def runExperimentPool(numObjects,
numLocations,
numFeatures,
numColumns,
networkType=["MultipleL4L2Columns"],
longDistanceConnectionsRange = [0.0],
numWorkers=7,
nTrials=1,
pointRange=1,
numPoints=10,
numInferenceRpts=1,
l2Params=None,
l4Params=None,
resultsName="convergence_results.pkl"):
"""
Allows you to run a number of experiments using multiple processes.
For each parameter except numWorkers, pass in a list containing valid values
for that parameter. The cross product of everything is run, and each
combination is run nTrials times.
Returns a list of dict containing detailed results from each experiment.
Also pickles and saves the results in resultsName for later analysis.
Example:
results = runExperimentPool(
numObjects=[10],
numLocations=[5],
numFeatures=[5],
numColumns=[2,3,4,5,6],
numWorkers=8,
nTrials=5)
"""
# Create function arguments for every possibility
args = []
for c in reversed(numColumns):
for o in reversed(numObjects):
for l in numLocations:
for f in numFeatures:
for n in networkType:
for p in longDistanceConnectionsRange:
for t in range(nTrials):
args.append(
{"numObjects": o,
"numLocations": l,
"numFeatures": f,
"numColumns": c,
"trialNum": t,
"pointRange": pointRange,
"numPoints": numPoints,
"networkType" : n,
"longDistanceConnections" : p,
"plotInferenceStats": False,
"settlingTime": 3,
"numInferenceRpts": numInferenceRpts,
"l2Params": l2Params,
"l4Params": l4Params
}
)
print "{} experiments to run, {} workers".format(len(args), numWorkers)
# Run the pool
if numWorkers > 1:
pool = Pool(processes=numWorkers)
result = pool.map(runExperiment, args)
else:
result = []
for arg in args:
result.append(runExperiment(arg))
# print "Full results:"
# pprint.pprint(result, width=150)
# Pickle results for later use
with open(resultsName,"wb") as f:
cPickle.dump(result,f)
return result
plt.close()
if __name__ == "__main__":
# This is how you run a specific experiment in single process mode. Useful
# for debugging, profiling, etc.
if True:
results = runExperiment(
{
"numObjects": 100,
"numPoints": 10,
"numLocations": 10,
"numFeatures": 10,
"numColumns": 1,
"trialNum": 4,
"pointRange": 1,
"featureNoise": 0.40,
"plotInferenceStats": True, # Outputs detailed graphs
"settlingTime": 3,
"includeRandomLocation": False
}
)
################
# These experiments look at the effect of topology
# Here we want to see how the number of columns affects convergence.
# This experiment is run using a process pool
if False:
numColumns=columnRange,
numPoints=10,
numWorkers=cpu_count() - 1,
nTrials=numTrials,
resultsName="object_convergence_multi_column_results.pkl")
# Analyze results
with open("object_convergence_multi_column_results.pkl", "rb") as f:
results = cPickle.load(f)
plotConvergenceByColumn(results, columnRange, featureRange, numTrials)
if __name__ == "__main__":
# This is how you run a specific experiment in single process mode. Useful
# for debugging, profiling, etc.
if True:
results = runExperiment({
"numObjects": 100,
"numPoints": 10,
"numLocations": 10,
"numFeatures": 10,
"numColumns": 1,
"trialNum": 4,
"featureNoise": 0.0,
"plotInferenceStats": False, # Outputs detailed graphs
"settlingTime": 2,
"includeRandomLocation": False
})
plt.savefig(plotPath)
plt.close()
if __name__ == "__main__":
# This is how you run a specific experiment in single process mode. Useful
# for debugging, profiling, etc.
if False:
results = runExperiment(
{
"numObjects": 100,
"numPoints": 10,
"numLocations": 10,
"numFeatures": 10,
"numColumns": 1,
"trialNum": 4,
"featureNoise": 0.6,
"plotInferenceStats": False, # Outputs detailed graphs
"settlingTime": 3,
"includeRandomLocation": False,
"l2Params": {"cellCount": 4096*4, "sdrSize": 40*2, "activationThresholdDistal": 14}
}
)
# This is for specifically testing how the distal activation threshold affect
# the classification results
if True:
activationThresholdDistalRange = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
results_by_thresholds = []
for i in range(len(activationThresholdDistalRange)):
results = runExperiment(
if __name__ == "__main__":
# This is how you run a specific experiment in single process mode. Useful
# for debugging, profiling, etc.
if False:
results = runExperiment({
"numObjects": 100,
"numPoints": 10,
"numLocations": 10,
"numFeatures": 10,
"numColumns": 1,
"trialNum": 4,
"featureNoise": 0.6,
"plotInferenceStats": False, # Outputs detailed graphs
"settlingTime": 3,
"includeRandomLocation": False,
"l2Params": {
"cellCount": 4096 * 4,
"sdrSize": 40 * 2,
"activationThresholdDistal": 14
}
})
# This is for specifically testing how the distal activation threshold affect
# the classification results
if True:
activationThresholdDistalRange = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
]
def runExperimentPool(numObjects,
numLocations,
numFeatures,
numColumns,
longDistanceConnectionsRange = [0.0],
numWorkers=7,
nTrials=1,
numPoints=10,
locationNoiseRange=[0.0],
featureNoiseRange=[0.0],
enableFeedback=[True],
ambiguousLocationsRange=[0],
numInferenceRpts=1,
l2Params=None,
l4Params=None,
resultsName="convergence_results.pkl"):
"""
Allows you to run a number of experiments using multiple processes.
For each parameter except numWorkers, pass in a list containing valid values
for that parameter. The cross product of everything is run, and each
combination is run nTrials times.
Returns a list of dict containing detailed results from each experiment.
Also pickles and saves the results in resultsName for later analysis.
Example:
results = runExperimentPool(
numObjects=[10],
numLocations=[5],
numFeatures=[5],
numColumns=[2,3,4,5,6],
numWorkers=8,
nTrials=5)
"""
# Create function arguments for every possibility
args = []
for c in reversed(numColumns):
for o in reversed(numObjects):
for l in numLocations:
for f in numFeatures:
for p in longDistanceConnectionsRange:
for t in range(nTrials):
for locationNoise in locationNoiseRange:
for featureNoise in featureNoiseRange:
for ambiguousLocations in ambiguousLocationsRange:
for feedback in enableFeedback:
args.append(
{"numObjects": o,
"numLocations": l,
"numFeatures": f,
"numColumns": c,
"trialNum": t,
"numPoints": numPoints,
"longDistanceConnections" : p,
"plotInferenceStats": False,
"settlingTime": 3,
"locationNoise": locationNoise,
"featureNoise": featureNoise,
"enableFeedback": feedback,
"numAmbiguousLocations": ambiguousLocations,
"numInferenceRpts": numInferenceRpts,
"l2Params": l2Params,
"l4Params": l4Params
}
)
print "{} experiments to run, {} workers".format(len(args), numWorkers)
# Run the pool
if numWorkers > 1:
pool = Pool(processes=numWorkers)
result = pool.map(runExperiment, args)
else:
result = []
for arg in args:
result.append(runExperiment(arg))
# print "Full results:"
# pprint.pprint(result, width=150)
# Pickle results for later use
with open(resultsName,"wb") as f:
cPickle.dump(result,f)
return result