def estimate_beta(x, y):
beta_initial = [random.random() for x_i in x[0]]
return minimize_stochastic(squared_error,
squared_error_gradient,
x, y,
beta_initial,
0.001)
def estimate_beta_ridge(x, y, alpha):
"""use gradient descent to fit a ridge regression
with penalty alpha"""
beta_initial = [random.random() for x_i in x[0]]
return minimize_stochastic(
partial(squared_error_ridge, alpha=alpha),
partial(squared_error_ridge_gradient, alpha=alpha), x, y, beta_initial,
0.001)
def estimate_beta_ridge(x, y, alpha):
"""применить градиентный спуск для подгонки гребневой регрессии
со штрафом alpha"""
beta_initial = [random.random() for x_i in x[0]]
return minimize_stochastic(partial(squared_error_ridge, alpha=alpha),
partial(squared_error_ridge_gradient,
alpha=alpha),
x, y,
beta_initial,
0.001)
28.08, 19.42, 29.79, 32.8, 35.99, 28.32, 27.79, 35.88, 29.06, 36.28,
14.1, 36.63, 37.49, 26.9, 18.58, 38.48, 24.48, 18.95, 33.55, 14.24,
29.04, 32.51, 25.63, 22.22, 19, 32.73, 15.16, 13.9, 27.2, 32.01, 29.27,
33, 13.74, 20.42, 27.32, 18.23, 35.35, 28.48, 9.08, 24.62, 20.12,
35.26, 19.92, 31.02, 16.49, 12.16, 30.7, 31.22, 34.65, 13.13, 27.51,
33.2, 31.57, 14.1, 33.42, 17.44, 10.12, 24.42, 9.82, 23.39, 30.93,
15.03, 21.67, 31.09, 33.29, 22.61, 26.89, 23.48, 8.38, 27.81, 32.35,
23.84
]
alpha, beta = least_squares_fit(num_friends_good, daily_minutes_good)
print("alpha", alpha)
print("beta", beta)
print("r-квадрат",
r_squared(alpha, beta, num_friends_good, daily_minutes_good))
print()
print("градиентный спуск:")
# выбрать случайное значение для запуска
random.seed(0)
theta = [random.random(), random.random()]
alpha, beta = minimize_stochastic(squared_error, squared_error_gradient,
num_friends_good, daily_minutes_good,
theta, 0.0001)
print("alpha", alpha)
print("beta", beta)
# In[ ]: