53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.colors import LogNorm
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import matplotlib.pyplot as plt
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import numpy as np
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import math
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def fonction(X, Y):
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return X*np.exp(-X**2-Y**2)+(X**2+Y**2)/20
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def gradient_fonction(X, Y):
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g_x=np.exp(-X**2-Y**2)+X*-2*X*np.exp(-X**2-Y**2)+X/10
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g_y=-2*Y*X*np.exp(-X**2-Y**2)+Y/10
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return g_x, g_y
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fig=plt.figure()
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fig.set_size_inches(9, 7, forward=True)
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ax=Axes3D(fig, azim=-29, elev=49)
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X=np.arange(-3, 3, 0.2)
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Y=np.arange(-3, 3, 0.2)
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X, Y=np.meshgrid(X, Y)
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Z=fonction(X, Y)
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ax.plot_wireframe(X, Y, Z, rstride=1, cstride=1)
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plt.xlabel("Paramètre 1 (x)")
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plt.ylabel("Paramètre 2 (y)")
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x1=x2=np.random.random_integers(-2, 2)+np.random.rand(1)[0]
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y1=y2=np.random.random_integers(-2, 2)+np.random.rand(1)[0]
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lr=0.2
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lr2=0.9
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correction_x1=0
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correction_y1=0
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i=0
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while True:
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g_x1, g_y1=gradient_fonction(x1, y1)
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g_x2, g_y2=gradient_fonction(x2, y2)
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correction_x1=lr2*correction_x1-lr*g_x1
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x1=x1+correction_x1
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correction_y1=lr2*correction_y1-lr*g_y1
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y1=y1+correction_y1
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x2=x2-lr*g_x2
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y2=y2-lr*g_y2
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ax.scatter(x1, y1, fonction(x1, y1), marker='o', s=10, color='#FF0000')
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ax.scatter(x2, y2, fonction(x2, y2), marker='o', s=10, color='#00FF00')
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plt.draw()
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print("iteration= {} x1={:+7.5f} y1={:+7.5f} x2={:+7.5f} y2={:+7.5f}".format(i, x1, y1, x2, y2))
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plt.pause(0.05)
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i+=1
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