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)