def menuNodeAddRegion_click(self, event):
"""
Add a feeder region to the selected region.
"""
# Ask for region's name
entered_text, ok = QtWidgets.QInputDialog.getText(
self, "Input Dialog", "Enter region's name:")
if ok:
valid_expr = QtCore.QRegExp('[a-zA-Z0-9_]+')
if not valid_expr.exactMatch(entered_text):
QtWidgets.QMessageBox.warning(
self, "Warning", "'" + entered_text +
"' is not a valid name. Only characters, numbers and _ are accepted."
)
return
self.main_window.markProjectChanges(True)
# Add new region below highlighted region
new_region = Region(entered_text)
Global.project.network.addFeederNode(new_region,
self.under_mouse_node)
# Redraw the tree to show the updates.
self.repaint()
self.updateCode()
def __init__(self):
"""
Initializes a new instance of this class.
"""
QtGui.QWidget.__init__(self)
#region Instance fields
self.topRegion = Region("TopRegion")
"""Node that is on top of the hierarchy."""
self.selectedNode = None
"""Node that is selected for visualization of its details."""
self.underMouseNode = None
"""Node that is highlighted due to mouse is on it."""
# Space to skip horizontally between siblings
# and vertically between generations
self._offsetHorizontal = 15
self._offsetVertical = 30
#endregion
self.initUI()
def new(self):
"""
Initializes a new instance of this class.
"""
# Initialize metadata
self.fileName = ''
self.name = "Untitled"
self.author = ""
self.description = ""
# Create the top region
topRegion = Region("TopRegion")
# Create the network and add topRegion as its starting node
self.network = Network()
self.network.nodes.append(topRegion)
self.network.preparePhases()
def __init__(self, main_window):
"""
Initializes a new instance of this class.
"""
QtWidgets.QWidget.__init__(self)
self.main_window = main_window
# Node that is on top of the hierarchy.
self.top_region = Region("TopRegion")
# Node that is selected for visualization of its details.
self.selected_node = None
# Node that is highlighted due to mouse is on it.
self.under_mouse_node = None
# Space to skip horizontally between siblings
# and vertically between generations
self.offset_horizontal = 15
self.offset_vertical = 30
self.initUI()
def __readNode(self, xmlReader):
# Read type of node
name = self.__getStringAttribute(xmlReader.attributes(), 'name')
type = self.__getStringAttribute(xmlReader.attributes(), 'type')
# Create a node from parameters
node = None
if type == 'Region':
node = Region(name)
elif type == 'Sensor':
node = Sensor(name)
node.width = self.__getIntegerAttribute(xmlReader.attributes(), 'width')
node.height = self.__getIntegerAttribute(xmlReader.attributes(), 'height')
# Read specific parameters according to node type
if type == 'Region':
node.enableSpatialLearning = self.__getBooleanAttribute(xmlReader.attributes(), 'enableSpatialLearning')
node.potentialRadius = self.__getIntegerAttribute(xmlReader.attributes(), 'potentialRadius')
node.potentialPct = self.__getFloatAttribute(xmlReader.attributes(), 'potentialPct')
node.globalInhibition = self.__getBooleanAttribute(xmlReader.attributes(), 'globalInhibition')
node.localAreaDensity = self.__getFloatAttribute(xmlReader.attributes(), 'localAreaDensity')
node.numActiveColumnsPerInhArea = self.__getFloatAttribute(xmlReader.attributes(), 'numActiveColumnsPerInhArea')
node.stimulusThreshold = self.__getIntegerAttribute(xmlReader.attributes(), 'stimulusThreshold')
node.proximalSynConnectedPerm = self.__getFloatAttribute(xmlReader.attributes(), 'proximalSynConnectedPerm')
node.proximalSynPermIncrement = self.__getFloatAttribute(xmlReader.attributes(), 'proximalSynPermIncrement')
node.proximalSynPermDecrement = self.__getFloatAttribute(xmlReader.attributes(), 'proximalSynPermDecrement')
node.minPctOverlapDutyCycle = self.__getFloatAttribute(xmlReader.attributes(), 'minPctOverlapDutyCycle')
node.minPctActiveDutyCycle = self.__getFloatAttribute(xmlReader.attributes(), 'minPctActiveDutyCycle')
node.dutyCyclePeriod = self.__getIntegerAttribute(xmlReader.attributes(), 'dutyCyclePeriod')
node.maxBoost = self.__getFloatAttribute(xmlReader.attributes(), 'maxBoost')
node.spSeed = self.__getIntegerAttribute(xmlReader.attributes(), 'spSeed')
node.enableTemporalLearning = self.__getBooleanAttribute(xmlReader.attributes(), 'enableTemporalLearning')
node.numCellsPerColumn = self.__getIntegerAttribute(xmlReader.attributes(), 'numCellsPerColumn')
node.learningRadius = self.__getIntegerAttribute(xmlReader.attributes(), 'learningRadius')
node.distalSynInitialPerm = self.__getFloatAttribute(xmlReader.attributes(), 'distalSynInitialPerm')
node.distalSynConnectedPerm = self.__getFloatAttribute(xmlReader.attributes(), 'distalSynConnectedPerm')
node.distalSynPermIncrement = self.__getFloatAttribute(xmlReader.attributes(), 'distalSynPermIncrement')
node.distalSynPermDecrement = self.__getFloatAttribute(xmlReader.attributes(), 'distalSynPermDecrement')
node.minThreshold = self.__getIntegerAttribute(xmlReader.attributes(), 'minThreshold')
node.activationThreshold = self.__getIntegerAttribute(xmlReader.attributes(), 'activationThreshold')
node.maxNumNewSynapses = self.__getIntegerAttribute(xmlReader.attributes(), 'maxNumNewSynapses')
node.tpSeed = self.__getIntegerAttribute(xmlReader.attributes(), 'tpSeed')
elif type == 'Sensor':
dataSourceType = self.__getStringAttribute(xmlReader.attributes(), 'dataSourceType')
if dataSourceType == "File":
node.dataSourceType = DataSourceType.file
node.fileName = self.__getStringAttribute(xmlReader.attributes(), 'fileName')
elif dataSourceType == "Database":
node.dataSourceType = DataSourceType.database
node.databaseConnectionString = self.__getStringAttribute(xmlReader.attributes(), 'databaseConnectionString')
node.databaseTable = self.__getStringAttribute(xmlReader.attributes(), 'databaseTable')
node.predictionsMethod = self.__getStringAttribute(xmlReader.attributes(), 'predictionsMethod')
if node.predictionsMethod == PredictionsMethod.classification:
node.enableClassificationLearning = self.__getBooleanAttribute(xmlReader.attributes(), 'enableClassificationLearning')
node.enableClassificationInference = self.__getBooleanAttribute(xmlReader.attributes(), 'enableClassificationInference')
# If still is not end of element it's because this node has encodings
token = xmlReader.readNext()
if not xmlReader.isEndElement():
while xmlReader.readNextStartElement():
encoding = self.__readEncoding(xmlReader)
node.encodings.append(encoding)
token = xmlReader.readNext()
return node
def __readNode(self, xmlReader):
# Read type of node
name = self.__getStringAttribute(xmlReader.attributes(), 'name')
type = self.__getStringAttribute(xmlReader.attributes(), 'type')
# Create a node from parameters
node = None
if type == 'Region':
node = Region(name)
elif type == 'Sensor':
node = Sensor(name)
node.width = self.__getIntegerAttribute(xmlReader.attributes(),
'width')
node.height = self.__getIntegerAttribute(xmlReader.attributes(),
'height')
# Read specific parameters according to node type
if type == 'Region':
node.enableSpatialLearning = self.__getBooleanAttribute(
xmlReader.attributes(), 'enableSpatialLearning')
node.potentialRadius = self.__getIntegerAttribute(
xmlReader.attributes(), 'potentialRadius')
node.potentialPct = self.__getFloatAttribute(
xmlReader.attributes(), 'potentialPct')
node.globalInhibition = self.__getBooleanAttribute(
xmlReader.attributes(), 'globalInhibition')
node.localAreaDensity = self.__getFloatAttribute(
xmlReader.attributes(), 'localAreaDensity')
node.numActiveColumnsPerInhArea = self.__getFloatAttribute(
xmlReader.attributes(), 'numActiveColumnsPerInhArea')
node.stimulusThreshold = self.__getIntegerAttribute(
xmlReader.attributes(), 'stimulusThreshold')
node.proximalSynConnectedPerm = self.__getFloatAttribute(
xmlReader.attributes(), 'proximalSynConnectedPerm')
node.proximalSynPermIncrement = self.__getFloatAttribute(
xmlReader.attributes(), 'proximalSynPermIncrement')
node.proximalSynPermDecrement = self.__getFloatAttribute(
xmlReader.attributes(), 'proximalSynPermDecrement')
node.minPctOverlapDutyCycle = self.__getFloatAttribute(
xmlReader.attributes(), 'minPctOverlapDutyCycle')
node.minPctActiveDutyCycle = self.__getFloatAttribute(
xmlReader.attributes(), 'minPctActiveDutyCycle')
node.dutyCyclePeriod = self.__getIntegerAttribute(
xmlReader.attributes(), 'dutyCyclePeriod')
node.maxBoost = self.__getFloatAttribute(xmlReader.attributes(),
'maxBoost')
node.spSeed = self.__getIntegerAttribute(xmlReader.attributes(),
'spSeed')
node.enableTemporalLearning = self.__getBooleanAttribute(
xmlReader.attributes(), 'enableTemporalLearning')
node.numCellsPerColumn = self.__getIntegerAttribute(
xmlReader.attributes(), 'numCellsPerColumn')
node.distalSynInitialPerm = self.__getFloatAttribute(
xmlReader.attributes(), 'distalSynInitialPerm')
node.distalSynConnectedPerm = self.__getFloatAttribute(
xmlReader.attributes(), 'distalSynConnectedPerm')
node.distalSynPermIncrement = self.__getFloatAttribute(
xmlReader.attributes(), 'distalSynPermIncrement')
node.distalSynPermDecrement = self.__getFloatAttribute(
xmlReader.attributes(), 'distalSynPermDecrement')
node.minThreshold = self.__getIntegerAttribute(
xmlReader.attributes(), 'minThreshold')
node.activationThreshold = self.__getIntegerAttribute(
xmlReader.attributes(), 'activationThreshold')
node.maxNumNewSynapses = self.__getIntegerAttribute(
xmlReader.attributes(), 'maxNumNewSynapses')
node.tpSeed = self.__getIntegerAttribute(xmlReader.attributes(),
'tpSeed')
elif type == 'Sensor':
dataSourceType = self.__getStringAttribute(xmlReader.attributes(),
'dataSourceType')
if dataSourceType == "File":
node.dataSourceType = DataSourceType.file
node.fileName = self.__getStringAttribute(
xmlReader.attributes(), 'fileName')
elif dataSourceType == "Database":
node.dataSourceType = DataSourceType.database
node.databaseConnectionString = self.__getStringAttribute(
xmlReader.attributes(), 'databaseConnectionString')
node.databaseTable = self.__getStringAttribute(
xmlReader.attributes(), 'databaseTable')
node.predictionsMethod = self.__getStringAttribute(
xmlReader.attributes(), 'predictionsMethod')
if node.predictionsMethod == PredictionsMethod.classification:
node.enableClassificationLearning = self.__getBooleanAttribute(
xmlReader.attributes(), 'enableClassificationLearning')
node.enableClassificationInference = self.__getBooleanAttribute(
xmlReader.attributes(), 'enableClassificationInference')
# If still is not end of element it's because this node has encodings
token = xmlReader.readNext()
if not xmlReader.isEndElement():
while xmlReader.readNextStartElement():
encoding = self.__readEncoding(xmlReader)
node.encodings.append(encoding)
token = xmlReader.readNext()
return node
def readNode(self, node_dict):
# Read type of node
name = node_dict['name']
type = node_dict['type']
# Create a node from parameters
node = None
if type == 'Region':
node = Region(name)
elif type == 'Sensor':
node = Sensor(name)
node.width = node_dict['width']
node.height = node_dict['height']
# Read specific parameters according to node type
if type == 'Region':
node.enable_spatial_learning = node_dict['enable_spatial_learning']
node.potential_radius = node_dict['potential_radius']
node.potential_pct = node_dict['potential_pct']
node.global_inhibition = node_dict['global_inhibition']
node.local_area_density = node_dict['local_area_density']
node.num_active_columns_per_inh_area = node_dict['num_active_columns_per_inh_area']
node.stimulus_threshold = node_dict['stimulus_threshold']
node.proximal_syn_connected_perm = node_dict['proximal_syn_connected_perm']
node.proximal_syn_perm_increment = node_dict['proximal_syn_perm_increment']
node.proximal_syn_perm_decrement = node_dict['proximal_syn_perm_decrement']
node.min_pct_overlap_duty_cycle = node_dict['min_pct_overlap_duty_cycle']
node.min_pct_active_duty_cycle = node_dict['min_pct_active_duty_cycle']
node.duty_cycle_period = node_dict['duty_cycle_period']
node.max_boost = node_dict['max_boost']
node.sp_seed = node_dict['sp_seed']
node.enable_temporal_learning = node_dict['enable_temporal_learning']
node.cells_per_column = node_dict['cells_per_column']
node.distal_syn_initial_perm = node_dict['distal_syn_initial_perm']
node.distal_syn_connected_perm = node_dict['distal_syn_connected_perm']
node.distal_syn_perm_increment = node_dict['distal_syn_perm_increment']
node.distal_syn_perm_decrement = node_dict['distal_syn_perm_decrement']
node.min_threshold = node_dict['min_threshold']
node.activation_threshold = node_dict['activation_threshold']
node.max_new_synapses = node_dict['max_new_synapses']
node.tp_seed = node_dict['tp_seed']
elif type == 'Sensor':
data_source_type = node_dict['data_source_type']
if data_source_type == "File":
node.data_source_type = DataSourceType.FILE
node.file_name = node_dict['file_name']
elif data_source_type == "Database":
node.data_source_type = DataSourceType.DATABASE
node.database_connection_string = node_dict['database_connection_string']
node.database_table = node_dict['database_table']
node.predictions_method = node_dict['predictions_method']
if node.predictions_method == PredictionsMethod.CLASSIFICATION:
node.enable_classification_learning = node_dict['enable_classification_learning']
node.enable_classification_inference = node_dict['enable_classification_inference']
# If still is not end of element it's because this node has encodings
for encoding_dict in node_dict['encodings']:
encoding = self.readEncoding(encoding_dict)
node.encodings.append(encoding)
return node