def initializeClassifiers(Nelements, encoder):
cla = CLAClassifier(steps=[0])
nn_classifier = NeuralNetClassifier(numInputs=encoder.n,
steps=[0], alpha=0.1)
patternNZ = list(numpy.where(encoder.encode(Nelements-1))[0])
classification = {'bucketIdx': Nelements-1, 'actValue': Nelements-1}
# feed in the pattern with the highest bucket index
claRetval = cla.compute(0, patternNZ, classification,
learn=True, infer=True)
nnRetval = nn_classifier.compute(0, patternNZ, classification,
learn=True, infer=True)
return cla, nn_classifier
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 classifier
numTMcells = model._getTPRegion().getSelf()._tfdr.numberOfCells()
nn_classifier = NeuralNetClassifier(numInputs=numTMcells,
steps=[5], alpha=0.005)
_METRIC_SPECS = getMetricSpecs(predictedField, stepsAhead=_options.stepsAhead)
metric = metrics.getModule(_METRIC_SPECS[0])
metricsManager = MetricsManager(_METRIC_SPECS, model.getFieldInfo(),
model.getInferenceType())
if plot:
plotCount = 1
plotHeight = max(plotCount * 3, 6)
fig = plt.figure(figsize=(14, plotHeight))
gs = gridspec.GridSpec(plotCount, 1)
plt.title(predictedField)
plt.ylabel('Data')
plt.xlabel('Timed')
plt.tight_layout()
