def demo_cluster_example_1():
X,_,_,_ = demo_cluster_data()
# plot the input data
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(X[:, 0], X[:, 1], s=50, color='gray')
# format the plot
mf.format_plot(ax, 'Input Data')
def demo_dimensionality_reduction_example_1():
X, _ = demo_swiss_roll_data()
# visualize data
fig, ax = plt.subplots()
ax.scatter(X[:, 0], X[:, 1], color='gray', s=30)
# format the plot
mf.format_plot(ax, 'Input Data')
def demo_cluster_example_2():
X,y,_,_ = demo_cluster_data()
# plot the data with cluster labels
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(X[:, 0], X[:, 1], s=50, c=y, cmap='viridis')
# format the plot
mf.format_plot(ax, 'Learned Cluster Labels')
def demo_regression_example_1():
X, y, _, _, _ = demo_regression_data()
# plot data points
fig, ax = plt.subplots()
points = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,
cmap='viridis')
# format plot
mf.format_plot(ax, 'Input Data')
ax.axis([-4, 4, -3, 3])
def demo_classification_example_1():
X, y, X2, y2, clf = demo_classification_data()
# plot the data
fig, ax = plt.subplots(figsize=(8, 6))
point_style = dict(cmap='Paired', s=50)
ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)
# format plot
mf.format_plot(ax, 'Input Data')
ax.axis([-1, 4, -2, 7])
def demo_dimensionality_reduction_example_2():
X, y = demo_swiss_roll_data()
# create model
model = Isomap(n_neighbors=8, n_components=1)
y_fit = model.fit_transform(X).ravel()
# visualize data
fig, ax = plt.subplots()
pts = ax.scatter(X[:, 0], X[:, 1], c=y_fit, cmap='viridis', s=30)
cb = fig.colorbar(pts, ax=ax)
# format the plot
mf.format_plot(ax, 'Learned Latent Parameter')
cb.set_ticks([])
cb.set_label('Latent Variable', color='gray')
def demo_regression_example_3():
X, y, _, _, model = demo_regression_data()
# plot data points
fig, ax = plt.subplots()
pts = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,
cmap='viridis', zorder=2)
# compute and plot model color mesh
xx, yy = np.meshgrid(np.linspace(-4, 4),
np.linspace(-3, 3))
Xfit = np.vstack([xx.ravel(), yy.ravel()]).T
yfit = model.predict(Xfit)
zz = yfit.reshape(xx.shape)
ax.pcolorfast([-4, 4], [-3, 3], zz, alpha=0.5,
cmap='viridis', norm=pts.norm, zorder=1)
# format plot
mf.format_plot(ax, 'Input Data with Linear Fit')
ax.axis([-4, 4, -3, 3])
def demo_classification_example_2():
X, y, X2, y2, clf = demo_classification_data()
# Get contours describing the model
xx = np.linspace(-1, 4, 10)
yy = np.linspace(-2, 7, 10)
xy1, xy2 = np.meshgrid(xx, yy)
point_style = dict(cmap='Paired', s=50)
Z = np.array([clf.decision_function([t])
for t in zip(xy1.flat, xy2.flat)]).reshape(xy1.shape)
# plot points and model
fig, ax = plt.subplots(figsize=(8, 6))
line_style = dict(levels = [-1.0, 0.0, 1.0],
linestyles = ['dashed', 'solid', 'dashed'],
colors = 'gray', linewidths=1)
ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)
ax.contour(xy1, xy2, Z, **line_style)
# format plot
mf.format_plot(ax, 'Model Learned from Input Data')
ax.axis([-1, 4, -2, 7])