def testAll(experiments):
experimentsDir = os.path.join(
os.path.split(os.path.dirname(__file__))[:-1])[0]
for experiment in experiments:
experimentBase = os.path.join(os.getcwd(), experimentsDir, experiment)
config, control = opfhelpers.loadExperiment(experimentBase)
if control['environment'] == 'opfExperiment':
experimentTasks = control['tasks']
task = experimentTasks[0]
datasetURI = task['dataset']['streams'][0]['source']
elif control['environment'] == 'nupic':
datasetURI = control['dataset']['streams'][0]['source']
metricSpecs = control['metrics']
datasetPath = datasetURI[len("file://"):]
for i in xrange(1024, 2176, 128):
#config['modelParams']['tmParams']['cellsPerColumn'] = 16
config['modelParams']['tmParams']['columnCount'] = i
config['modelParams']['spParams']['columnCount'] = i
print 'Running with 32 cells per column and %i columns.' % i
start = time.time()
result = runOneExperiment(config, control['inferenceArgs'],
metricSpecs, datasetPath)
print 'Total time: %d.' % (time.time() - start)
pprint(result)
def testAll(experiments):
experimentsDir = os.path.join(os.path.split(
os.path.dirname(__file__))[:-1])[0]
for experiment in experiments:
experimentBase = os.path.join(os.getcwd(), experimentsDir, experiment)
config, control = opfhelpers.loadExperiment(experimentBase)
if control['environment'] == 'opfExperiment':
experimentTasks = control['tasks']
task = experimentTasks[0]
datasetURI = task['dataset']['streams'][0]['source']
elif control['environment'] == 'nupic':
datasetURI = control['dataset']['streams'][0]['source']
metricSpecs = control['metrics']
datasetPath = datasetURI[len("file://"):]
for i in xrange(1024, 2176, 128):
#config['modelParams']['tpParams']['cellsPerColumn'] = 16
config['modelParams']['tpParams']['columnCount'] = i
config['modelParams']['spParams']['columnCount'] = i
print 'Running with 32 cells per column and {0:d} columns.'.format(i)
start = time.time()
result = runOneExperiment(config, control['inferenceArgs'], metricSpecs,
datasetPath)
print 'Total time: {0:d}.'.format((time.time() - start))
pprint(result)
def testAll(experiments):
experimentsDir = os.path.join(os.path.split(os.path.dirname(__file__))[:-1])[0]
for experiment in experiments:
experimentBase = os.path.join(os.getcwd(), experimentsDir, experiment)
config, control = opfhelpers.loadExperiment(experimentBase)
if control["environment"] == "opfExperiment":
experimentTasks = control["tasks"]
task = experimentTasks[0]
datasetURI = task["dataset"]["streams"][0]["source"]
elif control["environment"] == "grok":
datasetURI = control["dataset"]["streams"][0]["source"]
metricSpecs = control["metrics"]
datasetPath = datasetURI[len("file://") :]
for i in xrange(1024, 2176, 128):
# config['modelParams']['tpParams']['cellsPerColumn'] = 16
config["modelParams"]["tpParams"]["columnCount"] = i
config["modelParams"]["spParams"]["columnCount"] = i
print "Running with 32 cells per column and %i columns." % i
start = time.time()
result = runOneExperiment(config, control["inferenceArgs"], metricSpecs, datasetPath)
print "Total time: %d." % (time.time() - start)
pprint(result)
def getModelDescriptionLists(numProcesses, experiment):
config, control = opfhelpers.loadExperiment(experiment)
encodersList=getFieldPermutations(config, 'pounds')
ns=range(50, 140, 120)
clAlphas=np.arange(0.01, 0.16, 0.104)
synPermInactives=np.arange(0.01, 0.16, 0.105)
tpPamLengths=range(5, 8, 2)
tpSegmentActivations=range(13, 17, 12)
if control['environment'] == 'opfExperiment':
experimentTasks = control['tasks']
task = experimentTasks[0]
datasetURI = task['dataset']['streams'][0]['source']
elif control['environment'] == 'nupic':
datasetURI = control['dataset']['streams'][0]['source']
metricSpecs = control['metrics']
datasetPath = datasetURI[len("file://"):]
ModelSetUpData=[]
name=0
for n in ns:
for clAlpha in clAlphas:
for synPermInactive in synPermInactives:
for tpPamLength in tpPamLengths:
for tpSegmentActivation in tpSegmentActivations:
for encoders in encodersList:
encodersmod=copy.deepcopy(encoders)
configmod=copy.deepcopy(config)
configmod['modelParams']['sensorParams']['encoders']=encodersmod
configmod['modelParams']['clParams']['alpha']=clAlpha
configmod['modelParams']['spParams']['synPermInactiveDec']=synPermInactive
configmod['modelParams']['tpParams']['pamLength']=tpPamLength
configmod['modelParams']['tpParams']['activationThreshold']=tpSegmentActivation
for encoder in encodersmod:
if encoder['name']==predictedField:
encoder['n']=n
ModelSetUpData.append((name,{'modelConfig':configmod, 'inferenceArgs':control['inferenceArgs'], 'metricSpecs':metricSpecs, 'sourceSpec':datasetPath,'sinkSpec':None,}))
name=name+1
#print modelInfo['modelConfig']['modelParams']['tpParams']
#print modelInfo['modelConfig']['modelParams']['sensorParams']['encoders'][4]['n']
print "num Models"+str( len(ModelSetUpData))
shuffle(ModelSetUpData)
#print [ (m[1]['modelConfig']['modelParams']['tpParams']['pamLength'], m[1]['modelConfig']['modelParams']['sensorParams']['encoders']) for m in ModelSetUpData]
return list(chunk(ModelSetUpData,numProcesses))
def getModelDescriptionLists(numProcesses, experiment):
config, control = opfhelpers.loadExperiment(experiment)
encodersList = getFieldPermutations(config, 'pounds')
ns = range(50, 140, 120)
clAlphas = np.arange(0.01, 0.16, 0.104)
synPermInactives = np.arange(0.01, 0.16, 0.105)
tpPamLengths = range(5, 8, 2)
tpSegmentActivations = range(13, 17, 12)
if control['environment'] == 'opfExperiment':
experimentTasks = control['tasks']
task = experimentTasks[0]
datasetURI = task['dataset']['streams'][0]['source']
elif control['environment'] == 'nupic':
datasetURI = control['dataset']['streams'][0]['source']
metricSpecs = control['metrics']
datasetPath = datasetURI[len("file://"):]
ModelSetUpData = []
name = 0
for n in ns:
for clAlpha in clAlphas:
for synPermInactive in synPermInactives:
for tpPamLength in tpPamLengths:
for tpSegmentActivation in tpSegmentActivations:
for encoders in encodersList:
encodersmod = copy.deepcopy(encoders)
configmod = copy.deepcopy(config)
configmod['modelParams']['sensorParams'][
'encoders'] = encodersmod
configmod['modelParams']['clParams'][
'alpha'] = clAlpha
configmod['modelParams']['spParams'][
'synPermInactiveDec'] = synPermInactive
configmod['modelParams']['tpParams'][
'pamLength'] = tpPamLength
configmod['modelParams']['tpParams'][
'activationThreshold'] = tpSegmentActivation
for encoder in encodersmod:
if encoder['name'] == predictedField:
encoder['n'] = n
ModelSetUpData.append((name, {
'modelConfig':
configmod,
'inferenceArgs':
control['inferenceArgs'],
'metricSpecs':
metricSpecs,
'sourceSpec':
datasetPath,
'sinkSpec':
None,
}))
name = name + 1
#print modelInfo['modelConfig']['modelParams']['tpParams']
#print modelInfo['modelConfig']['modelParams']['sensorParams']['encoders'][4]['n']
print "num Models" + str(len(ModelSetUpData))
shuffle(ModelSetUpData)
#print [ (m[1]['modelConfig']['modelParams']['tpParams']['pamLength'], m[1]['modelConfig']['modelParams']['sensorParams']['encoders']) for m in ModelSetUpData]
return list(chunk(ModelSetUpData, numProcesses))