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 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()