def plot_showcase_with_clusters():
ds = Table('showcase')
X, = ds.to_numpy("a")
w, means, covars, priors = em(X, 3, 100)
stdevs = np.sqrt(covars)
m, M = X.min(axis=0), X.max(axis=0)
X = (X - m) / (M - m)
means = (means - m) / (M - m)
stdevs /= M - m
parallel_coordinates_plot("showcase-clusters.pdf", X, means, stdevs)
def plot_wine_with_km():
ds = Table('wine')
X, = ds.to_numpy("a")
X = X[:, (3,4,5,6,9)]
w, means, covars, priors = em(X, 3, 0)
stdevs = np.sqrt(covars)
m, M = X.min(axis=0), X.max(axis=0)
X = (X - m) / (M - m)
means = (means - m) / (M - m)
stdevs /= M - m
parallel_coordinates_plot("wine-km.pdf", X, means, stdevs)
def plot_yeast_with_km():
ds = Table('yeast-class-RPR')
from Orange.feature.imputation import AverageConstructor
ds = AverageConstructor()(ds)(ds)
X, = ds.to_numpy("a")
X = X[:, (72, 73, 74, 75, 76, 77, 78)]
w, means, covars, priors = em(X, 4, 0)
stdevs = np.sqrt(covars)
m, M = X.min(axis=0), X.max(axis=0)
X = (X - m) / (M - m)
means = (means - m) / (M - m)
stdevs /= M - m
parallel_coordinates_plot("yeast-km.pdf", X, means, stdevs)
def plot_showcase():
ds = Table('showcase')
X, = ds.to_numpy("a")
m, M = X.min(axis=0), X.max(axis=0)
X = (X - m) / (M - m)
def annotate_critical_areas(ax):
ax.add_artist(Ellipse(xy=(1., 0.6,), width=.1, height=0.3, facecolor='none'))
ax.annotate('1.', xy=(1, 0.6), xytext=(0.5, 0.4),
arrowprops=dict(facecolor='white', shrink=0.05))
ax.add_artist(Ellipse(xy=(2., 0.725,), width=.1, height=0.5, facecolor='none'))
ax.add_artist(Ellipse(xy=(3., 0.8,), width=.1, height=0.4, facecolor='none'))
ax.annotate('2.', xy=(2., 0.725), xytext=(2.5, 0.5),
arrowprops=dict(facecolor='white', shrink=0.05))
ax.annotate('2.', xy=(3., 0.725), xytext=(2.5, 0.5),
arrowprops=dict(facecolor='white', shrink=0.05))
parallel_coordinates_plot("showcase-lines.pdf", X, annotate=annotate_critical_areas)