def testIrisNoisy(trainDataFile, testDataFile, attrDataFile):
data = Preprocessor(trainDataFile, testDataFile, attrDataFile)
data.loadData()
testData = data.getMatrix(data.getTestData())
numInput = data.getNumInput()
numOutput = len(data.getClasses())
numHidden = 3
seed = 4
learningRate = 0.1
maxEpochs = 5000
momentum = 0.0
for rate in range(0, 21, 2):
noisyData = addNoise(data.getTrainData(), rate, data.getClasses())
trainData = data.getMatrix(noisyData)
print("\nNoise Rate (%): " + str(rate))
print("Generating neural network: %d-%d-%d" % (numInput, numHidden,numOutput))
nn = NeuralNetwork(numInput, numHidden, numOutput, seed)
nn.train(trainData, maxEpochs, learningRate, momentum, showEpochs=False, vRatio=0.85)
print("Training complete")
accTrain = nn.accuracy(trainData)
accTest = nn.accuracy(testData)
accValidTrain = nn.accuracy(trainData, validationOn=True)
accValidTest = nn.accuracy(testData, validationOn=True)
print("w/o validation set:")
print("Accuracy on train data = %0.4f " % accTrain)
print("Accuracy on test data = %0.4f " % accTest)
print("w/ validation set:")
print("Accuracy on train data = %0.4f " % accValidTrain)
print("Accuracy on test data = %0.4f " % accValidTest)
def testIdentity(trainDataFile):
trainData = np.genfromtxt(trainDataFile)
numInput = 8
numHidden = 3
numOutput = 8
seed = 3
learningRate = 0.3
maxEpochs = 5000
momentum = 0.0
print("Generating %d-%d-%d neural network " % (numInput, numHidden, numOutput))
nn = NeuralNetwork(numInput, numHidden, numOutput, seed)
nn.train(trainData, maxEpochs, learningRate, momentum, showHidden=True)
print("Training complete")
accTrain = nn.accuracy(trainData)
print("\nAccuracy on train data = %0.4f " % accTrain)
numHidden = 4
print("\nGenerating %d-%d-%d neural network " % (numInput, numHidden, numOutput))
nn = NeuralNetwork(numInput, numHidden, numOutput, seed)
nn.train(trainData, maxEpochs, learningRate, momentum, showHidden=True)
print("Training complete")
accTrain = nn.accuracy(trainData)
print("\nAccuracy on train data = %0.4f " % accTrain)
def testTennisOrIris(trainDataFile, testDataFile, attrDataFile):
data = Preprocessor(trainDataFile, testDataFile, attrDataFile)
data.loadData()
trainData = data.getMatrix(data.getTrainData())
testData = data.getMatrix(data.getTestData())
numInput = data.getNumInput()
numOutput = len(data.getClasses())
numHidden = 3
seed = 4
learningRate = 0.1
maxEpochs = 5000
momentum = 0.0
print("Generating neural network: %d-%d-%d" % (numInput, numHidden,numOutput))
nn = NeuralNetwork(numInput, numHidden, numOutput, seed)
nn.train(trainData, maxEpochs, learningRate, momentum)
print("Training complete")
# accTrain = nn.accuracy(trainData)
accTest = nn.accuracy(testData)
# print("\nAccuracy on train data = %0.4f " % accTrain)
print("Accuracy on test data = %0.4f " % accTest)
