Made final plotting of fitness and parameter history a function.

这个提交包含在:
Craig Warren
2015-12-22 17:37:30 +00:00
父节点 6cb3af53c8
当前提交 ce4eb3299c

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@@ -31,7 +31,6 @@ from enum import Enum
from collections import OrderedDict
import numpy as np
import matplotlib.pyplot as plt
from gprMax.constants import c, e0, m0, z0, floattype
from gprMax.exceptions import CmdInputError
@@ -79,7 +78,7 @@ def main():
# Process for Taguchi optimisation #
########################################
if args.opt_taguchi:
from user_libs.optimisations.taguchi import taguchi_code_blocks, select_OA, calculate_ranges_experiments, calculate_optimal_levels
from user_libs.optimisations.taguchi import taguchi_code_blocks, select_OA, calculate_ranges_experiments, calculate_optimal_levels, plot_optimisation_history
# Default maximum number of iterations of optimisation to perform (used if the stopping criterion is not achieved)
maxiterations = 20
@@ -97,7 +96,7 @@ def main():
# Store initial parameter ranges
optparamsinit = list(optparams.items())
# Dictionary to hold history of optmised values of parameters
optparamshist = OrderedDict((key, list()) for key in optparams)
@@ -250,20 +249,8 @@ def main():
print('\n{}\nTaguchi optimisation completed after {} iteration(s).\nHistory of optimal parameter values {} and of fitness values {}\n{}\n'.format(68*'*', i, dict(optparamshist), fitnessvalueshist, 68*'*'))
# Plot history of fitness values
fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Fitness value'), num='History of fitness values', figsize=(20, 10), facecolor='w', edgecolor='w')
ax.plot(fitnessvalueshist, 'r', marker='x', ms=10, lw=2)
ax.grid()
# Plot history of optimisation parameters
p = 0
for key, value in optparamshist.items():
fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Parameter value'), num='History of ' + key + ' parameter', figsize=(20, 10), facecolor='w', edgecolor='w')
ax.plot(optparamshist[key], 'r', marker='x', ms=10, lw=2)
# ax.set_ylim([optparamsinit[p][1][0], optparamsinit[p][1][1]])
ax.grid()
p += 1
plt.show()
# Plot the history of fitness values and each optimised parameter values for the optimisation
plot_optimisation_history(fitnessvalueshist, optparamshist, optparamsinit)
#######################################