Isomap with Scikit-learn
Play with Isomap. It can be used in almost the same way as PCA.
isomap.py
from sklearn.datasets import load_digits
from sklearn.manifold import Isomap
import matplotlib.pyplot as plt
##Data reading
digits = load_digits()
X = digits.data
y = digits.target
target_names = digits.target_names
## Isomap
n_neighbors=30
isomap = Isomap(n_neighbors=30, n_components=2)
X_iso = isomap.fit(X).transform(X)
## colors
colors = [plt.cm.nipy_spectral(i/10., 1) for i in range(10)]
## plot
plt.figure()
for c, target_name in zip(colors, target_names):
plt.scatter(X_iso[y == target_name, 0], X_iso[y == target_name, 1], c=c, label = target_name)
plt.legend()
plt.title('Isomap')
plt.show()
Execution result.
reference: Scikit-learn PCA sample Scikit-learn manifold learning sample
Recommended Posts