# -*- coding: utf-8 -*- """ Created on Wed Mar 23 15:03:28 2011 @author: - """ import matplotlib.pyplot as plt import plot_pca_functions; error_file1 = "/Users/isa/Experiments/PCA/CapitolBOXMSmall/10/weights.txt" error_file2_un = "/Users/isa/Experiments/BOF/learn_PCA/tests/weights.txt" error1 = plot_pca_functions.read_vector(error_file1); error2 = plot_pca_functions.read_vector(error_file2_un); plot_pca_functions.plot_error_per_sample(error1,56432) plot_pca_functions.plot_error_per_sample(error2,169296)
labels.append('Downtown Overall Error');
dim=125;
i= 0;
fig = plt.figure(1);
for pca_dir in dirs:
print (pca_dir)
if not os.path.isdir( pca_dir + '/'):
sys.exit(-1);
train_error_file = pca_dir + "/normalized_training_error.txt";
overall_error = plot_pca_functions.read_test_error(pca_dir, dim);
train_error = plot_pca_functions.read_vector(train_error_file);
print(train_error);
print(overall_error);
x = np.arange(0, len(train_error), 1);
plt.plot(x, train_error, label=labels[i]);
plt.hold(True);
x = np.arange(0, len(train_error)+1, 5);
plt.plot(x, overall_error, label=labels[i+1]);
i=i+2;
plt.title('Overall error vs training error ',fontsize= 14);
