tsne_donut.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_donut_data():
    N = 600
    R_inner = 10
    R_outer = 20

    # distance from origin is radius + random normal
    # angle theta is uniformly distributed between (0, 2pi)
    R1 = np.random.randn(N//2) + R_inner
    theta = 2*np.pi*np.random.random(N//2)
    X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T

    R2 = np.random.randn(N//2) + R_outer
    theta = 2*np.pi*np.random.random(N//2)
    X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T

    X = np.concatenate([ X_inner, X_outer ])
    Y = np.array([0]*(N//2) + [1]*(N//2))
    return X, Y


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