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.kohonenLearningFactorSelector.currentText()),
float(self.ui.kohonenLearningFactorInitialValue.text()),
int(self.ui.kohonenLearningIterations.text()),
)
conscience_threshold = (
float(self.ui.conscienceThresholdValue.text()) if self.ui.conscienceEnabled.isChecked() else 0
)
kohonen_learning_iterations = int(self.ui.kohonenLearningIterations.text())
kohonen_learning = CounterPropagationKohonenLearning(
self.network_layer, topology, self.metric, learning_factor, conscience_threshold
)
learning_data = self.input_view.get_data()
grossberg_learning_rule = RulesUtil.get_rule(self.ui.learningRuleSelector.currentText())
grossberg_learning_factor = LearningFactorUtil.get_factor(
str(self.ui.grossbergLearningFactorSelector.currentText()),
float(self.ui.grossbergLearningFactorInitialValue.text()),
int(self.ui.grossbergLearningIterations.text()),
)
grossberg_learning_iterations = int(self.ui.grossbergLearningIterations.text())
cp_learning = CounterPropagationLearning(
self.network, grossberg_learning_rule, grossberg_learning_factor, kohonen_learning
)
cp_learning.learn(kohonen_learning_iterations, grossberg_learning_iterations, learning_data)
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()