def nextStep(self):
"""
Perfoms actions related to time step progression.
"""
Node.nextStep(self)
for column in self.columns:
column.nextStep()
# Get input from sensors or lower regions and put into a single input map.
input = self.getInput()
# Send input to Spatial Pooler and get processed output (i.e. the active columns)
# First initialize the vector for representing the current record
columnDimensions = (self.width, self.height)
columnNumber = numpy.array(columnDimensions).prod()
activeColumns = numpy.zeros(columnNumber)
self.spatialPooler.compute(input, self.enableSpatialLearning, activeColumns)
# Send active columns to Temporal Pooler and get processed output (i.e. the predicting cells)
# First convert active columns from float array to integer set
activeColumnsSet = set()
for colIdx in range(len(activeColumns)):
if activeColumns[colIdx] == 1:
activeColumnsSet.add(colIdx)
self.temporalPooler.compute(activeColumnsSet, self.enableTemporalLearning)
# Update elements regarding spatial pooler
self.updateSpatialElements(activeColumns)
# Update elements regarding temporal pooler
self.updateTemporalElements()
# Get the predicted values
self.getPredictions()
def nextStep(self):
"""
Perfoms actions related to time step progression.
"""
Node.nextStep(self)
for column in self.columns:
column.nextStep()
# Get input from sensors or lower regions and put into a single input map.
input = self.getInput()
# Send input to Spatial Pooler and get processed output (i.e. the active columns)
# First initialize the vector for representing the current record
columnDimensions = (self.width, self.height)
columnNumber = numpy.array(columnDimensions).prod()
activeColumns = numpy.zeros(columnNumber)
self.spatialPooler.compute(input, self.enableSpatialLearning, activeColumns)
# Send active columns to Temporal Pooler and get processed output (i.e. the predicting cells)
# First convert active columns from float array to integer set
activeColumnsSet = set()
for colIdx in range(len(activeColumns)):
if activeColumns[colIdx] == 1:
activeColumnsSet.add(colIdx)
self.temporalPooler.compute(activeColumnsSet, self.enableTemporalLearning)
# Update elements regarding spatial pooler
self.updateSpatialElements(activeColumns)
# Update elements regarding temporal pooler
self.updateTemporalElements()
# Get the predicted values
self.getPredictions()
def nextStep(self):
"""
Performs actions related to time step progression.
"""
# Update states machine by remove the first element and add a new element in the end
for encoding in self.encodings:
encoding.currentValue.rotate()
if encoding.enableInference:
encoding.predictedValues.rotate()
encoding.bestPredictedValue.rotate()
Node.nextStep(self)
for bit in self.bits:
bit.nextStep()
# Get record value from data source
# If the last record was reached just rewind it
data = self.dataSource.getNextRecordDict()
if not data:
self.dataSource.rewind()
data = self.dataSource.getNextRecordDict()
# Pass raw values to encoder and get a concatenated array
outputArray = numpy.zeros(self.encoder.getWidth())
self.encoder.encodeIntoArray(data, outputArray)
# Get values obtained from the data source.
outputValues = self.encoder.getScalars(data)
# Get raw values and respective encoded bit array for each field
prevOffset = 0
for i in range(len(self.encodings)):
encoding = self.encodings[i]
# Convert the value to its respective data type
currValue = outputValues[i]
if encoding.encoderFieldDataType == FieldDataType.boolean:
currValue = bool(currValue)
elif encoding.encoderFieldDataType == FieldDataType.integer:
currValue = int(currValue)
elif encoding.encoderFieldDataType == FieldDataType.decimal:
currValue = float(currValue)
elif encoding.encoderFieldDataType == FieldDataType.dateTime:
currValue = dateutil.parser.parse(str(currValue))
elif encoding.encoderFieldDataType == FieldDataType.string:
currValue = str(currValue)
encoding.currentValue.setForCurrStep(currValue)
# Update sensor bits
for i in range(len(outputArray)):
if outputArray[i] > 0.:
self.bits[i].isActive.setForCurrStep(True)
else:
self.bits[i].isActive.setForCurrStep(False)
# Mark falsely predicted bits
for bit in self.bits:
if bit.isPredicted.atPreviousStep() and not bit.isActive.atCurrStep():
bit.isFalselyPredicted.setForCurrStep(True)
self._output = outputArray
def nextStep(self):
"""
Performs actions related to time step progression.
"""
# Update states machine by remove the first element and add a new element in the end
for encoding in self.encodings:
encoding.currentValue.rotate()
if encoding.enableInference:
encoding.predictedValues.rotate()
encoding.bestPredictedValue.rotate()
Node.nextStep(self)
for bit in self.bits:
bit.nextStep()
# Get record value from data source
# If the last record was reached just rewind it
data = self.dataSource.getNextRecordDict()
if not data:
self.dataSource.rewind()
data = self.dataSource.getNextRecordDict()
# Pass raw values to encoder and get a concatenated array
outputArray = numpy.zeros(self.encoder.getWidth())
self.encoder.encodeIntoArray(data, outputArray)
# Get values obtained from the data source.
outputValues = self.encoder.getScalars(data)
# Get raw values and respective encoded bit array for each field
prevOffset = 0
for i in range(len(self.encodings)):
encoding = self.encodings[i]
# Convert the value to its respective data type
currValue = outputValues[i]
if encoding.encoderFieldDataType == FieldDataType.boolean:
currValue = bool(currValue)
elif encoding.encoderFieldDataType == FieldDataType.integer:
currValue = int(currValue)
elif encoding.encoderFieldDataType == FieldDataType.decimal:
currValue = float(currValue)
elif encoding.encoderFieldDataType == FieldDataType.dateTime:
currValue = dateutil.parser.parse(str(currValue))
elif encoding.encoderFieldDataType == FieldDataType.string:
currValue = str(currValue)
encoding.currentValue.setForCurrStep(currValue)
# Update sensor bits
for i in range(len(outputArray)):
if outputArray[i] > 0.:
self.bits[i].isActive.setForCurrStep(True)
else:
self.bits[i].isActive.setForCurrStep(False)
# Mark falsely predicted bits
for bit in self.bits:
if bit.isPredicted.atPreviousStep(
) and not bit.isActive.atCurrStep():
bit.isFalselyPredicted.setForCurrStep(True)
self._output = outputArray
def nextStep(self):
"""
Performs actions related to time step progression.
"""
# Update states machine by remove the first element and add a new element in the end
for encoding in self.encodings:
encoding.current_value.rotate()
if encoding.enable_inference:
encoding.predicted_values.rotate()
encoding.best_predicted_value.rotate()
Node.nextStep(self)
for bit in self.bits:
bit.nextStep()
# Get record value from data source
# If the last record was reached just rewind it
data = self.data_source.getNextRecordDict()
if not data:
self.data_source.rewind()
data = self.data_source.getNextRecordDict()
# Pass raw values to encoder and get a concatenated array
output_array = numpy.zeros(self.encoder.getWidth())
self.encoder.encodeIntoArray(data, output_array)
# Get values obtained from the data source.
output_values = self.encoder.getScalars(data)
# Get raw values and respective encoded bit array for each field
for i in range(len(self.encodings)):
encoding = self.encodings[i]
# Convert the value to its respective data type
curr_value = output_values[i]
if encoding.encoder_field_data_type == FieldDataType.BOOLEAN:
curr_value = bool(curr_value)
elif encoding.encoder_field_data_type == FieldDataType.INTEGER:
curr_value = int(curr_value)
elif encoding.encoder_field_data_type == FieldDataType.DECIMAL:
curr_value = float(curr_value)
elif encoding.encoder_field_data_type == FieldDataType.DATE_TIME:
curr_value = dateutil.parser.parse(str(curr_value))
elif encoding.encoder_field_data_type == FieldDataType.STRING:
curr_value = str(curr_value)
encoding.current_value.setForCurrStep(curr_value)
# Update sensor bits
for i in range(len(output_array)):
if output_array[i] > 0.0:
self.bits[i].is_active.setForCurrStep(True)
else:
self.bits[i].is_active.setForCurrStep(False)
# Mark falsely predicted bits
for bit in self.bits:
if bit.is_predicted.atPreviousStep() and not bit.is_active.atCurrStep():
bit.is_falsely_predicted.setForCurrStep(True)
self.output = output_array
