if __name__ == '__main__':
print("Nelder-Mead Simplex")
print("===================")
# initial guess
x0 = [0.8, 1.2, 0.7]
# use Nelder-Mead to minimize the Rosenbrock function
solution = fmin(rosen, x0)
print(solution)
# plot the Rosenbrock function (one plot per axis)
x = [0.01 * i for i in range(200)]
plt.plot(x, [rosen([i, 1., 1.]) for i in x])
plt.plot(x, [rosen([1., i, 1.]) for i in x])
plt.plot(x, [rosen([1., 1., i]) for i in x])
# plot the solved minimum (for x)
plt.plot([solution[0]], [rosen(solution)], 'bo')
# draw the plot
plt.title("minimium of Rosenbrock's function")
plt.xlabel("x, y, z")
plt.ylabel("f(i) = Rosenbrock's function")
plt.legend(["f(x,1,1)", "f(1,y,1)", "f(1,1,z)"])
plt.show()
# end of file
import mystic.math.interpolate as ip
fx = ip._to_function(cost)
assert (fx(*x.T) - cost(x.T) ).sum() < 0.0001
f = ip.interpf(x, y, method='linear', arrays=True)
cf = ip._to_objective(f)
assert (f(*x.T) - cf(x.T) ).sum() < 0.0001
assert f(*x[0].T) == cf(x[0].T)
assert fx(*x[0].T) == cost(x[0].T)
assert ip.gradient(x, f, method='linear').shape \
== ip.gradient(x, fx, method='linear').shape
from mystic.models import rosen0der as rosen
y = rosen(x.T)
f = ip.interpf(x, y, method='linear')
fx = ip._to_function(rosen)
cf = ip._to_objective(f)
assert f(*x[0].T) == cf(x[0].T)
assert fx(*x[0].T) == rosen(x[0].T)
#print( fx(*x[0].T) - rosen(x[0].T) )
assert ip.gradient(x, f, method='linear').shape \
== ip.gradient(x, fx, method='linear').shape
import mystic.math.interpolate as ip
fx = ip._to_function(cost)
assert (fx(*x.T) - cost(x.T) ).sum() < 0.0001
f = ip.interpf(x, y, method='linear', arrays=True)
cf = ip._to_objective(f)
assert (f(*x.T) - cf(x.T) ).sum() < 0.0001
assert f(*x[0].T) == cf(x[0].T)
assert fx(*x[0].T) == cost(x[0].T)
assert ip.gradient(x, f, method='linear').shape \
== ip.gradient(x, fx, method='linear').shape
from mystic.models import rosen0der as rosen
y = rosen(x.T)
f = ip.interpf(x, y, method='linear')
fx = ip._to_function(rosen)
cf = ip._to_objective(f)
assert f(*x[0].T) == cf(x[0].T)
assert fx(*x[0].T) == rosen(x[0].T)
#print( fx(*x[0].T) - rosen(x[0].T) )
assert ip.gradient(x, f, method='linear').shape \
== ip.gradient(x, fx, method='linear').shape
if __name__ == '__main__':
print "Nelder-Mead Simplex"
print "==================="
# initial guess
x0 = [0.8,1.2,0.7]
# use Nelder-Mead to minimize the Rosenbrock function
solution = fmin(rosen,x0)
print solution
# plot the Rosenbrock function (one plot per axis)
x = [0.01*i for i in range(200)]
pylab.plot(x,[rosen([i,1.,1.]) for i in x])
pylab.plot(x,[rosen([1.,i,1.]) for i in x])
pylab.plot(x,[rosen([1.,1.,i]) for i in x])
# plot the solved minimum (for x)
pylab.plot([solution[0]],[rosen(solution)],'bo')
# draw the plot
pylab.title("minimium of Rosenbrock's function")
pylab.xlabel("x, y, z")
pylab.ylabel("f(i) = Rosenbrock's function")
pylab.legend(["f(x,1,1)","f(1,y,1)","f(1,1,z)"])
pylab.show()
# end of file
