def _save_img_list(img_list, save_path, config):
#_show_img_list(img_list)
metadata = dict(title='generator images', artist='Matplotlib', comment='Movie support!')
writer = ImageMagickWriter(fps=1,metadata=metadata)
ims = [np.transpose(i, (1, 2, 0)) for i in img_list]
fig, ax = plt.subplots()
with writer.saving(fig, "%s/img_list.gif" % save_path,500):
for i in range(len(ims)):
ax.imshow(ims[i])
ax.set_title("step {}".format(i * config["save_every"]))
writer.grab_frame()
def save_animation(self, fps=5, time=5, *, filename="robot_animation.gif"):
original_position = self.theta
number_of_frames = self.path.shape[1]
frames_to_animate = fps * time
if number_of_frames < frames_to_animate:
step = 1
fps = max(1, time // number_of_frames)
else:
step = number_of_frames // frames_to_animate
fig = plt.figure()
robot_animation = ImageMagickWriter(fps=fps)
with robot_animation.saving(fig, filename, dpi=150):
for column in np.arange(start=0, stop=number_of_frames, step=step):
self.theta = np.array(self.path[:, column])
self.plot(show=False)
robot_animation.grab_frame()
fig.clear()
self._set_plot_options()
self.theta = original_position # ??
plt.close()
train_x[:, 2],
s=5,
c=create_color_list(train_y))
scat = ax.scatter(weights[:, 0],
weights[:, 1],
weights[:, 2],
c='k',
marker='^')
max_iterations = 100
learning_rate = 0.005
old_weights = np.zeros(weights.shape)
writer = ImageMagickWriter(fps=4)
file_name = "kohonens.gif"
try:
with writer.saving(fig, file_name, dpi=100):
for k in range(max_iterations):
neuron_class = 4 * np.ones(weight_size)
print(k)
random.shuffle(train_set)
for x, y in train_set:
d = np.zeros(weight_size)
for i in range(weight_size):
d[i] = np.linalg.norm(x - weights[i, :])
winner_index = np.argmin(d)
neuron_class[winner_index] = y
h = neighbor(k, winner_index)
for j in range(weight_size):
delta_w = learning_rate * h[j] * (x - weights[j])
weights[j] += delta_w