def draw_contour():
import pylab, numpy
x, y = numpy.mgrid[-1:2:0.02,-0.5:2:0.02]
c = 0*x
s,t = x.shape
for i in range(s):
for j in range(t):
xx,yy = x[i,j], y[i,j]
c[i,j] = fOsc3D([xx,yy])
pylab.contourf(x,y,c,100)
def draw_contour():
import pylab, numpy
x, y = numpy.mgrid[-1:2:0.02, -0.5:2:0.02]
c = 0 * x
s, t = x.shape
for i in range(s):
for j in range(t):
xx, yy = x[i, j], y[i, j]
c[i, j] = fOsc3D([xx, yy])
pylab.contourf(x, y, c, 100)
def draw_contour():
import matplotlib.pyplot as plt, numpy
x, y = numpy.mgrid[-1:2:0.02, -0.5:2:0.02]
c = 0 * x
s, t = x.shape
for i in range(s):
for j in range(t):
xx, yy = x[i, j], y[i, j]
c[i, j] = fOsc3D([xx, yy])
plt.contourf(x, y, c, 100)
def draw_contour():
import matplotlib.pyplot as plt, numpy
x, y = numpy.mgrid[-1:2:0.02,-0.5:2:0.02]
c = 0*x
s,t = x.shape
for i in range(s):
for j in range(t):
xx,yy = x[i,j], y[i,j]
c[i,j] = fOsc3D([xx,yy])
plt.contourf(x,y,c,100)
solver.Solve(fOsc3D,termination=ChangeOverGeneration(1e-5, 30), \
strategy=strategy,CrossProbability=1.0,ScalingFactor=0.9)
return solver.Solution()
if __name__ == '__main__':
import pylab
from mystic.solvers import fmin
#from mystic._scipyoptimize import fmin
draw_contour()
solution = main()
print("solution: %s" % solution)
pylab.plot([solution[0]], [solution[1]], 'wo', markersize=10)
print("Differential Evolution: Min: %s, sol = %s" %
(fOsc3D(solution), solution))
print("\nTrying scipy.optimize.fmin (Nelder-Mead Simplex)...")
m = fmin(fOsc3D, [0.1, 0.1])
pylab.plot([m[0]], [m[1]], 'ro', markersize=5)
print("solution w/ initial conditions (0.1,0.1): %s\n" % m)
m = fmin(fOsc3D, [1, 1])
pylab.plot([m[0]], [m[1]], 'ro', markersize=5)
print("solution w/ initial conditions (1,1): %s\n" % m)
m = fmin(fOsc3D, [-1, 1])
print("solution w/ initial conditions (-1,1): %s\n" % m)
pylab.plot([m[0]], [m[1]], 'ro', markersize=5)
solver.Solve(fOsc3D,termination=ChangeOverGeneration(1e-5, 30), \
strategy=strategy,CrossProbability=1.0,ScalingFactor=0.9)
return solver.Solution()
if __name__ == '__main__':
import pylab
from mystic.solvers import fmin
#from mystic._scipyoptimize import fmin
draw_contour()
solution = main()
print "solution: ", solution
pylab.plot([solution[0]],[solution[1]],'wo',markersize=10)
print "Differential Evolution: Min: %s, sol = %s" % (fOsc3D(solution), solution)
print "\nTrying scipy.optimize.fmin (Nelder-Mead Simplex)..."
m = fmin(fOsc3D, [0.1, 0.1])
pylab.plot([m[0]],[m[1]],'ro',markersize=5)
print "solution w/ initial conditions (0.1,0.1): %s\n" % m
m = fmin(fOsc3D, [1, 1])
pylab.plot([m[0]],[m[1]],'ro',markersize=5)
print "solution w/ initial conditions (1,1): %s\n" % m
m = fmin(fOsc3D, [-1, 1])
print "solution w/ initial conditions (-1,1): %s\n" % m
pylab.plot([m[0]],[m[1]],'ro',markersize=5)
solver.Solve(fOsc3D,termination=ChangeOverGeneration(1e-5, 30), \
strategy=strategy,CrossProbability=1.0,ScalingFactor=0.9)
return solver.Solution()
if __name__ == '__main__':
import pylab
from mystic.solvers import fmin
#from mystic._scipyoptimize import fmin
draw_contour()
solution = main()
print "solution: ", solution
pylab.plot([solution[0]], [solution[1]], 'wo', markersize=10)
print "Differential Evolution: Min: %s, sol = %s" % (fOsc3D(solution),
solution)
print "\nTrying scipy.optimize.fmin (Nelder-Mead Simplex)..."
m = fmin(fOsc3D, [0.1, 0.1])
pylab.plot([m[0]], [m[1]], 'ro', markersize=5)
print "solution w/ initial conditions (0.1,0.1): %s\n" % m
m = fmin(fOsc3D, [1, 1])
pylab.plot([m[0]], [m[1]], 'ro', markersize=5)
print "solution w/ initial conditions (1,1): %s\n" % m
m = fmin(fOsc3D, [-1, 1])
print "solution w/ initial conditions (-1,1): %s\n" % m
pylab.plot([m[0]], [m[1]], 'ro', markersize=5)
