def transformSub(self, cumSums, cumSums_2, wCumSums, pos):
transformedSub = np.zeros(self.wordSize)
#window mean and std
if not (self.meanNorm or self.stdNorm):
meanSub = 0
sigmaSub = 1
elif self.stdNorm:
meanSub = (cumSums[pos + self.winLen] - cumSums[pos]) / self.winLen
meanSub_2 = (cumSums_2[pos + self.winLen] -
cumSums_2[pos]) / self.winLen
varSub = meanSub_2 - meanSub * meanSub
sigmaSub = np.sqrt(varSub) if varSub > 0 else 1
if not self.meanNorm:
meanSub = 0
else:
meanSub = (cumSums[pos + self.winLen] - cumSums[pos]) / self.winLen
sigmaSub = 1
#timestamp parameters
startPts = self.segStarts[:len(self.segStarts) - 1] + pos
finishPts = self.segStarts[1:] + pos
sum_X = gu.getAriSeqSum(startPts, finishPts - 1)
mean_X = sum_X / self.segSizes
mean_X2 = gu.getSumOfSquares(startPts, finishPts - 1) / self.segSizes
#segment parameters
# sumSegs = cumSums[self.segStarts[1 :]] - cumSums[self.segStarts[: len(self.segStarts) - 1]]
sumSegs = cumSums[finishPts] - cumSums[startPts]
meanSegs = (sumSegs / self.segSizes - meanSub) / sigmaSub
# wCumSegs = wCumSums[self.segStarts[1 :]] - wCumSums[self.segStarts[: len(self.segStarts) - 1]]
wCumSegs = wCumSums[finishPts] - wCumSums[startPts]
wMeanSegs = (wCumSegs - meanSub * sum_X) / self.segSizes / sigmaSub
#the coefficients
slopes = (wMeanSegs - mean_X * meanSegs) / (mean_X2 - mean_X * mean_X)
intercepts = meanSegs - slopes * mean_X
if self.posNorm:
intercepts += startPts * slopes #shift to the same starting timestamp of 0
transformedSub[0:self.wordSize - 1:2] = slopes
transformedSub[1:self.wordSize:2] = intercepts
return transformedSub
def getAllMeanX2(tsLen, segSize):
starts = np.arange(tsLen - segSize + 1)
finishes = np.arange(segSize - 1, tsLen)
return gu.getSumOfSquares(starts, finishes) / segSize