#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)
# Test the spatial pooler on testingVectors.
accuracy = tb.test(testingVectors, testingTags, clf, verbose=1)
#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)
# Test the spatial pooler on testingVectors.
accuracy = tb.test(testingVectors, testingTags, clf, verbose=1)
