if __name__ == '__main__':
inputTest = pd.read_csv('{}.csv'.format(
str(input('Enter Feature File name:\t'))),
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]
meal[i] = handlingNaN(meal[i])
for j in meal[i].columns:
meal[i][j] = list(meal[i][j])[::-1]
meal[i].columns = [i for i in range(len(meal[i].columns))]
noMeal[i] = DropIfMaxNaN(noMeal[i])
meal[i] = handlingNaN(noMeal[i])
for j in noMeal[i].columns:
noMeal[i][j] = list(noMeal[i][j])[::-1]
noMeal[i].columns = [i for i in range(len(noMeal[i].columns))]
DeviationFeature = pd.DataFrame(columns=[
'inRangeCount', 'LowCount', 'HighCount', 'LowMean', 'HighMean', 'Class'
])
for i in range(numFiles):
DeviationFeature = DeviationFeature.append(Features.Deviation(
meal[i], 'Meal'),
ignore_index=True)
for i in range(numFiles):
DeviationFeature = DeviationFeature.append(Features.Deviation(
noMeal[i], 'NoMeal'),
ignore_index=True)
mean_range_feature = pd.DataFrame(columns=['MeanRange', 'Class'])
for i in range(numFiles):
mean_range_feature = mean_range_feature.append(Features.meanRange(
meal[i], 'Meal'),
ignore_index=True)
for i in range(numFiles):
mean_range_feature = mean_range_feature.append(Features.meanRange(
noMeal[i], 'NoMeal'),
ignore_index=True)
