specified by testingDataset.
"""
trainingDataset = "DataSets/OCR/characters/cmr_hex.xml"
maxTrainingCycles = 20
testingDataset = "DataSets/OCR/characters/cmr_hex.xml"
import dataset_readers as data
import image_encoders as encoder
from nupic.research.spatial_pooler import SpatialPooler
from vision_testbench import VisionTestBench
from classifiers import exactMatch
if __name__ == "__main__":
# Get training images and convert them to vectors.
trainingImages, trainingTags = data.getImagesAndTags(trainingDataset)
trainingVectors = encoder.imagesToVectors(trainingImages)
# Instantiate the python spatial pooler
sp = SpatialPooler(
inputDimensions=32**2, # Size of image patch
columnDimensions=16, # Number of potential features
potentialRadius=10000, # Ensures 100% potential pool
potentialPct=1, # Neurons can connect to 100% of input
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,
trainingDataset = "DataSets/OCR/characters/cmr_hex.xml"
maxTrainingCycles = 20
testingDataset = "DataSets/OCR/characters/cmr_hex.xml"
import dataset_readers as data
import image_encoders as encoder
from nupic.research.spatial_pooler import SpatialPooler
from vision_testbench import VisionTestBench
from classifiers import exactMatch
if __name__ == "__main__":
# Get training images and convert them to vectors.
trainingImages, trainingTags = data.getImagesAndTags(trainingDataset)
trainingVectors = encoder.imagesToVectors(trainingImages)
# Instantiate the python spatial pooler
sp = SpatialPooler(
inputDimensions = 32**2, # Size of image patch
columnDimensions = 16, # Number of potential features
potentialRadius = 10000, # Ensures 100% potential pool
potentialPct = 1, # Neurons can connect to 100% of input
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,
