fixed the moving of plotting .py files

python/cma_multiplt.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+"""In a OS shell::
+
+    python cma_multiplt.py data_file_name
+    
+or in a python shell::
+
+    import cma_multiplt as lcmaplt
+    lcmaplt.plot(data_file_name)
+    
+"""
+# CMA-ES, Covariance Matrix Adaptation Evolution Strategy
+# Copyright (c) 2014 Inria
+# Author: Emmanuel Benazera <emmanuel.benazera@lri.fr>
+#
+# This file is part of libcmaes.
+#
+# libcmaes is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# libcmaes is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with libcmaes.  If not, see <http://www.gnu.org/licenses/>.
+##
+
+import sys, pylab, csv
+import numpy as np
+from matplotlib.pylab import figure, ioff, ion, subplot, semilogy, hold, grid, axis, title, text, xlabel, isinteractive, draw, gcf
+# TODO: the above direct imports clutter the interface in a Python shell
+
+# number of static variables at the head of every line (i.e. independent of problem dimension)
+single_values = 4
+
+def plot(filename):
+    # read data into numpy array
+    dat = np.loadtxt(filename,dtype=float)
+
+    dim = int(np.ceil(np.shape(dat)[1] - single_values) / 3) # we estimate the problem dimension from the data
+    #print dim
+
+    fvalue = np.absolute(dat[:,0])
+    fevals = dat[:,1]
+    sigma = dat[:,2]
+    kappa = dat[:,3]
+    if dim > 0:
+        eigenvc = []
+        for c in range(single_values,single_values+dim):
+            eigenvc.append(c)
+        eigenv = dat[:,eigenvc]
+        stdsc = []
+        for c in range(single_values+dim,single_values+2*dim):
+            stdsc.append(c)
+        stds = dat[:,stdsc]
+        minstds = np.amin(stds,axis=1)
+        maxstds = np.amax(stds,axis=1)
+        xmeanc = []
+        for c in range(single_values+2*dim,single_values+3*dim):
+            xmeanc.append(c)
+        xmean = dat[:,xmeanc]
+
+    # plot data.
+    pylab.rcParams['font.size'] = 10
+    xlab = "function evaluations"
+
+    # plot fvalue, sigma, kappa
+    if dim > 0:
+        subplot(221)
+    semilogy(fevals,fvalue,'b')
+    semilogy(fevals,sigma,'g')
+    semilogy(fevals,kappa,'r')
+    if dim > 0:
+        semilogy(fevals,sigma*minstds,'y')
+        semilogy(fevals,sigma*maxstds,'y')
+    title('f-value (blue), sigma (green), kappa (red)')
+    grid(True)
+
+    if dim == 0:
+        pylab.xlabel(xlab)
+        pylab.show();
+        msg = '  --- press return to continue --- '
+        raw_input(msg) if sys.version < '3' else input(msg)
+        sys.exit(1)
+
+    # plot xmean
+    subplot(222)
+    pylab.plot(fevals,xmean)
+    title('Object Variables (mean, ' + str(dim) + '-D)')
+    grid(True)
+
+    # plot eigenvalues
+    subplot(223)
+    semilogy(fevals,eigenv,'-b')
+    pylab.xlabel(xlab)
+    title('Eigenvalues')
+    grid(True)
+
+    # plot std deviations
+    subplot(224)
+    semilogy(fevals,stds)
+    pylab.xlabel(xlab)
+    title('Standard Deviation in all coordinates')
+    grid(True)
+
+    pylab.show()
+
+if __name__ == "__main__":
+    plot(sys.argv[1])
+    msg = '  --- press return to continue --- '
+    raw_input(msg) if sys.version < '3' else input(msg)

python/cma_multiplt_surr.py

@@ -0,0 +1,104 @@
+##
+# CMA-ES, Covariance Matrix Adaptation Evolution Strategy
+# Copyright (c) 2014 Inria
+# Author: Emmanuel Benazera <emmanuel.benazera@lri.fr>
+#
+# This file is part of libcmaes.
+#
+# libcmaes is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# libcmaes is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with libcmaes.  If not, see <http://www.gnu.org/licenses/>.
+##
+
+import sys, pylab, csv
+import numpy as np
+from matplotlib.pylab import figure, ioff, ion, subplot, semilogy, hold, plot, grid, axis, title, text, xlabel, isinteractive, draw, gcf
+from numpy import *
+
+# number of static variables at the head of every line (i.e. independent of problem dimension)
+single_values = 4
+
+# read data into numpy array
+dat = loadtxt(sys.argv[1],dtype=float)
+
+dim = int(ceil(np.shape(dat)[1] - single_values - 3) / 3) # we estimate the problem dimension from the data
+#print dim
+
+fvalue = np.absolute(dat[:,0])
+fevals = dat[:,1]
+sigma = dat[:,2]
+kappa = dat[:,3]
+eigenvc = []
+for c in range(single_values,single_values+dim):
+    eigenvc.append(c)
+eigenv = dat[:,eigenvc]
+stdsc = []
+for c in range(single_values+dim,single_values+2*dim):
+    stdsc.append(c)
+stds = dat[:,stdsc]
+minstds = np.amin(stds,axis=1)
+maxstds = np.amax(stds,axis=1)
+xmeanc = []
+for c in range(single_values+2*dim,single_values+3*dim):
+    xmeanc.append(c)
+xmean = dat[:,xmeanc]
+trainerr = dat[:,single_values+3*dim+1]
+testerr = dat[:,single_values+3*dim+2]
+stesterr = dat[:,single_values+3*dim+3]
+
+# plot data.
+pylab.rcParams['font.size'] = 10
+xlab = "function evaluations"
+
+# plot fvalue, sigma, kappa
+subplot(321)
+semilogy(fevals,fvalue,'b')
+semilogy(fevals,sigma,'g')
+semilogy(fevals,kappa,'r')
+semilogy(fevals,sigma*minstds,'y')
+semilogy(fevals,sigma*maxstds,'y')
+title('f-value (blue), sigma (green), kappa (red)')
+grid(True)
+
+# plot xmean
+subplot(322)
+plot(fevals,xmean)
+title('Object Variables (mean, ' + str(dim) + '-D)')
+grid(True)
+
+# plot eigenvalues
+subplot(323)
+semilogy(fevals,eigenv,'-b')
+pylab.xlabel(xlab)
+title('Eigenvalues')
+grid(True)
+
+# plot std deviations
+subplot(324)
+semilogy(fevals,stds)
+pylab.xlabel(xlab)
+title('Standard Deviation in all coordinates')
+grid(True)
+
+# plot std deviations
+subplot(325)
+pylab.ylim(0,1)
+plot(fevals,trainerr,'r')
+plot(fevals,testerr,'g')
+plot(fevals,stesterr,'b')
+title('Surrogate error: train (red), test (green), smoothed test (blue)')
+grid(True)
+
+pylab.show()
+
+msg = '  --- press return to continue --- '
+raw_input(msg) if sys.version < '3' else input(msg)