吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第二周编程作业

吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第一、二周所有jupyter notebook文件：

吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第一、二周所有jupyter notebook文件(包括实验室练习文件)

本次作业

Exercise 1

# UNQ_C1
# GRADED FUNCTION: compute_cost

def compute_cost(x, y, w, b): 
    """
    Computes the cost function for linear regression.
    
    Args:
        x (ndarray): Shape (m,) Input to the model (Population of cities) 
        y (ndarray): Shape (m,) Label (Actual profits for the cities)
        w, b (scalar): Parameters of the model
    
    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] 
    
    # You need to return this variable correctly
    total_cost = 0
    
    ### START CODE HERE ###
    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 
    
    ### END CODE HERE ### 

    return total_cost

Exercise 2

# UNQ_C2
# GRADED FUNCTION: compute_gradient
def compute_gradient(x, y, w, b): 
    """
    Computes the gradient for linear regression 
    Args:
      x (ndarray): Shape (m,) Input to the model (Population of cities) 
      y (ndarray): Shape (m,) Label (Actual profits for the cities)
      w, b (scalar): Parameters of the model  
    Returns
      dj_dw (scalar): The gradient of the cost w.r.t. the parameters w
      dj_db (scalar): The gradient of the cost w.r.t. the parameter b     
     """
    
    # Number of training examples
    m = x.shape[0]
    
    # You need to return the following variables correctly
    dj_dw = 0
    dj_db = 0
    
    ### START CODE HERE ### 
    for i in range(m):  
        f_wb = w * x[i] + b 
        dj_dw_i = (f_wb - y[i]) * x[i] 
        dj_db_i = f_wb - y[i] 
        dj_db += dj_db_i
        dj_dw += dj_dw_i 
    dj_dw = dj_dw / m 
    dj_db = dj_db / m 
    
    ### END CODE HERE ### 
        
    return dj_dw, dj_db