def getSVMTrainingData(datas): trainingDatas = [] for rawData, output in datas: channels = zip(*rawData) allBins = [] for channel in channels: downSampledData = fftDataExtraction.downSample(channel, rawSps, constants.samplesPerSecond, interpolate = True) transforms = fftDataExtraction.applyTransformsToWindows(fftDataExtraction.getFFTWindows(downSampledData), magnitude = True, polyFitSubtraction = 2) bins = fftDataExtraction.DoFrequencyBinning(transforms) allBins.append(bins) trainingInputs = map(lambda x: x[0] + x[1], zip(*allBins)) trainingDatas += [(input, output) for input in trainingInputs] return trainingDatas
fallDelayScores.append((score, fallDelay)) fallDelay = min(fallDelayScores)[1] sqWave = squareWave(dataLength * 2, riseDelay, fallDelay, outputTimes) #pylab.plot(outputTimes, sqWave, '-o') ;pylab.plot(outputTimes, predictions, '-o') ;pylab.plot([0.0, 0.0], [1.5, -0.5]) ;pylab.show() return riseDelay, fallDelay if __name__ == "__main__": constants.samplesPerSecond = int(constants.samplesPerSecond) #timeData, output = createChangingTimeDomainData(constants.baseFilename, low = 0, high = 10) timeData, output = createChangingTimeDomainDataPhaseMatch(constants.baseFilename, low = constants.lowPercent, high = constants.highPercent) dataWindows, outputs, outputTimes = getFFTWindows(timeData, output) transforms = fftDataExtraction.applyTransformsToWindows(dataWindows, magnitude = True) transforms = fftDataExtraction.DoFrequencyBinning(transforms) #svmAccuracy.printSvmValidationAccuracy(transforms, outputs) predictions = svmAccuracy.getAverageSVMPredictions(transforms, outputs) riseLat, fallLat = measureLatency(predictions, outputs, outputTimes) print 'rising latency: %dms' % riseLat print 'falling latency: %dms' % fallLat svmAccuracy.graphSvmLatency(predictions, outputs, timeData, outputTimes)
