# model = runMultiplePassSPonly(df, model, nMultiplePass, nTrain)
maxBucket = classifier_encoder.n - classifier_encoder.w + 1
likelihoodsVecAll = np.zeros((maxBucket, len(df)))
prediction_nstep = None
time_step = []
actual_data = []
patternNZ_track = []
predict_data = np.zeros((_options.stepsAhead, 0))
predict_data_ML = []
negLL_track = []
activeCellNum = []
trueBucketIndex = []
sp = model._getSPRegion().getSelf()._sfdr
spActiveCellsCount = np.zeros(sp.getColumnDimensions())
for i in xrange(len(df)):
inputRecord = getInputRecord(df, predictedField, i)
result = model.run(inputRecord)
trueBucketIndex.append(model._getClassifierInputRecord(inputRecord).bucketIndex)
# inspect SP
sp = model._getSPRegion().getSelf()._sfdr
spOutput = model._getSPRegion().getOutputData('bottomUpOut')
spActiveCellsCount[spOutput.nonzero()[0]] += 1
tp = model._getTPRegion()
tm = tp.getSelf()._tfdr
activeColumn = tm.getActiveCells()
likelihoodsVecAll = np.zeros((maxBucket, len(df)))
prediction_nstep = None
time_step = []
actual_data = []
patternNZ_track = []
predict_data = np.zeros((_options.stepsAhead, 0))
predict_data_ML = []
negLL_track = []
activeCellNum = []
predCellNum = []
predSegmentNum = []
predictedActiveColumnsNum = []
trueBucketIndex = []
sp = model._getSPRegion().getSelf()._sfdr
spActiveCellsCount = np.zeros(sp.getColumnDimensions())
output = nupic_output.NuPICFileOutput([dataSet])
for i in xrange(len(df)):
inputRecord = getInputRecord(df, predictedField, i)
tp = model._getTPRegion()
tm = tp.getSelf()._tfdr
prePredictiveCells = tm.getPredictiveCells()
prePredictiveColumn = np.array(
list(prePredictiveCells)) / tm.cellsPerColumn
result = model.run(inputRecord)
trueBucketIndex.append(
model._getClassifierInputRecord(inputRecord).bucketIndex)
likelihoodsVecAllNN = np.zeros((maxBucket, len(df)))
predictionNstep = None
timeStep = []
actualData = []
patternNZTrack = []
predictData = np.zeros((_options.stepsAhead, 0))
predictDataCLA = []
predictDataNN = []
negLLTrack = []
activeCellNum = []
predCellNum = []
predictedActiveColumnsNum = []
trueBucketIndex = []
sp = model._getSPRegion().getSelf()._sfdr
spActiveCellsCount = np.zeros(sp.getColumnDimensions())
if noise > 0:
datasetName = dataSet + "noise_{:.2f}".format(noise)
else:
datasetName = dataSet
output = nupic_output.NuPICFileOutput([datasetName])
for i in xrange(len(df)):
inputRecord = getInputRecord(df, predictedField, i, noise)
tp = model._getTPRegion()
tm = tp.getSelf()._tfdr
prePredictiveCells = tm.predictiveCells
prePredictiveColumn = np.array(list(prePredictiveCells)) / tm.cellsPerColumn