Added Part B, and accompanying results.

part-a/part_a.py

@@ -24,15 +24,15 @@ print("Created classifier with linear kernal.")
 # test the accuracy of the model
 print("Beginning accuracy testing...")
 averageAccuracy = 0
-for i in range(10):
-    print("Testing accuracy - iteration " + str(i) + "/10")
+for i in range(5):
+    print("Testing accuracy - iteration " + str(i) + "/5")
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.0167)
     classifier.fit(X_train, y_train)
     y_pred = classifier.predict(X_test)
     accuracy = metrics.accuracy_score(y_test, y_pred)
     print("Accuracy: ", accuracy)
     averageAccuracy += accuracy
-print("Average accuracy: ", (averageAccuracy/10))
+print("Average accuracy: ", (averageAccuracy/5))
     
 # re-train the model with whole data set
 classifier.fit(X, y)

part-b/part_b.py

+import csv
+
+from sklearn import svm, metrics
+from sklearn.model_selection import train_test_split
+
+# load training data - array of size (6000, 128) - and labels (1d array of length 6000)
+print("Loading training data...")
+X = []
+y = []
+# read the training csv file line by line and parse the data
+trainingFile = open("TrainingDataMulti.csv")
+reader = csv.reader(trainingFile)
+for line in reader:
+    # remove the label from the system trace and add it to the training data
+    y.append(line.pop()) 
+    # add the features of the system trace to the training data
+    X.append(line)
+print("Finished reading training data - read " + str(len(X)) + " system traces.")
+
+# create a SVM classifier with the linear kernel
+classifier = svm.SVC(kernel="linear")
+print("Created classifier with linear kernel.")
+
+# test the accuracy of the model
+print("Beginning accuracy testing...")
+averageAccuracy = 0
+for i in range(5):
+    print("Testing accuracy - iteration " + str(i) + "/5")
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.0167)
+    classifier.fit(X_train, y_train)
+    y_pred = classifier.predict(X_test)
+    accuracy = metrics.accuracy_score(y_test, y_pred)
+    print("Accuracy: ", accuracy)
+    averageAccuracy += accuracy
+print("Average accuracy: ", (averageAccuracy/5))
+    
+# re-train the model with whole data set
+classifier.fit(X, y)
+print("Trained model.")
+
+# read the test file
+testData = []
+testFile = open("TestingDataMulti.csv");
+reader = csv.reader(testFile)
+for line in reader:
+    # add the system trace to the list of test data
+    testData.append(line)
+print("Read test sytem traces.")
+
+# predict the classifications for the test system traces and write them to the results file
+print("Writing to results file...")
+resultsFile = open("TestingResultsMutli.csv", "w", newline="")
+writer = csv.writer(resultsFile)
+for trace in testData:
+    trace.append(classifier.predict([trace])[0])
+    writer.writerow(trace)
+resultsFile.close()
+print("Finished writing to results file.")