def pairwiseDists(seqs,
length,
norm='each',
tieDims=False,
pad=True,
removeZeros=True,
k=-1):
seqs = asListOrTuple(seqs)
nDims = 1
if len(seqs[0].shape) < 2 or tieDims:
Xnorm, _, _ = window.flattened_subseqs_of_length(seqs,
length,
norm=norm)
else:
nDims = seqs[0].shape[1]
# bypass flattening--each dim of each seq is treated as a separate
# 1D seq; we end up with a long list whose elements are 1D vectors,
# each of which was originally a column within some ND array in seqs
#
# note that this may do weird things if there's more than one seq
# because the dims for each seq are sequential, rather than the seqs
# for each dim
separatedByDim = map(lambda X: colsAsList(X), seqs)
flatSeqs = flattenListOfLists(separatedByDim)
flatSeqs = map(lambda v: v.flatten(),
flatSeqs) # col vects -> 1D arrays
Xnorm, _, _ = window.flattened_subseqs_of_length(flatSeqs,
length,
norm='each')
nSamples, m = Xnorm.shape
rowsPerDim = nSamples / nDims
print "----- pairwiseDists"
print "length", length
print "origSeqs[0] shape", seqs[0].shape
print "nsamples, m, rowsPerDim", Xnorm.shape, rowsPerDim
print "-----"
if pad:
paddingLen = length - 1
else:
paddingLen = 0
# print "Xnorm stats:", np.mean(Xnorm, axis=1), np.std(Xnorm, axis=1)
# D = np.zeros((nSamples, nSamples+paddingLen*nDims)) # 0 pad at end so samples line up
Dtensor = np.zeros((nDims, rowsPerDim, rowsPerDim + paddingLen))
# D = np.zeros((nSamples, nSamples))
maxPossibleDist = 2**2 * m
maxIdx = 0
for dim in range(nDims):
# extract subseqs associated with this dim
minIdx = maxIdx
maxIdx += rowsPerDim
Xdim = Xnorm[minIdx:maxIdx]
# compute dists to each one
for i, row in enumerate(Xdim):
if removeZeros:
if np.sum(row * row) < 1.e-6:
Dtensor[dim, i, :rowsPerDim] = maxPossibleDist
continue
diffs = Xdim - row
diffs_sq = diffs * diffs
# dMinIdx = minIdx + dim*paddingLen
# dMaxIdx = dMinIdx + rowsPerDim
dists = np.sum(diffs_sq, axis=1)
# D[minIdx + i, dMinIdx:dMaxIdx] = dists
Dtensor[dim, i, :rowsPerDim] = dists
# only keep k lowest dists
if k > 0:
for j in np.arange(rowsPerDim):
col = Dtensor[dim, :, j]
highestIdxs = np.argsort(col)[k:]
Dtensor[dim, highestIdxs, j] = maxPossibleDist
# return Dtensor, D, Xnorm
return Dtensor, Xnorm
def uniqueSubseqsInSignals(signal, length, maxDist, norm='each', tree=None):
X, _, _ = window.flattened_subseqs_of_length(signal, length, norm=norm)
Xnorm = zNormalizeRows(X, removeZeros=False)
# print("subseqsInSignals: signal has %d subseqs" % (len(Xnorm)))
# init kd tree--we can't give it any data yet because we only want to
# search through seqs that have been added to the dictionary
if tree is None:
width = Xnorm.shape[1]
tree = kd.create(dimensions=width)
signalOccurIdxs = {}
tree.add(Xnorm[0], 0)
for startIdx, subseq in enumerate(Xnorm[1:]):
if np.sum(subseq * subseq) < .001: # ignore zero seqs
continue
startIdx += 1 # since we skipped Xnorm[0]
neighbors = tree.search_knn(subseq, 2)
neighborIdx = -1
neighborDist = np.inf
# pull out whichever neighbor isn't the query
for node, dist in neighbors:
idx = node.metadata
if idx != startIdx:
neighborIdx = idx
neighborDist = dist
if neighborIdx < 0:
print "ERROR: knn returned <2 neighbors..."
print "Neighbors returned:", neighbors
assert (0)
# print "neighborDist", neighborDist, maxDist
if neighborDist < maxDist:
# store that the subseq happened at this idx too
l = signalOccurIdxs.get(neighborIdx, [])
l.append(startIdx)
# signalOccurIdxs[neighborIdx] = l
else:
# ah, so this can overwrite crap and yield too few features
signalOccurIdxs[startIdx] = [startIdx]
tree.add(subseq, startIdx)
# rebalance if startIdx is a power of 2, so we do so log(N) times
if 2**int(np.log2(startIdx)) == startIdx:
# print "rebalancing at start idx %d" % (startIdx,)
tree.rebalance()
# signalOccurIdxs[neighborIdx] = [startIdx]
# if res:
# nn, dist = res
# if dist <= maxDist:
# # store that the subseq happened at this idx too
# neighborID = nn.metadata
# signalOccurIdxs[neighborID].append(startIdx)
# continue
# neighborID = startIdx
# signalOccurIdxs[neighborID] = [startIdx]
# tree.add(subseq, neighborID)
return signalOccurIdxs, Xnorm # return Xnorm for convenience, although confusing...
def allZNormalizedSubseqs(seqs, length):
X, _, _ = window.flattened_subseqs_of_length(seqs, length, norm='each')
return zNormalizeRows(X, removeZeros=False)
def pairwiseDists(seqs, length, norm='each', tieDims=False, pad=True, removeZeros=True, k=-1): seqs = asListOrTuple(seqs) nDims = 1 if len(seqs[0].shape) < 2 or tieDims: Xnorm, _, _ = window.flattened_subseqs_of_length(seqs, length, norm=norm) else: nDims = seqs[0].shape[1] # bypass flattening--each dim of each seq is treated as a separate # 1D seq; we end up with a long list whose elements are 1D vectors, # each of which was originally a column within some ND array in seqs # # note that this may do weird things if there's more than one seq # because the dims for each seq are sequential, rather than the seqs # for each dim separatedByDim = map(lambda X: colsAsList(X), seqs) flatSeqs = flattenListOfLists(separatedByDim) flatSeqs = map(lambda v: v.flatten(), flatSeqs) # col vects -> 1D arrays Xnorm, _, _ = window.flattened_subseqs_of_length(flatSeqs, length, norm='each') nSamples, m = Xnorm.shape rowsPerDim = nSamples / nDims print "----- pairwiseDists" print "length", length print "origSeqs[0] shape", seqs[0].shape print "nsamples, m, rowsPerDim", Xnorm.shape, rowsPerDim print "-----" if pad: paddingLen = length - 1 else: paddingLen = 0 # print "Xnorm stats:", np.mean(Xnorm, axis=1), np.std(Xnorm, axis=1) # D = np.zeros((nSamples, nSamples+paddingLen*nDims)) # 0 pad at end so samples line up Dtensor = np.zeros((nDims, rowsPerDim, rowsPerDim+paddingLen)) # D = np.zeros((nSamples, nSamples)) maxPossibleDist = 2**2 * m maxIdx = 0 for dim in range(nDims): # extract subseqs associated with this dim minIdx = maxIdx maxIdx += rowsPerDim Xdim = Xnorm[minIdx:maxIdx] # compute dists to each one for i, row in enumerate(Xdim): if removeZeros: if np.sum(row*row) < 1.e-6: Dtensor[dim, i, :rowsPerDim] = maxPossibleDist continue diffs = Xdim - row diffs_sq = diffs * diffs # dMinIdx = minIdx + dim*paddingLen # dMaxIdx = dMinIdx + rowsPerDim dists = np.sum(diffs_sq, axis=1) # D[minIdx + i, dMinIdx:dMaxIdx] = dists Dtensor[dim, i,:rowsPerDim] = dists # only keep k lowest dists if k > 0: for j in np.arange(rowsPerDim): col = Dtensor[dim, :, j] highestIdxs = np.argsort(col)[k:] Dtensor[dim, highestIdxs, j] = maxPossibleDist # return Dtensor, D, Xnorm return Dtensor, Xnorm
def uniqueSubseqsInSignals(signal, length, maxDist, norm='each', tree=None):
X, _, _ = window.flattened_subseqs_of_length(signal, length, norm=norm)
Xnorm = zNormalizeRows(X, removeZeros=False)
# print("subseqsInSignals: signal has %d subseqs" % (len(Xnorm)))
# init kd tree--we can't give it any data yet because we only want to
# search through seqs that have been added to the dictionary
if tree is None:
width = Xnorm.shape[1]
tree = kd.create(dimensions=width)
signalOccurIdxs = {}
tree.add(Xnorm[0], 0)
for startIdx, subseq in enumerate(Xnorm[1:]):
if np.sum(subseq*subseq) < .001: # ignore zero seqs
continue
startIdx += 1 # since we skipped Xnorm[0]
neighbors = tree.search_knn(subseq, 2)
neighborIdx = -1
neighborDist = np.inf
# pull out whichever neighbor isn't the query
for node, dist in neighbors:
idx = node.metadata
if idx != startIdx:
neighborIdx = idx
neighborDist = dist
if neighborIdx < 0:
print "ERROR: knn returned <2 neighbors..."
print "Neighbors returned:", neighbors
assert(0)
# print "neighborDist", neighborDist, maxDist
if neighborDist < maxDist:
# store that the subseq happened at this idx too
l = signalOccurIdxs.get(neighborIdx, [])
l.append(startIdx)
# signalOccurIdxs[neighborIdx] = l
else:
# ah, so this can overwrite crap and yield too few features
signalOccurIdxs[startIdx] = [startIdx]
tree.add(subseq, startIdx)
# rebalance if startIdx is a power of 2, so we do so log(N) times
if 2**int(np.log2(startIdx)) == startIdx:
# print "rebalancing at start idx %d" % (startIdx,)
tree.rebalance()
# signalOccurIdxs[neighborIdx] = [startIdx]
# if res:
# nn, dist = res
# if dist <= maxDist:
# # store that the subseq happened at this idx too
# neighborID = nn.metadata
# signalOccurIdxs[neighborID].append(startIdx)
# continue
# neighborID = startIdx
# signalOccurIdxs[neighborID] = [startIdx]
# tree.add(subseq, neighborID)
return signalOccurIdxs, Xnorm # return Xnorm for convenience, although confusing...
def allZNormalizedSubseqs(seqs, length): X, _, _ = window.flattened_subseqs_of_length(seqs, length, norm='each') return zNormalizeRows(X, removeZeros=False)
