градиент спуск
import numpy as np
def hw(w0, w1, x):
final = w0 + w1*x
return final
def error(x,y,w0,w1):
return y - hw(w0, w1 ,x)
def sumError(training_points, w0, w1):
sum = 0
for i in training_points:
sum += error(i[0], i[1], w0, w1)
return sum
def sumError2(training_points, w0, w1):
sum = 0
for i in training_points:
sum += error(i[0], i[1], w0, w1) * i[0]
return sum
def updateW0(training_points, w0 , w1, learning_rate):
final = w0 + learning_rate * sumError(training_points, w0, w1)
final = np.round(final,round)
return final
def updateW1(training_points, w0, w1, learning_rate):
final = w1 + learning_rate * sumError2(training_points, w0, w1)
final = np.round(final,round)
return final
def update_iteration(number_of_iterations, training_points, w0, w1, learning_rate):
print("----Batch Gradient Descend----")
w0_new = 0
w1_new = 0
for i in range(number_of_iterations):
w0_new = updateW0(training_points, w0, w1, learning_rate)
w1_new =updateW1(training_points, w0, w1, learning_rate)
print("--------------------------------")
print(f"iteration {i+1}: w0 = {w0_new}")
print(f"iteration {i+1}: w1 = {w1_new}")
w0 = w0_new
w1 = w1_new
print("================================")
return w0_new, w1_new
# round 3 decimal points
round = 3
training_points = [
(1.5, 1),
(3.5, 3),
(3,2),
(5,3),
(2,2.5),
]
w0 = 0
w1 = 0
learning_rate = 0.01
number_of_iterations = 3
update_iteration(number_of_iterations,training_points,w0, w1,learning_rate)
SMR