def vizInterSequenceClusters(traces, outputDir, cellsType, numCells,
numClasses, ignoreNoise=True):
clusters = assignClusters(traces)
# compare inter-cluster distance over time
numRptsPerCategory = {}
categories = np.unique(traces['actualCategory'])
repetition = np.array(clusters['repetition'])
for category in categories:
numRptsPerCategory[category] = np.max(
repetition[np.array(traces['actualCategory']) == category])
SDRclusters = []
clusterAssignments = []
numRptsMin = np.min(numRptsPerCategory.values()).astype('int32')
for rpt in range(numRptsMin + 1):
idx0 = np.logical_and(np.array(traces['actualCategory']) == 0,
repetition == rpt)
idx1 = np.logical_and(np.array(traces['actualCategory']) == 1,
repetition == rpt)
idx2 = np.logical_and(np.array(traces['actualCategory']) == 2,
repetition == rpt)
c0slice = [traces['tmPredictedActiveCells'][i] for i in range(len(idx0)) if
idx0[i]]
c1slice = [traces['tmPredictedActiveCells'][i] for i in range(len(idx1)) if
idx1[i]]
c2slice = [traces['tmPredictedActiveCells'][i] for i in range(len(idx2)) if
idx2[i]]
if not ignoreNoise:
SDRclusters.append(c0slice)
clusterAssignments.append(0)
SDRclusters.append(c1slice)
clusterAssignments.append(1)
SDRclusters.append(c2slice)
clusterAssignments.append(2)
print " Presentation #{}: ".format(rpt)
if not ignoreNoise:
d01 = clusterDist(c0slice, c1slice, numCells)
print '=> d(c0, c1): %s' % d01
d02 = clusterDist(c0slice, c2slice, numCells)
print '=> d(c0, c2): %s' % d02
d12 = clusterDist(c1slice, c2slice, numCells)
print '=> d(c1, c2): %s' % d12
npos, distanceMat = projectClusters2D(SDRclusters, numCells)
title = 'Inter-sequence clusters in 2D (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
viz2DProjection(title, outputFile, numClasses, clusterAssignments, npos)
title = 'Inter-sequence clusters distances (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
plotDistanceMat(distanceMat, title, outputFile)
def vizInterCategoryClusters(traces, outputDir, cellsType,
numCells, pointsToPlot=100):
sdrs = convertNonZeroToSDR(traces[cellsType][-pointsToPlot:], numCells)
clusterAssignments = traces['actualCategory'][-pointsToPlot:]
numClasses = len(set(clusterAssignments))
npos, distanceMat = project2D(sdrs)
title = 'Actual category clusters in 2D (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
viz2DProjection(title, outputFile, numClasses, clusterAssignments, npos)
title = 'Actual category clusters distances (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
plotDistanceMat(distanceMat, title, outputFile)
def vizInterCategoryClusters(traces,
outputDir,
cellsType,
numCells,
pointsToPlot=100):
sdrs = convertNonZeroToSDR(traces[cellsType][-pointsToPlot:], numCells)
clusterAssignments = traces['actualCategory'][-pointsToPlot:]
numClasses = len(set(clusterAssignments))
npos, distanceMat = project2D(sdrs)
title = 'Actual category clusters in 2D (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
viz2DProjection(title, outputFile, numClasses, clusterAssignments, npos)
title = 'Actual category clusters distances (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
plotDistanceMat(distanceMat, title, outputFile)
def main():
numClasses = 7
numSDRsPerClass = 20
noiseLevel = 0.1
vizTitle = 'MDS, noise level: {}'.format(noiseLevel)
# SDR parameters
n = 1024
w = 20
sdrs = generateSDRs(numClasses, numSDRsPerClass, n, w, noiseLevel)
clusterAssignments = assignClusters(sdrs, numClasses, numSDRsPerClass)
npos, distanceMat = project2D(sdrs)
viz2DProjection(vizTitle, numClasses, clusterAssignments, npos)
plotDistanceMat(distanceMat)
def main():
numClasses = 7
numSDRsPerClass = 20
noiseLevel = 0.1
vizTitle = 'MDS, noise level: {}'.format(noiseLevel)
# SDR parameters
n = 1024
w = 20
sdrs = generateSDRs(numClasses, numSDRsPerClass, n, w, noiseLevel)
clusterAssignments = assignClusters(sdrs, numClasses, numSDRsPerClass)
npos, distanceMat = project2D(sdrs)
outputFile = '2d_projections.png'
viz2DProjection(vizTitle, outputFile, numClasses, clusterAssignments, npos)
outputFile = 'distance_matrix.png'
plotDistanceMat(distanceMat, 'Inter-cluster distances', outputFile,
showPlot=True)
def main():
numClasses = 7
numSDRsPerClass = 20
noiseLevel = 0.1
vizTitle = 'MDS, noise level: {}'.format(noiseLevel)
# SDR parameters
n = 1024
w = 20
sdrs = generateSDRs(numClasses, numSDRsPerClass, n, w, noiseLevel)
clusterAssignments = assignClusters(sdrs, numClasses, numSDRsPerClass)
npos, distanceMat = project2D(sdrs)
outputFile = '2d_projections.png'
viz2DProjection(vizTitle, outputFile, numClasses, clusterAssignments, npos)
outputFile = 'distance_matrix.png'
plotDistanceMat(distanceMat,
'Inter-cluster distances',
outputFile,
showPlot=True)
def vizInterSequenceClusters(traces,
outputDir,
cellsType,
numCells,
numClasses,
ignoreNoise=True):
clusters = assignClusters(traces)
# compare inter-cluster distance over time
numRptsPerCategory = {}
categories = np.unique(traces['actualCategory'])
repetition = np.array(clusters['repetition'])
for category in categories:
numRptsPerCategory[category] = np.max(
repetition[np.array(traces['actualCategory']) == category])
SDRclusters = []
clusterAssignments = []
numRptsMin = np.min(numRptsPerCategory.values()).astype('int32')
for rpt in range(numRptsMin + 1):
idx0 = np.logical_and(
np.array(traces['actualCategory']) == 0, repetition == rpt)
idx1 = np.logical_and(
np.array(traces['actualCategory']) == 1, repetition == rpt)
idx2 = np.logical_and(
np.array(traces['actualCategory']) == 2, repetition == rpt)
c0slice = [
traces['tmPredictedActiveCells'][i] for i in range(len(idx0))
if idx0[i]
]
c1slice = [
traces['tmPredictedActiveCells'][i] for i in range(len(idx1))
if idx1[i]
]
c2slice = [
traces['tmPredictedActiveCells'][i] for i in range(len(idx2))
if idx2[i]
]
if not ignoreNoise:
SDRclusters.append(c0slice)
clusterAssignments.append(0)
SDRclusters.append(c1slice)
clusterAssignments.append(1)
SDRclusters.append(c2slice)
clusterAssignments.append(2)
print " Presentation #{}: ".format(rpt)
if not ignoreNoise:
d01 = clusterDist(c0slice, c1slice, numCells)
print '=> d(c0, c1): %s' % d01
d02 = clusterDist(c0slice, c2slice, numCells)
print '=> d(c0, c2): %s' % d02
d12 = clusterDist(c1slice, c2slice, numCells)
print '=> d(c1, c2): %s' % d12
npos, distanceMat = projectClusters2D(SDRclusters, numCells)
title = 'Inter-sequence clusters in 2D (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
viz2DProjection(title, outputFile, numClasses, clusterAssignments, npos)
title = 'Inter-sequence clusters distances (using %s)' % cellsType
outputFile = '%s/%s' % (outputDir, title)
plotDistanceMat(distanceMat, title, outputFile)