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):
"""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
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()
executable = "../../../bin_test/testLeastSquares"
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)
fig_number = 1
directory = "/tmp/testLeastSquares/"
# Call the executable with the directory to which results should be written
command = executable + " " + directory
print(command)
subprocess.call(command, shell=True)
for dim in [1, 2]:
fig = plt.figure(fig_number, figsize=(15, 5))
fig_number = fig_number + 1
if (dim == 1):
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
cur_directory = directory + "/" + str(dim) + "D"
plotDataFromDirectory(cur_directory, ax)
plt.show()
def plotFunctionApproximatorTrainingFromDirectory(directory, ax):
"""zzz."""
plotLocallyWeightedLinesFromDirectory(directory, ax)
plotDataFromDirectory(directory, ax)
else:
ax.set_xlabel('input_1');
ax.set_ylabel('input_2');
ax.set_zlabel('output');
return True;
if __name__=='__main__':
"""Pass a directory argument, read inputs, targets and predictions from that directory, and plot."""
if (len(sys.argv)==2):
directory = str(sys.argv[1])
else:
print '\nUsage: '+sys.argv[0]+' <directory> (data is read from directory)\n';
sys.exit()
fig = plt.figure()
if (getDataDimFromDirectory(directory)==1):
ax = fig.gca()
else:
ax = Axes3D(fig)
plotDataFromDirectory(directory,ax)
plotLocallyWeightedLinesFromDirectory(directory,ax)
plt.show()
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
if __name__ == '__main__':
"""Pass a directory argument, read inputs, targets and predictions from that directory, and plot."""
if (len(sys.argv) == 2):
directory = str(sys.argv[1])
else:
print '\nUsage: ' + sys.argv[
0] + ' <directory> (data is read from directory)\n'
sys.exit()
fig = plt.figure()
if (getDataDimFromDirectory(directory) == 1):
ax = fig.gca()
else:
ax = Axes3D(fig)
plotDataFromDirectory(directory, ax)
plotLocallyWeightedLinesFromDirectory(directory, ax)
plt.show()
def plotFunctionApproximatorTrainingFromDirectory(directory,ax):
"""zzz."""
plotLocallyWeightedLinesFromDirectory(directory,ax)
plotDataFromDirectory(directory,ax)