def learn(self, kohonen_iterations, grossberg_iterations, learning_data):
inputs_count = self.network.inputs_count()
outputs_count = len(learning_data[0])
input_data = DataNormalizer.normalize([[case[i] for i in range(inputs_count)] for case in learning_data])
output_data = [[case[i] for i in range(inputs_count, outputs_count)] for case in learning_data]
self.kohonen_learning.learn(input_data, kohonen_iterations)
self.grossberg_layer_learning(grossberg_iterations, input_data, output_data)
def on_runButton_clicked(self):
error_metric = ErrorUtil.get_factor(str(self.ui.errorMetricSelector.currentText()))()
learning_factor = float(self.ui.learningFactorValue.text())
momentum = float(self.ui.momentumValue.text())
learning_iterations = int(self.ui.learningIterations.text())
bias_enabled = self.ui.biasEnabled.isChecked()
learning = BackpropagationLearning(self.network, error_metric, learning_factor, momentum, bias_enabled)
learning.learn(DataNormalizer.normalize(self.input_view.get_data()), learning_iterations)
self.accept()
def on_runButton_clicked(self):
if self.ui.neighbourhoodEnabled.isChecked():
neighbourhood = NeighbourhoodUtil.get_neighbourhood(
str(self.ui.neighbourhoodFunctionSelector.currentText()))()
topology = TopologyUtil.get_topology(str(self.ui.topologySelector.currentText()),
neighbourhood, int(self.ui.initialNeighbourhoodRadius.text()),
int(self.ui.topologyRowsValue.text()), int(self.ui.topologyColsValue.text()))
else:
topology = NoTopology()
self.metric = MetricUtil.get_metric(str(self.ui.metricSelector.currentText()))()
learning_factor = LearningFactorUtil.get_factor(str(self.ui.learningFactorSelector.currentText()),
float(self.ui.learningFactorInitialValue.text()), int(self.ui.learningIterations.text()))
conscience_threshold =\
float(self.ui.conscienceThresholdValue.text()) if self.ui.conscienceEnabled.isChecked() else 0
learning_iterations = int(self.ui.learningIterations.text())
learning = KohonenLearning(self.network_layer, topology, self.metric, learning_factor,
conscience_threshold)
learning.learn(DataNormalizer.normalize(self.input_view.get_data()), learning_iterations)
self.accept()