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 if self.inputFormat == InputFormat.raw: if len(self.currentValue) > maxPreviousSteps: self.currentValue.remove(self.currentValue[0]) self.predictedValues.remove(self.predictedValues[0]) self.currentValue.append(None) self.predictedValues.append(None) Node.nextStep(self) for bit in self.bits: bit.nextStep() # Get record value from data source recordValue = None if self.dataSourceType == DataSourceType.file: recordValue = self.__getNextFileRecord() elif self.dataSourceType == DataSourceType.database: pass # Handle the value according to its type self._output = [] if self.inputFormat == InputFormat.htm: # Initialize the array for representing the current record self._output = recordValue elif self.inputFormat == InputFormat.raw: # Convert the value to its respective data type rawValue = None if self.inputRawDataType == InputRawDataType.boolean: rawValue = bool(recordValue) elif self.inputRawDataType == InputRawDataType.integer: rawValue = int(recordValue) elif self.inputRawDataType == InputRawDataType.decimal: rawValue = float(recordValue) elif self.inputRawDataType == InputRawDataType.dateTime: rawValue = datetime.datetime.strptime(recordValue, "%m/%d/%y %H:%M") elif self.inputRawDataType == InputRawDataType.string: rawValue = str(recordValue) self.currentValue[maxPreviousSteps - 1] = rawValue # Pass raw value to encoder and get its respective array self._output = self.encoder.encode(rawValue) # Update sensor bits for i in range(len(self._output)): if self._output[i] > 0.: self.bits[i].isActive[maxPreviousSteps - 1] = True else: self.bits[i].isActive[maxPreviousSteps - 1] = False # Mark falsely predicted bits for bit in self.bits: if bit.isPredicted[maxPreviousSteps - 2] and not bit.isActive[maxPreviousSteps - 1]: bit.isFalselyPredicted[maxPreviousSteps - 1] = True
def nextStep(self): """ Perfoms actions related to time step progression. """ Node.nextStep(self) for child in self.children: child.nextStep() 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.enableSpatialPooling, 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.enableTemporalPooling) # Update elements regarding spatial pooler self.updateSpatialElements(activeColumns) # Update elements regarding temporal pooler self.updateTemporalElements()
def nextStep(self):
"""
Perfoms actions related to time step progression.
"""
Node.nextStep(self)
for bit in self.bits:
bit.nextStep()
if self.dataSourceType == DataSourceType.file:
self.__getNextFileRecord()
elif self.dataSourceType == DataSourceType.database:
pass
def nextStep(self):
"""
Perfoms actions related to time step progression.
"""
Node.nextStep(self)
for bit in self.bits:
bit.nextStep()
if self.dataSourceType == DataSourceType.file:
self.__getNextFileRecord()
elif self.dataSourceType == DataSourceType.database:
pass
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