gradient ascent algorithm python

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

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MarsNebulaSoup 110 points

                                    import matplotlib.pyplot as mp, numpy as np
# Primitive
origin = lambda x: 2 * x - x ** 2
x = np.linspace(0, 2, 9999)
mp.plot(x, origin(x), c='black')  # Visualization
# Derivative of the original function
derivative = lambda x: 2 - 2 * x
# Gradient rising demand
extreme_point = 0  # Initial value
alpha = 0.1  # Step, that is the learning rate
precision = 0.001  # Range of tolerance
while True:
    mp.scatter(extreme_point, origin(extreme_point))  # Visualization
    error = alpha * derivative(extreme_point)  # Climbing pace
    extreme_point += error  Climbing #
    if abs(error) &lt; precision:
        break  # Exit iterative error is small
mp.show()123456789101112131415161718

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