t-SNE Embedding

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import numpy,pylab,time

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from sklearn import manifold,datasets

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X,y=datasets.load_digits(return_X_y=True)

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tsne=manifold.TSNE(n_components=2,learning_rate=800.,init="random")

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t0=time.time(); X_emb=tsne.fit_transform(X)

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t=time.time()-t0

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x_min,x_max=numpy.min(X_emb,0),numpy.max(X_emb,0)

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X_emb=(X_emb-x_min)/(x_max-x_min)

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f,ax=pylab.subplots(1,figsize=(6,6))

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for i in range(X_emb.shape[0]):

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    pylab.text(X_emb[i,0],X_emb[i,1],str(y[i]),

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               color=pylab.cm.hsv(.1*y[i]))

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pylab.title("t-SNE embedding %f s"%t)

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pylab.axis("off"); pylab.show()

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