header=None) outputTest = pd.read_csv('{}.csv'.format( str(input('Enter Class File name:\t'))), header=None) outputTest.columns = ['Class'] inputTest = inputTest.T inputTest = DropIfMaxNaN(inputTest) inputTest = handlingNaN(inputTest) Test_FeatureMatrix = pd.concat([ Features.Deviation(inputTest, 'N/A'), Features.meanRange(inputTest, 'N/A')[['MeanRange']], Features.Range(inputTest, 'N/A')[['HighRange', 'LowRange']], Features.FFT(inputTest, 'N/A')[['varFFT', 'sdFFT', 'meanFFT']], Features.Quantile(inputTest, 'N/A')['Quantile'], ], axis=1) if int(input('Pass From PCA? 1: YES, 0: NO:\t')) == 1: columns = TopFeatures(Test_FeatureMatrix, len(Test_FeatureMatrix.columns) - 1) else: columns = list(Test_FeatureMatrix.columns) columns.remove('Class') Test_DF = Test_FeatureMatrix[columns] while True: name = str(
mean_range_feature = mean_range_feature.append(Features.meanRange( noMeal[i], 'NoMeal'), ignore_index=True) range_feature = pd.DataFrame(columns=['HighRange', 'LowRange', 'Class']) for i in range(numFiles): range_feature = range_feature.append(Features.Range(meal[i], 'Meal'), ignore_index=True) for i in range(numFiles): range_feature = range_feature.append(Features.Range( noMeal[i], 'NoMeal'), ignore_index=True) fftFeature = pd.DataFrame(columns=['varFFT', 'sdFFT', 'meanFFT', 'Class']) for i in range(numFiles): fftFeature = fftFeature.append(Features.FFT(meal[i], 'Meal'), ignore_index=True) for i in range(numFiles): fftFeature = fftFeature.append(Features.FFT(noMeal[i], 'NoMeal'), ignore_index=True) QuantileFeature = pd.DataFrame(columns=['Quantile', 'Class']) for i in range(numFiles): QuantileFeature = QuantileFeature.append(Features.Quantile( meal[i], 'Meal'), ignore_index=True) for i in range(numFiles): QuantileFeature = QuantileFeature.append(Features.Quantile( noMeal[i], 'NoMeal'), ignore_index=True)