def plot_data_set(dataset_name):
data_set = pickle.load(open('utils/data_sets.pickle', 'rb'), encoding='latin1')[dataset_name]
fig = plt.figure()
ax = fig.add_subplot(111)
ac.hide_top_and_right_axis(ax)
# plt.axis('off')
ac.plot_data(data_set, ax)
ac.save_plot(fig)
def create_iris_figure():
from sklearn import datasets
iris = datasets.load_iris()
data = np.append(iris.data.T[0:2], np.array([iris.target]), axis=0)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.axis('off')
ac.plot_data(data, ax)
ac.save_plot(fig)
def plot_data_set(dataset_name):
data_set = pickle.load(open('utils/data_sets.pickle', 'rb'),
encoding='latin1')[dataset_name]
fig = plt.figure()
ax = fig.add_subplot(111)
ac.hide_top_and_right_axis(ax)
# plt.axis('off')
ac.plot_data(data_set, ax)
ac.save_plot(fig)
def create_iris_figure():
from sklearn import datasets
iris = datasets.load_iris()
data = np.append(iris.data.T[0:2], np.array([iris.target]), axis=0)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.axis('off')
ac.plot_data(data, ax)
ac.save_plot(fig)
def plot_all_data_sets():
data_sets = pickle.load(open('../data_sets.pickle', 'rb'), encoding='latin1')
for key in data_sets:
data = data_sets[key]
fig = plt.figure()
ax = fig.add_subplot(111)
ac.hide_top_and_right_axis(ax)
# plt.axis('off')
ac.plot_data(data, ax)
ac.save_plot(fig)
def plot_all_data_sets():
data_sets = pickle.load(open('../data_sets.pickle', 'rb'),
encoding='latin1')
for key in data_sets:
data = data_sets[key]
fig = plt.figure()
ax = fig.add_subplot(111)
ac.hide_top_and_right_axis(ax)
# plt.axis('off')
ac.plot_data(data, ax)
ac.save_plot(fig)
def plot_points():
blue = np.array([[1.7, 1.2, .9, .7, 2.3, 1.75, 2.7, 3.3, 3.1],
[-0.23, 0.2, 1.05, 1.87, 0.15, 2.08, -.3, 1.1, 2.2],
[.0, .0, .0, .0, .0, .0, .0, .0, .0]])
red = np.array([[2.26, 1.8, 2.3], [1.38, 1.15, 0.8], [1, 1, 1]])
data = np.concatenate((red, blue), axis=1)
ax = plt.subplot(111)
ac.plot_data(data, ax)
plt.axis('off')
ac.save_plot()
plt.savefig('points.svg', bbox_inches='tight')
def plot_points():
blue = np.array([[1.7, 1.2, .9, .7, 2.3, 1.75, 2.7, 3.3, 3.1],
[-0.23, 0.2, 1.05, 1.87, 0.15, 2.08, -.3, 1.1, 2.2],
[.0, .0, .0, .0, .0, .0, .0, .0, .0]])
red = np.array([[2.26, 1.8, 2.3], [1.38, 1.15, 0.8], [1, 1, 1]])
data = np.concatenate((red, blue), axis=1)
ax = plt.subplot(111)
ac.plot_data(data, ax)
plt.axis('off')
ac.save_plot()
plt.savefig('points.svg', bbox_inches='tight')
def np_list_to_csv_string(npl):
return ",".join(list(map(lambda f: "{:.4f}".format(f), npl)))
csv = []
for arr in mean_results:
csv.append(np_list_to_csv_string(arr))
utils.save_object(mean_results, SAVE_FOLDER, 'results')
utils.save_string_to_file("\n".join(csv), SAVE_FOLDER, 'results.csv')
utils.save_dict(CLASSIFIER_CONFIG, SAVE_FOLDER, 'config.json')
data = np.array(mean_results)
x = range(data.shape[1])
fig, ax = plt.subplots()
plotter.hide_top_and_right_axis(ax)
ax.yaxis.grid(color='gray')
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Best polygon solution')
ax.set_prop_cycle(cycler('color', ['c', 'm', 'y', 'k', 'r', 'g', 'b']))
lines = []
for i in range(len(configurations)):
lines.append(ax.plot(x, data[i], label=labels[i]))
plt.legend(labels, loc='lower right')
plotter.save_plot(fig, SAVE_FOLDER, 'results')
# plt.show()
#fig1.savefig('fig1.eps')
def np_list_to_csv_string(npl):
return ",".join(list(map(lambda f: "{:.4f}".format(f), npl)))
csv = []
for arr in mean_results:
csv.append(np_list_to_csv_string(arr))
utils.save_object(mean_results, SAVE_FOLDER, 'results')
utils.save_string_to_file("\n".join(csv), SAVE_FOLDER, 'results.csv')
utils.save_dict(CLASSIFIER_CONFIG, SAVE_FOLDER, 'config.json')
data = np.array(mean_results)
x = range(data.shape[1])
fig, ax = plt.subplots()
plotter.hide_top_and_right_axis(ax)
ax.yaxis.grid(color='gray')
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Best polygon solution')
ax.set_prop_cycle(cycler('color', ['c', 'm', 'y', 'k', 'r', 'g', 'b']))
lines = []
for i in range(len(configurations)):
lines.append(ax.plot(x, data[i], label=labels[i]))
plt.legend(labels, loc='lower right')
plotter.save_plot(fig, SAVE_FOLDER, 'results')
# plt.show()
#fig1.savefig('fig1.eps')
