print(data.head())
data = StandardScaler().fit_transform(data.values)
data = pd.DataFrame(data, columns=columns)
return data
s = DataSetFormation()
s.read_csv()
s.createFeatureMatrixCGM()
mealFeatures = Features(4)
s.mealDataFrame.to_csv("myMealData.csv")
noMealFeatures = Features(4)
s.noMealDataFrame.to_csv("myNoMealData.csv")
finalMealDataFrame = pd.read_csv("myMealData.csv")
finalNoMealDataFrame = pd.read_csv("myNoMealData.csv")
meal = mealFeatures.completefeatures(finalMealDataFrame)
print(meal)
print("Final Meal DataSet")
mealPrincipalComponentDataFrame = s.normalizeData(meal)
nomeal = noMealFeatures.completefeatures(finalNoMealDataFrame)
print(nomeal)
print("Here", mealPrincipalComponentDataFrame)
mealPrincipalComponentDataFrame['Label'] = 1
print("Final NoMeal DataSet")
noMealPrincipalComponentDataFrame = s.normalizeData(nomeal)
noMealPrincipalComponentDataFrame['Label'] = 0
print("Here", noMealPrincipalComponentDataFrame)
#Concatinating 2 dataframes
finalMealNoMealDataFrame = pd.concat(
[mealPrincipalComponentDataFrame, noMealPrincipalComponentDataFrame],
test_dataframe = pd.read_csv('MealNoMealData/mealData3.csv', names=columns)
# print(test_dataframe)
row, column = test_dataframe.shape
for i in range(row):
test_dataframe.dropna(thresh=4, axis=0)
print("test_data")
# print(test_dataframe)
test_dataframe = test_dataframe.interpolate(method='linear',
limit_direction='backward')
print(test_dataframe)
# test_dataframe=test_dataframe.dropna()
# print(test_dataframe)
s = DataSetFormation()
f = Features(4)
data = f.completefeatures(test_dataframe)
data = normalized_data = s.normalizeData(data)
# data=s.applyPCA(data,3)
data["Label"] = 1
print(data)
column = [
'fft1', 'fft2', 'fft3', 'fft4', 'velocity1', 'velocity2', 'velocity3',
'velocity4', 'rolling1', 'rolling2', 'rolling3', 'rolling4', 'dwt1',
'dwt2', 'dwt3', 'dwt4'
]
column_p = ['pc1', 'pc2', 'pc3']
column_v = ['velocity1', 'velocity2', 'rolling2', 'rolling1']
value = loaded_model.predict(data[column_v])
print(value)
result = loaded_model.score(data[column_v], data['Label'])
print(result)
'rolling4', 'expwindow1', 'expwindow2', 'expwindow3', 'expwindow4',
'dwt1', 'dwt2', 'dwt3', 'dwt4'
]
data = pd.DataFrame(extracted_features, columns=columns)
data = data.dropna()
print(data.head())
data = MinMaxScaler().fit_transform(data.values)
data = pd.DataFrame(data, columns=columns)
self.applyPCA(data, 5, person, 'PCA')
print("""----------------------------------------|
| Enter a Person Number |
| |
|-----------------------------------------|""")
n = input()
directoryPath = os.getcwd()
access_right = 0o777
try:
if not os.path.isdir(directoryPath + '/Person' + str(n)):
os.mkdir(directoryPath + '/Person' + str(n), access_right)
except OSError:
print('Directoy not created')
s = DataSetFormation(int(n))
s.plotCGMData(int(n))
b = Features(4, s.CGMData)
final_extracted_feature_matrix = b.completefeatures(int(n))
df = pd.DataFrame(final_extracted_feature_matrix)
df.to_csv('FeaturesExtracted.csv')
s.normalizeData(final_extracted_feature_matrix, n)
