def findKMeansPerLabel(activations, k, labelCount, targetPath, indexLabelMapping):
labels = None
if indexLabelMapping != None:
labels = utility.getLabelStrings(indexLabelMapping)
# split activations by label
activationsByLabel = []
counter = 0
while counter < labelCount:
currentLabelIndex = activations.shape[1] - labelCount + counter
logger.debug(currentLabelIndex)
currentSelection = activations[activations[:, currentLabelIndex] == 1]
activationsByLabel.append(currentSelection)
counter += 1
counter = 0
clusters = []
iterations = []
for batch in activationsByLabel:
if labels != None:
logger.info('Running KMeans for label ' + labels[counter] + '.')
else:
logger.info('Running KMeans for label ' + str(counter))
logger.debug("Batch shape: " + str(batch.shape))
[c, i] = kMeans_core.runKMeans(batch, labelCount, k, MAX_ITERATIONS)
clusters.extend(c)
iterations.append(i)
counter += 1
kMeans_core.saveResults(clusters, iterations, targetPath)
return [clusters, iterations]
def findKMeans(activations, k, labelCount, targetPath):
logger.info("Running mixed KMeans, please wait...")
[clusters, iterations] = kMeans_core.runKMeans(activations, labelCount, k,
MAX_ITERATIONS)
kMeans_core.saveResults(clusters, iterations, targetPath)
return [clusters, iterations]
def findKMeans(activations, k, labelCount, targetPath):
logger.info("Running mixed KMeans, please wait...")
[clusters, iterations] = kMeans_core.runKMeans(activations, labelCount, k, MAX_ITERATIONS)
kMeans_core.saveResults(clusters, iterations, targetPath)
return [clusters, iterations]