def main(args):
if len(args) != 2:
print "Insufficient arguments provided"
print "Terminating..."
return
trainFilename = args[0]
testFilename = args[1]
resutlsFilename = args[1].split('.')[0] + '_rbf_results.txt'
resultStatsFilename = args[1].split('.')[0] + '_rbf_results_stats.txt'
# Loading train and test data
train_data = np.genfromtxt(trainFilename, dtype=int, delimiter='\t')
test_data = np.genfromtxt(testFilename, dtype=int, delimiter='\t')
trainEndIndex = len(train_data[0]) - 1
testEndIndex = len(test_data[0]) - 1
tempData = list()
tempLabels = list()
tempTest = list()
tempTestLabels = list()
for i in range(len(train_data)):
tempData.append(train_data[i][0:trainEndIndex])
if train_data[i][trainEndIndex] == 0:
tempLabels.append(-1)
else:
tempLabels.append(1)
for i in range(len(test_data)):
tempTest.append(test_data[i][0:testEndIndex])
if test_data[i][testEndIndex] == 0:
tempTestLabels.append(-1)
else:
tempTestLabels.append(1)
training_data = np.array(tempData)
training_labels = np.array(tempLabels)
testing_data = np.array(tempTest)
validationLabels = np.array(tempTestLabels, 'd')
# RBF Kernel test
clf = SVM(kernel=GaussianKernel(2.5), C=1.0)
val = Validator()
clf.fit(training_data, training_labels)
predictions = clf.predict(testing_data)
val.validate(validationLabels, predictions)
predFile = open(resutlsFilename, "w")
statFile = open(resultStatsFilename, "w")
predFile.write("Predicted\tActual\n")
for i in range(len(predictions)):
predFile.write("%d\t%d\n" % (predictions[i], validationLabels[i]))
statFile.write("%-20s %-5d\n" % ("True Positives:", val.truePositives()))
statFile.write("%-20s %-5d\n" % ("True Negatives:", val.trueNegatives()))
statFile.write("%-20s %-5d\n" % ("False Positives:", val.falsePositives()))
statFile.write("%-20s %-5d\n\n" %
("False Negatives:", val.falseNegatives()))
statFile.write("%-20s %-2.2f\n" % ("Accuracy:", val.accuracy()))
predFile.close()
statFile.close()
