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https://github.com/AtsushiSakai/PythonRobotics.git
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90 lines
1.7 KiB
Python
90 lines
1.7 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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import numpy as np
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import random
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import math
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delta = 0.1
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minXY=-5.0
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maxXY=5.0
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nContour=50
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alpha=0.001
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def Jacob(state):
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u"""
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jacobi matrix of Himmelblau's function
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"""
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x=state[0,0]
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y=state[0,1]
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dx=4*x**3+4*x*y-44*x+2*x+2*y**2-14
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dy=2*x**2+4*x*y+4*y**3-26*y-22
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J=np.matrix([dx,dy]).T
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return J
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def HimmelblauFunction(x,y):
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u"""
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Himmelblau's function
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see Himmelblau's function - Wikipedia, the free encyclopedia
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http://en.wikipedia.org/wiki/Himmelblau%27s_function
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"""
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return (x**2+y-11)**2+(x+y**2-7)**2
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def CreateMeshData():
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x = np.arange(minXY, maxXY, delta)
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y = np.arange(minXY, maxXY, delta)
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X, Y = np.meshgrid(x, y)
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Z=[HimmelblauFunction(x,y) for (x,y) in zip(X,Y)]
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return(X,Y,Z)
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def QuasiNewtonMethod(start,Jacob):
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u"""
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Quasi Newton Method Optimization
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"""
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result=start
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x=start
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H= np.identity(2)
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preJ=None
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preG=None
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while 1:
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J=Jacob(x)
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sumJ=abs(np.sum(J))
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if sumJ<=0.01:
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print("OK")
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break
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grad=-np.linalg.inv(H)*J
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x+=alpha*grad.T
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result=np.vstack((result,np.array(x)))
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if preJ is not None:
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y=J-preJ
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H=H+(y*y.T)/(y.T*preG)-(H*preG*preG.T*H)/(preG.T*H*preG)
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preJ=J
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preG=(alpha*grad.T).T
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return result
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# Main
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start=np.matrix([random.uniform(minXY,maxXY),random.uniform(minXY,maxXY)])
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result=QuasiNewtonMethod(start,Jacob)
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(X,Y,Z)=CreateMeshData()
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CS = plt.contour(X, Y, Z,nContour)
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plt.plot(start[0,0],start[0,1],"xr");
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optX=result[:,0]
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optY=result[:,1]
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plt.plot(optX,optY,"-r");
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plt.show()
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