tsne_xor.py

# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python
# https://www.udemy.com/unsupervised-deep-learning-in-python
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future

import numpy as np
import matplotlib.pyplot as plt

from sklearn.manifold import TSNE


def get_xor_data():
    X1 = np.random.random((100, 2))
    X2 = np.random.random((100, 2)) - np.array([1, 1])
    X3 = np.random.random((100, 2)) - np.array([1, 0])
    X4 = np.random.random((100, 2)) - np.array([0, 1])
    X = np.vstack((X1, X2, X3, X4))
    Y = np.array([0]*200 + [1]*200)
    return X, Y


def main():
    X, Y = get_xor_data()

    plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)
    plt.show()

    tsne = TSNE(perplexity=40)
    Z = tsne.fit_transform(X)
    plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5)
    plt.show()

if __name__ == '__main__':
    main()