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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
Example:
- Minimize Rosenbrock's Function with Nelder-Mead.
- Dynamic plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using callback
- solver interactivity
"""
# Nelder-Mead solver
from mystic.solvers import fmin
# Rosenbrock function
from mystic.models import rosen
# tools
from mystic.tools import getch
import matplotlib.pyplot as plt
plt.ion()
# draw the plot
def plot_frame():
plt.title("Rosenbrock parameter convergence")
plt.xlabel("Nelder-Mead solver iterations")
plt.ylabel("parameter value")
plt.draw()
plt.pause(0.001)
return
iter = 0
step, xval, yval, zval = [], [], [], []
# plot the parameter trajectories
def plot_params(params):
global iter, step, xval, yval, zval
step.append(iter)
xval.append(params[0])
yval.append(params[1])
zval.append(params[2])
plt.plot(step,xval,'b-')
plt.plot(step,yval,'g-')
plt.plot(step,zval,'r-')
plt.legend(["x", "y", "z"])
plt.draw()
plt.pause(0.001)
iter += 1
return
if __name__ == '__main__':
# initial guess
x0 = [0.8,1.2,0.7]
# suggest that the user interacts with the solver
print("NOTE: while solver is running, press 'Ctrl-C' in console window")
getch()
plot_frame()
# use Nelder-Mead to minimize the Rosenbrock function
solution = fmin(rosen,x0,disp=1,callback=plot_params,handler=True)
print(solution)
# don't exit until user is ready
getch()
# end of file
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