--- a
+++ b/preprocessing.py
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+'''
+About: Python script to preprocess the predictor and target matrices for the MLP classification program.
+Author: Iman Kafian-Attari
+Date: 20.07.2021
+Licence: MIT
+version: 0.1
+=========================================================
+How to use:
+1. Select the output directory.
+2. Select the file containing information on the predictor matrix.
+3. Select the file containing information on the target matrix.
+=========================================================
+Notes:
+1. This script is meant to create the training and test datasets for the MLP neural network for the classification problem.
+2. This script must be executed before running the main script.
+3. It requires the following inputs from the user:
+   - an output directory,
+   - a numpy 2D matrix containing the information on the predictor in the form of mxn
+     where m: number of observation and n: number of predictor variables,
+   - a numpy 2D matrix containing the information on the predictor in the form of mx1
+     where m: number of observation and 1: the only target variable,
+4. It randomly creates the training and test sets for the predictor and target variables,
+5. It transfer the range of values for the predictor variables to the range of [0, 1],
+6. It stores the following data:
+   - x_train,
+   - x_test,
+   - y_train,
+   - y_test,
+7. The output files are saved as a numpy 2D arrays.
+8. To use this program without any errors, the target variables should be in the form of mx1 where m: number of samples.
+=========================================================
+TODO for version O.2
+1. Modify the code in a functional form.
+2. Modify to code to work for any number of target variables.
+=========================================================
+'''
+
+print(__doc__)
+
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+import tkinter as tk
+from tkinter import filedialog
+
+root = tk.Tk()
+root.withdraw()
+
+output_dir = filedialog.askdirectory(parent=root, initialdir='C:\\', title='Select the output directory')
+
+# Import the data
+# PREDICTORS:
+us = np.loadtxt(filedialog.askopenfilename(parent=root, initialdir='C:\\', title='Select the input file, a 2D numpy array'))
+
+# REFERENCES:
+cells = np.loadtxt(filedialog.askopenfilename(parent=root, initialdir='C:\\', title='Select the output file, a 2D numpy array'))
+
+# Normalizing the data into [0, 1]
+scaler = MinMaxScaler()
+us = scaler.fit_transform(us)
+
+# Making the train and test set
+x_train, x_test, y_train, y_test = train_test_split(us, cells, test_size=0.25)
+np.savetxt(f'{output_dir}\\x_train.txt', x_train, delimiter='\t')
+np.savetxt(f'{output_dir}\\x_test.txt', x_test, delimiter='\t')
+np.savetxt(f'{output_dir}\\y_train.txt', y_train, delimiter='\t')
+np.savetxt(f'{output_dir}\\y_test.txt', y_test, delimiter='\t')