def dialog(network, learning_data):
layers = network.get_layers()
if Layers.get_type(layers[0]) == Layers.KOHONEN_LAYER:
if len(layers) == 2 and Layers.get_type(layers[1]) == Layers.GENERAL_LAYER:
return CounterPropagationLearningDialog(network)
else:
return KohonenLearningDialog(network)
else:
return BackPropagationLearningDialog(network, learning_data)
def from_network(network):
model = NetworkModel(empty=True)
model.set_network_input_neurons(network.inputs_count())
for layer in network.get_layers():
layer_model = model.add_layer()
neurons = layer.get_neurons()
layer_model.set_layer_type(Layers.get_type(layer))
layer_model.set_neurons(len(neurons))
for neuron_index in range(len(neurons)):
neuron = neurons[neuron_index]
layer_model.set_psp_for_neuron(neuron_index, neuron.get_psp().NAME)
layer_model.set_activation_for_neuron(neuron_index, neuron.get_activator().NAME)
layer_model.set_bias_for_neuron(neuron_index, neuron.get_bias().get_weight())
inputs = neuron.get_inputs()
for input_index in range(len(inputs)):
layer_model.set_weight_for_connection(neuron_index, input_index,
round(neuron.get_input_edge(input_index).get_weight(), 3))
return model
