def main():
# np.random.seed(123)
# ================================ consts for everything
# consts for generating data
# n = 1000
n = 500
# n = 300
# length = 8
# length = 16
length = 32
# length = 50
# nInstances = 3
exampleLengths = [55, 60, 65]
# exampleLengths = [60, 60, 60]
noiseStd = .5
# consts for algorithm
Lmin = max(20, length) # only needed for optimalAlignK() spacing
Lmax = 100 # loose upper bound on pattern length
minSim = .5 # loose cutoff for what counts as similar
# k0 = len(exampleLengths) # for version where we tell it k
answerIdxs = None
# ------------------------ synthetic data
# seq = synth.randconst(n, std=noiseStd)
# seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=4)
seq = synth.notSoRandomWalk(n,
std=noiseStd,
trendFilterLength=80,
lpfLength=16)
seq = embedExamples(seq, exampleLengths)
# ------------------------ msrc
from ..datasets import read_msrc as msrc
idxs = [2]
# idxs = [0]
downsampleBy = 2
recordings = msrc.getRecordings(idxs=idxs)
r = list(recordings)[0]
# seq = r.data
# seq = r.data[:, :40]
# seq = r.data[:, 20:23]
seq = r.data[:, 24:27]
# seq = r.data[:, 20:27]
print "orig seq shape", seq.shape
seq = ar.downsampleMat(seq, rowsBy=downsampleBy)
print "downsampled seq shape", seq.shape
length = max(8, Lmin / 2)
Lmin = len(seq) / 20
Lmax = len(seq) / 10
# Lmax = len(seq) / 20
# k0 = 10
minSim = .5
answerIdxs = r.gestureIdxs / downsampleBy
# print "seq shape", seq.shape
prePadLen = Lmax - length
postPadLen = length - 1
first = np.tile(seq[0], (prePadLen, 1))
last = np.tile(seq[-1], (postPadLen, 1))
seq = np.vstack(
(first, seq, last)) # pad with fixed val to allow all window positions
# ^ TODO pad simMat with zeros instead--this introduces fake subseqs
answerIdxs += prePadLen
# seq = np.vstack((seq, np.tile(flat, (length-1, 1)))) # lets it get the last rep
# print "seq shape", seq.shape
# r.plot()
# plt.figure()
# plt.plot(r.sampleTimes)
# answerIdxs = r.gestureIdxs / downsampleBy
# print r.gestureIdxs
# print answerIdxs
# plt.figure()
# plt.plot(seq)
# for idx in answerIdxs:
# ax = plt.gca()
# viz.plotVertLine(idx, ax=ax)
# plt.show()
# return
# noise = synth.randconst(seq.shape) # add noise for debugging
# seq = np.r_[noise, seq, noise]
# ================================ simMat
X = computeSimMat(seq, length)
X[X < minSim] = 0.
# Xorig = np.copy(X)
X = ff2.localMaxFilterSimMat(X)
Xblur = ff2.filterSimMat(X,
length - 1,
'hamming',
scaleFilterMethod='max1')
# Xblur = ff2.filterSimMat(X, Lmin-1, 'hamming', scaleFilterMethod='max1')
Xblur = np.minimum(Xblur, 1.)
print "simMat dims:", X.shape
Xnonzeros = np.count_nonzero(X)
print "simMat nonzeros, total, frac = ", Xnonzeros, X.size, Xnonzeros / float(
X.size)
# ================================ plotting crap
plt.figure()
axSeq = plt.subplot2grid((4, 1), (0, 0))
axSim = plt.subplot2grid((4, 1), (1, 0), rowspan=3)
for ax in (axSeq, axSim):
ax.autoscale(tight=True)
axSeq.plot(seq)
if answerIdxs is not None:
for idx in answerIdxs:
viz.plotVertLine(idx, ax=axSeq)
Xpad = synth.appendZeros(X, length - 1)
axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# im = axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# plt.colorbar(im, cax=axSim)
axSeq.set_title("Time Series")
axSim.set_title("Similarities Matrix")
# plt.figure()
# plt.imshow(Xorig, interpolation='nearest', aspect='auto')
# plt.colorbar()
# plt.figure()
# plt.imshow(X, interpolation='nearest', aspect='auto')
# plt.colorbar()
# # plt.figure()
# # Xfilt = ff2.localMaxFilterSimMat(X, allowEq=True)
# # plt.imshow(Xfilt, interpolation='nearest', aspect='auto')
# # plt.colorbar()
# # plt.figure()
# # Xfilt = ff2.localMaxFilterSimMat(X, allowEq=False)
# # plt.imshow(Xfilt, interpolation='nearest', aspect='auto')
# # plt.colorbar()
# plt.figure()
# plt.imshow(Xblur, interpolation='nearest', aspect='auto')
# plt.colorbar()
# plt.show()
# return
# ================================ science
# ------------------------ derived stats
kMax = int(X.shape[1] / Lmin + .5)
windowLen = Lmax - length + 1
# windowShape = (X.shape[0], Lmax)
# windowSize = np.prod(windowShape)
nLocs = X.shape[1] - windowLen + 1
p0 = np.mean(X) # fraction of entries that are 1 (roughly)
# intersections = computeIntersections(X, windowLen)
# windowSims = np.sum(intersections, axis=2)
# colSims = np.dot(X.T, X)
colSims = np.dot(X.T, Xblur)
filt = np.zeros((windowLen, windowLen)) + np.diag(
np.ones(windowLen)) # zeros except 1s on diag
windowSims = sig.convolve2d(colSims, filt, mode='valid')
windowVects = vectorizeWindowLocs(X, windowLen)
windowVectsBlur = vectorizeWindowLocs(Xblur, windowLen)
plt.figure()
plt.imshow(windowSims, interpolation='nearest', aspect='auto')
# plt.show()
# return
# ------------------------ find stuff
# #
# # Version where we we tell it k
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# # selfSims = np.diagonal(windowSims)
# # candidateRowIdxs = np.where(selfSims * k0 <= bsfScore)[0]
# # for i in candidateRowIdxs:
# # row = windowSims[i]
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# if windowSims[i, i] * k0 <= bsfScore:
# continue
# idxs = sub.optimalAlignK(row, Lmin, k0)
# intersection = windowVects[i]
# sz = 0
# for idx in idxs:
# intersection = np.minimum(intersection, windowVectsBlur[idx])
# sz = np.sum(intersection)
# if sz * k0 <= bsfScore:
# break
# score = sz * k0
# if score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k0, score))
# bsfScore = score
# bsfLocs = idxs
# bsfIntersection = np.copy(intersection)
# #
# # Version where we look for similarities to orig seq
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# # print("immediately abandoning window {}!".format(i))
# continue
# # print("not abandoning window {}!".format(i))
# # best combination of idxs such that none are within Lmin of each other
# idxs = sub.optimalAlignment(row, Lmin)
# # print i, ": ", idxs
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# intersection = np.minimum(intersection, windowVectsBlur[idx])
# sz = np.sum(intersection) # use apodization window
# # sz = np.count_nonzero(intersection) # just max-pool
# score = sz * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(intersection)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # print("early abandoning window {} at k={}".format(i, k))
# break
# #
# # Version where we look for similarities to orig seq and use nearest
# # enemy dist as M0
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# # print("immediately abandoning window {}!".format(i))
# continue
# # best combination of idxs such that none are within Lmin of each other
# # validRow = row[:(-length + 1)] # can't go past end of ts
# # idxs = sub.optimalAlignment(validRow, Lmin)
# idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# # allZeros = np.zeros(intersection.shape)
# nextIntersection = np.minimum(intersection, windowVectsBlur[sortedIdxs[0]])
# nextSz = np.sum(nextIntersection)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# intersection = np.copy(nextIntersection)
# sz = nextSz
# if k < numIdxs:
# nextIdx = sortedIdxs[k] # since k = j+1
# nextIntersection = np.minimum(intersection, windowVectsBlur[nextIdx])
# nextSz = np.sum(nextIntersection) # sum -> use apodization window
# else:
# nextSz = sz * p0
# score = (sz - nextSz) * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(intersection)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # print("early abandoning window {} at k={}".format(i, k))
# break
#
# Version where we look for similarities to orig seq and use nearest
# enemy dist as M0, and use mean values instead of intersection
#
bsfScore = 0
bsfLocs = None
bsfIntersection = None
for i, row in enumerate(windowSims):
if i % 20 == 0:
print("computing stuff for row {}".format(i))
# early abandon if this location has so little stuff that no
# intersection with it can possibly beat the best score
if windowSims[
i,
i] * kMax <= bsfScore: # highest score is kMax identical locs
continue
# best combination of idxs such that none are within Lmin of each other
# validRow = row[:(-length + 1)] # can't go past end of ts
# idxs = sub.optimalAlignment(validRow, Lmin)
idxs = sub.optimalAlignment(row,
Lmin) # goes past end of ts, but better
# order idxs by descending order of associated score
sizes = windowSims[i, idxs]
sortedSizesOrder = np.argsort(sizes)[::-1]
sortedIdxs = idxs[sortedSizesOrder]
# iteratively intersect with another near neighbor, compute the
# associated score, and check if it's better (or if we can early abandon)
intersection = windowVects[i]
numIdxs = len(sortedIdxs)
nextSz = np.sum(intersection)
nextFilt = np.array(intersection, dtype=np.float)
nextFiltSum = np.array(nextFilt, dtype=np.float)
for j, idx in enumerate(sortedIdxs):
k = j + 1
filt = np.copy(nextFilt)
sz = nextSz
if k < numIdxs:
nextIdx = sortedIdxs[k] # since k = j+1
nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
nextFiltSum += nextIntersection
nextFilt = nextFiltSum / (
k + 1) # avg value of each feature in intersections
# nextSz = np.sum(nextFilt) # big even if like no intersection...
nextSz = np.sum(nextIntersection)
bigEnoughIntersection = nextIntersection[
nextIntersection > minSim]
nextSz = np.sum(bigEnoughIntersection)
else:
nextSz = sz * p0
score = (sz - nextSz) * k
if k > 1 and score > bsfScore:
print("window {0}, k={1}, score={2} is the new best!".format(
i, k, score))
print("sortedIdxs = {}".format(str(sortedIdxs)))
print("sortedIdxScores = {}".format(
str(windowSims[i, sortedIdxs])))
print("------------------------")
bsfScore = score
bsfLocs = sortedIdxs[:k]
bsfIntersection = np.copy(filt)
# early abandon if this can't possibly beat the best score, which
# is the case exactly when the intersection is so small that perfect
# matches at all future locations still wouldn't be good enough
elif sz * numIdxs <= bsfScore:
# TODO can we actually early abandon here? next window loc
# could increase filt, and thus score for a given loc isn't
# necessarily non-increasing...
# -can't abandon using this test, but pretty sure there's
# a lower bound to be had here somewhere
# print("early abandoning window {} at k={}".format(i, k))
break
#
# Version where we look for similarities to orig seq and use nearest
# enemy dist as M0, and use mean values instead of intersection,
# and don't sort the indices, but instead care about overlap
#
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# continue
# # best combination of idxs such that none are within Lmin of each other
# # validRow = row[:(-length + 1)] # can't go past end of ts
# # idxs = sub.optimalAlignment(validRow, Lmin)
# idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# nextSz = np.sum(intersection)
# nextFilt = np.array(intersection, dtype=np.float)
# nextFiltSum = np.array(nextFilt, dtype=np.float)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# filt = np.copy(nextFilt)
# sz = nextSz
# if k < numIdxs:
# nextIdx = sortedIdxs[k] # since k = j+1
# nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
# nextFiltSum += nextIntersection
# nextFilt = nextFiltSum / (k+1) # avg value of each feature in intersections
# # nextSz = np.sum(nextFilt) # big even if like no intersection...
# nextSz = np.sum(nextIntersection)
# bigEnoughIntersection = nextIntersection[nextIntersection > minSim]
# nextSz = np.sum(bigEnoughIntersection)
# else:
# nextSz = sz * p0
# score = (sz - nextSz) * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(filt)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # TODO can we actually early abandon here? next window loc
# # could increase filt, and thus score for a given loc isn't
# # necessarily non-increasing...
# # -can't abandon using this test, but pretty sure there's
# # a lower bound to be had here somewhere
# # print("early abandoning window {} at k={}".format(i, k))
# break
# ------------------------ recover original ts
bsfIntersection *= bsfIntersection >= minSim
bsfIntersectionWindow = bsfIntersection.reshape((-1, windowLen))
sums = np.sum(bsfIntersectionWindow, axis=0)
kBest = len(bsfLocs)
p0 = np.power(p0, kBest)
# expectedOnesPerCol = p0 * X.shape[1]
expectedOnesPerCol = p0 * X.shape[1] * 2
sums -= expectedOnesPerCol
plt.plot(sums)
start, end, _ = maxSubarray(sums)
patStart, patEnd = start, end + 1 + length
# ================================ show output
print "bestScore = {}".format(bsfScore)
print "bestLocations = {}".format(str(bsfLocs))
for idx in bsfLocs:
viz.plotRect(axSim, idx, idx + windowLen)
# print bsfIntersectionWindow.shape
# print sums.shape
# plt.plot(sums)
# viz.plotRect(plt.gca(), start, end + 1)
for idx in bsfLocs:
viz.plotRect(axSeq, idx + patStart, idx + patEnd)
plt.figure()
plt.imshow(bsfIntersectionWindow, interpolation='nearest', aspect='auto')
plt.tight_layout()
plt.show()
def main():
# np.random.seed(123)
# ================================ consts for everything
# consts for generating data
# n = 1000
n = 500
# n = 300
# length = 8
# length = 16
# length = 32
# length = 50
# nInstances = 3
exampleLengths = [55, 60, 65]
# exampleLengths = [60, 60, 60]
noiseStd = .5
# consts for algorithm
# Lmin = max(20, length) # only needed for optimalAlignK() spacing
Lmin = 20 # only needed for optimalAlignK() spacing
Lmax = 100 # loose upper bound on pattern length
# minSim = .5
minSim = 0.
length = Lmin // 2
# length = Lmin // 4
# length = 3
answerIdxs = None
USE_MSRC = True
# USE_MSRC = False
# ================================ data
# ------------------------ synthetic data
# seq = synth.randconst(n, std=noiseStd)
seq = synth.randwalk(n, std=noiseStd)
# seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=4)
# seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=16)
seq = embedExamples(seq, exampleLengths)
# seq = synth.appendZeros(seq, Lmax)
# ------------------------ msrc
if USE_MSRC:
from ..datasets import read_msrc as msrc
# idxs = [0]
# idxs = [1]
# idxs = [2]
# idxs = [7] # length 1500, but instances of length like 20
# idxs = [8] # gets owned on this one cuz patterns of length like 100
# idxs = [9] # missing an annotation, it appears
idxs = [10] # something crazy about feature rep here # TODO fix
# idxs = [11] # crap cuz bad, low-variance signals
# idxs = [12] # has garbagey sections like [10]
# idxs = [13] # empty feature mat # TODO
# idxs = [14]
downsampleBy = 2
# downsampleBy = 1
recordings = msrc.getRecordings(idxs=idxs)
r = list(recordings)[0]
# seq = r.data
# seq = r.data[:, :40]
# seq = r.data[:, 20:23]
seq = r.data[:, 24:27]
# seq = r.data[:, 20:27]
print "orig seq shape", seq.shape
seq = ar.downsampleMat(seq, rowsBy=downsampleBy)
print "downsampled seq shape", seq.shape
# length = max(8, Lmin / 2)
Lmin = len(seq) // 20
# Lmax = len(seq) // 8
Lmax = len(seq) // 10
length = Lmin // 2
# Lmax = len(seq) / 20
# k0 = 10
# minSim = .5
answerIdxs = r.gestureIdxs / downsampleBy
# print "seq shape", seq.shape
prePadLen = Lmax - length
# postPadLen = length - 1
postPadLen = Lmax - length
first = np.tile(seq[0], (prePadLen, 1))
last = np.tile(seq[-1], (postPadLen, 1))
seq = np.vstack((first, seq, last)) # pad with fixed val to allow all window positions
# ^ TODO pad simMat with zeros instead--this introduces fake subseqs
answerIdxs += prePadLen
# seq = np.vstack((seq, np.tile(flat, (length-1, 1)))) # lets it get the last rep
# print "seq shape", seq.shape
# ================================ feature construction
logMaxLength = int(np.floor(np.log2(Lmax)))
# logMaxLength = int(np.ceil(np.log2(Lmax)))
# logMinLength = 3 # -> length 8
# logMinLength = 4 # -> length 16
logMinLength = int(np.floor(np.log2(Lmin)))
lengths = np.arange(logMinLength, logMaxLength + 1)
lengths = 2 ** lengths
# lengths = [16]
cardinality = 8
breakpoints = rep.saxBreakpoints(cardinality)
X = rep.multiNormalizeAndSparseQuantize(seq, lengths, breakpoints)
# X = rep.multiSparseLineProject(seq, lengths, breakpoints, removeZeroRows=False)
# lengths2 = np.arange(3, logMaxLength + 1)
# lengths2 = 2 ** lengths2
lengths2 = lengths # TODO uncomment after debug
# lengths2 = [8, 32]
# breakpoints2 = rep.defaultSparseLineBreakpoints(seq, scaleHowMany=2)
breakpoints2 = rep.defaultSparseLineBreakpoints(seq)
X2 = rep.multiSparseLineProject(seq, lengths2, breakpoints2)
# X2 = X2 > minSim
X2 = X2 > 0. # ignore correlations
# print "shapes:"
# print X.shape
# print X2.shape
X = np.vstack((X, X2))
# plt.figure()
# # viz.imshowBetter(X)
# viz.imshowBetter(X2)
# plt.figure()
# viz.imshowBetter(X2 > 0.)
# plt.show()
# print seq.shape
# plt.figure()
# plt.plot(seq[:,0]) # bit of pattern, but only varies between -.4 and .2
# okay, so 1st dim is all zeros
# variances = rep.slidingVariance(seq, 8)
# for dim in range(len(variances)):
# plt.figure()
# plt.plot(variances[dim].flatten())
# print variances.shape
# variances = rep.vstack3Tensor(variances.T)
# print variances.shape
# plt.plot(variances)
# plt.show()
# return
X = localMaxFilterSimMat(X)
# Xbool = np.copy(X)
featureMeans = np.mean(X, axis=1).reshape((-1, 1))
# print featureMeans
X *= -np.log2(featureMeans) # variable encoding costs for rows
# X /= -np.log(featureMeans)
# Xblur = localMaxFilterSimMat(X) # try only maxFiltering Xblur
Xblur = filterSimMat(X, length-1, 'hamming', scaleFilterMethod='max1')
# plt.figure()
# viz.imshowBetter(X)
# plt.figure()
# viz.imshowBetter(Xblur)
print "featureMat dims:", X.shape
Xnonzeros = np.count_nonzero(X)
print "featureMat nonzeros, total, frac = ", Xnonzeros, X.size, Xnonzeros / float(X.size)
# plt.show()
# return
# ================================ plotting crap
plt.figure()
axSeq = plt.subplot2grid((4,1), (0,0))
axSim = plt.subplot2grid((4,1), (1,0), rowspan=3)
for ax in (axSeq, axSim):
ax.autoscale(tight=True)
axSeq.plot(seq)
# if answerIdxs is not None:
# for idx in answerIdxs:
# viz.plotVertLine(idx, ax=axSeq)
padLen = len(seq) - X.shape[1]
Xpad = synth.appendZeros(X, padLen)
axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# im = axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# plt.colorbar(im, cax=axSim)
axSeq.set_title("Time Series")
axSim.set_title("Feature Matrix")
# plt.show()
# return
# ================================ science
# ------------------------ derived stats
kMax = int(X.shape[1] / Lmin + .5)
windowLen = Lmax - length + 1
p0 = np.mean(X) # fraction of entries that are 1 (roughly)
# p0 = 2 * np.mean(X) # lambda for l0 reg based on features being bernoulli at 2 locs
minSim = p0
# p0 = -np.log(np.mean(Xbool)) # fraction of entries that are 1 (roughly)
# noiseSz = p0 * X.shape[0] * windowLen # way too hard to beat
expectedOnesPerWindow = p0 * X.shape[0] * windowLen
noiseSz = p0 * expectedOnesPerWindow # num ones to begin with
# intersections = computeIntersections(X, windowLen)
# windowSims = np.sum(intersections, axis=2)
# colSims = np.dot(X.T, X)
colSims = np.dot(X.T, Xblur)
filt = np.zeros((windowLen, windowLen)) + np.diag(np.ones(windowLen)) # zeros except 1s on diag
windowSims = sig.convolve2d(colSims, filt, mode='valid')
windowVects = vectorizeWindowLocs(X, windowLen)
windowVectsBlur = vectorizeWindowLocs(Xblur, windowLen)
# plt.figure()
# plt.imshow(windowSims, interpolation='nearest', aspect='auto')
# ------------------------ find stuff
#
# Version where we look for similarities to orig seq and use nearest
# enemy dist as M0, and use mean values instead of intersection
#
bsfScore = 0
bsfLocs = None
bsfIntersection = None
for i, row in enumerate(windowSims):
if i % 20 == 0:
print("computing stuff for row {}".format(i))
# early abandon if this location has so little stuff that no
# intersection with it can possibly beat the best score
if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
continue
# best combination of idxs such that none are within Lmin of each other
# validRow = row[:(-length + 1)] # can't go past end of ts
# idxs = sub.optimalAlignment(validRow, Lmin)
idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# order idxs by descending order of associated score
sizes = windowSims[i, idxs]
sortedSizesOrder = np.argsort(sizes)[::-1]
sortedIdxs = idxs[sortedSizesOrder]
# iteratively intersect with another near neighbor, compute the
# associated score, and check if it's better (or if we can early abandon)
intersection = windowVects[i]
numIdxs = len(sortedIdxs)
nextSz = np.sum(intersection)
nextFilt = np.array(intersection, dtype=np.float)
nextFiltSum = np.array(nextFilt, dtype=np.float)
for j, idx in enumerate(sortedIdxs):
k = j + 1
filt = np.copy(nextFilt)
sz = nextSz
if k < numIdxs:
nextIdx = sortedIdxs[k] # since k = j+1
nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
nextFiltSum += nextIntersection
nextFilt = nextFiltSum / (k+1) # avg value of each feature in intersections
# nextSz = np.sum(nextFilt) # big even if like no intersection...
nextSz = np.sum(nextIntersection)
bigEnoughIntersection = nextIntersection[nextIntersection > minSim]
nextSz = np.sum(bigEnoughIntersection)
else:
nextSz = sz * p0
# nextSz = -1
enemySz = max(nextSz, noiseSz)
score = (sz - enemySz) * k
if k > 1 and score > bsfScore:
print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
print("sortedIdxs = {}".format(str(sortedIdxs)))
print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
print("------------------------")
bsfScore = score
bsfLocs = sortedIdxs[:k]
bsfIntersection = np.copy(filt)
# early abandon if this can't possibly beat the best score, which
# is the case exactly when the intersection is so small that perfect
# matches at all future locations still wouldn't be good enough
elif sz * numIdxs <= bsfScore:
# TODO can we actually early abandon here? next window loc
# could increase filt, and thus score for a given loc isn't
# necessarily non-increasing...
# -can't abandon using this test, but pretty sure there's
# a lower bound to be had here somewhere
# print("early abandoning window {} at k={}".format(i, k))
break
elif noiseSz > nextSz:
break
# #
# # Version where we look for similarities to orig seq and use nearest
# # enemy dist as M0, and use mean values instead of intersection,
# # and don't sort the indices, but instead care about overlap
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# continue
# # best combination of idxs such that none are within Lmin of each other
# # validRow = row[:(-length + 1)] # can't go past end of ts
# # idxs = sub.optimalAlignment(validRow, Lmin)
# idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# nextSz = np.sum(intersection)
# nextFilt = np.array(intersection, dtype=np.float)
# nextFiltSum = np.array(nextFilt, dtype=np.float)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# filt = np.copy(nextFilt)
# sz = nextSz
# if k < numIdxs:
# nextIdx = sortedIdxs[k] # since k = j+1
# nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
# nextFiltSum += nextIntersection
# nextFilt = nextFiltSum / (k+1) # avg value of each feature in intersections
# # nextSz = np.sum(nextFilt) # big even if like no intersection...
# nextSz = np.sum(nextIntersection)
# bigEnoughIntersection = nextIntersection[nextIntersection > minSim]
# nextSz = np.sum(bigEnoughIntersection)
# else:
# nextSz = sz * p0
# score = (sz - nextSz) * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(filt)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # TODO can we actually early abandon here? next window loc
# # could increase filt, and thus score for a given loc isn't
# # necessarily non-increasing...
# # -can't abandon using this test, but pretty sure there's
# # a lower bound to be had here somewhere
# # print("early abandoning window {} at k={}".format(i, k))
# break
# ------------------------ recover original ts
bsfIntersection *= bsfIntersection >= minSim
bsfIntersectionWindow = bsfIntersection.reshape((-1, windowLen))
sums = np.sum(bsfIntersectionWindow, axis=0)
kBest = len(bsfLocs)
p0 = np.power(p0, kBest)
# expectedOnesPerCol = p0 * X.shape[1] * 2
# expectedOnesPerCol = p0 * X.shape[1]
expectedOnesPerCol = p0 * X.shape[0]
sums -= expectedOnesPerCol
# plt.figure()
# plt.plot(sums)
start, end, _ = maxSubarray(sums)
# patStart, patEnd = start, end + 1 + length
patStart, patEnd = start, end + 1
# patStart, patEnd = start + length // 2, end + 1 + length
# ================================ show output
print "bestScore = {}".format(bsfScore)
print "bestLocations = {}".format(str(bsfLocs))
for idx in bsfLocs:
viz.plotRect(axSim, idx, idx+windowLen)
# print bsfIntersectionWindow.shape
# print sums.shape
# plt.plot(sums)
# viz.plotRect(plt.gca(), start, end + 1)
for idx in bsfLocs:
viz.plotRect(axSeq, idx + patStart, idx + patEnd)
if answerIdxs is not None:
for idx in answerIdxs:
viz.plotVertLine(idx, ax=axSeq)
plt.figure()
plt.imshow(bsfIntersectionWindow, interpolation='nearest', aspect='auto')
plt.tight_layout()
plt.show()
def main():
# np.random.seed(123)
# ================================ consts for everything
# consts for generating data
# n = 1000
n = 500
# n = 300
# length = 8
# length = 16
length = 32
# length = 50
# nInstances = 3
exampleLengths = [55, 60, 65]
# exampleLengths = [60, 60, 60]
noiseStd = .5
# consts for algorithm
Lmin = max(20, length) # only needed for optimalAlignK() spacing
Lmax = 100 # loose upper bound on pattern length
minSim = .5 # loose cutoff for what counts as similar
# k0 = len(exampleLengths) # for version where we tell it k
answerIdxs = None
# ------------------------ synthetic data
# seq = synth.randconst(n, std=noiseStd)
# seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=4)
seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=16)
seq = embedExamples(seq, exampleLengths)
# ------------------------ msrc
from ..datasets import read_msrc as msrc
idxs = [2]
# idxs = [0]
downsampleBy = 2
recordings = msrc.getRecordings(idxs=idxs)
r = list(recordings)[0]
# seq = r.data
# seq = r.data[:, :40]
# seq = r.data[:, 20:23]
seq = r.data[:, 24:27]
# seq = r.data[:, 20:27]
print "orig seq shape", seq.shape
seq = ar.downsampleMat(seq, rowsBy=downsampleBy)
print "downsampled seq shape", seq.shape
length = max(8, Lmin / 2)
Lmin = len(seq) / 20
Lmax = len(seq) / 10
# Lmax = len(seq) / 20
# k0 = 10
minSim = .5
answerIdxs = r.gestureIdxs / downsampleBy
# print "seq shape", seq.shape
prePadLen = Lmax - length
postPadLen = length - 1
first = np.tile(seq[0], (prePadLen, 1))
last = np.tile(seq[-1], (postPadLen, 1))
seq = np.vstack((first, seq, last)) # pad with fixed val to allow all window positions
# ^ TODO pad simMat with zeros instead--this introduces fake subseqs
answerIdxs += prePadLen
# seq = np.vstack((seq, np.tile(flat, (length-1, 1)))) # lets it get the last rep
# print "seq shape", seq.shape
# r.plot()
# plt.figure()
# plt.plot(r.sampleTimes)
# answerIdxs = r.gestureIdxs / downsampleBy
# print r.gestureIdxs
# print answerIdxs
# plt.figure()
# plt.plot(seq)
# for idx in answerIdxs:
# ax = plt.gca()
# viz.plotVertLine(idx, ax=ax)
# plt.show()
# return
# noise = synth.randconst(seq.shape) # add noise for debugging
# seq = np.r_[noise, seq, noise]
# ================================ simMat
X = computeSimMat(seq, length)
X[X < minSim] = 0.
# Xorig = np.copy(X)
X = ff2.localMaxFilterSimMat(X)
Xblur = ff2.filterSimMat(X, length-1, 'hamming', scaleFilterMethod='max1')
# Xblur = ff2.filterSimMat(X, Lmin-1, 'hamming', scaleFilterMethod='max1')
Xblur = np.minimum(Xblur, 1.)
print "simMat dims:", X.shape
Xnonzeros = np.count_nonzero(X)
print "simMat nonzeros, total, frac = ", Xnonzeros, X.size, Xnonzeros / float(X.size)
# ================================ plotting crap
plt.figure()
axSeq = plt.subplot2grid((4,1), (0,0))
axSim = plt.subplot2grid((4,1), (1,0), rowspan=3)
for ax in (axSeq, axSim):
ax.autoscale(tight=True)
axSeq.plot(seq)
if answerIdxs is not None:
for idx in answerIdxs:
viz.plotVertLine(idx, ax=axSeq)
Xpad = synth.appendZeros(X, length-1)
axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# im = axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# plt.colorbar(im, cax=axSim)
axSeq.set_title("Time Series")
axSim.set_title("Similarities Matrix")
# plt.figure()
# plt.imshow(Xorig, interpolation='nearest', aspect='auto')
# plt.colorbar()
# plt.figure()
# plt.imshow(X, interpolation='nearest', aspect='auto')
# plt.colorbar()
# # plt.figure()
# # Xfilt = ff2.localMaxFilterSimMat(X, allowEq=True)
# # plt.imshow(Xfilt, interpolation='nearest', aspect='auto')
# # plt.colorbar()
# # plt.figure()
# # Xfilt = ff2.localMaxFilterSimMat(X, allowEq=False)
# # plt.imshow(Xfilt, interpolation='nearest', aspect='auto')
# # plt.colorbar()
# plt.figure()
# plt.imshow(Xblur, interpolation='nearest', aspect='auto')
# plt.colorbar()
# plt.show()
# return
# ================================ science
# ------------------------ derived stats
kMax = int(X.shape[1] / Lmin + .5)
windowLen = Lmax - length + 1
# windowShape = (X.shape[0], Lmax)
# windowSize = np.prod(windowShape)
nLocs = X.shape[1] - windowLen + 1
p0 = np.mean(X) # fraction of entries that are 1 (roughly)
# intersections = computeIntersections(X, windowLen)
# windowSims = np.sum(intersections, axis=2)
# colSims = np.dot(X.T, X)
colSims = np.dot(X.T, Xblur)
filt = np.zeros((windowLen, windowLen)) + np.diag(np.ones(windowLen)) # zeros except 1s on diag
windowSims = sig.convolve2d(colSims, filt, mode='valid')
windowVects = vectorizeWindowLocs(X, windowLen)
windowVectsBlur = vectorizeWindowLocs(Xblur, windowLen)
plt.figure()
plt.imshow(windowSims, interpolation='nearest', aspect='auto')
# plt.show()
# return
# ------------------------ find stuff
# #
# # Version where we we tell it k
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# # selfSims = np.diagonal(windowSims)
# # candidateRowIdxs = np.where(selfSims * k0 <= bsfScore)[0]
# # for i in candidateRowIdxs:
# # row = windowSims[i]
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# if windowSims[i, i] * k0 <= bsfScore:
# continue
# idxs = sub.optimalAlignK(row, Lmin, k0)
# intersection = windowVects[i]
# sz = 0
# for idx in idxs:
# intersection = np.minimum(intersection, windowVectsBlur[idx])
# sz = np.sum(intersection)
# if sz * k0 <= bsfScore:
# break
# score = sz * k0
# if score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k0, score))
# bsfScore = score
# bsfLocs = idxs
# bsfIntersection = np.copy(intersection)
# #
# # Version where we look for similarities to orig seq
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# # print("immediately abandoning window {}!".format(i))
# continue
# # print("not abandoning window {}!".format(i))
# # best combination of idxs such that none are within Lmin of each other
# idxs = sub.optimalAlignment(row, Lmin)
# # print i, ": ", idxs
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# intersection = np.minimum(intersection, windowVectsBlur[idx])
# sz = np.sum(intersection) # use apodization window
# # sz = np.count_nonzero(intersection) # just max-pool
# score = sz * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(intersection)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # print("early abandoning window {} at k={}".format(i, k))
# break
# #
# # Version where we look for similarities to orig seq and use nearest
# # enemy dist as M0
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# # print("immediately abandoning window {}!".format(i))
# continue
# # best combination of idxs such that none are within Lmin of each other
# # validRow = row[:(-length + 1)] # can't go past end of ts
# # idxs = sub.optimalAlignment(validRow, Lmin)
# idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# # allZeros = np.zeros(intersection.shape)
# nextIntersection = np.minimum(intersection, windowVectsBlur[sortedIdxs[0]])
# nextSz = np.sum(nextIntersection)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# intersection = np.copy(nextIntersection)
# sz = nextSz
# if k < numIdxs:
# nextIdx = sortedIdxs[k] # since k = j+1
# nextIntersection = np.minimum(intersection, windowVectsBlur[nextIdx])
# nextSz = np.sum(nextIntersection) # sum -> use apodization window
# else:
# nextSz = sz * p0
# score = (sz - nextSz) * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(intersection)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # print("early abandoning window {} at k={}".format(i, k))
# break
#
# Version where we look for similarities to orig seq and use nearest
# enemy dist as M0, and use mean values instead of intersection
#
bsfScore = 0
bsfLocs = None
bsfIntersection = None
for i, row in enumerate(windowSims):
if i % 20 == 0:
print("computing stuff for row {}".format(i))
# early abandon if this location has so little stuff that no
# intersection with it can possibly beat the best score
if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
continue
# best combination of idxs such that none are within Lmin of each other
# validRow = row[:(-length + 1)] # can't go past end of ts
# idxs = sub.optimalAlignment(validRow, Lmin)
idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# order idxs by descending order of associated score
sizes = windowSims[i, idxs]
sortedSizesOrder = np.argsort(sizes)[::-1]
sortedIdxs = idxs[sortedSizesOrder]
# iteratively intersect with another near neighbor, compute the
# associated score, and check if it's better (or if we can early abandon)
intersection = windowVects[i]
numIdxs = len(sortedIdxs)
nextSz = np.sum(intersection)
nextFilt = np.array(intersection, dtype=np.float)
nextFiltSum = np.array(nextFilt, dtype=np.float)
for j, idx in enumerate(sortedIdxs):
k = j + 1
filt = np.copy(nextFilt)
sz = nextSz
if k < numIdxs:
nextIdx = sortedIdxs[k] # since k = j+1
nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
nextFiltSum += nextIntersection
nextFilt = nextFiltSum / (k+1) # avg value of each feature in intersections
# nextSz = np.sum(nextFilt) # big even if like no intersection...
nextSz = np.sum(nextIntersection)
bigEnoughIntersection = nextIntersection[nextIntersection > minSim]
nextSz = np.sum(bigEnoughIntersection)
else:
nextSz = sz * p0
score = (sz - nextSz) * k
if k > 1 and score > bsfScore:
print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
print("sortedIdxs = {}".format(str(sortedIdxs)))
print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
print("------------------------")
bsfScore = score
bsfLocs = sortedIdxs[:k]
bsfIntersection = np.copy(filt)
# early abandon if this can't possibly beat the best score, which
# is the case exactly when the intersection is so small that perfect
# matches at all future locations still wouldn't be good enough
elif sz * numIdxs <= bsfScore:
# TODO can we actually early abandon here? next window loc
# could increase filt, and thus score for a given loc isn't
# necessarily non-increasing...
# -can't abandon using this test, but pretty sure there's
# a lower bound to be had here somewhere
# print("early abandoning window {} at k={}".format(i, k))
break
#
# Version where we look for similarities to orig seq and use nearest
# enemy dist as M0, and use mean values instead of intersection,
# and don't sort the indices, but instead care about overlap
#
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# continue
# # best combination of idxs such that none are within Lmin of each other
# # validRow = row[:(-length + 1)] # can't go past end of ts
# # idxs = sub.optimalAlignment(validRow, Lmin)
# idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# nextSz = np.sum(intersection)
# nextFilt = np.array(intersection, dtype=np.float)
# nextFiltSum = np.array(nextFilt, dtype=np.float)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# filt = np.copy(nextFilt)
# sz = nextSz
# if k < numIdxs:
# nextIdx = sortedIdxs[k] # since k = j+1
# nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
# nextFiltSum += nextIntersection
# nextFilt = nextFiltSum / (k+1) # avg value of each feature in intersections
# # nextSz = np.sum(nextFilt) # big even if like no intersection...
# nextSz = np.sum(nextIntersection)
# bigEnoughIntersection = nextIntersection[nextIntersection > minSim]
# nextSz = np.sum(bigEnoughIntersection)
# else:
# nextSz = sz * p0
# score = (sz - nextSz) * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(filt)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # TODO can we actually early abandon here? next window loc
# # could increase filt, and thus score for a given loc isn't
# # necessarily non-increasing...
# # -can't abandon using this test, but pretty sure there's
# # a lower bound to be had here somewhere
# # print("early abandoning window {} at k={}".format(i, k))
# break
# ------------------------ recover original ts
bsfIntersection *= bsfIntersection >= minSim
bsfIntersectionWindow = bsfIntersection.reshape((-1, windowLen))
sums = np.sum(bsfIntersectionWindow, axis=0)
kBest = len(bsfLocs)
p0 = np.power(p0, kBest)
# expectedOnesPerCol = p0 * X.shape[1]
expectedOnesPerCol = p0 * X.shape[1] * 2
sums -= expectedOnesPerCol
plt.plot(sums)
start, end, _ = maxSubarray(sums)
patStart, patEnd = start, end + 1 + length
# ================================ show output
print "bestScore = {}".format(bsfScore)
print "bestLocations = {}".format(str(bsfLocs))
for idx in bsfLocs:
viz.plotRect(axSim, idx, idx+windowLen)
# print bsfIntersectionWindow.shape
# print sums.shape
# plt.plot(sums)
# viz.plotRect(plt.gca(), start, end + 1)
for idx in bsfLocs:
viz.plotRect(axSeq, idx + patStart, idx + patEnd)
plt.figure()
plt.imshow(bsfIntersectionWindow, interpolation='nearest', aspect='auto')
plt.tight_layout()
plt.show()
def neighborSimsMain():
import matplotlib.pyplot as plt
from ..datasets import datasets
from ..viz import viz_utils as viz
from ..utils import files
from ..utils import arrays as ar
from scipy import signal
from scipy.ndimage import filters
from ff2 import localMaxFilterSimMat
saveDir = None
# saveDir = 'figs/repr'
if saveDir:
files.ensureDirExists(saveDir)
howMany = 4
instPerTs = 2
# np.random.seed(12345)
np.random.seed(123)
syntheticDatasets = ['triangles','shapes','rects','sines']
# saveDir = 'figs/highlight/tidigits/'
# tsList = datasets.loadDataset('triangles', whichExamples=range(howMany))
# tsList = datasets.loadDatasets(syntheticDatasets, whichExamples=range(howMany))
# tsList = datasets.loadDataset('tidigits_grouped_mfcc', whichExamples=range(howMany))
tsList = datasets.loadDataset('ucr_short', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_CBF', whichExamples=range(5), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_CBF', whichExamples=range(2), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_Gun_Point', whichExamples=range(2), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_pairs', whichExamples=range(1))
# tsList = datasets.loadDataset('ucr_adiac', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_wafer', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_MedicalImages', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_ECG200', whichExamples=range(1), instancesPerTs=instPerTs)
tsList = datasets.loadDataset('dishwasher_groups', whichExamples=range(howMany), instancesPerTs=5)
# tsList = datasets.loadDataset('msrc', whichExamples=range(howMany))
print '------------------------'
# lengths = [16]
# lengths = np.array([1./8])
# lengths = np.array([1./16])
# lengths = np.array([25])
# lengths = np.array([1./32])
# lengths = np.array([1./32, 1./16])
# lengths = np.array([1./64, 1./32])
# lengths = np.array([1./64, 1./32, 1./16])
# lengths = np.array([1./128, 1./64, 1./32, 1./16])
# lengths = np.array([50, 100])
# lengths = np.array([50])
# lengths = np.array([16])
# lengths = np.array([8, 16, 32])
# lengths = np.array([16, 32, 64])
# lengths = np.array([8, 1./20, 1./10])
# from ff8 import logSpacedLengths
# lengths = 2 ** np.arange(4, 8) # 16,...,128
lengths = 2 ** np.arange(3, 8) # 8,...,128
# lengths = 2 ** np.arange(3, 8, .5) # 8,...,256, step by sqrt(2)
for ts in tsList:
print "running on ts ", ts.name
# print "dim stds:", np.std(ts.data, axis=0)
# ts.data = filters.convolve1d(ts.data, weights=np.hamming(8), axis=0, mode='constant')
# ts.data = ts.data[:, 0]
# ts.data = ts.data[:, [6,10]] # super large values in dishwasher
# ts.data = ts.data[:, [5,9]] # dishwasher pattern dims
# ts.data = ts.data[:, 5]
# ts.data = ts.data[:, 9]
if ts.data.shape[1] > 30: # if msrc, basically
ts.data = ts.data[:, 23:38]
ts.data = ar.ensure2D(ts.data)
lens = np.copy(lengths)
fractionIdxs = lengths < 1.
lens[fractionIdxs] = lengths[fractionIdxs] * len(ts.data)
lens = lens.astype(np.int)
lens = np.unique(lens)
lens = lens[lens >= 8]
lens = lens[lens < len(ts.data) // 4]
print "main(): using lengths", lens, lens.shape, lens.dtype
# continue
# lengths = np.array([25])
plt.figure(figsize=(8, 10))
gridShape = (9, 2)
ax1 = plt.subplot2grid(gridShape, (0,0), rowspan=2, colspan=2)
ax2 = plt.subplot2grid(gridShape, (2,0), rowspan=6, colspan=2)
ax3 = plt.subplot2grid(gridShape, (8,0), rowspan=1, colspan=2)
ts.plot(ax=ax1)
# numNeighbors = np.max(lens)
numNeighbors = np.log2(np.max(lens))
# numNeighbors = int(np.log2(len(ts.data)))
# numNeighbors = 10 * np.max(lens)
# numNeighbors = len(ts.data) - np.min(lens) # "sample" everything
# sims = multiNeighborSims(ts.data, lens, numNeighbors)
sims = multiNeighborSims(ts.data, lens, numNeighbors, maxDist=.25)
# print "after sims", ts.data.shape
# sims = multiNeighborSims(ts.data, lens, numNeighbors, maxDist=.5)
sims2 = multiVariance(ts.data, lens)
# print "after variance", ts.data.shape
# sims3 = multiNormalizeAndSparseQuantize(ts.data, lens)
# diffs = ts.data[1:] - ts.data[:-1]
# diffs = ar.addZeroRows(diffs, howMany=1, prepend=True) # same length as data
# sims4 = multiVariance(diffs, lens)
sims = np.vstack((sims, sims2))
# sims = np.vstack((sims, sims2, sims3))
# sims = np.vstack((sims, sims2, sims3, sims4))
# from ff8 import buildFeatureMat
# # sims = buildFeatureMat(ts.data, 1./16, 1./8, includeNeighbors=True,
# sims = buildFeatureMat(ts.data, 1./16, 1./8, includeNeighbors=True,
# detrend=False, maxDist=.25)
# sims = multiNeighborSims(ts.data, lengths, numNeighbors, maxDist=.5)
# print "nonzeros before localMaxFilter", np.count_nonzero(sims)
# sims = localMaxFilterSimMat(sims) # makes it look much worse
# print "nonzeros after localMaxFilter", np.count_nonzero(sims)
maxFilterSims = localMaxFilterSimMat(sims)
sums = np.sum(maxFilterSims, axis=1)
# sums = np.sum(sims, axis=1)
# print "keeping {} / {} rows".format(np.sum(sums > 1), sims.shape[0])
# numFeaturesPossible = len(lens) * numNeighbors * ts.data.shape[1]
numFeaturesPossible = np.sum(sums > 0) # pure zero reows only if empty
print "keeping {} / {} rows".format(np.sum(sums > 1), numFeaturesPossible)
sims = sims[sums > 1.]
# sims = sims[np.mean(sims, axis=1) > np.max(lengths)]
print "sims shape, stats", sims.shape, np.mean(sims), np.count_nonzero(sims)
sims = ar.centerInMatOfSize(sims, -1, len(ts.data))
viz.imshowBetter(sims, ax=ax2)
colSums = np.sum(sims, axis=0)
ax3.plot(colSums)
ax3.set_xlim((0, len(colSums)))
plt.tight_layout()
if saveDir:
savePath = os.path.join(saveDir, ts.name + '.pdf')
plt.savefig(savePath)
plt.show()
def neighborSimsMain():
import matplotlib.pyplot as plt
from ..datasets import datasets
from ..viz import viz_utils as viz
from ..utils import files
from ..utils import arrays as ar
from scipy import signal
from scipy.ndimage import filters
from ff2 import localMaxFilterSimMat
saveDir = None
# saveDir = 'figs/repr'
if saveDir:
files.ensureDirExists(saveDir)
howMany = 4
instPerTs = 2
# np.random.seed(12345)
np.random.seed(123)
syntheticDatasets = ['triangles', 'shapes', 'rects', 'sines']
# saveDir = 'figs/highlight/tidigits/'
# tsList = datasets.loadDataset('triangles', whichExamples=range(howMany))
# tsList = datasets.loadDatasets(syntheticDatasets, whichExamples=range(howMany))
# tsList = datasets.loadDataset('tidigits_grouped_mfcc', whichExamples=range(howMany))
tsList = datasets.loadDataset('ucr_short',
whichExamples=range(1),
instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_CBF', whichExamples=range(5), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_CBF', whichExamples=range(2), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_Gun_Point', whichExamples=range(2), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_pairs', whichExamples=range(1))
# tsList = datasets.loadDataset('ucr_adiac', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_wafer', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_MedicalImages', whichExamples=range(1), instancesPerTs=instPerTs)
# tsList = datasets.loadDataset('ucr_ECG200', whichExamples=range(1), instancesPerTs=instPerTs)
tsList = datasets.loadDataset('dishwasher_groups',
whichExamples=range(howMany),
instancesPerTs=5)
# tsList = datasets.loadDataset('msrc', whichExamples=range(howMany))
print '------------------------'
# lengths = [16]
# lengths = np.array([1./8])
# lengths = np.array([1./16])
# lengths = np.array([25])
# lengths = np.array([1./32])
# lengths = np.array([1./32, 1./16])
# lengths = np.array([1./64, 1./32])
# lengths = np.array([1./64, 1./32, 1./16])
# lengths = np.array([1./128, 1./64, 1./32, 1./16])
# lengths = np.array([50, 100])
# lengths = np.array([50])
# lengths = np.array([16])
# lengths = np.array([8, 16, 32])
# lengths = np.array([16, 32, 64])
# lengths = np.array([8, 1./20, 1./10])
# from ff8 import logSpacedLengths
# lengths = 2 ** np.arange(4, 8) # 16,...,128
lengths = 2**np.arange(3, 8) # 8,...,128
# lengths = 2 ** np.arange(3, 8, .5) # 8,...,256, step by sqrt(2)
for ts in tsList:
print "running on ts ", ts.name
# print "dim stds:", np.std(ts.data, axis=0)
# ts.data = filters.convolve1d(ts.data, weights=np.hamming(8), axis=0, mode='constant')
# ts.data = ts.data[:, 0]
# ts.data = ts.data[:, [6,10]] # super large values in dishwasher
# ts.data = ts.data[:, [5,9]] # dishwasher pattern dims
# ts.data = ts.data[:, 5]
# ts.data = ts.data[:, 9]
if ts.data.shape[1] > 30: # if msrc, basically
ts.data = ts.data[:, 23:38]
ts.data = ar.ensure2D(ts.data)
lens = np.copy(lengths)
fractionIdxs = lengths < 1.
lens[fractionIdxs] = lengths[fractionIdxs] * len(ts.data)
lens = lens.astype(np.int)
lens = np.unique(lens)
lens = lens[lens >= 8]
lens = lens[lens < len(ts.data) // 4]
print "main(): using lengths", lens, lens.shape, lens.dtype
# continue
# lengths = np.array([25])
plt.figure(figsize=(8, 10))
gridShape = (9, 2)
ax1 = plt.subplot2grid(gridShape, (0, 0), rowspan=2, colspan=2)
ax2 = plt.subplot2grid(gridShape, (2, 0), rowspan=6, colspan=2)
ax3 = plt.subplot2grid(gridShape, (8, 0), rowspan=1, colspan=2)
ts.plot(ax=ax1)
# numNeighbors = np.max(lens)
numNeighbors = np.log2(np.max(lens))
# numNeighbors = int(np.log2(len(ts.data)))
# numNeighbors = 10 * np.max(lens)
# numNeighbors = len(ts.data) - np.min(lens) # "sample" everything
# sims = multiNeighborSims(ts.data, lens, numNeighbors)
sims = multiNeighborSims(ts.data, lens, numNeighbors, maxDist=.25)
# print "after sims", ts.data.shape
# sims = multiNeighborSims(ts.data, lens, numNeighbors, maxDist=.5)
sims2 = multiVariance(ts.data, lens)
# print "after variance", ts.data.shape
# sims3 = multiNormalizeAndSparseQuantize(ts.data, lens)
# diffs = ts.data[1:] - ts.data[:-1]
# diffs = ar.addZeroRows(diffs, howMany=1, prepend=True) # same length as data
# sims4 = multiVariance(diffs, lens)
sims = np.vstack((sims, sims2))
# sims = np.vstack((sims, sims2, sims3))
# sims = np.vstack((sims, sims2, sims3, sims4))
# from ff8 import buildFeatureMat
# # sims = buildFeatureMat(ts.data, 1./16, 1./8, includeNeighbors=True,
# sims = buildFeatureMat(ts.data, 1./16, 1./8, includeNeighbors=True,
# detrend=False, maxDist=.25)
# sims = multiNeighborSims(ts.data, lengths, numNeighbors, maxDist=.5)
# print "nonzeros before localMaxFilter", np.count_nonzero(sims)
# sims = localMaxFilterSimMat(sims) # makes it look much worse
# print "nonzeros after localMaxFilter", np.count_nonzero(sims)
maxFilterSims = localMaxFilterSimMat(sims)
sums = np.sum(maxFilterSims, axis=1)
# sums = np.sum(sims, axis=1)
# print "keeping {} / {} rows".format(np.sum(sums > 1), sims.shape[0])
# numFeaturesPossible = len(lens) * numNeighbors * ts.data.shape[1]
numFeaturesPossible = np.sum(sums > 0) # pure zero reows only if empty
print "keeping {} / {} rows".format(np.sum(sums > 1),
numFeaturesPossible)
sims = sims[sums > 1.]
# sims = sims[np.mean(sims, axis=1) > np.max(lengths)]
print "sims shape, stats", sims.shape, np.mean(sims), np.count_nonzero(
sims)
sims = ar.centerInMatOfSize(sims, -1, len(ts.data))
viz.imshowBetter(sims, ax=ax2)
colSums = np.sum(sims, axis=0)
ax3.plot(colSums)
ax3.set_xlim((0, len(colSums)))
plt.tight_layout()
if saveDir:
savePath = os.path.join(saveDir, ts.name + '.pdf')
plt.savefig(savePath)
plt.show()
def main():
# np.random.seed(123)
# ================================ consts for everything
# consts for generating data
# n = 1000
n = 500
# n = 300
# length = 8
# length = 16
# length = 32
# length = 50
# nInstances = 3
exampleLengths = [55, 60, 65]
# exampleLengths = [60, 60, 60]
noiseStd = .5
# consts for algorithm
# Lmin = max(20, length) # only needed for optimalAlignK() spacing
Lmin = 20 # only needed for optimalAlignK() spacing
Lmax = 100 # loose upper bound on pattern length
# minSim = .5
minSim = 0.
length = Lmin // 2
# length = Lmin // 4
# length = 3
answerIdxs = None
USE_MSRC = True
# USE_MSRC = False
# ================================ data
# ------------------------ synthetic data
# seq = synth.randconst(n, std=noiseStd)
seq = synth.randwalk(n, std=noiseStd)
# seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=4)
# seq = synth.notSoRandomWalk(n, std=noiseStd, trendFilterLength=80, lpfLength=16)
seq = embedExamples(seq, exampleLengths)
# seq = synth.appendZeros(seq, Lmax)
# ------------------------ msrc
if USE_MSRC:
from ..datasets import read_msrc as msrc
# idxs = [0]
# idxs = [1]
# idxs = [2]
# idxs = [7] # length 1500, but instances of length like 20
# idxs = [8] # gets owned on this one cuz patterns of length like 100
# idxs = [9] # missing an annotation, it appears
idxs = [10] # something crazy about feature rep here # TODO fix
# idxs = [11] # crap cuz bad, low-variance signals
# idxs = [12] # has garbagey sections like [10]
# idxs = [13] # empty feature mat # TODO
# idxs = [14]
downsampleBy = 2
# downsampleBy = 1
recordings = msrc.getRecordings(idxs=idxs)
r = list(recordings)[0]
# seq = r.data
# seq = r.data[:, :40]
# seq = r.data[:, 20:23]
seq = r.data[:, 24:27]
# seq = r.data[:, 20:27]
print "orig seq shape", seq.shape
seq = ar.downsampleMat(seq, rowsBy=downsampleBy)
print "downsampled seq shape", seq.shape
# length = max(8, Lmin / 2)
Lmin = len(seq) // 20
# Lmax = len(seq) // 8
Lmax = len(seq) // 10
length = Lmin // 2
# Lmax = len(seq) / 20
# k0 = 10
# minSim = .5
answerIdxs = r.gestureIdxs / downsampleBy
# print "seq shape", seq.shape
prePadLen = Lmax - length
# postPadLen = length - 1
postPadLen = Lmax - length
first = np.tile(seq[0], (prePadLen, 1))
last = np.tile(seq[-1], (postPadLen, 1))
seq = np.vstack(
(first, seq,
last)) # pad with fixed val to allow all window positions
# ^ TODO pad simMat with zeros instead--this introduces fake subseqs
answerIdxs += prePadLen
# seq = np.vstack((seq, np.tile(flat, (length-1, 1)))) # lets it get the last rep
# print "seq shape", seq.shape
# ================================ feature construction
logMaxLength = int(np.floor(np.log2(Lmax)))
# logMaxLength = int(np.ceil(np.log2(Lmax)))
# logMinLength = 3 # -> length 8
# logMinLength = 4 # -> length 16
logMinLength = int(np.floor(np.log2(Lmin)))
lengths = np.arange(logMinLength, logMaxLength + 1)
lengths = 2**lengths
# lengths = [16]
cardinality = 8
breakpoints = rep.saxBreakpoints(cardinality)
X = rep.multiNormalizeAndSparseQuantize(seq, lengths, breakpoints)
# X = rep.multiSparseLineProject(seq, lengths, breakpoints, removeZeroRows=False)
# lengths2 = np.arange(3, logMaxLength + 1)
# lengths2 = 2 ** lengths2
lengths2 = lengths # TODO uncomment after debug
# lengths2 = [8, 32]
# breakpoints2 = rep.defaultSparseLineBreakpoints(seq, scaleHowMany=2)
breakpoints2 = rep.defaultSparseLineBreakpoints(seq)
X2 = rep.multiSparseLineProject(seq, lengths2, breakpoints2)
# X2 = X2 > minSim
X2 = X2 > 0. # ignore correlations
# print "shapes:"
# print X.shape
# print X2.shape
X = np.vstack((X, X2))
# plt.figure()
# # viz.imshowBetter(X)
# viz.imshowBetter(X2)
# plt.figure()
# viz.imshowBetter(X2 > 0.)
# plt.show()
# print seq.shape
# plt.figure()
# plt.plot(seq[:,0]) # bit of pattern, but only varies between -.4 and .2
# okay, so 1st dim is all zeros
# variances = rep.slidingVariance(seq, 8)
# for dim in range(len(variances)):
# plt.figure()
# plt.plot(variances[dim].flatten())
# print variances.shape
# variances = rep.vstack3Tensor(variances.T)
# print variances.shape
# plt.plot(variances)
# plt.show()
# return
X = localMaxFilterSimMat(X)
# Xbool = np.copy(X)
featureMeans = np.mean(X, axis=1).reshape((-1, 1))
# print featureMeans
X *= -np.log2(featureMeans) # variable encoding costs for rows
# X /= -np.log(featureMeans)
# Xblur = localMaxFilterSimMat(X) # try only maxFiltering Xblur
Xblur = filterSimMat(X, length - 1, 'hamming', scaleFilterMethod='max1')
# plt.figure()
# viz.imshowBetter(X)
# plt.figure()
# viz.imshowBetter(Xblur)
print "featureMat dims:", X.shape
Xnonzeros = np.count_nonzero(X)
print "featureMat nonzeros, total, frac = ", Xnonzeros, X.size, Xnonzeros / float(
X.size)
# plt.show()
# return
# ================================ plotting crap
plt.figure()
axSeq = plt.subplot2grid((4, 1), (0, 0))
axSim = plt.subplot2grid((4, 1), (1, 0), rowspan=3)
for ax in (axSeq, axSim):
ax.autoscale(tight=True)
axSeq.plot(seq)
# if answerIdxs is not None:
# for idx in answerIdxs:
# viz.plotVertLine(idx, ax=axSeq)
padLen = len(seq) - X.shape[1]
Xpad = synth.appendZeros(X, padLen)
axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# im = axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
# plt.colorbar(im, cax=axSim)
axSeq.set_title("Time Series")
axSim.set_title("Feature Matrix")
# plt.show()
# return
# ================================ science
# ------------------------ derived stats
kMax = int(X.shape[1] / Lmin + .5)
windowLen = Lmax - length + 1
p0 = np.mean(X) # fraction of entries that are 1 (roughly)
# p0 = 2 * np.mean(X) # lambda for l0 reg based on features being bernoulli at 2 locs
minSim = p0
# p0 = -np.log(np.mean(Xbool)) # fraction of entries that are 1 (roughly)
# noiseSz = p0 * X.shape[0] * windowLen # way too hard to beat
expectedOnesPerWindow = p0 * X.shape[0] * windowLen
noiseSz = p0 * expectedOnesPerWindow # num ones to begin with
# intersections = computeIntersections(X, windowLen)
# windowSims = np.sum(intersections, axis=2)
# colSims = np.dot(X.T, X)
colSims = np.dot(X.T, Xblur)
filt = np.zeros((windowLen, windowLen)) + np.diag(
np.ones(windowLen)) # zeros except 1s on diag
windowSims = sig.convolve2d(colSims, filt, mode='valid')
windowVects = vectorizeWindowLocs(X, windowLen)
windowVectsBlur = vectorizeWindowLocs(Xblur, windowLen)
# plt.figure()
# plt.imshow(windowSims, interpolation='nearest', aspect='auto')
# ------------------------ find stuff
#
# Version where we look for similarities to orig seq and use nearest
# enemy dist as M0, and use mean values instead of intersection
#
bsfScore = 0
bsfLocs = None
bsfIntersection = None
for i, row in enumerate(windowSims):
if i % 20 == 0:
print("computing stuff for row {}".format(i))
# early abandon if this location has so little stuff that no
# intersection with it can possibly beat the best score
if windowSims[
i,
i] * kMax <= bsfScore: # highest score is kMax identical locs
continue
# best combination of idxs such that none are within Lmin of each other
# validRow = row[:(-length + 1)] # can't go past end of ts
# idxs = sub.optimalAlignment(validRow, Lmin)
idxs = sub.optimalAlignment(row,
Lmin) # goes past end of ts, but better
# order idxs by descending order of associated score
sizes = windowSims[i, idxs]
sortedSizesOrder = np.argsort(sizes)[::-1]
sortedIdxs = idxs[sortedSizesOrder]
# iteratively intersect with another near neighbor, compute the
# associated score, and check if it's better (or if we can early abandon)
intersection = windowVects[i]
numIdxs = len(sortedIdxs)
nextSz = np.sum(intersection)
nextFilt = np.array(intersection, dtype=np.float)
nextFiltSum = np.array(nextFilt, dtype=np.float)
for j, idx in enumerate(sortedIdxs):
k = j + 1
filt = np.copy(nextFilt)
sz = nextSz
if k < numIdxs:
nextIdx = sortedIdxs[k] # since k = j+1
nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
nextFiltSum += nextIntersection
nextFilt = nextFiltSum / (
k + 1) # avg value of each feature in intersections
# nextSz = np.sum(nextFilt) # big even if like no intersection...
nextSz = np.sum(nextIntersection)
bigEnoughIntersection = nextIntersection[
nextIntersection > minSim]
nextSz = np.sum(bigEnoughIntersection)
else:
nextSz = sz * p0
# nextSz = -1
enemySz = max(nextSz, noiseSz)
score = (sz - enemySz) * k
if k > 1 and score > bsfScore:
print("window {0}, k={1}, score={2} is the new best!".format(
i, k, score))
print("sortedIdxs = {}".format(str(sortedIdxs)))
print("sortedIdxScores = {}".format(
str(windowSims[i, sortedIdxs])))
print("------------------------")
bsfScore = score
bsfLocs = sortedIdxs[:k]
bsfIntersection = np.copy(filt)
# early abandon if this can't possibly beat the best score, which
# is the case exactly when the intersection is so small that perfect
# matches at all future locations still wouldn't be good enough
elif sz * numIdxs <= bsfScore:
# TODO can we actually early abandon here? next window loc
# could increase filt, and thus score for a given loc isn't
# necessarily non-increasing...
# -can't abandon using this test, but pretty sure there's
# a lower bound to be had here somewhere
# print("early abandoning window {} at k={}".format(i, k))
break
elif noiseSz > nextSz:
break
# #
# # Version where we look for similarities to orig seq and use nearest
# # enemy dist as M0, and use mean values instead of intersection,
# # and don't sort the indices, but instead care about overlap
# #
# bsfScore = 0
# bsfLocs = None
# bsfIntersection = None
# for i, row in enumerate(windowSims):
# if i % 20 == 0:
# print("computing stuff for row {}".format(i))
# # early abandon if this location has so little stuff that no
# # intersection with it can possibly beat the best score
# if windowSims[i,i] * kMax <= bsfScore: # highest score is kMax identical locs
# continue
# # best combination of idxs such that none are within Lmin of each other
# # validRow = row[:(-length + 1)] # can't go past end of ts
# # idxs = sub.optimalAlignment(validRow, Lmin)
# idxs = sub.optimalAlignment(row, Lmin) # goes past end of ts, but better
# # order idxs by descending order of associated score
# sizes = windowSims[i, idxs]
# sortedSizesOrder = np.argsort(sizes)[::-1]
# sortedIdxs = idxs[sortedSizesOrder]
# # iteratively intersect with another near neighbor, compute the
# # associated score, and check if it's better (or if we can early abandon)
# intersection = windowVects[i]
# numIdxs = len(sortedIdxs)
# nextSz = np.sum(intersection)
# nextFilt = np.array(intersection, dtype=np.float)
# nextFiltSum = np.array(nextFilt, dtype=np.float)
# for j, idx in enumerate(sortedIdxs):
# k = j + 1
# filt = np.copy(nextFilt)
# sz = nextSz
# if k < numIdxs:
# nextIdx = sortedIdxs[k] # since k = j+1
# nextIntersection = np.minimum(filt, windowVectsBlur[nextIdx])
# nextFiltSum += nextIntersection
# nextFilt = nextFiltSum / (k+1) # avg value of each feature in intersections
# # nextSz = np.sum(nextFilt) # big even if like no intersection...
# nextSz = np.sum(nextIntersection)
# bigEnoughIntersection = nextIntersection[nextIntersection > minSim]
# nextSz = np.sum(bigEnoughIntersection)
# else:
# nextSz = sz * p0
# score = (sz - nextSz) * k
# if k > 1 and score > bsfScore:
# print("window {0}, k={1}, score={2} is the new best!".format(i, k, score))
# print("sortedIdxs = {}".format(str(sortedIdxs)))
# print("sortedIdxScores = {}".format(str(windowSims[i, sortedIdxs])))
# print("------------------------")
# bsfScore = score
# bsfLocs = sortedIdxs[:k]
# bsfIntersection = np.copy(filt)
# # early abandon if this can't possibly beat the best score, which
# # is the case exactly when the intersection is so small that perfect
# # matches at all future locations still wouldn't be good enough
# elif sz * numIdxs <= bsfScore:
# # TODO can we actually early abandon here? next window loc
# # could increase filt, and thus score for a given loc isn't
# # necessarily non-increasing...
# # -can't abandon using this test, but pretty sure there's
# # a lower bound to be had here somewhere
# # print("early abandoning window {} at k={}".format(i, k))
# break
# ------------------------ recover original ts
bsfIntersection *= bsfIntersection >= minSim
bsfIntersectionWindow = bsfIntersection.reshape((-1, windowLen))
sums = np.sum(bsfIntersectionWindow, axis=0)
kBest = len(bsfLocs)
p0 = np.power(p0, kBest)
# expectedOnesPerCol = p0 * X.shape[1] * 2
# expectedOnesPerCol = p0 * X.shape[1]
expectedOnesPerCol = p0 * X.shape[0]
sums -= expectedOnesPerCol
# plt.figure()
# plt.plot(sums)
start, end, _ = maxSubarray(sums)
# patStart, patEnd = start, end + 1 + length
patStart, patEnd = start, end + 1
# patStart, patEnd = start + length // 2, end + 1 + length
# ================================ show output
print "bestScore = {}".format(bsfScore)
print "bestLocations = {}".format(str(bsfLocs))
for idx in bsfLocs:
viz.plotRect(axSim, idx, idx + windowLen)
# print bsfIntersectionWindow.shape
# print sums.shape
# plt.plot(sums)
# viz.plotRect(plt.gca(), start, end + 1)
for idx in bsfLocs:
viz.plotRect(axSeq, idx + patStart, idx + patEnd)
if answerIdxs is not None:
for idx in answerIdxs:
viz.plotVertLine(idx, ax=axSeq)
plt.figure()
plt.imshow(bsfIntersectionWindow, interpolation='nearest', aspect='auto')
plt.tight_layout()
plt.show()