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 gradient.minimize_stochastic(
partial(squared_error_ridge, alpha=alpha),
partial(squared_error_gradient, alpha=alpha), x, y, beta_initial,
0.001)
def estimate_beta(x, y):
"""Find the optimal beta using stochastic gradient descent"""
beta_initial = [random.random() for x_i in x[0]]
return gradient.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 gradient.minimize_stochastic(partial(squared_error_ridge,
alpha=alpha),
partial(squared_error_gradient,
alpha=alpha),
x,
y,
beta_initial,
0.001)
def estimate_beta(x, y):
"""Find the optimal beta using stochastic gradient descent"""
beta_initial = [random.random() for x_i in x[0]]
return gradient.minimize_stochastic(squared_error, squared_error_gradient,
x, y, beta_initial, 0.001)
