python-scikit-learnTSNe embedding example

import numpy as np
from sklearn import manifold

X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
Xe = manifold.TSNE(n_components=2, learning_rate='auto', init='random').fit_transform(X)ctrl + cgithub

`import numpy` import Numpy module	`from sklearn import` import module from scikit-learn
`.TSNE(` creates T-distributed Stochastic Neighbor Embedding model	`n_components=2` reduce dataset to 2 features
`.fit_transform(` train and transform given dataset	`Xe` will contain embedded dataset

Usage example

import numpy as np
from sklearn.manifold import TSNE

X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
print(X.shape)

Xe = TSNE(n_components=2, learning_rate='auto', init='random').fit_transform(X)
print(Xe.shape)

output

(4, 3)
(4, 2)

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