linear regression

Author: Simon

Artificial Intelligence/linear_regression.py

@@ -0,0 +1,26 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def coefficient(x, y):
+    n = np.size(x)
+    mean_x, mean_y = np.mean(x), np.mean(y)
+    cross_deviation_sum = np.sum(x * y) - n * mean_x * mean_y
+    square_deviation_sum = np.sum(x * x) - n * mean_x * mean_x
+    b_1 = cross_deviation_sum / square_deviation_sum
+    b_0 = mean_y - b_1 * mean_x
+
+    return (b_0, b_1)
+
+def plot_line( x, y, b):
+    plt.scatter(x, y, color = "r")
+    y_pred = b[0] + b[1] * x
+    plt.plot( x, y_pred, color = "g")
+    plt.xlabel('x')
+    plt.ylabel('y')
+    plt.show()
+
+x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+y = np.array([1, 2, 4, 5, 7, 8, 3, 6, 9, 10 ])
+b = coefficient( x, y)
+print("The coefficients are : {} and {}".format(b[0], b[1]))
+plot_line( x, y, b)