tsne visualization

gistfile1.txt

from time import time
import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.manifold import TSNE


def get_data():
    digits = datasets.load_digits(n_class=10)
    data = digits.data
    label = digits.target
    n_samples, n_features = data.shape
    return data, label, n_samples, n_features


def plot_embedding(data, label, title):
    x_min, x_max = np.min(data, 0), np.max(data, 0)
    data = (data - x_min) / (x_max - x_min)

    fig = plt.figure()
    label_colors = np.random.random([10 , 3])
    colors = label_colors[label]

    plt.scatter(data[:,0], data[:,1], c=colors)
    plt.xticks([])
    plt.yticks([])
    plt.title(title)
    plt.show()
    return

def main():
    data, label, n_samples, n_features = get_data()
    print('Computing t-SNE embedding')
    tsne = TSNE(n_components=2, init='pca', random_state=0)
    t0 = time()
    result = tsne.fit_transform(data)
    plot_embedding(result, label,
                         't-SNE embedding of the digits (time %.2fs)'
                         % (time() - t0))


if __name__ == '__main__':
    main()