def decision_boundary(self, x, y, name: str = '', ax: plt.axis = None):
if ax is None:
fig, ax = plt.subplots(figsize=(10, 10))
h = 0.02
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
print(np.c_[xx.ravel(), yy.ravel()].shape)
z = self.predict(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(xx.shape)
ax.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.coolwarm)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
if name:
plt.savefig(os.path.join(os.getcwd(), name + '.png'))
plt.clf()
else:
plt.show()
plt.clf()
def decision_boundary(self, x, y, ax: plt.axis = None):
"""
Plot decision boundary and labeled data
:param x: data
:param y: true labels
:param ax: matplotlib axes (optional)
:return: None
"""
if ax is None:
fig, ax = plt.subplots(figsize=(10, 10))
h = 0.02
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
z = self.predict(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(xx.shape)
ax.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.coolwarm)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
plt.show()
def plot(ax: plt.axis, x, y, u, v, trajectory, pause=0.2, title=""):
"""
Plot function for subtask 1
"""
ax.set_aspect('equal')
arrowed_spines(ax)
ax.set_xlim([min(x) * 1, max(x) * 1])
ax.set_ylim([min(y) * 1, max(y) * 1])
ax.set_title(title)
ax.streamplot(x, y, u, v)
ax.plot(trajectory[:, 0], trajectory[:, 1])
def set_inset_spectrum(axis: plt.axis, data: np.ndarray, current_index: int,
peak_collection: PeakCollection) -> plt.axis:
axis.plot(data) # Show intensity spectrum
axis = plot_peaks(axis=axis, collection=peak_collection) # Show peaks
y_bot, y_top = axis.get_ylim()
text_height = y_bot + 0.6 * (y_top - y_bot) # Data coordinates
_margin = 50
x_lim = [
max(0, current_index - _margin),
min(len(data) - 1, current_index + _margin)
]
# Plot clusters
# for cluster in peak_collection.get_clusters:
# bound_left, bound_right = cluster.get_value_slice
# if bound_right > x_lim[0] or bound_left < x_lim[1]:
# axis.axvspan(bound_left, bound_right, alpha=0.5, color='green')
# if x_lim[0] < cluster.get_avg_x < x_lim[1]:
# axis.text(x=cluster.get_avg_x, y=text_height, s=r'$\tilde{m}$'+f'={cluster.get_transverse_mode_id}')
axis.axvline(x=current_index, color='r')
axis.set_xlim(x_lim)
return axis