kmeans_fail.py

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


import numpy as np
from kmeans import plot_k_means


def donut():
    N = 1000
    D = 2

    R_inner = 5
    R_outer = 10

    # 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 ])
    return X


def main():
    # donut
    X = donut()
    plot_k_means(X, 2, beta=0.1, show_plots=True)

    # elongated clusters
    X = np.zeros((1000, 2))
    X[:500,:] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500)
    X[500:,:] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500)
    plot_k_means(X, 2, beta=0.1, show_plots=True)

    # different density
    X = np.zeros((1000, 2))
    X[:950,:] = np.array([0,0]) + np.random.randn(950, 2)
    X[950:,:] = np.array([3,0]) + np.random.randn(50, 2)
    plot_k_means(X, 2, show_plots=True)



if __name__ == '__main__':
    main()