python3-scikilearn-lle-manifold

python3-scikilearn-lle-manifold.py

import matplotlib.pyplot as plt
# This import is needed to modify the way figure behaves
from mpl_toolkits.mplot3d import Axes3D
#----------------------------------------------------------------------
# Locally linear embedding of the swiss roll
from sklearn import manifold, datasets
X, color = datasets.samples_generator.make_swiss_roll(n_samples=1500)
print("Computing LLE embedding")
X_r, err = manifold.locally_linear_embedding(X, n_neighbors=12,
                                             n_components=2)
print("Done. Reconstruction error: %g" % err)
#----------------------------------------------------------------------
# Plot result
fig = plt.figure()
try:
    # compatibility matplotlib < 1.0
    ax = fig.add_subplot(211, projection='3d')
    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
except:
    ax = fig.add_subplot(211)
    ax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)

ax.set_title("Original data")
ax = fig.add_subplot(212)
ax.scatter(X_r[:, 0], X_r[:, 1], c=color, cmap=plt.cm.Spectral)
plt.axis('tight')
plt.xticks([]), plt.yticks([])
plt.title('Projected data')
plt.show()

requirements.txt

matplotlib==2.0.2
scikit_learn==0.18.2

スクリーンショット 2017-07-10 12.06.26.png