class Perceptron(object):
def __init__(self, input_size, lrn_rate=1):
"""'input_size' is the length of the input.
'lrn_rate' is the learning rate.
"""
self.neuron = Neuron([0]*input_size, 0, signal)
self.lrn_rate = lrn_rate
self.fire = self.neuron.fire
def training(self, examples):
epochs = 0
while True:
epochs = epochs + 1
error_count = 0
for (input_vector, desired_output) in examples:
actual_output = self.neuron.fire(input_vector)
error = desired_output - actual_output
if error != 0:
learned = self.lrn_rate*error
self.neuron.update(input_vector, learned)
error_count = error_count + 1
if error_count == 0:
break
return epochs
def __str__(self):
ret = 'lrn_rate: %s' % self.lrn_rate
ret = '%s\n%s' % (ret, self.neuron.__str__())
return ret
class Perceptron(object):
def __init__(self, input_size, lrn_rate=1):
"""'input_size' is the length of the input.
'lrn_rate' is the learning rate.
"""
self.neuron = Neuron([0] * input_size, 0, signal)
self.lrn_rate = lrn_rate
self.fire = self.neuron.fire
def training(self, examples):
epochs = 0
while True:
epochs = epochs + 1
error_count = 0
for (input_vector, desired_output) in examples:
actual_output = self.neuron.fire(input_vector)
error = desired_output - actual_output
if error != 0:
learned = self.lrn_rate * error
self.neuron.update(input_vector, learned)
error_count = error_count + 1
if error_count == 0:
break
return epochs
def __str__(self):
ret = 'lrn_rate: %s' % self.lrn_rate
ret = '%s\n%s' % (ret, self.neuron.__str__())
return ret
class Perceptron(object):
"""Online learning Perceptron.
"""
def __init__(self, input_size, lrn_rate=1, activation=signal):
"""'input_size' is the length of the input.
'lrn_rate' is the learning rate.
"""
self.neuron = Neuron([0] * input_size, 0, activation)
self.lrn_rate = lrn_rate
self.fire = self.neuron.fire
def training(self, inputs_vector, outputs, max_epochs):
"""Not checking if inputs_vector and outputs have the same size.
"""
epochs = 0
while True:
epochs = epochs + 1
error_count = 0
for (inputs, output) in zip(inputs_vector, outputs):
actual_output = self.fire(inputs)
error = output - actual_output
if error != 0:
learned = self.lrn_rate * error
self.neuron.update(inputs, learned)
error_count = error_count + 1
if error_count == 0:
break
elif max_epochs and (epochs > max_epochs):
return False
return epochs
def __str__(self):
ret = 'lrn_rate: %s' % self.lrn_rate
ret = '%s\n%s' % (ret, self.neuron.__str__())
return ret
class Perceptron(object):
"""Online learning Perceptron.
"""
def __init__(self, input_size, lrn_rate=1, activation=signal):
"""'input_size' is the length of the input.
'lrn_rate' is the learning rate.
"""
self.neuron = Neuron([0]*input_size, 0, activation)
self.lrn_rate = lrn_rate
self.fire = self.neuron.fire
def training(self, inputs_vector, outputs, max_epochs):
"""Not checking if inputs_vector and outputs have the same size.
"""
epochs = 0
while True:
epochs = epochs + 1
error_count = 0
for (inputs, output) in zip(inputs_vector, outputs):
actual_output = self.fire(inputs)
error = output - actual_output
if error != 0:
learned = self.lrn_rate*error
self.neuron.update(inputs, learned)
error_count = error_count + 1
if error_count == 0:
break
elif max_epochs and (epochs > max_epochs):
return False
return epochs
def __str__(self):
ret = 'lrn_rate: %s' % self.lrn_rate
ret = '%s\n%s' % (ret, self.neuron.__str__())
return ret
