def partitionIndices(self, _):
"""
Sets self.partitions for the number of tokens for each sample in the
training and test sets.
The order of sequences is already specified by the network data files; if
generated by the experiment, these are in order or randomized as specified
by the orderedSplit arg.
"""
if self.experimentType == "k-folds":
for fold in xrange(self.folds):
dataFile = self.dataFiles[fold]
numTokens = NetworkDataGenerator.getNumberOfTokens(dataFile)
self.partitions = KFolds(self.folds).split(numTokens, randomize=False)
else:
for trial, split in enumerate(self.trainSizes):
dataFile = self.dataFiles[trial]
numTokens = NetworkDataGenerator.getNumberOfTokens(dataFile)
self.partitions.append((numTokens[:split], numTokens[split:]))
def partitionIndices(self, _):
"""
Sets self.partitions for the number of tokens for each sample in the
training and test sets.
The order of sequences is already specified by the network data files; if
generated by the experiment, these are in order or randomized as specified
by the orderedSplit arg.
"""
if self.experimentType == "k-folds":
for fold in xrange(self.folds):
dataFile = self.dataFiles[fold]
numTokens = NetworkDataGenerator.getNumberOfTokens(dataFile)
self.partitions = KFolds(self.folds).split(numTokens,
randomize=False)
else:
for trial, split in enumerate(self.trainSizes):
dataFile = self.dataFiles[trial]
numTokens = NetworkDataGenerator.getNumberOfTokens(dataFile)
self.partitions.append((numTokens[:split], numTokens[split:]))
def partitionIndices(self, seed=42, numInference=10):
"""
Sets self.partitions for the buckets' querying and ranking sets. The
corresponding numbers of tokens for each sequence are stored in
self.numTokens.
The order of sequences is already specified by the network data files; if
generated by the experiment, these are in order or randomized as specified
by the orderedSplit arg.
"""
super(BucketHTMRunner, self).partitionIndices(
seed=seed, numInference=numInference)
# Get the number of tokens in each bucket file so the network knows how many
# iterations to run. The order of buckets in self.bucketFiles is not
# necessarily the same
ndg = NetworkDataGenerator()
for dataFile in self.bucketFiles:
self.numTokens.append(ndg.getNumberOfTokens(dataFile))
animals, vegetables = getAnimalVegetableList()
vegetable = {}
animal = {}
tmCellUnion = []
tmInputUnion = []
tpOutput = []
categoryLabel = []
accuracy = []
accuracyTp = []
knnInLastNSequences = 20
knnNumber = 1
plt.close('all')
plt.figure(1)
plt.show()
numTokens = NetworkDataGenerator.getNumberOfTokens(args.dataPath)
for numSample in xrange(len(numTokens)):
# union SDR for this sequence
tmCellActivation = np.zeros((tmRegion._tfdr.cellsPerColumn * tmRegion._tfdr.columnDimensions[0],))
tmInputActivation = np.zeros((tmRegion._tfdr.columnDimensions[0],))
print
for word in xrange(numTokens[numSample]):
sensorInput = None
sensorOutput = {'categoryOut': np.array([0]),
'resetOut': [None],
'sourceOut': None,
'sequenceIdOut': [None],
'encodingOut': None,
'dataOut': np.zeros((sensorRegion.encoder.n, ))}
sensorRegion.compute(sensorInput, sensorOutput)
