9. adding of eeg dataset with bayesian tests

imputegap/wrapper/AlgoPython/MRNN/Data_Loader.py

@@ -1,120 +1,11 @@
 '''
-Jinsung Yoon (09/06/2018)
+Quentin Nater (27/09/2024)
 Data Loading
 '''
 
 #%% Necessary Packages
 import numpy as np
 
-#%% Google data loading
-
-'''
-1. train_rate: training / testing set ratio
-2. missing_rate: the amount of introducing missingness
-'''
-
-def Data_Loader_Incomplete(seq_length, xy):
-
-    #%% Normalization
-    def MinMaxScaler(data):
-        dmin = np.nanmin(data, 0)
-        dmax = np.nanmax(data, 0)
-        numerator = data - dmin
-        denominator = dmax - dmin
-        return numerator / (denominator + 1e-8), dmin, dmax
-
-
-    xy, dmin, dmax = MinMaxScaler(xy)
-    x = xy
-
-    #%% Parameters
-    col_no = len(x[0,:])
-    row_no = len(x[:,0]) - seq_length
-
-    # Dataset build
-    dataX = []
-    for i in range(0, len(x[:,0]) - seq_length):
-        _x = x[i:i + seq_length]
-        dataX.append(_x)
-
-    train_size = 0
-
-    for i in range(0, len(x)):
-        anynan = False
-
-        for j in range(0, len(x[i])):
-            if np.isnan(x[i][j]):
-                anynan = True
-
-        if anynan:
-            train_size = i - int(i/3.0)
-            break
-
-    #%% Introduce Missingness (MCAR)
-
-    dataZ = []
-    dataM = []
-    dataT = []
-
-    for i in range(row_no):
-
-        #%% Missing matrix construct
-        m = np.ones([seq_length, col_no])
-        m[np.where(np.isnan(dataX[i])==1)] = 0
-
-        dataM.append(m)
-
-        #%% Introduce missingness to the original data
-        z = np.copy(dataX[i])    
-        z[np.where(m==0)] = 0
-
-        dataZ.append(z)
-
-        #%% Time gap generation
-        t = np.ones([seq_length, col_no])
-        for j in range(col_no):
-            for k in range(seq_length):
-                if (k > 0):
-                    if (m[k,j] == 0):
-                        t[k,j] = t[k-1,j] + 1
-
-        dataT.append(t)
-
-    #%% Building the dataset
-    '''
-    X: Original Feature
-    Z: Feature with Missing
-    M: Missing Matrix
-    T: Time Gap
-    '''
-
-    #%% Train / Test Division   
-    #train_size = int(len(dataX) * train_rate)
-
-    trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)])
-    trainZ, testZ = np.array(dataZ[0:train_size]), np.array(dataZ[train_size:len(dataX)])
-    trainM, testM = np.array(dataM[0:train_size]), np.array(dataM[train_size:len(dataX)])
-    trainT, testT = np.array(dataT[0:train_size]), np.array(dataT[train_size:len(dataX)])
-
-    return [trainX, trainZ, trainM, trainT, testX, testZ, testM, testT, dmin, dmax, train_size, x]
-
-
-'''    
-#%% Data with missingness
-    
-xy = np.loadtxt('/home/jinsung/Documents/Jinsung/MRNN/MRNN_New_Revision/Data/GOOGLE_Missing.csv', delimiter = ",",skiprows = 1)
-row_no = len(xy[:,0])
-col_no = len(xy[0,:])
-
-temp_m = np.random.uniform(0,1,[row_no, col_no]) 
-m = np.zeros([row_no, col_no])
-m[np.where(temp_m >= 0.2)] = 1
-        
-xy[np.where(m==0)] = np.nan
-
-np.savetxt('/home/jinsung/Documents/Jinsung/MRNN/MRNN_New_Revision/Data/GOOGLE_Missing.csv',xy)
-'''
-
 def Data_Loader_With_Dataset(seq_length, data):
     # %% Normalization
     def MinMaxScaler(data):