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:
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