globalInhibition=True,
localAreaDensity=-1, # Using numActiveColumnsPerInhArea
#localAreaDensity = 0.02, # one percent of columns active at a time
#numActiveColumnsPerInhArea = -1, # Using percentage instead
numActiveColumnsPerInhArea=1, # Only one feature active at a time
# All input activity can contribute to feature output
stimulusThreshold=0,
synPermInactiveDec=0.3,
synPermActiveInc=0.3,
synPermConnected=0.3, # Connected threshold
maxBoost=2,
seed=1956, # The seed that Grok uses
spVerbosity=1)
# Instantiate the spatial pooler test bench.
tb = VisionTestBench(sp)
# Instantiate the classifier
clf = exactMatch()
# Train the spatial pooler on trainingVectors.
numCycles = tb.train(trainingVectors, trainingTags, clf, maxTrainingCycles)
# View the permanences and connections after training.
tb.showPermsAndConns()
#tb.savePermsAndConns('perms_and_conns.jpg')
# Get testing images and convert them to vectors.
testingImages, testingTags = data.getImagesAndTags(testingDataset)
testingVectors = encoder.imagesToVectors(testingImages)
globalInhibition = True,
localAreaDensity = -1, # Using numActiveColumnsPerInhArea
#localAreaDensity = 0.02, # one percent of columns active at a time
#numActiveColumnsPerInhArea = -1, # Using percentage instead
numActiveColumnsPerInhArea = 1, # Only one feature active at a time
# All input activity can contribute to feature output
stimulusThreshold = 0,
synPermInactiveDec = 0.3,
synPermActiveInc = 0.3,
synPermConnected = 0.3, # Connected threshold
maxBoost = 2,
seed = 1956, # The seed that Grok uses
spVerbosity = 1)
# Instantiate the spatial pooler test bench.
tb = VisionTestBench(sp)
# Instantiate the classifier
clf = exactMatch()
# Train the spatial pooler on trainingVectors.
numCycles = tb.train(trainingVectors, trainingTags, clf, maxTrainingCycles)
# View the permanences and connections after training.
tb.showPermsAndConns()
#tb.savePermsAndConns('perms_and_conns.jpg')
# Get testing images and convert them to vectors.
testingImages, testingTags = data.getImagesAndTags(testingDataset)
testingVectors = encoder.imagesToVectors(testingImages)
potentialRadius=10000, # Ensures 100% potential pool
potentialPct=0.8,
globalInhibition=True,
localAreaDensity=-1, # Using numActiveColumnsPerInhArea
numActiveColumnsPerInhArea=64,
# All input activity can contribute to feature output
stimulusThreshold=0,
synPermInactiveDec=synPermDec,
synPermActiveInc=synPermInc,
synPermConnected=synPermConn,
maxBoost=1.0,
seed=1956, # The seed that Grok uses
spVerbosity=1)
# Instantiate the spatial pooler test bench.
tb = VisionTestBench(sp)
# Instantiate the classifier
clf = KNNClassifier()
# Train the spatial pooler on trainingVectors.
numCycles = tb.train(trainingVectors, trainingTags, clf, maxTrainingCycles,
minAccuracy)
# Save the permanences and connections after training.
#tb.savePermanences('perms.jpg')
#tb.showPermanences()
#tb.showConnections()
# Get testing images and convert them to vectors.
testingImages, testingTags = data.getImagesAndTags(testingDataset)
#numActiveColumnsPerInhArea = -1, # Using percentage instead
numActiveColumnsPerInhArea = 64,
# All input activity can contribute to feature output
stimulusThreshold = 0,
synPermInactiveDec = 0.001,
synPermActiveInc = 0.001,
synPermConnected = 0.3,
minPctOverlapDutyCycle=0.001,
minPctActiveDutyCycle=0.001,
dutyCyclePeriod=1000,
maxBoost = 1.0,
seed = 1956, # The seed that Grok uses
spVerbosity = 1)
# Instantiate the spatial pooler test bench.
tb = VisionTestBench(sp)
# Instantiate the classifier
clf = KNNClassifier()
# Get training images and convert them to vectors.
trainingImages, trainingTags = data.getImagesAndTags(trainingDataset)
trainingVectors = encoder.imagesToVectors(trainingImages)
# Train the spatial pooler on trainingVectors.
numCycles = tb.train(trainingVectors, trainingTags, clf, maxTrainingCycles,
minAccuracy)
# Save the permanences and connections after training.
#tb.savePermanences('perms.jpg')
#tb.showPermanences()
localAreaDensity = -1, # Using numActiveColumnsPerInhArea
#localAreaDensity = 0.02, # one percent of columns active at a time
#numActiveColumnsPerInhArea = -1, # Using percentage instead
numActiveColumnsPerInhArea = 64,
# All input activity can contribute to feature output
stimulusThreshold = 0,
synPermInactiveDec = synPermDec,
synPermActiveInc = synPermInc,
synPermConnected = synPermConn, # Connected threshold
maxBoost = 3,
seed = 1956, # The seed that Grok uses
spVerbosity = 1)
# Instantiate the spatial pooler test bench.
tb = VisionTestBench(sp)
# Train the spatial pooler on trainingVectors.
trainSDRIs, numCycles = tb.train(trainingVectors, trainingTags,
maxTrainingCycles, usePPM=False)
# Save the permanences and connections after training.
tb.savePermsAndConns('perms_and_conns.jpg')
#tb.showPermsAndConns()
# Get testing images and convert them to vectors.
testingImages, testingTags = data.getImagesAndTags(testingDataset)
testingVectors = encoder.imagesToVectors(testingImages)
# Test the spatial pooler on testingVectors.
testSDRIs = tb.test(testingVectors, testingTags)
localAreaDensity = -1, # Using numActiveColumnsPerInhArea
#localAreaDensity = 0.02, # one percent of columns active at a time
#numActiveColumnsPerInhArea = -1, # Using percentage instead
numActiveColumnsPerInhArea = 1, # Only one feature active at a time
# All input activity can contribute to feature output
stimulusThreshold = 0,
synPermInactiveDec = 0.1,
synPermActiveInc = 0.1,
synPermConnected = 0.1, # Connected threshold
maxBoost = 3,
seed = 1956, # The seed that Grok uses
spVerbosity = 1)
# Instantiate the spatial pooler test bench.
tb = VisionTestBench(sp)
# Train the spatial pooler on trainingVectors.
SDRs, numCycles = tb.train(trainingVectors, trainingTags, maxTrainingCycles)
# View the permanences and connections after training.
tb.showPermsAndConns()
#tb.savePermsAndConns('perms_and_conns.jpg')
# Get testing images and convert them to vectors.
testingImages, testingTags = data.getImagesAndTags(testingDataset)
testingVectors = encoder.imagesToVectors(testingImages)
# Test the spatial pooler on testingVectors.
testSDRs = tb.test(testingVectors, testingTags)
if testSDRs != SDRs:
