def plotFunctionApproximatorTrainingFromDirectory(directory,ax,ax2=None):
"""Load data related to function approximator training from a directory and plot it."""
if ax2 != None:
plotLocallyWeightedLinesFromDirectory(directory,ax2)
else:
plotLocallyWeightedLinesFromDirectory(directory,ax)
plotDataFromDirectory(directory,ax)
def plotFunctionApproximatorTrainingFromDirectory(directory,ax):
"""zzz."""
plotDataFromDirectory(directory,ax)
data_read_successfully = True
cur_directory_number=0
while (data_read_successfully):
cur_dir = directory+'/perturbation'+str(cur_directory_number)+'/'
data_read_successfully = plotLocallyWeightedLinesFromDirectory(cur_dir,ax)
cur_directory_number+=1
def plotFunctionApproximatorTrainingFromDirectory(directory,ax):
"""Load data related to function approximator training from a directory and plot it."""
plotDataFromDirectory(directory,ax)
data_read_successfully = True
cur_directory_number=0
while (data_read_successfully):
cur_dir = directory+'/perturbation'+str(cur_directory_number)+'/'
data_read_successfully = plotLocallyWeightedLinesFromDirectory(cur_dir,ax)
cur_directory_number+=1
def plotFunctionApproximatorTrainingFromDirectory(directory, ax):
"""zzz."""
plotDataFromDirectory(directory, ax)
data_read_successfully = True
cur_directory_number = 0
while data_read_successfully:
cur_dir = directory + "/perturbation" + str(cur_directory_number) + "/"
data_read_successfully = plotLocallyWeightedLinesFromDirectory(cur_dir, ax)
cur_directory_number += 1
from plotLocallyWeightedLines import plotLocallyWeightedLinesFromDirectory
if __name__=='__main__':
executable = "../../../bin_test/testBasisFunctionsLWR"
if (not os.path.isfile(executable)):
print ""
print "ERROR: Executable '"+executable+"' does not exist."
print "Please call 'make install' in the build directory first."
print ""
sys.exit(-1);
# Call the executable with the directory to which results should be written
directory = "/tmp/testBasisFunctionsLWR"
subprocess.call([executable, directory])
# Plot the results in each directory
fig = plt.figure()
subplot_number = 1;
for sym_label in ["symmetric","Asymmetric"]:
ax = fig.add_subplot(1,2,subplot_number)
subplot_number += 1;
directory_fa = directory +"/1D_" + sym_label
plotDataFromDirectory(directory_fa,ax)
plotLocallyWeightedLinesFromDirectory(directory_fa,ax)
#ax.legend(['targets','predictions'])
ax.set_title(sym_label)
plt.show()
sys.exit(-1);
# Call the executable with the directory to which results should be written
directory = "/tmp/demoTrainFunctionApproximators"
subprocess.call([executable, directory])
# Plot the results in each directory
function_approximator_names = ["LWR","LWPR","GMR","IRFRLS"]
fig = plt.figure()
subplot_number = 1;
for name in function_approximator_names:
ax = fig.add_subplot(1,len(function_approximator_names),subplot_number)
subplot_number += 1;
directory_fa = directory +"/"+ name
try:
plotDataFromDirectory(directory_fa,ax)
if (name=="LWR" or name=="LWPR"):
plotLocallyWeightedLinesFromDirectory(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(['targets','predictions'])
plt.show()
def plotFunctionApproximatorTrainingFromDirectory(directory, ax):
"""zzz."""
plotLocallyWeightedLinesFromDirectory(directory, ax)
plotDataFromDirectory(directory, ax)
def plotFunctionApproximatorTrainingFromDirectory(directory,ax):
"""zzz."""
plotLocallyWeightedLinesFromDirectory(directory,ax)
plotDataFromDirectory(directory,ax)
