def plotLocallyWeightedLinesFromDirectory(directory, ax):
"""Read activations from file, and plot them."""
try:
inputs = numpy.loadtxt(directory + '/inputs_grid.txt')
except IOError:
return False
try:
lines = numpy.loadtxt(directory + '/lines_grid.txt')
except IOError:
# If there are no lines, assume this to be a weighted sum of basis functions instead
return plotBasisFunctionsFromDirectory(directory, ax)
n_dims = len(numpy.atleast_1d(inputs[0]))
if (n_dims > 2):
sys.exit('Cannot plot input data with a dimensionality of ' +
str(n_dims) + '.')
try:
filename = directory + '/n_samples_per_dim.txt'
n_samples_per_dim = numpy.loadtxt(filename, dtype=int)
except IOError:
# Assume data is 1D
n_samples_per_dim = len(inputs)
#try:
# predictions = numpy.loadtxt(directory+'/predictions_grid.txt')
# plotDataPredictionsGrid(inputs,predictions,ax,n_samples_per_dim)
#except IOError:
# predictions = [];
try:
activations_unnormalized = numpy.loadtxt(
directory + '/activations_unnormalized_grid.txt')
except IOError:
activations_unnormalized = None
try:
activations = numpy.loadtxt(directory + '/activations_grid.txt')
except IOError:
activations = None
plotLocallyWeightedLines(inputs, lines, ax, n_samples_per_dim, activations,
activations_unnormalized)
if (n_dims == 1):
ax.set_xlabel('input')
ax.set_ylabel('output')
else:
ax.set_xlabel('input_1')
ax.set_ylabel('input_2')
ax.set_zlabel('output')
return True
def plotLocallyWeightedLinesFromDirectory(directory,ax):
"""Read activations from file, and plot them."""
try:
inputs = numpy.loadtxt(directory+'/inputs_grid.txt')
except IOError:
return False;
try:
lines = numpy.loadtxt(directory+'/lines_grid.txt')
except IOError:
# If there are no lines, assume this to be a weighted sum of basis functions instead
return plotBasisFunctionsFromDirectory(directory,ax)
n_dims = len(numpy.atleast_1d(inputs[0]))
if (n_dims>2):
sys.exit('Cannot plot input data with a dimensionality of '+str(n_dims)+'.')
try:
n_samples_per_dim = numpy.loadtxt(directory+'/n_samples_per_dim.txt')
except IOError:
# Assume data is 1D
n_samples_per_dim = len(inputs)
#try:
# predictions = numpy.loadtxt(directory+'/predictions_grid.txt')
# plotDataPredictionsGrid(inputs,predictions,ax,n_samples_per_dim)
#except IOError:
# predictions = [];
try:
activations_unnormalized = numpy.loadtxt(directory+'/activations_unnormalized_grid.txt')
except IOError:
activations_unnormalized = None
try:
activations = numpy.loadtxt(directory+'/activations_grid.txt')
except IOError:
activations = None
plotLocallyWeightedLines(inputs,lines,ax,n_samples_per_dim,activations,activations_unnormalized)
if (n_dims==1):
ax.set_xlabel('input');
ax.set_ylabel('output');
else:
ax.set_xlabel('input_1');
ax.set_ylabel('input_2');
ax.set_zlabel('output');
return True;
fig_number = 1
for name in function_approximator_names:
fig = plt.figure(fig_number)
directory_fa = directory + "/" + name
if (getDataDimFromDirectory(directory_fa) == 1):
ax = fig.add_subplot(111)
else:
ax = fig.add_subplot(111, projection='3d')
fig_number += 1
try:
if (name == "WLS" or name == "LWR" or name == "LWPR"
or name == "GMR"):
plotLocallyWeightedLinesFromDirectory(directory_fa, ax)
elif (name == "RBFN" or name == "GPR" or name == "IRFRLS"):
plotBasisFunctionsFromDirectory(directory_fa, ax)
plotDataFromDirectory(directory_fa, ax)
ax.set_ylim(-1.0, 1.5)
except IOError:
print "WARNING: Could not find data for function approximator " + name
ax.set_title(name)
ax.legend(['f(x)', '+std', '-std', 'residuals'])
plt.show()
#fig.savefig("lwr.svg")
fig_number = 1;
for name in function_approximator_names:
fig = plt.figure(fig_number)
directory_fa = directory +"/"+ name
if (getDataDimFromDirectory(directory_fa)==1):
ax = fig.add_subplot(111)
else:
ax = fig.add_subplot(111, projection='3d')
fig_number += 1;
try:
if (name=="WLS" or name=="LWR" or name=="LWPR" or name=="GMR"):
plotLocallyWeightedLinesFromDirectory(directory_fa,ax)
elif (name=="RBFN" or name=="GPR" or name=="IRFRLS"):
plotBasisFunctionsFromDirectory(directory_fa,ax)
plotDataFromDirectory(directory_fa,ax)
ax.set_ylim(-1.0,1.5)
except IOError:
print "WARNING: Could not find data for function approximator "+name
ax.set_title(name)
ax.legend(['f(x)','+std','-std','residuals'])
plt.show()
#fig.savefig("lwr.svg")
directory_fa = directory + "/" + name
if (getDataDimFromDirectory(directory_fa) == 1):
ax = fig.add_subplot(111)
else:
ax = fig.add_subplot(111, projection='3d')
fig_number += 1
try:
plotDataFromDirectory(directory_fa, ax)
if (name == "WLS" or name == "LWR" or name == "LWPR"
or name == "GPR"):
plotLocallyWeightedLinesFromDirectory(directory_fa, ax)
elif (name == "RBFN"):
plot_normalized = False
plotBasisFunctionsFromDirectory(directory_fa, ax,
plot_normalized)
ax.set_ylim(-1.0, 1.5)
except IOError:
print "WARNING: Could not find data for function approximator " + name
ax.set_title(name + " (" + devel[name] + ")")
ax.legend(['f(x)', '+std', '-std', 'residuals'])
print "stable/testing/unstable in the titles refers to the development status of the algorithm"
print " * stable - tested and should work"
print " * testing - seems ok, but still needs testing"
print " * unstable - seems ok, but still needs testing, and is still being modified regularly"
plt.show()
#fig.savefig("lwr.svg")
directory_fa = directory +"/"+ name
if (getDataDimFromDirectory(directory_fa)==1):
ax = fig.add_subplot(111)
else:
ax = fig.add_subplot(111, projection='3d')
fig_number += 1;
try:
plotDataFromDirectory(directory_fa,ax)
if (name=="WLS" or name=="LWR" or name=="LWPR" or name=="GPR"):
plotLocallyWeightedLinesFromDirectory(directory_fa,ax)
elif (name=="RBFN"):
plot_normalized=False
plotBasisFunctionsFromDirectory(directory_fa,ax,plot_normalized)
ax.set_ylim(-1.0,1.5)
except IOError:
print "WARNING: Could not find data for function approximator "+name
ax.set_title(name+" ("+devel[name]+")")
ax.legend(['f(x)','+std','-std','residuals'])
print "stable/testing/unstable in the titles refers to the development status of the algorithm"
print " * stable - tested and should work"
print " * testing - seems ok, but still needs testing"
print " * unstable - seems ok, but still needs testing, and is still being modified regularly"
plt.show()
#fig.savefig("lwr.svg")
