integration': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'hudson': ([1, 0, 0, 0, 0], [1, 1, 1, 1, 0]),\n 'cobertura': ([1, 0, 0, 0, 0], [1, 1, 1, 1, 0]),\n 'refresh': ([0, 0, 0, 1, 0], [0, 1, 1, 1, 1]),\n 'newline': ([1, 0, 0], [1, 1, 1]),\n 'fencepost': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'date': ([0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'ruby-on-rails': ([0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'metaprogramming': ([0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'code-translation': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'pickle': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'reportlab': ([0, 0, 1, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'search': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'keyboard-input': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'pycurl': ([1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]),\n 'text': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'nlp': ([1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'words': ([1, 0, 1, 1, 0], [1, 1, 1, 0, 0]),\n 'wordnet': ([1, 0, 1, 1, 0], [1, 1, 1, 0, 0]),\n 'header': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'aop': ([0, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'figures': ([1, 0, 0], [1, 1, 0]),\n 'jython': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'eoferror': ([0], [1]),\n 'frameworks': ([0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'encryption': ([0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0]),\n 'gnupg': ([0, 0, 1, 0, 0], [1, 1, 1, 0, 1]),\n 'pgp': ([0, 0, 1, 0, 0], [1, 1, 1, 0, 1]),\n 'language-features': ([1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0]),\n 'copy': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'file-copying': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0]),\n 'curl': ([0, 1, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'mapping': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'reverse': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'entities': ([1, 0, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'cookies': ([1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'upload': ([0, 1, 0, 0, 0, 0, 1], [1, 1, 1, 0, 1, 0, 1]),\n 'multipartform-data': ([0, 1, 0, 0, 0, 0], [1, 1, 1, 0, 1, 0]),\n 'static-methods': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'javascript': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'lua': ([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'pdb': ([1, 1, 0, 1, 0, 1, 1, 0, 1, 0], [1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'compare': ([1, 1, 0, 0, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'elementtree': ([0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1]),\n 'parameters': ([1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'file-upload': ([1, 0], [1, 1]),\n 'linked-list': ([0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'tzinfo': ([1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 1, 1]),\n 'cartesian-product': ([1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'passwords': ([0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1]),\n 'prompt': ([0, 0], [1, 1]),\n 'interactive': ([0, 0], [1, 1]),\n 'webserver': ([1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1],\n [1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'simplehttpserver': ([1, 0, 0, 0], [1, 0, 1, 0]),\n 'return': ([0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'program-entry-point': ([0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1]),\n 'pep': ([0, 0, 0, 1, 1], [1, 1, 1, 1, 1]),\n 'httplib': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'if-statement': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1]),\n 'security': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'media': ([1], [1]),\n 'aggregate-functions': ([1, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1]),\n 'binary': ([1, 1, 0, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'int': ([1, 1, 1, 0, 0, 0, 0, 1], [1, 1, 1, 0, 0, 1, 1, 1]),\n 'bit': ([1, 1, 1, 0, 0], [1, 1, 1, 0, 0]),\n 'casting': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'abap': ([1, 0, 0, 0, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'sap-basis': ([1, 0, 0, 0, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'pyrfc': ([1, 0, 0, 0, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'twisted': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'dependency-injection': ([0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1]),\n 'versioning': ([0, 1, 1], [1, 1, 0]),\n 'diagram': ([1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'literals': ([1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'rawstring': ([1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'python-stackless': ([0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'mod-wsgi': ([1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'c-preprocessor': ([0, 0, 0, 0, 0], [1, 0, 1, 1, 1]),\n 'urlfetch': ([1, 1], [1, 1]),\n 'excel': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'base64': ([0, 0, 0], [1, 1, 1]),\n 'dynamic': ([1, 0, 1], [1, 1, 1]),\n 'eval': ([1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n 'exec': ([1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]),\n 'svn': ([1, 0, 0, 0, 1, 0, 0, 1, 0], [1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'inheritance': ([1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'subclass': ([1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 0, 1]),\n 'optimization': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'time-complexity': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'types': ([1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'compatibility': ([1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0],\n [1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0]),\n 'exception-handling': ([1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'eclipse': ([1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'syntax': ([1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'pydev': ([1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1]),\n 'precompute': ([0, 0, 0, 0, 1], [1, 1, 1, 1, 1]),\n 'phpbb': ([0, 0, 0], [1, 1, 1]),\n 'packaging': ([1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0]),\n 'web-applications': ([0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1]),\n 'kill': ([0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'zombie-process': ([0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'code-golf': ([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1]),\n 'post': ([0, 0, 0], [1, 1, 1]),\n 'multipart': ([0, 0, 0], [1, 1, 1]),\n 'methods': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'graphics': ([0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]),\n 'fft': ([0, 1, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'getopt': ([0, 0, 0, 0, 0], [1, 1, 0, 1, 0]),\n 'getopt-long': ([0, 0, 0, 0, 0], [1, 1, 0, 1, 0]),\n 'egg': ([0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'euclidean-distance': ([1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'geolocation': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'maps': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'comments': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1]),\n 'makefile': ([0, 0, 0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'scons': ([0, 0, 0], [1, 1, 1]),\n 'finally': ([0, 1, 1, 1], [1, 1, 1, 1]),\n 'factorial': ([0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'internet-explorer': ([0, 0, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'factory': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'hex': ([1, 1, 0], [1, 1, 1]),\n 'console-output': ([0, 0, 1, 1, 0, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'django-database': ([1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0]),\n 'text-extraction': ([0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'pdf-scraping': ([0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'package': ([0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'redo': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'transactions': ([1, 1, 0, 0, 0, 1, 1], [1, 1, 0, 1, 1, 1, 1]),\n 'sax': ([1, 0, 0, 1, 0, 0, 1, 1], [1, 1, 0, 1, 1, 1, 1, 1]),\n 'sdl': ([0, 1, 1, 0, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'pygame': ([0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1]),\n 'scope': ([0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'nested': ([0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'inner-classes': ([0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'rest': ([0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'output': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'serial-port': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'ubuntu': ([0, 1, 0, 1, 0, 1, 1, 0], [1, 1, 0, 1, 0, 1, 1, 1]),\n 'apt-get': ([0, 1, 0, 1, 0, 1], [1, 1, 0, 1, 0, 1]),\n 'permissions': ([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0]),\n 'tk': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1]),\n 'relative-path': ([1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1]),\n 'job-queue': ([1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'printing': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'formatting': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'arguments': ([1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1]),\n 'collections': ([0, 0, 1, 1, 0], [1, 1, 1, 1, 0]),\n 'break': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'control-flow': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'screenshot': ([0, 0, 1, 0], [1, 1, 1, 0]),\n 'server-side-scripting': ([0, 0, 1, 0], [1, 1, 1, 0]),\n 'pythonpath': ([1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1]),\n 'sys.path': ([1, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'doxygen': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'docstring': ([1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'text-files': ([0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0]),\n 'line-count': ([0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'using': ([0, 1, 1, 1, 1, 1], [1, 1, 1, 0, 1, 1]),\n 'image-manipulation': ([0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'tesseract': ([0, 0, 0, 0, 0], [0, 1, 1, 1, 0]),\n 'django-testing': ([0, 1], [1, 1]),\n 'analysis': ([0, 0, 0, 0, 1], [1, 1, 1, 0, 1]),\n 'pyqt4': ([1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1]),\n 'operators': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'django-apps': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'locale': ([1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1]),\n 'scientific-notation': ([1, 1, 1], [1, 1, 1]),\n 'pass-by-value': ([0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0]),\n 'http-status-code-404': ([0, 0, 1, 1, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 0, 1, 0, 1, 1]),\n 'conflict': ([0, 0, 0, 1, 1, 0], [1, 1, 1, 1, 1, 0]),\n 'emacs': ([1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'python-module': ([0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'timestamp': ([0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'opencv': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'computer-vision': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'motion-detection': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'plone': ([0, 0, 0], [1, 1, 1]),\n 'zope': ([0, 0, 0], [1, 1, 1]),\n 'zodb': ([0, 0, 0], [1, 1, 1]),\n 'blobstorage': ([0, 0, 0], [1, 1, 1]),\n 'css': ([0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'minify': ([0, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'haskell': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0]),\n 'xmpp': ([1, 0, 0, 0, 1, 0], [1, 1, 0, 1, 1, 1]),\n 'currency': ([1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'hosting': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'webpage': ([0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 0, 1, 1, 1]),\n 'default-value': ([1, 1, 0, 0, 1], [1, 1, 1, 1, 1]),\n 'named-parameters': ([1, 1, 0, 0, 1], [1, 1, 1, 1, 1]),\n 'stringtemplate': ([1], [1]),\n 'race-condition': ([1, 0, 0, 0, 1, 1], [1, 0, 1, 1, 1, 1]),\n 'string-interpolation': ([0, 0, 0, 1, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'benchmarking': ([1, 0, 0, 0, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'clock': ([1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),\n 'unified-diff': ([0, 0], [1, 1]),\n 'generator-expression': ([1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 0, 1]),\n 'background-subtraction': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'image-comparison': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'timelapse': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0]),\n 'ocaml': ([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1],\n [1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1]),\n 'apache-pig': ([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1],\n [1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1]),\n 'rounded-corners': ([1, 1, 1, 0], [1, 1, 1, 0]),\n 'llvm': ([0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 0, 1, 1]),\n 'unladen-swallow': ([0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 0, 1, 1]),\n 'events': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1]),\n 'event-handling': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 0, 1, 1, 1, 0]),\n 'drive': ([0, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 0, 0, 1]),\n 'generator': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'protocol-buffers': ([0, 0, 0, 0, 0], [1, 1, 1, 0, 1]),\n 'python-internals': ([1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'sysadmin': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'sudo': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0]),\n 'root': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0]),\n 'function-calls': ([1, 0, 0, 1, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'sudoku': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'daemon': ([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]),\n 'python-multithreading': ([0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'overloading': ([0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1]),\n 'objective-c': ([1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'programming-languages': ([1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'utc': ([1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'rewrite': ([1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'nonblocking': ([0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]),\n 'openid': ([0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'multiple-inheritance': ([0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'prettify': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'raku': ([0, 1, 1, 0, 1, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'ironpython': ([0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'cython': ([1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'md5': ([1, 0, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'pywin32': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'cairo': ([1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1]),\n 'methodology': ([0, 1, 0, 1, 0, 0, 0], [1, 1, 1, 0, 1, 1, 0]),\n 'scapy': ([1, 0, 0, 0, 1], [1, 1, 1, 0, 0]),\n 'singleton': ([1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'properties': ([1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'hidden-features': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1]),\n 'limit': ([0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0],\n [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'type-conversion': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0]),\n 'hp-ux': ([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 1, 1, 1, 1, 0]),\n 'configuration-files': ([1], [1]),\n 'memory-leaks': ([0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1]),\n 'fibonacci': ([1, 1, 1, 0], [0, 1, 1, 1]),\n 'http-status-code-304': ([1, 1], [1, 1]),\n 'connection': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'exchange-server': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'diacritics': ([1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'setter': ([0, 0, 0, 1], [1, 1, 1, 1]),\n 'getter-setter': ([0, 0, 0, 1], [1, 1, 1, 1]),\n 'concurrency': ([0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'printf': ([0, 0, 0], [1, 1, 1]),\n 'deprecated': ([0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'dns': ([1, 0, 0, 1], [1, 1, 0, 0]),\n 'names': ([1, 0, 0, 1], [1, 1, 0, 0]),\n 'strong-typing': ([1, 0, 0, 0], [1, 1, 1, 1]),\n 'weak-typing': ([1, 0, 0, 0], [1, 1, 1, 1]),\n 'syntax-error': ([1, 0, 1, 0, 0, 1, 0], [1, 1, 1, 0, 1, 1, 1]),\n 'sum': ([1, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'python-2.5': ([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1]),\n 'configparser': ([1, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 0, 1, 1, 1, 0, 1, 0]),\n 'defaults': ([1, 1, 0, 0, 0], [1, 1, 1, 0, 1]),\n 'shuffle': ([1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1]),\n 'python-asyncio': ([0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'coroutine': ([0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'closures': ([1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'micro-optimization': ([1, 0, 1, 0], [1, 1, 1, 1]),\n 'graph': ([1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'pca': ([0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'rtos': ([1, 0, 0, 0, 1], [1, 1, 1, 0, 1]),\n 'qnx': ([1, 0, 0, 0, 1], [1, 1, 1, 0, 1]),\n 'drop-down-menu': ([0, 1, 0, 0], [1, 1, 1, 0]),\n 'inspect': ([1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0]),\n 'rtf': ([0, 1, 1, 0, 1, 1, 0, 0, 0, 1], [1, 1, 1, 0, 1, 0, 1, 1, 1, 1]),\n 'in-memory': ([0, 1], [1, 1]),\n 'access-modifiers': ([0, 1, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'call-graph': ([1, 0, 0], [1, 1, 1]),\n 'indexing': ([0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'lexical-analysis': ([1, 0, 0, 0, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0]),\n 'command-line-arguments': ([1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'tee': ([1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0]),\n 'destructor': ([1, 1, 1, 0], [0, 1, 1, 0]),\n 'shutdown': ([1, 1, 1, 0], [0, 1, 1, 0]),\n 'abstract-syntax-tree': ([0, 1, 0, 0, 1], [0, 1, 1, 1, 1]),\n 'python-2.7': ([0, 0, 0, 0], [1, 1, 1, 0]),\n 'vba': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'python.net': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'wav': ([1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'share': ([0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'virtualenvwrapper': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1]),\n 'interpolation': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'spline': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'burndowncharts': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'design-patterns': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'non-printable': ([0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'gnuplot': ([1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'graphing': ([1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'lxml': ([1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1]),\n 'deployment': ([0, 0, 0, 1, 0, 0], [1, 0, 1, 1, 1, 1]),\n 'scripting-language': ([0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'embedded-language': ([0, 0, 0, 1], [1, 0, 1, 1]),\n 'dynamic-typing': ([1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'bit-manipulation': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0]),\n 'bitwise-operators': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'logarithm': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'expect': ([1, 1, 0, 1, 0, 1], [1, 1, 1, 1, 0, 1]),\n 'char': ([1, 1, 0, 0, 1, 0, 0], [1, 1, 1, 0, 1, 1, 0]),\n 'increment': ([1, 1, 0, 0, 1, 0, 0], [1, 1, 1, 0, 1, 1, 0]),\n 'instantiation': ([1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'string-comparison': ([1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'fuzzy-comparison': ([1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'simplify': ([0, 0, 0], [1, 1, 1]),\n 'binary-tree': ([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0]),\n 'breadth-first-search': ([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0]),\n 'tree-traversal': ([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],\n [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0]),\n 'mutable': ([1, 0, 0, 1, 1], [1, 1, 1, 1, 0]),\n 'raise': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0]),\n 'cubic': ([0, 0, 0, 0, 0], [1, 1, 0, 0, 0]),\n 'stub': ([1, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1]),\n 'aggregate': ([0, 0, 0, 0, 1, 1], [0, 1, 1, 1, 1, 1]),\n 'readline': ([0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]),\n 'executable': ([1, 1, 1], [1, 1, 0]),\n 'github': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'python-dateutil': ([1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0]),\n 'permutation': ([0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'return-value': ([0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1]),\n 'difflib': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 0]),\n 'pylons': ([1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1],\n [1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'web-frameworks': ([1, 0, 1, 0], [1, 1, 1, 0]),\n 'turbogears': ([1, 0, 1, 0], [1, 1, 1, 0]),\n 'inline': ([1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 0]),\n 'text-parsing': ([0, 0, 1, 0, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'migration': ([0, 0, 1, 1, 0, 0, 1, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'filtering': ([0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 0, 0, 1, 0]),\n 'transformation': ([0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 0, 0, 1, 0]),\n 'boolean': ([1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'code-formatting': ([1, 1, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'identity': ([0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'keyword': ([0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1]),\n 'statements': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'sleep': ([0, 0, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'buildout': ([0, 1, 0, 0], [0, 1, 1, 1]),\n 'switch-statement': ([0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'sql-order-by': ([1, 1, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0]),\n 'jpeg': ([1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'exif': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'geotagging': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'mobile': ([0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0]),\n 'user-agent': ([0, 1, 0], [1, 1, 1]),\n 'bit.ly': ([1], [1]),\n 'windows-console': ([0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'credit-card': ([0, 0], [1, 1]),\n 'mime': ([1, 0, 1], [1, 1, 0]),\n 'qitemdelegate': ([0, 0], [1, 1]),\n 'qlistview': ([0, 0], [1, 1]),\n 'timeit': ([1, 1, 1, 0, 1], [1, 1, 1, 0, 1]),\n 'equation': ([0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'wxwidgets': ([1, 0, 0, 1, 1, 0], [1, 0, 1, 1, 1, 1]),\n 'fortran': ([1, 1], [1, 1]),\n 'cpython': ([0, 0, 1, 0, 0, 1], [1, 1, 1, 1, 1, 1]),\n 'alphabet': ([1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0]),\n 'birthday-paradox': ([0, 0], [1, 1]),\n 'png': ([1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'continuations': ([0, 1, 1], [1, 1, 1]),\n 'development-environment': ([1, 1, 0, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 0]),\n 'ubuntu-9.04': ([1, 1, 0, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 0]),\n 'namedtuple': ([0, 0, 1, 1, 1], [1, 1, 1, 1, 0]),\n 'parameter-passing': ([1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'polygon': ([0, 0], [1, 1]),\n 'shapely': ([0, 0], [1, 1]),\n 'set-operations': ([1, 1, 1, 0, 1, 0], [1, 1, 1, 1, 1, 1]),\n 'boost': ([0, 1], [1, 1]),\n 'class-attributes': ([0, 0, 1, 1], [1, 0, 1, 1]),\n 'windows-mobile': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'magic-methods': ([1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'callback': ([0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 0]),\n 'converter': ([0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0]),\n 'mongodb': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'couchdb': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'nosql': ([0, 0, 1, 0], [1, 1, 1, 1]),\n 'call': ([0, 1, 0, 1, 1, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'html-table': ([0, 0], [1, 1]),\n 'html-parsing': ([0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'torch': ([0, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'hashtable': ([1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'interface': ([0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'duplicates': ([0, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'pass-by-reference': ([0, 0, 0, 1, 0, 0], [1, 0, 1, 1, 1, 0]),\n 'htmlunit': ([0, 0, 0, 1, 1, 0, 1], [0, 0, 1, 1, 1, 1, 1]),\n '64-bit': ([0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'long-integer': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'owl': ([0, 1, 0, 0, 0, 0], [1, 1, 0, 1, 1, 1]),\n 'project-structure': ([1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'code-structure': ([1, 1, 1], [1, 1, 1]),\n 'iterable-unpacking': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 0]),\n 'units-of-measurement': ([0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'tracking': ([0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0, 1]),\n 'local': ([0, 0, 0, 0, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0]),\n 'glade': ([1, 1], [1, 1]),\n 'gtkbuilder': ([1, 1], [1, 1]),\n 'scripting': ([0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1],\n [1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'yahoo': ([0, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'user-controls': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'nltk': ([0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]),\n 'memcached': ([0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'scalability': ([0, 0, 0, 0, 0, 1, 0, 1, 1, 0],\n [1, 1, 1, 0, 1, 1, 0, 1, 1, 1]),\n 'pyinstaller': ([0, 0, 0, 0, 0], [1, 1, 0, 0, 1]),\n 'brython': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'transcrypt': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'rapydscript': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'scgi': ([1, 1], [1, 1]),\n 'alias': ([1, 1, 0, 1, 1], [1, 1, 1, 1, 1]),\n 'matrix': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'haml': ([1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'geometry': ([0, 0, 0, 0, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'routing': ([0, 1, 1, 0], [1, 1, 1, 1]),\n 'grep': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'kcachegrind': ([1, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'cprofile': ([1, 0, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'open-source': ([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'division': ([1, 1, 1, 1, 1], [1, 1, 1, 1, 1]),\n 'pretty-print': ([1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'financial': ([1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0]),\n 'reference': ([1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'camelcasing': ([0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0],\n [0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0]),\n 'groovy': ([0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1]),\n 'standardized': ([0, 1, 1], [1, 1, 1]),\n 'pythonw': ([1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 0]),\n 'ownership': ([1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1]),\n 'code-organization': ([0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0]),\n 'api': ([0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'nth-root': ([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1],\n [0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1]),\n 'content-type': ([0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1]),\n 'string-formatting': ([0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'configure': ([0, 0, 1, 0, 1], [1, 1, 1, 1, 0]),\n 'model-view-controller': ([0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 0, 1]),\n 'flatten': ([0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0]),\n 'nginx': ([0, 0, 0, 0, 0, 1, 0, 0, 1], [1, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'fastcgi': ([0, 0, 0, 0, 0], [1, 0, 1, 1, 1]),\n 'client': ([0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0]),\n 'path-separator': ([0], [1]),\n 'python-unittest': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1]),\n 'parity': ([0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 0, 1, 1, 1, 1, 0]),\n 'alpha': ([1, 1, 1, 1], [1, 0, 1, 1]),\n 'blit': ([1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'menu': ([1, 1], [1, 1]),\n 'splat': ([1, 0, 0, 1, 0, 0, 1, 1, 0], [1, 1, 1, 1, 1, 0, 1, 1, 1]),\n 'sha256': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'button': ([1, 1, 1], [1, 1, 1]),\n 'android': ([0, 0, 0, 1, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 1, 0]),\n 'dalvik': ([0, 0, 0, 1, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 1, 0]),\n 'temporary-directory': ([0, 0, 1, 0, 0], [1, 1, 1, 1, 0]),\n 'macros': ([0, 0, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1]),\n 'rss': ([1], [1]),\n 'feedparser': ([1], [1]),\n 'caching': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1]),\n 'memoization': ([1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'pyside': ([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1]),\n 'elisp': ([0, 0, 0], [0, 1, 1]),\n 'concatenation': ([1, 0, 0, 0, 0], [1, 1, 0, 1, 1]),\n 'rfc822': ([1, 1, 1], [1, 0, 1]),\n 'gzip': ([0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'zlib': ([0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'refactoring': ([0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1]),\n 'distributed': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'findall': ([0], [1]),\n 'pylint': ([0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 0,\n 1,\n 0,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1]),\n 'fluent-interface': ([0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0]),\n 'unix-timestamp': ([0, 1, 0, 1], [1, 1, 1, 1]),\n 'multiple-return-values': ([1, 1, 0, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 0, 1]),\n 'activepython': ([0, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0]),\n 'cron': ([0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]),\n 'geometry-surface': ([0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'wikipedia': ([0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0]),\n 'message-queue': ([0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0]),\n 'gettext': ([1, 1, 1], [1, 1, 1]),\n 'architecture': ([1, 0, 0], [1, 1, 1]),\n 'memcpy': ([1, 0, 0], [1, 1, 1]),\n 'jenkins': ([1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'buildbot': ([1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'wave': ([0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0]),\n 'filter': ([0, 0, 0, 0], [1, 1, 1, 1]),\n 'generics': ([1, 0], [1, 1]),\n 'formset': ([1, 0], [1, 1]),\n 'thread-safety': ([1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'deque': ([1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),\n 'erlang': ([0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0]),\n 'robotics': ([0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0]),\n 'terminate': ([0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0,\n 0]),\n 'dom': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'next': ([1, 1], [1, 1]),\n 'language-implementation': ([1], [1]),\n 'rename': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'batch-rename': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0]),\n 'timsort': ([1, 1], [1, 1]),\n 'ntfs': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'junction': ([1, 1, 1, 1, 0], [1, 1, 1, 1, 1]),\n 'jit': ([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n 'psyco': ([1, 1, 0], [1, 1, 1]),\n 'autovivification': ([1, 1, 1, 1], [1, 1, 1, 1]),\n 'mkdir': ([0, 1, 0], [1, 1, 1]),\n 'coercion': ([1, 1], [1, 1]),\n 'hpricot': ([0, 0, 0, 1, 0], [1, 1, 0, 1, 1]),\n 'parallel-processing': ([0, 0, 1, 1, 0, 0, 0], [1, 1, 1, 1, 0, 1, 1]),\n 'cluster-computing': ([0, 0, 1, 1, 0, 0, 0], [1, 1, 1, 1, 0, 1, 1]),\n 'directory-structure': ([1, 1, 0, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'organization': ([1, 1, 0, 0, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]),\n 'weak-references': ([1, 1], [1, 1]),\n 'django-signals': ([1, 1], [1, 1]),\n 'edi': ([0, 1, 1, 0, 0, 0, 0, 1], [0, 1, 1, 0, 0, 1, 1, 1]),\n 'reverse-lookup': ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0]),\n 'levenshtein-distance': ([1, 1, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'edit-distance': ([1, 1, 1, 0, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'dialog': ([1, 1, 1, 1, 1, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'zenity': ([1, 1, 1, 1, 1, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'partial': ([0, 0, 0], [1, 0, 1]),\n 'parsec': ([0, 0, 0, 0, 0, 1], [1, 1, 0, 1, 1, 1]),\n 'combinators': ([0, 0, 0, 0, 0, 1], [1, 1, 0, 1, 1, 1]),\n 'automated-tests': ([0, 1, 0, 1, 1, 0, 0, 1], [1, 1, 1, 1, 0, 1, 1, 0]),\n 'sql-parametrized-query': ([1, 1, 1, 0, 0], [1, 1, 1, 1, 0]),\n 'chat': ([0, 0, 1, 0, 0], [1, 1, 1, 1, 1]),\n 'private-messaging': ([0, 0, 1, 0, 0], [1, 1, 1, 1, 1]),\n 'doctest': ([0, 1, 0, 0, 1], [1, 1, 1, 1, 1]),\n 'transparent': ([1, 1, 1, 0, 0], [1, 1, 1, 1, 1]),\n 'builder': ([1, 1, 1, 0, 1, 1], [0, 1, 1, 1, 1, 1]),\n 'iso8601': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'datetime-parsing': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'rfc3339': ([1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1]),\n 'simpy': ([0, 0, 0], [1, 1, 1]),\n 'debian': ([0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 0,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 0]),\n 'typeof': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]),\n 'sequences': ([0, 0, 1, 1], [1, 1, 1, 1]),\n 'itertools': ([0], [1]),\n 'interpreter': ([1, 1, 0, 0], [1, 0, 0, 1]),\n 'pypy': ([1, 1, 0, 0], [1, 0, 0, 1]),\n 'precision': ([1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 0,\n 0,\n 1,\n 0,\n 1,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 0,\n 1,\n 1],\n [1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1,\n 0,\n 1,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 1]),\n 'python-egg-cache': ([0, 0, 1, 0, 1, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0]),\n 'visualization': ([0, 0, 1, 0, 0, 0, 0], [1, 0, 0, 1, 1, 1, 1]),\n 'workflow': ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]),\n 'progress-bar': ([0, 0, 0, 0], [1, 1, 1, 0]),\n 'httpclient': ([0, 0, 0, 0], [1, 1, 1, 0]),\n 'variable-variables': ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]),\n 'key-value-store': ([0, 0, 0, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1]),\n 'anonymous-types': ([1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0]),\n 'resources': ([1, 0, 1, 1, 1], [1, 0, 1, 1, 1]),\n 'indentation': ([1, 1, 0, 0, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n ...}"
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -248,7 +287,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"outputs": [],
"source": [
"from sklearn.metrics import f1_score, accuracy_score\n",
@@ -264,14 +303,14 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"outputs": [
{
"data": {
- "text/plain": " tag f1 accuracy count\n176 zsh 1.000000 1.0 4\n167 time 1.000000 1.0 10\n432 import 1.000000 1.0 2\n433 console 1.000000 1.0 2\n434 namespaces 1.000000 1.0 8\n.. ... ... ... ...\n268 visual-c++ 0.333333 0.2 10\n117 timer 0.333333 0.2 10\n65 apache 0.333333 0.5 16\n66 cgi 0.333333 0.5 16\n103 mechanize 0.333333 0.2 10\n\n[504 rows x 4 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tag</th>\n <th>f1</th>\n <th>accuracy</th>\n <th>count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>176</th>\n <td>zsh</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>4</td>\n </tr>\n <tr>\n <th>167</th>\n <td>time</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>10</td>\n </tr>\n <tr>\n <th>432</th>\n <td>import</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>2</td>\n </tr>\n <tr>\n <th>433</th>\n <td>console</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>2</td>\n </tr>\n <tr>\n <th>434</th>\n <td>namespaces</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>8</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>268</th>\n <td>visual-c++</td>\n <td>0.333333</td>\n <td>0.2</td>\n <td>10</td>\n </tr>\n <tr>\n <th>117</th>\n <td>timer</td>\n <td>0.333333</td>\n <td>0.2</td>\n <td>10</td>\n </tr>\n <tr>\n <th>65</th>\n <td>apache</td>\n <td>0.333333</td>\n <td>0.5</td>\n <td>16</td>\n </tr>\n <tr>\n <th>66</th>\n <td>cgi</td>\n <td>0.333333</td>\n <td>0.5</td>\n <td>16</td>\n </tr>\n <tr>\n <th>103</th>\n <td>mechanize</td>\n <td>0.333333</td>\n <td>0.2</td>\n <td>10</td>\n </tr>\n </tbody>\n</table>\n<p>504 rows × 4 columns</p>\n</div>"
+ "text/plain": " tag f1 accuracy count\n908 feedparser 1.0 1.00 2\n219 version 1.0 1.00 10\n99 spring 1.0 1.00 6\n1090 captcha 1.0 1.00 4\n1091 recaptcha 1.0 1.00 4\n... ... ... ... ...\n738 python-2.7 0.0 0.25 8\n337 windows-7-x64 0.0 0.50 20\n129 tree-structure 0.0 0.00 14\n1047 mta 0.0 0.00 6\n1057 inequality 0.0 0.20 10\n\n[1212 rows x 4 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tag</th>\n <th>f1</th>\n <th>accuracy</th>\n <th>count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>908</th>\n <td>feedparser</td>\n <td>1.0</td>\n <td>1.00</td>\n <td>2</td>\n </tr>\n <tr>\n <th>219</th>\n <td>version</td>\n <td>1.0</td>\n <td>1.00</td>\n <td>10</td>\n </tr>\n <tr>\n <th>99</th>\n <td>spring</td>\n <td>1.0</td>\n <td>1.00</td>\n <td>6</td>\n </tr>\n <tr>\n <th>1090</th>\n <td>captcha</td>\n <td>1.0</td>\n <td>1.00</td>\n <td>4</td>\n </tr>\n <tr>\n <th>1091</th>\n <td>recaptcha</td>\n <td>1.0</td>\n <td>1.00</td>\n <td>4</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>738</th>\n <td>python-2.7</td>\n <td>0.0</td>\n <td>0.25</td>\n <td>8</td>\n </tr>\n <tr>\n <th>337</th>\n <td>windows-7-x64</td>\n <td>0.0</td>\n <td>0.50</td>\n <td>20</td>\n </tr>\n <tr>\n <th>129</th>\n <td>tree-structure</td>\n <td>0.0</td>\n <td>0.00</td>\n <td>14</td>\n </tr>\n <tr>\n <th>1047</th>\n <td>mta</td>\n <td>0.0</td>\n <td>0.00</td>\n <td>6</td>\n </tr>\n <tr>\n <th>1057</th>\n <td>inequality</td>\n <td>0.0</td>\n <td>0.20</td>\n <td>10</td>\n </tr>\n </tbody>\n</table>\n<p>1212 rows × 4 columns</p>\n</div>"
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -287,29 +326,29 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\liamb\\AppData\\Local\\Temp\\ipykernel_15204\\3198782478.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
- " top_20 = results.sort_values(by='f1', ascending=False)[results['count'] > 15].head(20)\n"
+ "C:\\Users\\liamb\\AppData\\Local\\Temp\\ipykernel_17348\\1473026940.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " top_15 = results.sort_values(by='f1', ascending=False)[results['count'] > 10].head(15)\n"
]
},
{
"data": {
- "text/plain": " tag f1 accuracy count\n236 xhtml 1.000000 1.000000 16\n465 pickle 1.000000 1.000000 16\n5 easy-install 1.000000 1.000000 20\n222 naming-conventions 0.975610 0.954545 44\n282 command 0.975207 0.951613 124\n221 oop 0.974359 0.954545 44\n223 identifier 0.967742 0.941176 34\n450 simplejson 0.960000 0.923077 26\n38 portability 0.960000 0.928571 28\n281 terminal 0.960000 0.923077 130\n127 perl 0.952381 0.913043 92\n47 wxpython 0.952381 0.916667 24\n246 audio 0.952381 0.909091 22\n33 subprocess 0.950000 0.908046 174\n1 strptime 0.947368 0.900000 20\n160 browser 0.947368 0.900000 20\n161 remote-execution 0.947368 0.900000 20\n197 python-2.x 0.947368 0.909091 22\n9 django-models 0.947368 0.909091 44\n253 use-strict 0.941176 0.900000 20",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tag</th>\n <th>f1</th>\n <th>accuracy</th>\n <th>count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>236</th>\n <td>xhtml</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>465</th>\n <td>pickle</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>5</th>\n <td>easy-install</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>222</th>\n <td>naming-conventions</td>\n <td>0.975610</td>\n <td>0.954545</td>\n <td>44</td>\n </tr>\n <tr>\n <th>282</th>\n <td>command</td>\n <td>0.975207</td>\n <td>0.951613</td>\n <td>124</td>\n </tr>\n <tr>\n <th>221</th>\n <td>oop</td>\n <td>0.974359</td>\n <td>0.954545</td>\n <td>44</td>\n </tr>\n <tr>\n <th>223</th>\n <td>identifier</td>\n <td>0.967742</td>\n <td>0.941176</td>\n <td>34</td>\n </tr>\n <tr>\n <th>450</th>\n <td>simplejson</td>\n <td>0.960000</td>\n <td>0.923077</td>\n <td>26</td>\n </tr>\n <tr>\n <th>38</th>\n <td>portability</td>\n <td>0.960000</td>\n <td>0.928571</td>\n <td>28</td>\n </tr>\n <tr>\n <th>281</th>\n <td>terminal</td>\n <td>0.960000</td>\n <td>0.923077</td>\n <td>130</td>\n </tr>\n <tr>\n <th>127</th>\n <td>perl</td>\n <td>0.952381</td>\n <td>0.913043</td>\n <td>92</td>\n </tr>\n <tr>\n <th>47</th>\n <td>wxpython</td>\n <td>0.952381</td>\n <td>0.916667</td>\n <td>24</td>\n </tr>\n <tr>\n <th>246</th>\n <td>audio</td>\n <td>0.952381</td>\n <td>0.909091</td>\n <td>22</td>\n </tr>\n <tr>\n <th>33</th>\n <td>subprocess</td>\n <td>0.950000</td>\n <td>0.908046</td>\n <td>174</td>\n </tr>\n <tr>\n <th>1</th>\n <td>strptime</td>\n <td>0.947368</td>\n <td>0.900000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>160</th>\n <td>browser</td>\n <td>0.947368</td>\n <td>0.900000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>161</th>\n <td>remote-execution</td>\n <td>0.947368</td>\n <td>0.900000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>197</th>\n <td>python-2.x</td>\n <td>0.947368</td>\n <td>0.909091</td>\n <td>22</td>\n </tr>\n <tr>\n <th>9</th>\n <td>django-models</td>\n <td>0.947368</td>\n <td>0.909091</td>\n <td>44</td>\n </tr>\n <tr>\n <th>253</th>\n <td>use-strict</td>\n <td>0.941176</td>\n <td>0.900000</td>\n <td>20</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ "text/plain": " tag f1 accuracy count\n1029 setup.py 1.000000 1.000000 20\n1030 python-packaging 1.000000 1.000000 20\n421 celery 1.000000 1.000000 12\n939 deque 1.000000 1.000000 14\n282 command 0.984127 0.969697 132\n389 ansi-colors 0.976000 0.953125 128\n590 output 0.970149 0.942029 138\n223 identifier 0.969697 0.941176 34\n281 terminal 0.961240 0.927536 276\n151 temporary-files 0.960000 0.928571 28\n565 euclidean-distance 0.954545 0.913043 46\n437 focus 0.952381 0.909091 22\n1164 keyboardinterrupt 0.952381 0.909091 22\n626 python-module 0.951220 0.914894 94\n339 assertion 0.944444 0.894737 38",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tag</th>\n <th>f1</th>\n <th>accuracy</th>\n <th>count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1029</th>\n <td>setup.py</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>1030</th>\n <td>python-packaging</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>421</th>\n <td>celery</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>12</td>\n </tr>\n <tr>\n <th>939</th>\n <td>deque</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>14</td>\n </tr>\n <tr>\n <th>282</th>\n <td>command</td>\n <td>0.984127</td>\n <td>0.969697</td>\n <td>132</td>\n </tr>\n <tr>\n <th>389</th>\n <td>ansi-colors</td>\n <td>0.976000</td>\n <td>0.953125</td>\n <td>128</td>\n </tr>\n <tr>\n <th>590</th>\n <td>output</td>\n <td>0.970149</td>\n <td>0.942029</td>\n <td>138</td>\n </tr>\n <tr>\n <th>223</th>\n <td>identifier</td>\n <td>0.969697</td>\n <td>0.941176</td>\n <td>34</td>\n </tr>\n <tr>\n <th>281</th>\n <td>terminal</td>\n <td>0.961240</td>\n <td>0.927536</td>\n <td>276</td>\n </tr>\n <tr>\n <th>151</th>\n <td>temporary-files</td>\n <td>0.960000</td>\n <td>0.928571</td>\n <td>28</td>\n </tr>\n <tr>\n <th>565</th>\n <td>euclidean-distance</td>\n <td>0.954545</td>\n <td>0.913043</td>\n <td>46</td>\n </tr>\n <tr>\n <th>437</th>\n <td>focus</td>\n <td>0.952381</td>\n <td>0.909091</td>\n <td>22</td>\n </tr>\n <tr>\n <th>1164</th>\n <td>keyboardinterrupt</td>\n <td>0.952381</td>\n <td>0.909091</td>\n <td>22</td>\n </tr>\n <tr>\n <th>626</th>\n <td>python-module</td>\n <td>0.951220</td>\n <td>0.914894</td>\n <td>94</td>\n </tr>\n <tr>\n <th>339</th>\n <td>assertion</td>\n <td>0.944444</td>\n <td>0.894737</td>\n <td>38</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "top_20 = results.sort_values(by='f1', ascending=False)[results['count'] > 15].head(20)\n",
- "top_20"
+ "top_15 = results.sort_values(by='f1', ascending=False)[results['count'] > 10].head(15)\n",
+ "top_15"
],
"metadata": {
"collapsed": false
@@ -317,29 +356,29 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\liamb\\AppData\\Local\\Temp\\ipykernel_15204\\1442870573.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
- " bottom_20 = results.sort_values(by='f1', ascending=False)[results['count'] > 15].tail(20)\n"
+ "C:\\Users\\liamb\\AppData\\Local\\Temp\\ipykernel_17348\\2240084834.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " bottom_15 = results.sort_values(by='f1', ascending=False)[results['count'] > 15].tail(20).sort_values(by='count', ascending=False)\n"
]
},
{
"data": {
- "text/plain": " tag f1 accuracy count\n284 python-imaging-library 0.648649 0.580645 62\n87 performance 0.646154 0.510638 94\n352 rounding 0.640000 0.550000 40\n295 gmail 0.625000 0.500000 24\n130 replace 0.615385 0.444444 18\n24 lisp 0.600000 0.500000 16\n25 clojure 0.600000 0.500000 16\n202 large-files 0.600000 0.500000 16\n76 preferences 0.600000 0.555556 18\n460 fencepost 0.600000 0.515152 66\n479 frameworks 0.545455 0.375000 16\n431 smtplib 0.545455 0.444444 18\n430 smtp 0.545455 0.444444 18\n3 qt 0.500000 0.333333 18\n337 windows-7-x64 0.500000 0.600000 20\n141 32bit-64bit 0.421053 0.312500 32\n192 primes 0.400000 0.689655 58\n354 osx-snow-leopard 0.375000 0.285714 28\n65 apache 0.333333 0.500000 16\n66 cgi 0.333333 0.500000 16",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tag</th>\n <th>f1</th>\n <th>accuracy</th>\n <th>count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>284</th>\n <td>python-imaging-library</td>\n <td>0.648649</td>\n <td>0.580645</td>\n <td>62</td>\n </tr>\n <tr>\n <th>87</th>\n <td>performance</td>\n <td>0.646154</td>\n <td>0.510638</td>\n <td>94</td>\n </tr>\n <tr>\n <th>352</th>\n <td>rounding</td>\n <td>0.640000</td>\n <td>0.550000</td>\n <td>40</td>\n </tr>\n <tr>\n <th>295</th>\n <td>gmail</td>\n <td>0.625000</td>\n <td>0.500000</td>\n <td>24</td>\n </tr>\n <tr>\n <th>130</th>\n <td>replace</td>\n <td>0.615385</td>\n <td>0.444444</td>\n <td>18</td>\n </tr>\n <tr>\n <th>24</th>\n <td>lisp</td>\n <td>0.600000</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>25</th>\n <td>clojure</td>\n <td>0.600000</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>202</th>\n <td>large-files</td>\n <td>0.600000</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>76</th>\n <td>preferences</td>\n <td>0.600000</td>\n <td>0.555556</td>\n <td>18</td>\n </tr>\n <tr>\n <th>460</th>\n <td>fencepost</td>\n <td>0.600000</td>\n <td>0.515152</td>\n <td>66</td>\n </tr>\n <tr>\n <th>479</th>\n <td>frameworks</td>\n <td>0.545455</td>\n <td>0.375000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>431</th>\n <td>smtplib</td>\n <td>0.545455</td>\n <td>0.444444</td>\n <td>18</td>\n </tr>\n <tr>\n <th>430</th>\n <td>smtp</td>\n <td>0.545455</td>\n <td>0.444444</td>\n <td>18</td>\n </tr>\n <tr>\n <th>3</th>\n <td>qt</td>\n <td>0.500000</td>\n <td>0.333333</td>\n <td>18</td>\n </tr>\n <tr>\n <th>337</th>\n <td>windows-7-x64</td>\n <td>0.500000</td>\n <td>0.600000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>141</th>\n <td>32bit-64bit</td>\n <td>0.421053</td>\n <td>0.312500</td>\n <td>32</td>\n </tr>\n <tr>\n <th>192</th>\n <td>primes</td>\n <td>0.400000</td>\n <td>0.689655</td>\n <td>58</td>\n </tr>\n <tr>\n <th>354</th>\n <td>osx-snow-leopard</td>\n <td>0.375000</td>\n <td>0.285714</td>\n <td>28</td>\n </tr>\n <tr>\n <th>65</th>\n <td>apache</td>\n <td>0.333333</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>66</th>\n <td>cgi</td>\n <td>0.333333</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ "text/plain": " tag f1 accuracy count\n192 primes 0.0 0.533333 90\n202 large-files 0.0 0.214286 28\n946 rename 0.0 0.142857 28\n947 batch-rename 0.0 0.142857 28\n964 reverse-lookup 0.0 0.384615 26\n1058 digits 0.0 0.090909 22\n665 sysadmin 0.0 0.090909 22\n866 matrix 0.0 0.181818 22\n669 sudoku 0.0 0.090909 22\n376 data-migration 0.0 0.200000 20\n337 windows-7-x64 0.0 0.500000 20\n370 integration 0.0 0.000000 18\n694 hp-ux 0.0 0.333333 18\n850 tracking 0.0 0.333333 18\n333 ocr 0.0 0.222222 18\n896 parity 0.0 0.125000 16\n928 message-queue 0.0 0.250000 16\n788 transformation 0.0 0.500000 16\n836 html-parsing 0.0 0.000000 16\n787 filtering 0.0 0.500000 16",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tag</th>\n <th>f1</th>\n <th>accuracy</th>\n <th>count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>192</th>\n <td>primes</td>\n <td>0.0</td>\n <td>0.533333</td>\n <td>90</td>\n </tr>\n <tr>\n <th>202</th>\n <td>large-files</td>\n <td>0.0</td>\n <td>0.214286</td>\n <td>28</td>\n </tr>\n <tr>\n <th>946</th>\n <td>rename</td>\n <td>0.0</td>\n <td>0.142857</td>\n <td>28</td>\n </tr>\n <tr>\n <th>947</th>\n <td>batch-rename</td>\n <td>0.0</td>\n <td>0.142857</td>\n <td>28</td>\n </tr>\n <tr>\n <th>964</th>\n <td>reverse-lookup</td>\n <td>0.0</td>\n <td>0.384615</td>\n <td>26</td>\n </tr>\n <tr>\n <th>1058</th>\n <td>digits</td>\n <td>0.0</td>\n <td>0.090909</td>\n <td>22</td>\n </tr>\n <tr>\n <th>665</th>\n <td>sysadmin</td>\n <td>0.0</td>\n <td>0.090909</td>\n <td>22</td>\n </tr>\n <tr>\n <th>866</th>\n <td>matrix</td>\n <td>0.0</td>\n <td>0.181818</td>\n <td>22</td>\n </tr>\n <tr>\n <th>669</th>\n <td>sudoku</td>\n <td>0.0</td>\n <td>0.090909</td>\n <td>22</td>\n </tr>\n <tr>\n <th>376</th>\n <td>data-migration</td>\n <td>0.0</td>\n <td>0.200000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>337</th>\n <td>windows-7-x64</td>\n <td>0.0</td>\n <td>0.500000</td>\n <td>20</td>\n </tr>\n <tr>\n <th>370</th>\n <td>integration</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>18</td>\n </tr>\n <tr>\n <th>694</th>\n <td>hp-ux</td>\n <td>0.0</td>\n <td>0.333333</td>\n <td>18</td>\n </tr>\n <tr>\n <th>850</th>\n <td>tracking</td>\n <td>0.0</td>\n <td>0.333333</td>\n <td>18</td>\n </tr>\n <tr>\n <th>333</th>\n <td>ocr</td>\n <td>0.0</td>\n <td>0.222222</td>\n <td>18</td>\n </tr>\n <tr>\n <th>896</th>\n <td>parity</td>\n <td>0.0</td>\n <td>0.125000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>928</th>\n <td>message-queue</td>\n <td>0.0</td>\n <td>0.250000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>788</th>\n <td>transformation</td>\n <td>0.0</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>836</th>\n <td>html-parsing</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>16</td>\n </tr>\n <tr>\n <th>787</th>\n <td>filtering</td>\n <td>0.0</td>\n <td>0.500000</td>\n <td>16</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "bottom_20 = results.sort_values(by='f1', ascending=False)[results['count'] > 15].tail(20)\n",
- "bottom_20"
+ "bottom_15 = results.sort_values(by='f1', ascending=False)[results['count'] > 15].tail(20).sort_values(by='count', ascending=False)\n",
+ "bottom_15"
],
"metadata": {
"collapsed": false
@@ -347,20 +386,20 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"outputs": [
{
"data": {
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+ "text/plain": "[Text(5, 0, '20'),\n Text(5, 0, '20'),\n Text(5, 0, '12'),\n Text(5, 0, '14'),\n Text(5, 0, '132'),\n Text(5, 0, '128'),\n Text(5, 0, '138'),\n Text(5, 0, '34'),\n Text(5, 0, '276'),\n Text(5, 0, '28'),\n Text(5, 0, '46'),\n Text(5, 0, '22'),\n Text(5, 0, '22'),\n Text(5, 0, '94'),\n Text(5, 0, '38')]"
},
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": "<Figure size 1000x800 with 1 Axes>",
- "image/png": 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\n"
+ "image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
@@ -373,11 +412,11 @@
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 8))\n",
- "ax = sns.barplot(y=top_20['tag'], x=top_20['f1'])\n",
+ "ax = sns.barplot(y=top_15['tag'], x=top_15['f1'])\n",
"plt.xticks(rotation=90)\n",
"plt.title('Top 20 Tags by F1 Score')\n",
"# Label with counts\n",
- "ax.bar_label(ax.containers[0], labels=top_20['count'], padding=5)"
+ "ax.bar_label(ax.containers[0], labels=top_15['count'], padding=5)"
],
"metadata": {
"collapsed": false
@@ -385,20 +424,20 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"outputs": [
{
"data": {
- "text/plain": "[Text(5, 0, '62'),\n Text(5, 0, '94'),\n Text(5, 0, '40'),\n Text(5, 0, '24'),\n Text(5, 0, '18'),\n Text(5, 0, '16'),\n Text(5, 0, '16'),\n Text(5, 0, '16'),\n Text(5, 0, '18'),\n Text(5, 0, '66'),\n Text(5, 0, '16'),\n Text(5, 0, '18'),\n Text(5, 0, '18'),\n Text(5, 0, '18'),\n Text(5, 0, '20'),\n Text(5, 0, '32'),\n Text(5, 0, '58'),\n Text(5, 0, '28'),\n Text(5, 0, '16'),\n Text(5, 0, '16')]"
+ "text/plain": "[Text(5, 0, '90'),\n Text(5, 0, '28'),\n Text(5, 0, '28'),\n Text(5, 0, '28'),\n Text(5, 0, '26'),\n Text(5, 0, '22'),\n Text(5, 0, '22'),\n Text(5, 0, '22'),\n Text(5, 0, '22'),\n Text(5, 0, '20'),\n Text(5, 0, '20'),\n Text(5, 0, '18'),\n Text(5, 0, '18'),\n Text(5, 0, '18'),\n Text(5, 0, '18'),\n Text(5, 0, '16'),\n Text(5, 0, '16'),\n Text(5, 0, '16'),\n Text(5, 0, '16'),\n Text(5, 0, '16')]"
},
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": "<Figure size 1000x800 with 1 Axes>",
- "image/png": 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\n"
+ "image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
@@ -406,11 +445,11 @@
],
"source": [
"plt.figure(figsize=(10, 8))\n",
- "ax = sns.barplot(y=bottom_20['tag'], x=bottom_20['f1'])\n",
+ "ax = sns.barplot(y=bottom_15['tag'], x=bottom_15['f1'])\n",
"plt.xticks(rotation=90)\n",
- "plt.title('Bottom 20 Tags by F1 Score')\n",
+ "plt.title('Bottom 15 Tags by F1 Score')\n",
"# Label with counts\n",
- "ax.bar_label(ax.containers[0], labels=bottom_20['count'], padding=5)"
+ "ax.bar_label(ax.containers[0], labels=bottom_15['count'], padding=5)"
],
"metadata": {
"collapsed": false
@@ -418,7 +457,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"outputs": [],
"source": [],
"metadata": {
@@ -427,7 +466,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"outputs": [],
"source": [],
"metadata": {
@@ -436,7 +475,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"outputs": [],
"source": [],
"metadata": {
diff --git a/embeddings/precision_test.ipynb b/embeddings/precision_test.ipynb
index 399cb7c0243c84de71c535a6255f5cf3dd97324c..5192f545b9ae7ac2821ff55bc52113b4622fc5f7 100644
--- a/embeddings/precision_test.ipynb
+++ b/embeddings/precision_test.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 51,
"outputs": [],
"source": [
"import os\n",
@@ -17,30 +17,35 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 52,
+ "outputs": [],
+ "source": [
+ "ROOT = '../data/'\n",
+ "MODEL = '../models/SAGE_3l_60e_64h.pt'"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2023-03-25 18:07:10 INFO Proceeding with cpu . .\n"
- ]
- },
{
"data": {
- "text/plain": "HeteroGNN(\n (convs): ModuleList(\n (0): HeteroConv(num_relations=10)\n (1): HeteroConv(num_relations=10)\n (2): HeteroConv(num_relations=10)\n )\n (lin1): Linear(3392, 2, bias=True)\n (lin2): Linear(64, 2, bias=True)\n (softmax): Softmax(dim=-1)\n)"
+ "text/plain": "HeteroGAT(\n (convs): ModuleList(\n (0): HeteroConv(num_relations=10)\n (1): HeteroConv(num_relations=10)\n )\n (lin1): Linear(3392, 64, bias=True)\n (lin2): Linear(64, 2, bias=True)\n (softmax): Softmax(dim=-1)\n)"
},
- "execution_count": 2,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "from hetero_GAT import HeteroGNN\n",
+ "from hetero_GAT import HeteroGAT\n",
"import torch\n",
"# Load model (EXP 1)\n",
- "model = HeteroGNN(64, 2, 3)\n",
- "model.load_state_dict(torch.load('../models/qa_40e_64h_3l.pt', map_location=torch.device('cpu')))\n",
+ "model = HeteroGAT(out_channels=2, num_layers=2, hidden_channels=64)\n",
+ "model.load_state_dict(torch.load(MODEL, map_location=torch.device('cpu')), strict=False)\n",
"model.eval()"
],
"metadata": {
@@ -49,11 +54,11 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 54,
"outputs": [],
"source": [
- "question_ids = fetch_question_ids()\n",
- "train_ids = list(question_ids)[:int(len(question_ids) * 0.7)]\n",
+ "question_ids = fetch_question_ids(ROOT)\n",
+ "train_ids = list(question_ids)[:int(len(question_ids) * 0.8)]\n",
"test_ids = [x for x in question_ids if x not in train_ids]"
],
"metadata": {
@@ -62,20 +67,20 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 55,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-03-25 18:07:11 INFO PostEmbedding instantiated!\n"
+ "2023-04-16 20:08:39 INFO PostEmbedding instantiated!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
+ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
@@ -83,7 +88,7 @@
],
"source": [
"# Instantiate the dataset class for access to database\n",
- "ugd = UserGraphDataset('../data/', db_address='../stackoverflow.db', skip_processing=True)"
+ "ugd = UserGraphDataset(ROOT, db_address='../stackoverflow.db', skip_processing=True)"
],
"metadata": {
"collapsed": false
@@ -91,7 +96,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 56,
"outputs": [],
"source": [
"import torch\n",
@@ -100,11 +105,11 @@
"\n",
"\n",
"def fetch_answer_files(question_id: int):\n",
- " for f in os.listdir('../data/processed/'):\n",
+ " for f in os.listdir(os.path.join(ROOT, 'processed')):\n",
" question_id_search = re.search(r\"id_(\\d+)\", f)\n",
" if question_id_search:\n",
" if int(question_id_search.group(1)) == question_id:\n",
- " yield torch.load(os.path.join('../data/processed/', f))\n"
+ " yield torch.load(os.path.join(ROOT, 'processed', f))\n"
],
"metadata": {
"collapsed": false
@@ -112,544 +117,50 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 57,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2126\n",
- "1759455\n",
- "1464548\n",
- "2087148\n",
- "1857780\n",
- "809210\n",
- "2218238\n",
- "1595649\n",
- "88325\n",
- "1890570\n",
- "579856\n",
- "2054416\n",
- "1956142\n",
- "842031\n",
- "2087216\n",
- "1530161\n",
- "973106\n",
- "1431862\n",
- "350519\n",
- "1792312\n",
- "1169725\n",
- "252221\n",
- "874815\n",
- "2218304\n",
- "514371\n",
- "842059\n",
- "1661262\n",
- "842075\n",
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+ "Precision@1: 0.36915077989601386\n"
]
}
],
"source": [
"from torch_geometric.loader import DataLoader\n",
+ "from sklearn.metrics import ndcg_score\n",
+ "\n",
+ "for _ in range(1):\n",
+ " NDCGS = []\n",
+ " CORRECT = 0\n",
+ " COUNT = 0\n",
+ " for i in test_ids[:600]:\n",
+ " graph_files = list(fetch_answer_files(i))\n",
+ " dataloader = DataLoader(graph_files, batch_size=1, shuffle=False)\n",
"\n",
- "CORRECT = 0\n",
- "COUNT = 0\n",
- "print(len(test_ids))\n",
- "for i in test_ids[:500]:\n",
- " graph_files = list(fetch_answer_files(i))\n",
- " dataloader = DataLoader(graph_files, batch_size=1, shuffle=False)\n",
+ " results = []\n",
+ " for answer, graph in zip(ugd.fetch_answers_for_question(i).itertuples(), dataloader):\n",
"\n",
- " results = []\n",
- " print(i)\n",
- " for answer, graph in zip(ugd.fetch_answers_for_question(i).itertuples(), dataloader):\n",
+ " post_emb = torch.cat([graph.question_emb, graph.answer_emb], dim=1)\n",
"\n",
- " post_emb = torch.cat([graph.question_emb, graph.answer_emb], dim=1)\n",
+ " out = model(graph.x_dict, graph.edge_index_dict, graph.batch_dict, post_emb) # Perform a single forward pass.\n",
+ " results.append((out[0][1].item(), answer.Score))\n",
+ " if len(results) > 1:\n",
+ " # calculate ndcg\n",
+ " results.sort(key=lambda x: x[0], reverse=True)\n",
+ " NDCGS.append(ndcg_score([[x[1] for x in results]], [[x[0] for x in results]], k=5))\n",
"\n",
- " out = model(graph.x_dict, graph.edge_index_dict, graph.batch_dict, post_emb) # Perform a single forward pass.\n",
- " results.append((out[0][1].item(), answer.Score))\n",
- " if len(results) > 1:\n",
- " # calculate precision@1\n",
- " results.sort(key=lambda x: x[0], reverse=True)\n",
- " if results[0][1] > max([x[1] for x in results[1:]]):\n",
- " #print('Correct')\n",
- " CORRECT += 1\n",
- " else:\n",
- " #print('Incorrect')\n",
- " pass\n",
- " COUNT += 1\n",
+ " # calculate precision@1\n",
+ " results.sort(key=lambda x: x[0], reverse=True)\n",
+ " if results[0][1] >= max([x[1] for x in results[1:]]):\n",
+ " #print('Correct')\n",
+ " CORRECT += 1\n",
+ " else:\n",
+ " #print('Incorrect')\n",
+ " pass\n",
+ " COUNT += 1\n",
+ " print(f'Precision@1: {CORRECT / COUNT}')\n",
"\n"
],
"metadata": {
@@ -658,13 +169,42 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 58,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.7361403483124718"
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sum(NDCGS)/ len(NDCGS)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
"outputs": [
{
"data": {
- "text/plain": "161"
+ "text/plain": "213"
},
- "execution_count": 7,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -678,7 +218,27 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 60,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.36915077989601386"
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "CORRECT/ COUNT"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
"outputs": [],
"source": [],
"metadata": {
diff --git a/embeddings/static_graph_construction.py b/embeddings/static_graph_construction.py
index 4a661b538c80923b11f5ee3d33768e8bb210db07..dc393b8aa64b80a898f9bbc6e370ffc0db5a61f5 100644
--- a/embeddings/static_graph_construction.py
+++ b/embeddings/static_graph_construction.py
@@ -149,7 +149,7 @@ class StaticGraphConstruction:
return tags
def process_module_names(self, import_statements: List[Import]):
- modules = [i.module[0] for i in import_statements if i.module]
+ modules = [i.module for i in import_statements if i.module]
for m in modules:
if m not in self._known_modules:
self._known_modules[m] = len(self._known_modules)
@@ -170,6 +170,12 @@ class StaticGraphConstruction:
tag_nodes = list(self.process_tags())
module_nodes = list(self.process_modules())
+ print(len(question_nodes), len(answer_nodes), len(comment_nodes), len(tag_nodes), len(module_nodes), sum([len(question_nodes), len(answer_nodes), len(comment_nodes), len(tag_nodes), len(module_nodes)]))
+ # Print tags and modules with their indices (including offset)
+ OFFSET = len(question_nodes) + len(answer_nodes) + len(comment_nodes)
+ p1 = {v+(OFFSET+len(tag_nodes)):k for k,v in self._known_modules.items()}
+ p2 = {v+OFFSET:k for k,v in self._known_tags.items()}
+ print(f"TAGS {p2} MODULES {p1}")
# Assign node features
self._data['question'].x = torch.stack(question_nodes) if len(question_nodes) else torch.empty(0, 1536)
@@ -190,6 +196,9 @@ class StaticGraphConstruction:
# Remove isolated nodes, and convert to undirected graph
graph_out = T.remove_isolated_nodes.RemoveIsolatedNodes()(self._data)
+
+ print(sum([len(graph_out['question'].x), len(graph_out['answer'].x), len(graph_out['comment'].x), len(graph_out['tag'].x), len(graph_out['module'].x)]))
+
graph_out = T.ToUndirected()(graph_out)
graph_out.metadata()
diff --git a/embeddings/training_set_builder.ipynb b/embeddings/training_set_builder.ipynb
index 79dc96d4958dd987ba1f269142205027dbef2481..b1ae01aa8a9dfafc708190df9a0deaab1759d977 100644
--- a/embeddings/training_set_builder.ipynb
+++ b/embeddings/training_set_builder.ipynb
@@ -61,6 +61,26 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "'Number of unique users in training set: 4567'"
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"Number of unique users in training set: {}\".format(valid_questions['question_user_id'].nunique())"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 4,
diff --git a/evaluation/kfold.ipynb b/evaluation/kfold.ipynb
index a78804ede959e8f1232072e24c09335e5122484e..8031420883bf8b1e8c9904ceec2aa9d75b6ccbd6 100644
--- a/evaluation/kfold.ipynb
+++ b/evaluation/kfold.ipynb
@@ -11,13 +11,13 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 22,
"outputs": [
{
"data": {
"text/plain": "dict_keys(['accuracy', 'f1-score', 'loss', 'table', 'preds', 'trues'])"
},
- "execution_count": 1,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -26,6 +26,7 @@
"# load in k-fold cross validation results\n",
"import pickle\n",
"import numpy as np\n",
+ "from sklearn.metrics import f1_score\n",
"\n",
"model_configuration = {}\n",
"\n",
@@ -49,13 +50,13 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 23,
"outputs": [
{
"data": {
"text/plain": "[0.7379937163375224,\n 0.7423181872108198,\n 0.7391304347826086,\n 0.7803654604084558,\n 0.7387200736648251]"
},
- "execution_count": 2,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -69,13 +70,13 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 24,
"outputs": [
{
"data": {
"text/plain": "[0.7512742475843565,\n 0.756445407195294,\n 0.7525616000605592,\n 0.7709828880147491,\n 0.7516097705290494]"
},
- "execution_count": 3,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -171,6 +172,48 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.569332447661765"
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "with_answer_macro_f1_mean = np.mean([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['with_answer']])\n",
+ "with_answer_macro_f1_mean"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.005519649859141665"
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "with_answer_macro_f1_std = np.std([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['with_answer']])\n",
+ "with_answer_macro_f1_std"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "markdown",
"source": [
@@ -201,18 +244,6 @@
"collapsed": false
}
},
- {
- "cell_type": "code",
- "execution_count": 8,
- "outputs": [],
- "source": [],
- "metadata": {
- "collapsed": false,
- "pycharm": {
- "name": "#%%md dsa\n"
- }
- }
- },
{
"cell_type": "code",
"execution_count": 9,
@@ -276,6 +307,57 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.5604108683247334"
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "without_answer_macro_f1_mean = np.mean([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['without_answer']])\n",
+ "without_answer_macro_f1_mean"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.008649111537000575"
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "without_answer_macro_f1_std = np.std([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['without_answer']])\n",
+ "without_answer_macro_f1_std"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Baseline"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 12,
@@ -360,6 +442,48 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.4632465948110549"
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_macro_f1_mean = np.mean([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['baseline']])\n",
+ "baseline_macro_f1_mean"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.0068289565909087305"
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_macro_f1_std = np.std([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['baseline']])\n",
+ "baseline_macro_f1_std"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 16,
@@ -444,6 +568,48 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.544839920300357"
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_q_a_macro_f1_mean = np.mean([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['baseline_q+a']])\n",
+ "baseline_q_a_macro_f1_mean"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "0.010762765137994366"
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baseline_q_a_macro_f1_std = np.std([f1_score(x['trues'], x['preds'], average='macro') for x in model_configuration['baseline_q+a']])\n",
+ "baseline_q_a_macro_f1_std"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 20,
@@ -457,27 +623,27 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 35,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "model f1-score f1-score std accuracy accuracy std\n",
- "-------------- ---------- -------------- ---------- --------------\n",
- "with_answer 0.756575 0.0074359 0.747706 0.0163969\n",
- "without_answer 0.741596 0.0135423 0.724281 0.0225911\n",
- "baseline 0.593263 0.0137716 0.532296 0.0139245\n",
- "baseline_q+a 0.681144 0.0129791 0.634223 0.0158274\n"
+ "Model F1 (weighted) F1 std F1 (macro) F1 std (macro) Accuracy Accuracy std\n",
+ "-------------- --------------- --------- ------------ ---------------- ---------- --------------\n",
+ "with_answer 0.756575 0.0074359 0.569332 0.00551965 0.747706 0.0163969\n",
+ "without_answer 0.741596 0.0135423 0.560411 0.00864911 0.724281 0.0225911\n",
+ "baseline 0.593263 0.0137716 0.463247 0.00682896 0.532296 0.0139245\n",
+ "baseline_q+a 0.681144 0.0129791 0.54484 0.0107628 0.634223 0.0158274\n"
]
}
],
"source": [
- "print(tabulate([['with_answer', with_answer_mean, with_answer_f1_std, with_answer_acc_mean, with_answer_acc_std],\n",
- " ['without_answer', without_answer_mean, without_answer_f1_std, without_answer_acc_mean, without_answer_acc_std],\n",
- " ['baseline', baseline_mean, baseline_f1_std, baseline_acc_mean, baseline_acc_std],\n",
- " ['baseline_q+a', baseline_q_a_mean, baseline_q_a_f1_std, baseline_q_a_acc_mean, baseline_q_a_acc_std]],\n",
- " headers=['model', 'f1-score', 'f1-score std', 'accuracy', 'accuracy std']))"
+ "print(tabulate([['with_answer', with_answer_mean, with_answer_f1_std, with_answer_macro_f1_mean, with_answer_macro_f1_std, with_answer_acc_mean, with_answer_acc_std],\n",
+ " ['without_answer', without_answer_mean, without_answer_f1_std, without_answer_macro_f1_mean, without_answer_macro_f1_std, without_answer_acc_mean, without_answer_acc_std],\n",
+ " ['baseline', baseline_mean, baseline_f1_std, baseline_macro_f1_mean, baseline_macro_f1_std, baseline_acc_mean, baseline_acc_std],\n",
+ " ['baseline_q+a', baseline_q_a_mean, baseline_q_a_f1_std, baseline_q_a_macro_f1_mean, baseline_q_a_macro_f1_std, baseline_q_a_acc_mean, baseline_q_a_acc_std]],\n",
+ " headers=['Model', 'F1 (weighted)', 'F1 std', 'F1 (macro)', 'F1 std (macro)', 'Accuracy', 'Accuracy std']))"
],
"metadata": {
"collapsed": false
diff --git a/evaluation/time_to_construct.ipynb b/evaluation/time_to_construct.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dd8bd5449dbce476769859f960e2dfc770755f13
--- /dev/null
+++ b/evaluation/time_to_construct.ipynb
@@ -0,0 +1,82 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
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+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "<Figure size 640x480 with 1 Axes>",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "plt.rcParams.update(plt.rcParamsDefault)\n",
+ "\n",
+ "# plot distribution of times\n",
+ "sns.histplot(data=times, kde=True, bins=40)\n",
+ "# set x axis label\n",
+ "plt.xlabel('Time (s)')\n",
+ "# increase number of x ticks\n",
+ "plt.xticks(range(0, 70, 5))\n",
+ "\n",
+ "# save figure to appear in latex\n",
+ "plt.savefig('times.pdf', bbox_inches='tight')\n",
+ "\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
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