def __init__(self, trainingDataContainer, alpha, maxIterations):
self.log = LoggerFetcher().fetchLogger("p1", "perceptron")
self.trainingDataContainer = trainingDataContainer
self.alpha = alpha
self.maxIterations = maxIterations
self.initialiseWeightVector()
def testNonExistingLogConfig(self):
configFileName = 'non-existing-conf'
package = 'p1'
fetcher = LoggerFetcher()
try:
fetcher.fetchLogger(package, configFileName)
except IOError:
pass
def testNonExistingLogConfig(self):
configFileName = 'non-existing-conf'
package = 'p1'
fetcher = LoggerFetcher()
try:
fetcher.fetchLogger(package, configFileName)
except IOError:
pass
def __init__(self, trainingDataContainer, alpha, maxIterations):
self.log = LoggerFetcher().fetchLogger("p1", "perceptron")
self.trainingDataContainer = trainingDataContainer
self.alpha = alpha
self.maxIterations = maxIterations
self.initialiseWeightVector()
class Main(object):
def __init__(self):
self.log = LoggerFetcher().fetchLogger("p1", "main")
def classifyDataSet(self, dataSetName, perceptron):
self.log.info('Classifying dataset %s', dataSetName)
container = DataFetcher().fetchDataSet(dataSetName)
labels = container.getLabels()
samples = container.getDataVectors()
errors = 0
for i, sample in enumerate(samples):
cls = perceptron.classify(sample)
if cls != labels[i]:
errors += 1
self.log.info('Misclassified sample %s as %s', labels[i], cls)
setSize = container.getDataSetSize()
errorRate = errors * 1.0 / setSize * 100
self.log.info('Error rate while classifying was %s', errorRate)
def trainPerceptron(self, dataSetName, alpha, iterations):
container = DataFetcher().fetchDataSet(dataSetName)
perceptron = Perceptron(container, 0.1, 15)
perceptron.train()
return perceptron
def main(self):
dataSets = ['buffer_dataset', 'inverter_dataset']
for dataSetName in dataSets:
perceptron = self.trainPerceptron(dataSetName, 0.1, 15)
self.classifyDataSet(dataSetName, perceptron)
class Main(object):
def __init__(self):
self.log = LoggerFetcher().fetchLogger("p1", "main")
def classifyDataSet(self, dataSetName, perceptron):
self.log.info('Classifying dataset %s', dataSetName)
container = DataFetcher().fetchDataSet(dataSetName)
labels = container.getLabels()
samples = container.getDataVectors()
errors = 0
for i, sample in enumerate(samples):
cls = perceptron.classify(sample)
if cls != labels[i]:
errors += 1
self.log.info('Misclassified sample %s as %s', labels[i], cls)
setSize = container.getDataSetSize()
errorRate = errors * 1.0 / setSize * 100
self.log.info('Error rate while classifying was %s', errorRate)
def trainPerceptron(self, dataSetName, alpha, iterations):
container = DataFetcher().fetchDataSet(dataSetName)
perceptron = Perceptron(container, 0.1, 15)
perceptron.train()
return perceptron
def main(self):
dataSets = ['buffer_dataset', 'inverter_dataset']
for dataSetName in dataSets:
perceptron = self.trainPerceptron(dataSetName, 0.1, 15)
self.classifyDataSet(dataSetName, perceptron)
class Perceptron(object):
def __init__(self, trainingDataContainer, alpha, maxIterations):
self.log = LoggerFetcher().fetchLogger("p1", "perceptron")
self.trainingDataContainer = trainingDataContainer
self.alpha = alpha
self.maxIterations = maxIterations
self.initialiseWeightVector()
def initialiseWeightVector(self):
self.log.debug('Initializing weight vector')
vectorLength = self.trainingDataContainer.getDataVectorLength() + 1
self.weightVector = [random.random() for i in xrange(vectorLength)]
def classify(self, dataVector):
self.log.info('Classifying sample %s', dataVector)
if len(dataVector) == self.getWeightVectorLength() - 1:
activation = self.getActivation(self.weightVector, dataVector)
self.log.debug('Activation is %s', activation)
classification = 0 if activation < 0 else 1
self.log.info('Classification is %s' ,classification)
return classification
self.log.warn('Expected vector length %s, but found %s',
self.getWeightVectorLength() - 1, len(dataVector) )
return -1
def train(self):
self.log.info('Starting training')
labels = self.trainingDataContainer.getLabels()
vectors = self.trainingDataContainer.getDataVectors()
errors = -1
iteration = 0
while errors !=0 and iteration <= self.maxIterations :
errors = 0
iteration += 1
self.log.info('Iteration %s', iteration )
for i, target in enumerate(labels):
self.log.info('Label %s, sample [%s]',target, i)
classification = self.classify(vectors[i])
error = target - classification
if error != 0:
self.log.info('Made an error while classifying')
errors += 1
delta = self.alpha * error
self.weightVector = self.updateWeights(
self.weightVector, delta, vectors[i])
self.log.info('Finished training')
def updateWeights(self, weightVector, delta, dataVector):
self.log.info('Updating weights: %s', weightVector)
for index in xrange(len(dataVector)):
weightVector[index] += delta * dataVector[index]
weightVector[-1] += delta * (-1)
self.log.info('New weights: %s', weightVector)
return weightVector
def getActivation(self, weightVector, dataVector):
activation = sum(value * weightVector[index] for index, value in
enumerate(dataVector))
activation += weightVector[-1] * (-1)
return activation
def getWeightVector(self):
return self.weightVector
def getWeightVectorLength(self):
return len(self.weightVector)
def __init__(self):
self.log = LoggerFetcher().fetchLogger("p1", "main")
def __init__(self):
self.log = LoggerFetcher().fetchLogger("p1", "main")
class Perceptron(object):
def __init__(self, trainingDataContainer, alpha, maxIterations):
self.log = LoggerFetcher().fetchLogger("p1", "perceptron")
self.trainingDataContainer = trainingDataContainer
self.alpha = alpha
self.maxIterations = maxIterations
self.initialiseWeightVector()
def initialiseWeightVector(self):
self.log.debug('Initializing weight vector')
vectorLength = self.trainingDataContainer.getDataVectorLength() + 1
self.weightVector = [random.random() for i in xrange(vectorLength)]
def classify(self, dataVector):
self.log.info('Classifying sample %s', dataVector)
if len(dataVector) == self.getWeightVectorLength() - 1:
activation = self.getActivation(self.weightVector, dataVector)
self.log.debug('Activation is %s', activation)
classification = 0 if activation < 0 else 1
self.log.info('Classification is %s', classification)
return classification
self.log.warn('Expected vector length %s, but found %s',
self.getWeightVectorLength() - 1, len(dataVector))
return -1
def train(self):
self.log.info('Starting training')
labels = self.trainingDataContainer.getLabels()
vectors = self.trainingDataContainer.getDataVectors()
errors = -1
iteration = 0
while errors != 0 and iteration <= self.maxIterations:
errors = 0
iteration += 1
self.log.info('Iteration %s', iteration)
for i, target in enumerate(labels):
self.log.info('Label %s, sample [%s]', target, i)
classification = self.classify(vectors[i])
error = target - classification
if error != 0:
self.log.info('Made an error while classifying')
errors += 1
delta = self.alpha * error
self.weightVector = self.updateWeights(
self.weightVector, delta, vectors[i])
self.log.info('Finished training')
def updateWeights(self, weightVector, delta, dataVector):
self.log.info('Updating weights: %s', weightVector)
for index in xrange(len(dataVector)):
weightVector[index] += delta * dataVector[index]
weightVector[-1] += delta * (-1)
self.log.info('New weights: %s', weightVector)
return weightVector
def getActivation(self, weightVector, dataVector):
activation = sum(value * weightVector[index]
for index, value in enumerate(dataVector))
activation += weightVector[-1] * (-1)
return activation
def getWeightVector(self):
return self.weightVector
def getWeightVectorLength(self):
return len(self.weightVector)
