This two codes for Gradient Descent algorithm for two vectors

the first code for visualizing in 3D AND the second visualizing the function in 2D

I want to update these two codes in order to replace the exist function with sphere function

please give me excutable codes without error message with simple explanations since can understand your updated codes

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The first code :-

import numpy as np

from mpl_toolkits.mplot3d import Axes3D

import matplotlib.pyplot as plt

class GradientDescent:

def __init__(self, function, gradient, initial_solution, learning_rate=0.1, max_iter=100, tolerance=0.0000001):

self.function = function

self.gradient= gradient

self.solution = initial_solution

self.learning_rate = learning_rate

self.max_iter = max_iter

self.tolerance = tolerance

def run(self):

t = 0

while t

diff = -self.learning_rate*self.gradient(*self.solution)

if np.linalg.norm(diff)

break

self.solution += diff

t += 1

return self.solution, self.function(*self.solution)

def fun2(x, y):

return x**2 + y**2

def gradient2(x, y):

return np.array([2*x, 2*y])

bounds = [-3, 3]

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

X = np.linspace(bounds[0], bounds[1], 100)

Y = np.linspace(bounds[0], bounds[1], 100)

X, Y = np.meshgrid(X, Y)

Z = fun2(X, Y)

ax.plot_surface(X, Y, Z)

random_solution = np.random.uniform(bounds[0], bounds[1], size=2)

gd = GradientDescent(fun2, gradient2, random_solution)

best_solution, best_value = gd.run()

ax.scatter(best_solution[0], best_solution[1], best_value, color='r', s=100)

plt.show()

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The second code :-

import numpy as np from matplotlib import pyplot as plt

class GradientDescent: def __init__(self, function, gradient, initial_solution, learning_rate=0.1, max_iter=100, tolerance=0.0000001): self.function = function self.gradient = gradient self.solution = initial_solution self.learning_rate = learning_rate self.max_iter = max_iter self.tolerance = tolerance

def run(self): t = 0 while t

def fun1(x, y): return x ** 2 + y ** 2

def gradient1(x, y): return np.array([2 * x, 2 * y])

bounds = [-3, 3]

plt.figure()

x, y = np.meshgrid(np.linspace(bounds[0], bounds[1], 100), np.linspace(bounds[0], bounds[1], 100)) z = fun1(x, y) plt.contour(x, y, z,levels=20)

random_solution = np.random.uniform(bounds[0], bounds[1], size=2)

gd = GradientDescent(fun1, gradient1, random_solution)

best_solution, best_value = gd.run()

plt.plot(best_solution[0], best_solution[1], marker='o') plt.plot(best_solution[0], best_solution[1])

plt.show()