gradient ascent algorithm python

1import matplotlib.pyplot as mp, numpy as np
2# Primitive
3origin = lambda x: 2 * x - x ** 2
4x = np.linspace(0, 2, 9999)
5mp.plot(x, origin(x), c='black')  # Visualization
6# Derivative of the original function
7derivative = lambda x: 2 - 2 * x
8# Gradient rising demand
9extreme_point = 0  # Initial value
10alpha = 0.1  # Step, that is the learning rate
11presision = 0.001  # Range of tolerance
12while True:
13    mp.scatter(extreme_point, origin(extreme_point))  # Visualization
14    error = alpha * derivative(extreme_point)  # Climbing pace
15    extreme_point += error  Climbing #
16    if abs(error) < presision:
17        break  # Exit iterative error is small
18mp.show()123456789101112131415161718

1import matplotlib.pyplot as mp, numpy as np
2# Primitive
3origin = lambda x: 2 * x - x ** 2
4x = np.linspace(0, 2, 9999)
5mp.plot(x, origin(x), c='black')  # Visualization
6# Derivative of the original function
7derivative = lambda x: 2 - 2 * x
8# Gradient rising demand
9extreme_point = 0  # Initial value
10alpha = 0.1  # Step, that is the learning rate
11precision = 0.001  # Range of tolerance
12while True:
13    mp.scatter(extreme_point, origin(extreme_point))  # Visualization
14    error = alpha * derivative(extreme_point)  # Climbing pace
15    extreme_point += error  Climbing #
16    if abs(error) < precision:
17        break  # Exit iterative error is small
18mp.show()123456789101112131415161718