def findOutcastInstances(Xnorm,
seed,
length,
maxOverlapFraction=.1,
fromSeq=None):
minSpacing = max(int((1. - maxOverlapFraction) * length), 1)
dists = distsToRows(Xnorm, seed)
minimaIdxs = nonOverlappingMinima(dists, minSpacing, fromSeq=fromSeq)
minimaDists = dists[minimaIdxs]
# sort indices of relative minima in increasing order of distance
sortIdxs = np.argsort(minimaDists)
idxs = minimaIdxs[sortIdxs]
dists = minimaDists[sortIdxs]
centroidSums = seed
centroid = np.copy(seed)
distSum_pattern = 0
vectLen = len(seed)
bestGap = -np.inf
bestIdxs = None
for i, idx in enumerate(idxs[1:]):
k = i + 2.
# pattern model
x = Xnorm[idx]
diff = centroid - x
distSum_pattern += np.dot(diff, diff) / vectLen
centroidSums += x
centroid = centroidSums / k
# random walk
AVG_DIST_TO_RAND_WALK = 1.
# AVG_DIST_TO_RAND_WALK = .5
distSum_walk = AVG_DIST_TO_RAND_WALK * k
# nearest enemy
distSum_enemy = np.inf
if k < len(idxs):
nextIdx = idxs[k]
nextX = Xnorm[nextIdx]
diff_enemy = centroid - nextX
distSum_enemy = np.dot(diff_enemy, diff_enemy) / vectLen * k
rivalSum = min(distSum_walk, distSum_enemy)
gap = rivalSum - distSum_pattern
if gap > bestGap:
bestGap = gap
bestIdxs = idxs[:k]
return OutcastInfo(score=bestGap,
idxs=bestIdxs,
length=length,
fromSeq=fromSeq)
def old_findOutcastInstances(Xnorm, seed, length, maxOverlapFraction=0.1, fromSeq=None):
minSpacing = max(int((1.0 - maxOverlapFraction) * length), 1)
dists = distsToRows(Xnorm, seed)
minimaIdxs = nonOverlappingMinima(dists, minSpacing, fromSeq=fromSeq)
# invertMinimaIdxs = np.arange(len(dists))[minimaIdxs]
# print "dists shape: ", dists.shape
# print "found minimaIdxs: ", minimaIdxs
minimaDists = dists[minimaIdxs]
# sort indices of relative minima in increasing order of distance
# TODO use a min heap, since that's O(n) and this is O(nlgn)
sortIdxs = np.argsort(minimaDists)
# unsortIdxs = np.arange(len(minimaDists))[sortIdxs]
minimaIdxs = minimaIdxs[sortIdxs]
minimaDists = minimaDists[sortIdxs]
# initialize with best pair so we don't return anomalies
idxs = [minimaIdxs[0], minimaIdxs[1]]
# totalDist = 2 * minimaDists[1] # don't count self distance, since 0
# maxDist = minimaDists[1]
dist = minimaDists[1]
nextIdx, nextDist = minimaIdxs[2], minimaDists[2]
# bestScore = nextDist * len(idxs) - totalDist
# bestScore = (nextDist - dist) * len(idxs)
# bestScore = (nextDist - dist) * np.log(len(idxs))
bestScore = (nextDist / dist) * np.log(len(idxs))
bestIdxs = idxs[:]
np.set_printoptions(precision=0)
# print "minimaDists:", minimaDists
print "minima diffs:", np.r_[0, minimaDists[1:] - minimaDists[:-1]]
for i in range(2, len(minimaIdxs) - 1):
idx, dist = nextIdx, nextDist
nextIdx, nextDist = minimaIdxs[i + 1], minimaDists[i + 1]
idxs.append(idx)
# totalDist += dist
# score = nextDist * len(idxs) - totalDist
# score = (nextDist - dist) * len(idxs)
# score = (nextDist - dist) * np.log(len(idxs))
score = (nextDist / dist) * np.log(len(idxs))
if score > bestScore:
# print "new best score {} for idxs {}".format(score, idxs)
bestScore = score
bestIdxs = idxs[:]
# else:
# break
bestIdxs = sorted(bestIdxs)
return OutcastInfo(score=bestScore, idxs=bestIdxs, length=length, fromSeq=fromSeq)
def findOutcastInstances(Xnorm, seed, length, maxOverlapFraction=0.1, fromSeq=None):
minSpacing = max(int((1.0 - maxOverlapFraction) * length), 1)
dists = distsToRows(Xnorm, seed)
minimaIdxs = nonOverlappingMinima(dists, minSpacing, fromSeq=fromSeq)
minimaDists = dists[minimaIdxs]
# sort indices of relative minima in increasing order of distance
sortIdxs = np.argsort(minimaDists)
idxs = minimaIdxs[sortIdxs]
dists = minimaDists[sortIdxs]
centroidSums = seed
centroid = np.copy(seed)
distSum_pattern = 0
vectLen = len(seed)
bestGap = -np.inf
bestIdxs = None
for i, idx in enumerate(idxs[1:]):
k = i + 2.0
# pattern model
x = Xnorm[idx]
diff = centroid - x
distSum_pattern += np.dot(diff, diff) / vectLen
centroidSums += x
centroid = centroidSums / k
# random walk
AVG_DIST_TO_RAND_WALK = 1.0
# AVG_DIST_TO_RAND_WALK = .5
distSum_walk = AVG_DIST_TO_RAND_WALK * k
# nearest enemy
distSum_enemy = np.inf
if k < len(idxs):
nextIdx = idxs[k]
nextX = Xnorm[nextIdx]
diff_enemy = centroid - nextX
distSum_enemy = np.dot(diff_enemy, diff_enemy) / vectLen * k
rivalSum = min(distSum_walk, distSum_enemy)
gap = rivalSum - distSum_pattern
if gap > bestGap:
bestGap = gap
bestIdxs = idxs[:k]
return OutcastInfo(score=bestGap, idxs=bestIdxs, length=length, fromSeq=fromSeq)
def findOutcastInstancesMDL(Xnorm, seed, length, maxOverlapFraction=0.1, fromSeq=None, mdlBits=6, useEnemy=True):
minSpacing = max(int((1.0 - maxOverlapFraction) * length), 1)
dists = distsToRows(Xnorm, seed)
minimaIdxs = nonOverlappingMinima(dists, minSpacing, fromSeq=fromSeq)
minimaDists = dists[minimaIdxs]
# sort indices of relative minima in increasing order of distance
sortIdxs = np.argsort(minimaDists)
idxs = minimaIdxs[sortIdxs]
dists = minimaDists[sortIdxs]
# instanceIdxs = [idx1, idx2]
# compute quantized subsequences
numLevels = int(2 ** mdlBits)
mins = np.min(Xnorm, axis=1).reshape((-1, 1))
maxs = np.max(Xnorm, axis=1).reshape((-1, 1))
ranges = maxs - mins
Xquant = (Xnorm - mins) / ranges * (numLevels - 1) # 8 bits -> {0..255}
Xquant = Xquant.astype(np.int)
# initialize MDL stats
row = Xquant[idxs[0]]
centroidSums = np.copy(row)
hypothesisEnt = entropy(row)
origEnt = hypothesisEnt
bitsave = -np.inf # ensure 2nd subseq gets added
instanceIdxs = [idxs[0]]
for i, idx in enumerate(idxs[1:]):
k = i + 2.0
subseq = Xquant[idx]
# compute original entropy of this instance along with current ones
newOrigEnt = origEnt + entropy(subseq)
# compute centroid when this instance is added
newCentroidSums = centroidSums + subseq
newCentroid = (newCentroidSums / k).astype(np.int)
# compute coded entropy when this instance is added
newInstanceIdxs = instanceIdxs[:]
newInstanceIdxs.append(idx)
# diffs = Xquant[instanceIdxs] - newCentroid # works better, but nonsensical
diffs = Xquant[newInstanceIdxs] - newCentroid
newCodedEnt = np.sum(entropy(diffs, axis=1))
# compute total bitsave if this instance is added
newCodingSave = newOrigEnt - newCodedEnt
newHypothesisEnt = entropy(newCentroid)
newBitsave = newCodingSave - newHypothesisEnt
# divide by 2 as heuristic to reduce entropy, since description length
# doesn't correspond to any obvious probabilistic model
# noiseDiffs = Xquant[newInstanceIdxs] // 2
# noiseCodedEnt = np.sum(entropy(noiseDiffs, axis=1))
noiseCodedEnt = newCodedEnt / 2
enemyCodedEnt = -np.inf
if k < len(idxs):
nextIdx = idxs[k]
enemySubseq = Xquant[nextIdx]
enemyDiffs = Xquant[newInstanceIdxs] - enemySubseq
enemyCodedEnt = np.sum(entropy(enemyDiffs, axis=1))
rivalEnt = min(noiseCodedEnt, enemyCodedEnt)
newBitsave += rivalEnt
if newBitsave > bitsave:
bitsave = newBitsave
origEnt = newOrigEnt
centroidSums = newCentroidSums
instanceIdxs = newInstanceIdxs
# else:
# break
bestIdxs = sorted(instanceIdxs)
return OutcastInfo(score=bitsave, idxs=bestIdxs, length=length, fromSeq=fromSeq)
def old_findOutcastInstances(Xnorm,
seed,
length,
maxOverlapFraction=.1,
fromSeq=None):
minSpacing = max(int((1. - maxOverlapFraction) * length), 1)
dists = distsToRows(Xnorm, seed)
minimaIdxs = nonOverlappingMinima(dists, minSpacing, fromSeq=fromSeq)
# invertMinimaIdxs = np.arange(len(dists))[minimaIdxs]
# print "dists shape: ", dists.shape
# print "found minimaIdxs: ", minimaIdxs
minimaDists = dists[minimaIdxs]
# sort indices of relative minima in increasing order of distance
# TODO use a min heap, since that's O(n) and this is O(nlgn)
sortIdxs = np.argsort(minimaDists)
# unsortIdxs = np.arange(len(minimaDists))[sortIdxs]
minimaIdxs = minimaIdxs[sortIdxs]
minimaDists = minimaDists[sortIdxs]
# initialize with best pair so we don't return anomalies
idxs = [minimaIdxs[0], minimaIdxs[1]]
# totalDist = 2 * minimaDists[1] # don't count self distance, since 0
# maxDist = minimaDists[1]
dist = minimaDists[1]
nextIdx, nextDist = minimaIdxs[2], minimaDists[2]
# bestScore = nextDist * len(idxs) - totalDist
# bestScore = (nextDist - dist) * len(idxs)
# bestScore = (nextDist - dist) * np.log(len(idxs))
bestScore = (nextDist / dist) * np.log(len(idxs))
bestIdxs = idxs[:]
np.set_printoptions(precision=0)
# print "minimaDists:", minimaDists
print "minima diffs:", np.r_[0, minimaDists[1:] - minimaDists[:-1]]
for i in range(2, len(minimaIdxs) - 1):
idx, dist = nextIdx, nextDist
nextIdx, nextDist = minimaIdxs[i + 1], minimaDists[i + 1]
idxs.append(idx)
# totalDist += dist
# score = nextDist * len(idxs) - totalDist
# score = (nextDist - dist) * len(idxs)
# score = (nextDist - dist) * np.log(len(idxs))
score = (nextDist / dist) * np.log(len(idxs))
if score > bestScore:
# print "new best score {} for idxs {}".format(score, idxs)
bestScore = score
bestIdxs = idxs[:]
# else:
# break
bestIdxs = sorted(bestIdxs)
return OutcastInfo(score=bestScore,
idxs=bestIdxs,
length=length,
fromSeq=fromSeq)
def findOutcastInstancesMDL(Xnorm,
seed,
length,
maxOverlapFraction=.1,
fromSeq=None,
mdlBits=6,
useEnemy=True):
minSpacing = max(int((1. - maxOverlapFraction) * length), 1)
dists = distsToRows(Xnorm, seed)
minimaIdxs = nonOverlappingMinima(dists, minSpacing, fromSeq=fromSeq)
minimaDists = dists[minimaIdxs]
# sort indices of relative minima in increasing order of distance
sortIdxs = np.argsort(minimaDists)
idxs = minimaIdxs[sortIdxs]
dists = minimaDists[sortIdxs]
# instanceIdxs = [idx1, idx2]
# compute quantized subsequences
numLevels = int(2**mdlBits)
mins = np.min(Xnorm, axis=1).reshape((-1, 1))
maxs = np.max(Xnorm, axis=1).reshape((-1, 1))
ranges = (maxs - mins)
Xquant = (Xnorm - mins) / ranges * (numLevels - 1) # 8 bits -> {0..255}
Xquant = Xquant.astype(np.int)
# initialize MDL stats
row = Xquant[idxs[0]]
centroidSums = np.copy(row)
hypothesisEnt = entropy(row)
origEnt = hypothesisEnt
bitsave = -np.inf # ensure 2nd subseq gets added
instanceIdxs = [idxs[0]]
for i, idx in enumerate(idxs[1:]):
k = i + 2.
subseq = Xquant[idx]
# compute original entropy of this instance along with current ones
newOrigEnt = origEnt + entropy(subseq)
# compute centroid when this instance is added
newCentroidSums = centroidSums + subseq
newCentroid = (newCentroidSums / k).astype(np.int)
# compute coded entropy when this instance is added
newInstanceIdxs = instanceIdxs[:]
newInstanceIdxs.append(idx)
# diffs = Xquant[instanceIdxs] - newCentroid # works better, but nonsensical
diffs = Xquant[newInstanceIdxs] - newCentroid
newCodedEnt = np.sum(entropy(diffs, axis=1))
# compute total bitsave if this instance is added
newCodingSave = newOrigEnt - newCodedEnt
newHypothesisEnt = entropy(newCentroid)
newBitsave = newCodingSave - newHypothesisEnt
# divide by 2 as heuristic to reduce entropy, since description length
# doesn't correspond to any obvious probabilistic model
# noiseDiffs = Xquant[newInstanceIdxs] // 2
# noiseCodedEnt = np.sum(entropy(noiseDiffs, axis=1))
noiseCodedEnt = newCodedEnt / 2
enemyCodedEnt = -np.inf
if k < len(idxs):
nextIdx = idxs[k]
enemySubseq = Xquant[nextIdx]
enemyDiffs = Xquant[newInstanceIdxs] - enemySubseq
enemyCodedEnt = np.sum(entropy(enemyDiffs, axis=1))
rivalEnt = min(noiseCodedEnt, enemyCodedEnt)
newBitsave += rivalEnt
if newBitsave > bitsave:
bitsave = newBitsave
origEnt = newOrigEnt
centroidSums = newCentroidSums
instanceIdxs = newInstanceIdxs
# else:
# break
bestIdxs = sorted(instanceIdxs)
return OutcastInfo(score=bitsave,
idxs=bestIdxs,
length=length,
fromSeq=fromSeq)
