LinearRegression.py

#y = 2x + 3
train_data = [(0, 3), (1, 5), (2, 7), (3, 9), (4, 11), (5, 13), (6, 15), (7, 17), (8, 19), (9, 21), (10, 23)]
test_data = [11, 12, 13, 14, 15]

#Gradient Descent
def grad_des_w(w, cost_der_w):
    temp_w = w - 0.057*(cost_der_w)
    return temp_w
def grad_des_b(b, cost_der_b):
    temp_b = b - 0.057*(cost_der_b)
    return temp_b

#Squared Mean Cost Function
def cost_func(test_dat, w, b):
    total_sq = 0
    for data in test_dat:
        total_sq += ((w*data[0] + b) - data[1])**2
    print(f'Current value: w = {w}, b = {b}')
    print(f'Cost Function: {total_sq/(2*len(test_dat))}')
    return total_sq/(2*len(test_dat))

#Partial Derivatives
def cost_func_w(test_dat, w, b):
    total = 0
    for data in test_dat:
        total += ((w*data[0] + b) - data[1])*data[0]
    return total/len(train_data)

def cost_func_b(test_dat, w, b):
    total = 0
    for data in test_dat:
        total += ((w*data[0] + b) - data[1])
    return total/len(train_data)

#Training
w = 0
b = 0
for _ in range(1000):
    w = grad_des_w(w, cost_func_w(train_data, w, b))
    b = grad_des_b(b, cost_func_b(train_data, w, b))
    cost_func(train_data, w, b)

#Testing
for _ in range(5):
    print(f'Value for {test_data[_]}: {w*test_data[_] + b}')