class Adaline:
def __init__(self,
learning_rate: float = 0.1,
activation_function: callable = None):
self._learning_rate = learning_rate
self.activation_function = \
activation_function if activation_function is not None else default_activation_function
self.neuron = Neuron(learning_rate, self.activation_function)
self.list_sources_target = []
self.cycles = 0
self.total_square_error_by_cycle = []
@property
def learning_rate(self):
return self._learning_rate
@learning_rate.setter
def learning_rate(self, learning_rate):
self._learning_rate = learning_rate
self.neuron.learning_rate = learning_rate
def add_sources(self, sources, target):
sources_target = SourcesTarget(sources, target)
self.list_sources_target.append(sources_target)
def reset_weights(self, random_init=False):
self.total_square_error_by_cycle = []
n_inputs = len(self.list_sources_target[0].sources)
self.neuron.initialize_weights(n_inputs=n_inputs,
random_init=random_init)
def output(self, inputs):
output = self.neuron.output(inputs)
return output
def train(self,
random_starting_weights=False,
max_cycles: int = None,
weight_adjustment_tolerance: float = None,
square_error_tolerance: float = None,
verbose=True):
self.reset_weights(random_starting_weights)
self.cycles = 0
while True:
self.cycles += 1
weight_adjustments = []
total_square_error = 0
for sources_target in self.list_sources_target:
sources, target = sources_target.sources, sources_target.target
output = self.neuron.output(sources)
total_square_error += 0.5 * (target - output)**2
bias_adjustment = self.neuron.adjust_bias(target, output)
weight_adjustments.append(bias_adjustment)
for index, source in enumerate(sources):
weight_adjustment = self.neuron.adjust_weight(
index, source, target, output)
weight_adjustments.append(weight_adjustment)
self.total_square_error_by_cycle.append(total_square_error)
if verbose:
print(f"Cycle: {self.cycles}")
if total_square_error > SQUARE_ERROR_DIVERGENCE:
print(
f"Total square error diverging: {total_square_error:.5f}")
break
if max_cycles is not None and self.cycles >= max_cycles:
print(f"Max cycles exceed")
break
max_weight_adjustment = max(weight_adjustments)
if weight_adjustment_tolerance is not None and max_weight_adjustment <= weight_adjustment_tolerance:
print(
f"Maximum weight adjustment of {max_weight_adjustment:.5f}. "
f"(Threshold: f{weight_adjustment_tolerance:.5f}")
break
if square_error_tolerance is not None and total_square_error <= square_error_tolerance:
print(f"Total square error of {total_square_error:.5f}. "
f"(Threshold: f{square_error_tolerance:.5f}")
break
class Adaline:
def __init__(self, learning_rate: float = 0.1, activation_function: callable = None):
self._learning_rate = learning_rate
self.activation_function = activation_function
self.neuron = Neuron(learning_rate, activation_function)
self.list_sources_target = []
self.cycles = 0
self.total_square_error_by_cycle = []
@property
def learning_rate(self):
return self._learning_rate
@learning_rate.setter
def learning_rate(self, learning_rate):
self._learning_rate = learning_rate
self.neuron.learning_rate = learning_rate
def add_sources(self, sources, target):
sources_target = SourcesTarget(sources, target)
self.list_sources_target.append(sources_target)
def reset_weights(self, random_init=False):
self.total_square_error_by_cycle = []
n_inputs = len(self.list_sources_target[0].sources)
self.neuron.initialize_weights(n_inputs=n_inputs, random_init=random_init)
def output(self, inputs):
output = self.neuron.output(inputs)
return output
def train(self, random_starting_weights=False, max_cycles: int = None,
weight_adjustment_tolerance: float = None, square_error_tolerance: float = None):
self.reset_weights(random_starting_weights)
self.cycles = 0
while True:
self.cycles += 1
weight_adjustments = []
total_square_error = 0
for sources_target in self.list_sources_target:
sources, target = sources_target.sources, sources_target.target
output = self.neuron.output(sources)
total_square_error += 0.5*(target-output)**2
bias_adjustment = self.neuron.adjust_bias(target, output)
weight_adjustments.append(bias_adjustment)
for index, source in enumerate(sources):
weight_adjustment = self.neuron.adjust_weight(index, source, target, output)
weight_adjustments.append(weight_adjustment)
self.total_square_error_by_cycle.append(total_square_error)
if total_square_error > 100:
break
if max_cycles is not None and self.cycles >= max_cycles:
break
if weight_adjustment_tolerance is not None and max(weight_adjustments) <= weight_adjustment_tolerance:
break
if square_error_tolerance is not None and total_square_error <= square_error_tolerance:
break
