diff --git a/gradient_descent_comparison.ipynb b/extra_gradient_descent_comparison.ipynb
similarity index 100%
rename from gradient_descent_comparison.ipynb
rename to extra_gradient_descent_comparison.ipynb
diff --git a/gradient_descent_comparison.py b/gradient_descent_comparison.py
deleted file mode 100644
index d463ec6..0000000
--- a/gradient_descent_comparison.py
+++ /dev/null
@@ -1,129 +0,0 @@
-from __future__ import print_function, division, unicode_literals
-import sys
-from math import sqrt
-import numpy as np
-import matplotlib.pyplot as plt
-
-m = 100
-X = 2*np.random.rand(m, 1)
-X_b = np.c_[np.ones((m, 1)), X]
-y = 4 + 3*X + np.random.rand(m, 1)
-
-fig = plt.figure(figsize=(10, 5))
-data_ax = fig.add_subplot(121)
-cost_ax = fig.add_subplot(122)
-
-def batch_gradient_descent():
-    n_iterations = 1000
-    learning_rate = 0.05
-    thetas = np.random.randn(2, 1)
-    thetas_path = [thetas]
-    for i in range(n_iterations):
-        gradients = 2*X_b.T.dot(X_b.dot(thetas) - y)/m
-        thetas = thetas - learning_rate*gradients
-        thetas_path.append(thetas)
-
-    return thetas_path
-
-def stochastic_gradient_descent():
-    n_epochs = 50
-    t0, t1 = 5, 50
-    thetas = np.random.randn(2, 1)
-    thetas_path = [thetas]
-    for epoch in range(n_epochs):
-        for i in range(m):
-            random_index = np.random.randint(m)
-            xi = X_b[random_index:random_index+1]
-            yi = y[random_index:random_index+1]
-            gradients = 2*xi.T.dot(xi.dot(thetas) - yi)
-            eta = learning_schedule(epoch*m + i, t0, t1)
-            thetas = thetas - eta*gradients
-            thetas_path.append(thetas)
-
-    return thetas_path
-
-def mini_batch_gradient_descent():
-    n_iterations = 50
-    minibatch_size = 20
-    t0, t1 = 200, 1000
-    thetas = np.random.randn(2, 1)
-    thetas_path = [thetas]
-    t = 0
-    for epoch in range(n_iterations):
-        shuffled_indices = np.random.permutation(m)
-        X_b_shuffled = X_b[shuffled_indices]
-        y_shuffled = y[shuffled_indices]
-        for i in range(0, m, minibatch_size):
-            t += 1
-            xi = X_b_shuffled[i:i+minibatch_size]
-            yi = y_shuffled[i:i+minibatch_size]
-            gradients = 2*xi.T.dot(xi.dot(thetas) - yi)/minibatch_size
-            eta = learning_schedule(t, t0, t1)
-            thetas = thetas - eta*gradients
-            thetas_path.append(thetas)
-
-    return thetas_path
-
-def compute_mse(theta):
-    return np.sum((np.dot(X_b, theta) - y)**2)/m
-
-def learning_schedule(t, t0, t1):
-    return t0/(t+t1)
-
-if __name__ == '__main__':
-    plt.ion()
-
-    theta0, theta1 = np.meshgrid(np.arange(0, 5, 0.1), np.arange(0, 5, 0.1))
-    r, c = theta0.shape
-    cost_map = np.array([[0 for _ in range(c)] for _ in range(r)])
-    for i in range(r):
-        for j in range(c):
-            theta = np.array([theta0[i,j], theta1[i,j]])
-            cost_map[i,j] = compute_mse(theta)
-
-    exact_solution = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)
-    bgd_thetas = np.array(batch_gradient_descent())
-    sgd_thetas = np.array(stochastic_gradient_descent())
-    mbgd_thetas = np.array(mini_batch_gradient_descent())
-
-    bgd_len = len(bgd_thetas)
-    sgd_len = len(sgd_thetas)
-    mbgd_len = len(mbgd_thetas)
-    n_iter = min(bgd_len, sgd_len, mbgd_len)
-
-    cost_ax.plot(exact_solution[0,0], exact_solution[1,0], 'y*')
-    cost_img = cost_ax.pcolor(theta0, theta1, cost_map)
-    fig.colorbar(cost_img)
-
-    for i in range(n_iter):
-        data_ax.cla()
-        cost_ax.cla()
-
-        data_ax.plot(X, y, 'k.')
-
-        cost_ax.plot(exact_solution[0,0], exact_solution[1,0], 'y*')
-        cost_ax.pcolor(theta0, theta1, cost_map)
-
-        data_ax.plot(X, X_b.dot(bgd_thetas[i,:]), 'r-')
-        cost_ax.plot(bgd_thetas[:i,0], bgd_thetas[:i,1], 'r--')
-
-        data_ax.plot(X, X_b.dot(sgd_thetas[i,:]), 'g-')
-        cost_ax.plot(sgd_thetas[:i,0], sgd_thetas[:i,1], 'g--')
-
-        data_ax.plot(X, X_b.dot(mbgd_thetas[i,:]), 'b-')
-        cost_ax.plot(mbgd_thetas[:i,0], mbgd_thetas[:i,1], 'b--')
-
-        data_ax.set_xlim([0, 2])
-        data_ax.set_ylim([0, 15])
-        cost_ax.set_xlim([0, 5])
-        cost_ax.set_ylim([0, 5])
-
-        data_ax.set_xlabel(r'$x_1$')
-        data_ax.set_ylabel(r'$y$')
-        cost_ax.set_xlabel(r'$\theta_0$')
-        cost_ax.set_ylabel(r'$\theta_1$')
-
-        data_ax.legend(('Data', 'BGD', 'SGD', 'MBGD'))
-        cost_ax.legend(('Normal Equation', 'BGD', 'SGD', 'MBGD'))
-
-        plt.pause(1e-5)
\ No newline at end of file