add NewtonMethod

This commit is contained in:
AtsushiSakai
2016-05-02 21:37:07 +09:00
parent 74d17c6095
commit 5c5fde0241

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@@ -0,0 +1,94 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import random
delta = 0.1
minXY=-5.0
maxXY=5.0
nContour=50
alpha=0.01
def Hessian(state):
u"""
Hessian matrix of Himmelblau's function
"""
x=state[0]
y=state[1]
dxx=12*x**2+4*y-42;
dxy=4*x+4*y
dyy=4*x+12*y**2-26
H=np.array([[dxx,dxy],[dxy,dyy]])
return H
def Jacob(state):
u"""
jacobi matrix of Himmelblau's function
"""
x=state[0]
y=state[1]
dx=4*x**3+4*x*y-44*x+2*x+2*y**2-14
dy=2*x**2+4*x*y+4*y**3-26*y-22
J=[dx,dy]
return J
def HimmelblauFunction(x,y):
u"""
Himmelblau's function
see Himmelblau's function - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Himmelblau%27s_function
"""
return (x**2+y-11)**2+(x+y**2-7)**2
def CreateMeshData():
x = np.arange(minXY, maxXY, delta)
y = np.arange(minXY, maxXY, delta)
X, Y = np.meshgrid(x, y)
Z=[HimmelblauFunction(x,y) for (x,y) in zip(X,Y)]
return(X,Y,Z)
def SteepestDescentMethod(start,Jacob):
u"""
Steepest Descent Method Optimization
"""
result=start
x=start
while 1:
J=Jacob(x)
H=Hessian(x)
sumJ=sum([abs(alpha*j) for j in J])
if sumJ<=0.01:
print("OK")
break
grad=-np.linalg.inv(H).dot(J)
print(grad)
x=x+[alpha*j for j in grad]
result=np.vstack((result,x))
return result
# Main
start=np.array([random.uniform(minXY,maxXY),random.uniform(minXY,maxXY)])
result=SteepestDescentMethod(start,Jacob)
(X,Y,Z)=CreateMeshData()
CS = plt.contour(X, Y, Z,nContour)
# plt.clabel(CS, inline=1, fontsize=10)
# plt.title('Simplest default with labels')
plt.plot(start[0],start[1],"xr");
optX=[x[0] for x in result]
optY=[x[1] for x in result]
plt.plot(optX,optY,"-r");
plt.show()