class ga:
def __init__(self, inputs, hidden, outputs, mutation_rate, mutation_power):
self.nn = NeuralNetwork(inputs, hidden, outputs)
self.fitness = 0
self.mutation_rate = mutation_rate
self.mutation_power = mutation_power
def predict(self, inputs_array):
return self.nn.predict(inputs_array)
def mutate(self, func = basic_mutate):
self.nn.weights_inputs_hidden.BigMap(func, self.mutation_rate, self.mutation_power)
self.nn.weights_hidden_outputs.BigMap(func, self.mutation_rate, self.mutation_power)
self.nn.bias_hidden.BigMap(func, self.mutation_rate, self.mutation_power)
self.nn.bias_output.BigMap(func, self.mutation_rate, self.mutation_power)
def copy(self):
cop = self.nn.copy()
g = ga(self.nn.input_nodes, self.nn.hidden_nodes, self.nn.output_nodes, self.mutation_rate, self.mutation_power)
g.nn = cop
return g
def crossOver(self, partner):
child = self.copy()
child.nn.weights_inputs_hidden.VariableMap(myCrossOver, partner.nn.weights_inputs_hidden)
child.nn.weights_hidden_outputs.VariableMap(myCrossOver, partner.nn.weights_hidden_outputs)
child.nn.bias_hidden.VariableMap(myCrossOver, partner.nn.bias_hidden)
child.nn.bias_output.VariableMap(myCrossOver, partner.nn.bias_output)
return child
