cost function

 import numpy as np

%matplotlib widget

import matplotlib.pyplot as plt

from lab_utils_uni import plt_intuition, plt_stationary, plt_update_onclick, soup_bowl

plt.style.use('./deeplearning.mplstyle')

 x_train = np.array([1.0, 2.0])           #(size in 1000 square feet)

    y_train = np.array([300.0, 500.0])           #(price in 1000s of dollars)

def compute_cost(x, y, w, b): 

    """

    Computes the cost function for linear regression.

    

    Args:

      x (ndarray (m,)): Data, m examples 

      y (ndarray (m,)): target values

      w,b (scalar)    : model parameters  

    

    Returns

        total_cost (float): The cost of using w,b as the parameters for linear regression

               to fit the data points in x and y

    """

    # number of training examples

    m = x.shape[0] 

    

    cost_sum = 0 

    for i in range(m): 

        f_wb = w * x[i] + b   

        cost = (f_wb - y[i]) ** 2  

        cost_sum = cost_sum + cost  

    total_cost = (1 / (2 * m)) * cost_sum  

    return total_cost

plt_intuition(x_train,y_train)

x_train = np.array([1.0, 1.7, 2.0, 2.5, 3.0, 3.2])

y_train = np.array([250, 300, 480,  430,   630, 730,])

plt.close('all') 

fig, ax, dyn_items = plt_stationary(x_train, y_train)

updater = plt_update_onclick(fig, ax, x_train, y_train, dyn_items)

soup_bowl()