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Commit c7232ee5 authored by jj1g18's avatar jj1g18 :cartwheel:
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added svm classifier and test files

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TrainingData.txt 0 → 100644
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main.py 0 → 100644
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# This is the main file for the scheduler algorithm
# What I need to do is read in each hour's usage and then compare it to what it should be
# by factoring in the actual user's usage in the excel sheet
# What I need to do is create a model that solves for the user's task energy usage and minimizes their cost
# by using their task times from the excel sheet and guideline pricing data from the TrainingData and TestingData texts
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
#function reads from given file and adds all values to a list, removing newline and spacing
#Takes param:
#filename of type String
#Returns:
#list_of_datasets of type List of List of String
def read_from_file(filename) :
file = open(filename, "r")
list_of_datasets = []
for line in file:
separated = line.split(",")
lastItems = separated[len(separated)-1].split("\n")
separated.pop(len(separated)-1)
separated.append(lastItems[0].strip())
list_of_datasets.append(separated)
return list_of_datasets
#print(read_from_file("TestingData.txt"))
data = pd.read_csv("TrainingData.txt", header=None)
#Can index but references [column][row]
#Use these features: Mean Standarad Deviation Variance Skewness Curtosis Entropy Class
#print(data)
#x_train.T.mean() to get mean, have to transpose to get mean along row
#print(x_train)
#print(x_train.shape)
#print(y_train)
#print(y_train.shape)
#Splits the data into a ratio of 80:20 for training and testing
#Splitting these further into X and Y for features and classes
train_set, test_set = train_test_split(data, test_size=0.2, random_state=1)
x_train = train_set.iloc[:,0:24].values
x_train = pd.DataFrame(data = x_train)
y_train = train_set.iloc[:,24].values
x_test = test_set.iloc[:,0:24].values
x_test = pd.DataFrame(data = x_test)
y_test = test_set.iloc[:,24].values
trainMean = x_train.T.mean()
trainStd = x_train.T.std()
trainVar = x_train.T.var()
trainSkew = x_train.T.skew()
trainMed = x_train.T.quantile(q=0.5)
trainLQ = x_train.T.quantile(q=0.25)
trainUQ = x_train.T.quantile(q=0.75)
testMean = x_test.T.mean()
testStd = x_test.T.std()
testVar = x_test.T.var()
testSkew = x_test.T.skew()
testMed = x_test.T.quantile(q=0.5)
testLQ = x_test.T.quantile(q=0.25)
testUQ = x_test.T.quantile(q=0.75)
x_train["Mean"] = trainMean
x_train["Std"] = trainStd
x_train["Var"] = trainVar
#x_train["Skew"] = trainSkew
x_train["Median"] = trainMed
x_train["LowQ"] = trainLQ
x_train["UpQ"] = trainUQ
print(x_train)
x_test["Mean"] = testMean
x_test["Std"] = testStd
x_test["Var"] = testVar
#x_test["Skew"] = testSkew
x_test["Median"] = testMed
x_test["LowQ"] = testLQ
x_test["UpQ"] = testUQ
print(x_test)
clfSvm = svm.SVC()
clfSvm.fit(x_train, y_train)
print(clfSvm.score(x_test, y_test))
newTestData = pd.read_csv("TestingData.txt", header=None)
testData = pd.read_csv("TestingData.txt", header=None)
print(newTestData)
testMean = testData.T.mean()
testStd = testData.T.std()
testVar = testData.T.var()
testMed = testData.T.quantile(q=0.5)
testLQ = testData.T.quantile(q=0.25)
testUQ = testData.T.quantile(q=0.75)
testData["Mean"] = testMean
testData["Std"] = testStd
testData["Var"] = testVar
testData["Median"] = testMed
testData["LowQ"] = testLQ
testData["UpQ"] = testUQ
y_testData = clfSvm.predict(testData)
newTestData[24] = y_testData
print(newTestData)
newTestData.to_csv(path_or_buf="TestingResults.txt", header=False, index=False)
\ No newline at end of file
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