class BackPropagationLearningDialog(QDialog):
def __init__(self, network, test_data=None):
QDialog.__init__(self)
self.ui = Ui_LearningLauncher()
self.ui.setupUi(self)
self.network = network
self._setup_gui()
self.input_view = DataView(self.ui.learningDataView, header="Input", model=test_data,
columns=network.inputs_count() + len(network.get_layer(-1).get_neurons()))
# self.input_view.randomize_data()
@pyqtSlot()
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()
@pyqtSlot()
def on_randomizeButton_clicked(self):
self.input_view.randomize_data()
@pyqtSlot()
def on_readInputButton_clicked(self):
filePath = QFileDialog().getOpenFileName(None, "Select input data file")
try:
with open(filePath, "r") as file:
file_lines = file.readlines()
rows_count = len(file_lines)
self.ui.dataCount.setValue(rows_count)
for (line, row) in zip(file_lines, range(rows_count)):
for (value, column) in zip(line.split(), range(self.input_view.column_count())):
self.input_view.set_data(row, column, float(value))
except Exception as e:
print e
@pyqtSlot(int)
def on_dataCount_valueChanged(self, value):
old_rows_count = self.input_view.row_count()
self.input_view.set_rows(value)
self.input_view.randomize_data(old_rows_count)
def _setup_gui(self):
self.ui.errorMetricSelector.addItems(ErrorUtil.registered_factors())
self.ui.errorMetricSelector.setCurrentIndex(self.ui.errorMetricSelector.findText(ErrorUtil.default_factor()))
def get_model(self):
return self.input_view.get_model()
def __init__(self, network, test_data=None):
QDialog.__init__(self)
self.ui = Ui_LearningLauncher()
self.ui.setupUi(self)
self.network = network
self._setup_gui()
self.input_view = DataView(self.ui.learningDataView, header="Input", model=test_data,
columns=network.inputs_count() + len(network.get_layer(-1).get_neurons()))