def run(args): start = time.time() root = os.path.dirname(os.path.realpath(__file__)) args.resultsDir = os.path.join(root, args.resultsDir) if args.modelName == "HTMNetwork": runner = HTMRunner(**args.__dict__) runner.initModel(0) else: runner = Runner(**args.__dict__) runner.initModel(args.modelName) print "Reading in data and preprocessing." dataTime = time.time() runner.setupData(args.textPreprocess) print( "Data setup complete; elapsed time is {0:.2f} seconds.\nNow encoding " "the data".format(time.time() - dataTime)) encodeTime = time.time() runner.encodeSamples(args.writeEncodings) print( "Encoding complete; elapsed time is {0:.2f} seconds.\nNow running the " "experiment.".format(time.time() - encodeTime)) runner.runExperiment(args.seed) runner.writeOutClassifications() resultCalcs = runner.calculateResults() runner.evaluateCumulativeResults(resultCalcs) print "Saving..." runner.saveModel() print "Experiment complete in {0:.2f} seconds.".format(time.time() - start) if args.validation: print "Validating experiment against expected classifications..." print runner.validateExperiment(args.validation)
def run(args): start = time.time() root = os.path.dirname(os.path.realpath(__file__)) args.resultsDir = os.path.join(root, args.resultsDir) if args.modelName == "HTMNetwork": runner = HTMRunner(**args.__dict__) runner.initModel(0) else: runner = Runner(**args.__dict__) runner.initModel(args.modelName) print "Reading in data and preprocessing." dataTime = time.time() runner.setupData(args.textPreprocess) print ("Data setup complete; elapsed time is {0:.2f} seconds.\nNow encoding " "the data".format(time.time() - dataTime)) encodeTime = time.time() runner.encodeSamples(args.writeEncodings) print ("Encoding complete; elapsed time is {0:.2f} seconds.\nNow running the " "experiment.".format(time.time() - encodeTime)) runner.runExperiment(args.seed) runner.writeOutClassifications() resultCalcs = runner.calculateResults() runner.evaluateCumulativeResults(resultCalcs) print "Saving..." runner.saveModel() print "Experiment complete in {0:.2f} seconds.".format(time.time() - start) if args.validation: print "Validating experiment against expected classifications..." print runner.validateExperiment(args.validation)
retinaScaling=args.retinaScaling, retina=args.retina, apiKey=args.apiKey, numClasses=args.numClasses, plots=args.plots, orderedSplit=args.orderedSplit, folds=args.folds, trainSizes=args.trainSizes, verbosity=args.verbosity, generateData=args.generateData, votingMethod=args.votingMethod, classificationFile=args.classificationFile, seed=args.seed) runner.initModel(0) runner.setupData(args.textPreprocess) runner.encodeSamples() runner.partitionIndices(args.seed) sensorRegion, spRegion, tmRegion, tpRegion, knnRegion = getNupicRegions(runner.model.network) tmRegion.learningMode = True tmRegion.computePredictedActiveCellIndices = True categoryList = ['animal-eats-vegetable', 'vegetable-eats-animal'] animals, vegetables = getAnimalVegetableList() vegetable = {} animal = {} tmCellUnion = [] tmInputUnion = [] tpOutput = [] categoryLabel = [] accuracy = []