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)]