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
predictiveCells = tm.predictiveCells
predCellNum.append(len(predictiveCells))
predColumn = np.array(list(predictiveCells)) / tm.cellsPerColumn
patternNZ = tpOutput.reshape(-1).nonzero()[0]
activeColumn = patternNZ / tm.cellsPerColumn
activeCellNum.append(len(patternNZ))
predictedActiveColumns = np.intersect1d(prePredictiveColumn, activeColumn)
predictedActiveColumnsNum.append(len(predictedActiveColumns))
# fed input to the new classifier
classification = {'bucketIdx': result.classifierInput.bucketIndex,
'actValue': result.classifierInput.dataRow}
nnRetval = nn_classifier.compute(i, patternNZ, classification,
learn=True, infer=True)
nnPrediction = nnRetval['actualValues'][np.argmax(nnRetval[5])]
predictDataNN.append(nnPrediction)
output.write([i], [inputRecord[predictedField]], [float(nnPrediction)])
likelihoodsVecAllNN[0:len(nnRetval[5]), i] = nnRetval[5]
result.metrics = metricsManager.update(result)
negLL = result.metrics["multiStepBestPredictions:multiStep:"
"errorMetric='negativeLogLikelihood':steps=%d:window=1000:"
"field=%s" % (_options.stepsAhead, predictedField)]
if i % 100 == 0 and i > 0:
negLL = result.metrics["multiStepBestPredictions:multiStep:"
"errorMetric='negativeLogLikelihood':steps=%d:window=1000:"
"field=%s" % (_options.stepsAhead, predictedField)]
