modelParams['modelParams']['clParams']['steps'] = str(_options.stepsAhead)
modelParams['modelParams']['clParams']['regionName'] = classifierType
modelParams['modelParams']['spParams']['boostStrength'] = boostStrength
print "Creating model from %s..." % dataSet
# use customized CLA model
model = CLAModel_custom(**modelParams['modelParams'])
model.enableInference({"predictedField": predictedField})
model.enableLearning()
model._spLearningEnabled = bool(trainSP)
model._tpLearningEnabled = True
print model._spLearningEnabled
printTPRegionParams(model._getTPRegion())
inputData = "%s/%s.csv" % (DATA_DIR, dataSet.replace(" ", "_"))
sensor = model._getSensorRegion()
encoderList = sensor.getSelf().encoder.getEncoderList()
if sensor.getSelf().disabledEncoder is not None:
classifier_encoder = sensor.getSelf().disabledEncoder.getEncoderList()
classifier_encoder = classifier_encoder[0]
else:
classifier_encoder = None
_METRIC_SPECS = getMetricSpecs(predictedField, stepsAhead=_options.stepsAhead)
metric = metrics.getModule(_METRIC_SPECS[0])
metricsManager = MetricsManager(_METRIC_SPECS, model.getFieldInfo(),
model.getInferenceType())
modelParams = getModelParamsFromName("nyc_taxi")
else:
modelParams = getModelParamsFromName(dataSet)
modelParams['modelParams']['clParams']['steps'] = str(_options.stepsAhead)
modelParams['modelParams']['clParams']['regionName'] = classifierType
print "Creating model from %s..." % dataSet
# use customized CLA model
model = CLAModel_custom(**modelParams['modelParams'])
model.enableInference({"predictedField": predictedField})
model.enableLearning()
model._spLearningEnabled = True
model._tpLearningEnabled = True
printTPRegionParams(model._getTPRegion())
inputData = "%s/%s.csv" % (DATA_DIR, dataSet.replace(" ", "_"))
sensor = model._getSensorRegion()
encoderList = sensor.getSelf().encoder.getEncoderList()
if sensor.getSelf().disabledEncoder is not None:
classifier_encoder = sensor.getSelf().disabledEncoder.getEncoderList()
classifier_encoder = classifier_encoder[0]
else:
classifier_encoder = None
_METRIC_SPECS = getMetricSpecs(predictedField,
stepsAhead=_options.stepsAhead)
metric = metrics.getModule(_METRIC_SPECS[0])
metricsManager = MetricsManager(_METRIC_SPECS, model.getFieldInfo(),
print "Noise Amount: ", noise
DATE_FORMAT, predictedField = getDateFormatAndPredictedField(dataSet)
modelParams = getModelParamsFromName(dataSet)
modelParams['modelParams']['clParams']['steps'] = str(_options.stepsAhead)
# use customized CLA model
print "Creating model from %s..." % dataSet
model = CLAModel_custom(**modelParams['modelParams'])
model.enableInference({"predictedField": predictedField})
model.enableLearning()
model._spLearningEnabled = True
model._tpLearningEnabled = True
printTPRegionParams(model._getTPRegion())
inputData = "%s/%s.csv" % (DATA_DIR, dataSet.replace(" ", "_"))
sensor = model._getSensorRegion()
encoderList = sensor.getSelf().encoder.getEncoderList()
if sensor.getSelf().disabledEncoder is not None:
classifier_encoder = sensor.getSelf().disabledEncoder.getEncoderList()
classifier_encoder = classifier_encoder[0]
else:
classifier_encoder = None
# initialize new SDR classifier
numTMcells = model._getTPRegion().getSelf()._tfdr.numberOfCells()
sdrClassifier = SDRClassifier(steps=[5], alpha=0.005)