def sqerror_ridge_gradient(x: Vector, y: float, beta: Vector,
alpha: float) -> Vector:
"""
the gradient corresponding to the ith squared error term
including the ridge penalty
"""
return add(sqerror_gradient(x, y, beta),
ridge_penalty_gradient(beta, alpha))
def sqerror_ridge_gradient(x: Vector, y: float, beta: Vector,
alpha: float) -> Vector:
"""
Gradient odpowiadający czynnikowi i-tego błędu
podniesionego do kwadratu w tym również kary grzbietowej.
"""
return add(sqerror_gradient(x, y, beta),
ridge_penalty_gradient(beta, alpha))
def gradient_step(v: Vector, gradient: Vector, step_size: float) -> Vector:
"""v에서 step_size만큼 이동하기"""
assert len(v) == len(gradient)
step = scalar_multiply(step_size, gradient)
retrun add(v, step)
def gradient_step(v: Vector, gradient: Vector, step_size: float) -> Vector:
"""Moves `step_size` in the `gradient` direction from `v`"""
assert len(v) == len(gradient)
step = scalar_multiply(step_size, gradient)
return add(v, step)
def gradient_step(v: Vector, gradient: Vector, step_size: float) -> Vector:
step = scalar_multiply(step_size, gradient)
return add(v, step)
def gradient_step(v: Vector, gradient: Vector, step_size: float) -> Vector:
"""Moves `step_size` in the `gradient` direction from `v`"""
assert len(v) == len(gradient)
step = scalar_multiply(step_size, gradient)
return add(v, step)
def sqerror_ridge_gradient(x: Vector, y: float, beta: Vector,
alpha: float) -> Vector:
return add(sqerror_gradient(x, y, beta),
ridge_penalty_gradient(beta, alpha))
def estimate_gradient(f, v, h=0.00001):
return [partial_difference_quotient(f: Callable[Vector], v:Vector, i:int, h:float =0.0001):
for i, in range(len(v))]
#116p
import random
from scratch.linear_algebra import distance, add, scalar_multiply
assert len(v) == len(gradient)
step = scalat_multiply(step_size, gradient)
return add (v, step)
def sum_of_squares_gradient( v: Vector)-> Vector :
return [ 2 * v_i for v_i in v]
v = [random.uniform(-10,10) for i in range (3) ]
for epoch in range(1000) :
grad = sum_of_squares_gradient(v)
v= gradient_step(v, grad, -0.01)
print(epoch, v)
#117p
def gradient_step(v: Vector, gradient: Vector, step_size: float) -> Vector:
"""Przejdź o step_size w kierunku gradient od punktu v."""
assert len(v) == len(gradient)
step = scalar_multiply(step_size, gradient)
return add(v, step)
def gradient_step(v:np.array,gradient:np.array,step_size:float)->np.array:
step = scalar_multiply(step_size,gradient)
return add(v,step)