def predict(self, input_array):
inputs = Matrix.fromArray(input_array)
hidden = Matrix.algebra_multiply(self.weights_inputs_hidden, inputs)
hidden.add(self.bias_hidden)
hidden.map(sigmoid)
output = Matrix.algebra_multiply(self.weights_hidden_outputs, hidden)
output.add(self.bias_output)
output.map(sigmoid)
return output.toArray()
def train(self, inputs_array, targets):
inputs = Matrix.fromArray(inputs_array)
hidden = Matrix.algebra_multiply(self.weights_inputs_hidden, inputs)
hidden.add(self.bias_hidden)
hidden.map(sigmoid)
outputs = Matrix.algebra_multiply(self.weights_hidden_outputs, hidden)
outputs.add(self.bias_output)
outputs.map(sigmoid)
target = Matrix.fromArray(targets)
output_errors = Matrix.sub(target, outputs)
gradients = Matrix.staticMap(outputs, dsigmoid)
gradients.multiply(output_errors)
gradients.multiply(self.learning_rate)
hidden_t = Matrix.transpose(hidden)
weight_hidden_out_deltas = Matrix.algebra_multiply(gradients, hidden_t)
self.weights_hidden_outputs.add(weight_hidden_out_deltas)
self.bias_output.add(gradients)
who_t = Matrix.transpose(self.weights_hidden_outputs)
hidden_errors = Matrix.algebra_multiply(who_t, output_errors)
hidden_gradient = Matrix.staticMap(hidden, dsigmoid)
hidden_gradient.multiply(hidden_errors)
hidden_gradient.multiply(self.learning_rate)
inputs_t = Matrix.transpose(inputs)
weight_ih_deltas = Matrix.algebra_multiply(hidden_gradient, inputs_t)
self.weights_inputs_hidden.add(weight_ih_deltas)
self.bias_hidden.add(hidden_gradient)
