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diff --git a/main.py b/main.py
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+import os
+import time
+from colorama import init, Fore, Style
+# import math
+# import random
+import numpy as np
+# from numpy.linalg import cholesky, eig
+# import matplotlib.pyplot as plt
+from read_nc_data import read_nc_data
+from read_db_edata import read_db_edata
+from c_matrix import c_matrix
+from lj_matrix import lj_matrix
+# from frob_norm import frob_norm
+from gauss_kernel import gauss_kernel
+from cholesky_solve import cholesky_solve
+
+# Initialization.
+init_time = time.perf_counter()
+init()
+
+# Move to data folder.
+init_path = os.getcwd()
+os.chdir('../../data')
+data_path = os.getcwd()
+
+print(Fore.CYAN
+      + 'Data reading started.'
+      + Style.RESET_ALL)
+tic = time.perf_counter()
+
+# Read nuclear charge data.
+zi_data = read_nc_data(data_path)
+
+# Read molecule data.
+molecules, nuclear_charge, energy_pbe0, energy_delta = \
+    read_db_edata(zi_data, data_path)
+
+toc = time.perf_counter()
+print(Fore.GREEN
+      + '\tData reading took {:.4f} seconds.'.format(toc-tic)
+      + Style.RESET_ALL)
+
+# Go back to main folder.
+os.chdir(init_path)
+
+print(Fore.CYAN
+      + 'Coulomb Matrices calculation started.'
+      + Style.RESET_ALL)
+tic = time.perf_counter()
+
+cm_data = np.array([c_matrix(mol, nc, as_eig=True)
+                    for mol, nc in zip(molecules, nuclear_charge)])
+
+toc = time.perf_counter()
+print(Fore.GREEN
+      + '\tCoulomb matrices calculation took {:.4f} seconds.'.format(toc-tic)
+      + Style.RESET_ALL)
+
+print(Fore.CYAN
+      + 'L-J Matrices calculation started.'
+      + Style.RESET_ALL)
+tic = time.perf_counter()
+
+ljm_data = np.array([lj_matrix(mol, nc, as_eig=True)
+                     for mol, nc in zip(molecules, nuclear_charge)])
+
+toc = time.perf_counter()
+print(Fore.GREEN
+      + '\tL-J matrices calculation took {:.4f} seconds.'.format(toc-tic)
+      + Style.RESET_ALL)
+
+#
+# Problem solving with Coulomb Matrix.
+#
+print(Fore.CYAN
+      + 'CM ML started.'
+      + Style.RESET_ALL)
+tic = time.perf_counter()
+
+sigma = 1000.0
+
+Xcm_training = cm_data[:6000]
+Ycm_training = energy_pbe0[:6000]
+Kcm_training = gauss_kernel(Xcm_training, Xcm_training, sigma)
+alpha_cm = cholesky_solve(Kcm_training, Ycm_training)
+
+Xcm_test = cm_data[-1000:]
+Ycm_test = energy_pbe0[-1000:]
+Kcm_test = gauss_kernel(Xcm_test, Xcm_training, sigma)
+Ycm_predicted = np.dot(Kcm_test, alpha_cm)
+
+print('\tMean absolute error for CM: {}'.format(np.mean(np.abs(Ycm_predicted
+                                                               - Ycm_test))))
+
+toc = time.perf_counter()
+print(Fore.GREEN
+      + '\tCM ML took {:.4f} seconds.'.format(toc-tic)
+      + Style.RESET_ALL)
+
+
+#
+# Problem solving with L-J Matrix.
+#
+print(Fore.CYAN
+      + 'L-JM ML started.'
+      + Style.RESET_ALL)
+tic = time.perf_counter()
+
+sigma = 1000.0
+
+Xljm_training = ljm_data[:6000]
+Yljm_training = energy_pbe0[:6000]
+Kljm_training = gauss_kernel(Xljm_training, Xljm_training, sigma)
+alpha_ljm = cholesky_solve(Kljm_training, Yljm_training)
+
+Xljm_test = ljm_data[-1000:]
+Yljm_test = energy_pbe0[-1000:]
+Kljm_test = gauss_kernel(Xljm_test, Xljm_training, sigma)
+Yljm_predicted = np.dot(Kljm_test, alpha_ljm)
+
+print('\tMean absolute error for LJM: {}'.format(np.mean(np.abs(Yljm_predicted
+                                                                - Yljm_test))))
+
+toc = time.perf_counter()
+print(Fore.GREEN
+      + '\tL-JM ML took {:.4f} seconds.'.format(toc-tic)
+      + Style.RESET_ALL)
+
+
+# End of program
+end_time = time.perf_counter()
+print(Fore.CYAN
+      + 'The program took {:.4f} seconds of runtime.'.format(end_time
+                                                             - init_time)
+      + Style.RESET_ALL)