"""Pass a directory argument, read inputs, targets and predictions from that directory, and plot."""
executable = "../../../bin_test/testPerturbModelParameterLWR"
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/testPerturbModelParameterLWR/"
# 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(dim)
cur_directory = directory + "/" + str(dim) + "D"
if (getDataDimFromDirectory(cur_directory) == 1):
ax = fig.gca()
else:
ax = Axes3D(fig)
plotFunctionApproximatorTrainingFromDirectory(cur_directory, ax)
plt.show()
fa_names = ["RBFN","GPR","IRFRLS","LWR", "LWPR", "GMR"]
for fa_name in fa_names:
# Call the executable with the directory to which results should be written
command = executable+" "+directory+" "+fa_name
#print command
subprocess.call(command, shell=True)
for fa_name in fa_names:
print("Plotting "+fa_name+" results")
fig = plt.figure(fig_number,figsize=(15,5))
fig_number = fig_number+1
for dim in [1, 2]:
cur_directory = directory+fa_name+"_"+str(dim)+"D";
if (getDataDimFromDirectory(cur_directory)==1):
ax = fig.add_subplot(1, 3, 1)
ax2 = None
else:
ax = fig.add_subplot(1, 3, 2, projection='3d')
ax2 = fig.add_subplot(1, 3, 3, projection='3d')
plotFunctionApproximatorTrainingFromDirectory(cur_directory,ax,ax2)
ax.set_title(fa_name+" ("+str(dim)+"D data)")
if ax2 != None:
ax2.set_title(fa_name+" ("+str(dim)+"D basis functions)")
plt.show()
print ""
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 = ["WLS","LWR","LWPR","IRFRLS","GMR","RBFN","GPR"]
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
# 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 = [
"WLS", "LWR", "LWPR", "IRFRLS", "GMR", "RBFN", "GPR"
]
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:
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
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)
plotFunctionApproximatorTrainingFromDirectory(directory, ax)
plt.show()
