assignment.py

import numpy as np
x = np.array([-3.18518622,  1.74645156,  4.09636192, -2.09368588,  0.44610755,
       -3.98466071,  2.16960775, -4.6211409 , -0.73887275,  4.46604947,
       -3.00068391,  2.70825331, -1.02376892,  0.65867202, -4.79664901,
        4.35680149,  0.51000849,  0.39525456, -0.08423315, -1.13446538])
y = np.array([-3.2955604 , -1.59089425, -0.3995805 , -3.44415531, -1.85278474,
       -4.98282339, -1.93126611, -4.31494598, -1.48874351, -0.80045999,
       -2.95927634, -1.82392489, -2.09714382, -2.03732532, -4.82875516,
       -0.0902187 , -2.88278428, -1.52174129, -2.13553524, -3.14660371])
def grad_des(x,y):
    a=b=0
    n = len(x)
    lr = 0.01
    c = []
    d = []

    for i in range (6000):
        y_pred =  a * x + b
        cost = (1/n)*sum([val**2 for val in (y - y_predicted)])
        ad = (-2/n)*sum(x*(y-y_pred))
        bd = (-2/n)*sum(y-y_pred)
        c.append(cost)
        d.append([m_curr,b_curr])
        a = a - (lr * ad)
        b = b - (lr* bd)
        print(cost)


    e = b.index(min(b))
    print("The best fit line is: ")
    print(f"y = ({d[e][0]})x + ({d[e][1]})")

    grad_des(x,y)