t-SNE Dimensionality Reduction

from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
from sklearn import manifold, datasets, decomposition, ensemble,discriminant_analysis, random_projection

digits = datasets.load_digits(n_class=10)
X = digits.data
y = digits.target

xx=[]
for i in range(0,len(X)):
    xx.append((X[i] - np.min(X)) / (np.max(X) - np.min(X)))
X=xx

pca=decomposition.TruncatedSVD(n_components=2)
X_pca = decomposition.TruncatedSVD(n_components=2).fit_transform(X)
pca2=pca.fit(X)
pca2.components_
pca3=pca2.transform(X)

tsne = manifold.TSNE(n_components=2, init='pca', random_state=0,perplexity=50,verbose=1,n_iter=1500)
X_tsne = tsne.fit_transform(X)

fig = plt.figure(figsize=(10,5))
plt.subplot2grid((1,2), (0,0))
plt.title('PRINCIPAL COMPONENTS ANALYSIS')
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=digits.target)
plt.subplot2grid((1,2), (0,1), rowspan=1, colspan=2)
plt.title('t-SNE')
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=digits.target)
plt.show()

## ORIGINAL DATA DIMENSIONS
print('ORIGINAL DATA DIMENSION:',np.array(X).shape)

## DIMENSIONS AFTER t-SNE
print('DIMENSIONS AFTER t-SNE',np.array(X_tsne).shape)