from decision_tree import DecisionTree
import os
feature_matrix = [
'Glucose_max', 'Glucose_min', 'Glucose_variance', 'velocity_max',
'velocity_min', 'velocity_variance', 'rms_max', 'rms_min', 'rms_variance'
]
target = ['target']
for i in range(1, 6):
meal_data = Preprocess(
f'D:\\python projects\\CSE572\\project2\\mealData{i}.csv')
meal_data_df = meal_data.get_dataframe()
meal_feature = Features(meal_data_df)
temp1_matrix = meal_feature.features_extraction()
feature_matrix = np.row_stack((feature_matrix, temp1_matrix))
# print(len(temp1_matrix))
target.extend([1] * len(temp1_matrix))
nomeal_data = Preprocess(
f'D:\\python projects\\CSE572\\project2\\Nomeal{i}.csv')
nomeal_data_df = nomeal_data.get_dataframe()
nomeal_feature = Features(nomeal_data_df)
temp2_matrix = nomeal_feature.features_extraction()
feature_matrix = np.row_stack((feature_matrix, temp2_matrix))
target.extend([0] * len(temp2_matrix))
feature_matrix = np.array(feature_matrix)
target = np.array([target])
new_feature_matrix = np.concatenate((feature_matrix, target.T), axis=1)
from preprocess import Preprocess
from features import Features
import pandas as pd
cgm_series_lunch = Preprocess(
'D:\Desktop\ASU\CSE572\DataFolder\CGMSeriesLunchPat1.csv')
time_series_lunch = Preprocess(
'D:\Desktop\ASU\CSE572\DataFolder\CGMDatenumLunchPat1.csv')
cgm_series_lunch1 = cgm_series_lunch.get_dataframe()
time_series_lunch1 = time_series_lunch.get_dataframe()
# print(cgm_series_lunch1)
# print(time_series_lunch1)
feature = Features(cgm_series_lunch1, time_series_lunch1)
feature.features_extraction()
# feature.save_to_csv()
final_pca = pd.DataFrame(feature.pca_decomposition())
final_pca.to_csv('final_pca.csv', index=False)
feature.plot_time_series()
