def run(inputFiles,
networkConfigsFile,
plotResults,
runClustering):
with open(networkConfigsFile, 'rb') as jsonFile:
networkConfigurations = simplejson.load(jsonFile)
expResults = []
for networkConfig in networkConfigurations:
for inputFile in inputFiles:
expResult = runExperiment(networkConfig, inputFile, runClustering)
expResults.append(expResult)
if plotResults:
# traces = loadTraces(fileName)
traces = expResult['expTrace']
tmParams = networkConfig['tmRegionConfig']['regionParams']
numCells = tmParams['cellsPerColumn'] * tmParams['inputWidth']
numClusters = len(set(traces['actualCategory']))
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
os.makedirs(OUTPUT_DIR)
cellsType = CELLS_TO_CLUSTER
numSteps = len(traces['recordNumber'])
pointsToPlot = numSteps / 10
vizInterCategoryClusters(traces,
OUTPUT_DIR,
cellsType,
numCells,
pointsToPlot)
vizInterSequenceClusters(traces, OUTPUT_DIR, cellsType, numCells,
numClusters)
xl = None
plotTemporalMemoryStates = False
title = inputFile.split('/')[-1]
outputFile = '%s.png' % inputFile[:-4]
plotTraces(xl, traces, title, ANOMALY_SCORE, outputFile,
runClustering)
traceOutputDir = os.path.join(OUTPUT_DIR, 'traces')
if not os.path.exists(traceOutputDir):
os.makedirs(traceOutputDir)
saveTraces(os.path.join(traceOutputDir, '%s.csv'), expResults)
def run(inputFiles, networkConfigsFile, plotResults, runClustering):
with open(networkConfigsFile, 'rb') as jsonFile:
networkConfigurations = simplejson.load(jsonFile)
expResults = []
for networkConfig in networkConfigurations:
for inputFile in inputFiles:
expResult = runExperiment(networkConfig, inputFile, runClustering)
expResults.append(expResult)
if plotResults:
# traces = loadTraces(fileName)
traces = expResult['expTrace']
tmParams = networkConfig['tmRegionConfig']['regionParams']
numCells = tmParams['cellsPerColumn'] * tmParams['inputWidth']
numClusters = len(set(traces['actualCategory']))
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
os.makedirs(OUTPUT_DIR)
cellsType = CELLS_TO_CLUSTER
numSteps = len(traces['recordNumber'])
pointsToPlot = numSteps / 10
vizInterCategoryClusters(traces, OUTPUT_DIR, cellsType,
numCells, pointsToPlot)
vizInterSequenceClusters(traces, OUTPUT_DIR, cellsType,
numCells, numClusters)
xl = None
plotTemporalMemoryStates = False
title = inputFile.split('/')[-1]
outputFile = '%s.png' % inputFile[:-4]
plotTraces(xl, traces, title, outputFile,
plotTemporalMemoryStates)
traceOutputDir = os.path.join(OUTPUT_DIR, 'traces')
if not os.path.exists(traceOutputDir):
os.makedirs(traceOutputDir)
saveTraces(os.path.join(traceOutputDir, '%s.csv'), expResults)
perms = list(permutations(cluster, 2))
for (sdr1, sdr2) in perms:
overlap = percentOverlap(sdr1, sdr2)
overlaps.append(overlap)
return sum(overlaps) / len(overlaps)
if __name__ == "__main__":
(_options, _args) = _getArgs()
fileName = _options.fileName
traces = loadTraces(fileName)
outputDir = fileName[:-4]
if not os.path.exists(outputDir):
os.makedirs(outputDir)
cellsType = CELLS_TO_CLUSTER
numCells = 2048 * 32
numSteps = len(traces['recordNumber'])
pointsToPlot = numSteps / 10
numClasses = len(set(traces['actualCategory']))
vizInterCategoryClusters(traces,
outputDir,
cellsType,
numCells,
pointsToPlot)
vizInterSequenceClusters(traces, outputDir, cellsType, numCells,
numClasses)