demo1.py

# the original test : https://pythonhosted.org/scikit-fuzzy/auto_examples/plot_cmeans.html#example-plot-cmeans-py

from PFCM import pfcm, pfcm_predict
import numpy as np
import matplotlib.pyplot as plt


colors = ['b', 'orange', 'g', 'r', 'c', 'm', 'y', 'k', 'Brown', 'ForestGreen']

# Define three cluster centers
centers = [[4, 2],
           [1, 7],
           [5, 6]]

# Define three cluster sigmas in x and y, respectively
sigmas = [[0.8, 0.3],
          [0.3, 0.5],
          [1.1, 0.7]]

# Generate test data
np.random.seed(42)  # Set seed for reproducibility
xpts = np.zeros(1)
ypts = np.zeros(1)
labels = np.zeros(1)
for i, ((xmu, ymu), (xsigma, ysigma)) in enumerate(zip(centers, sigmas)):
    xpts = np.hstack((xpts, np.random.standard_normal(200) * xsigma + xmu))
    ypts = np.hstack((ypts, np.random.standard_normal(200) * ysigma + ymu))
    labels = np.hstack((labels, np.ones(200) * i))

# Visualize the test data
fig0, ax0 = plt.subplots()
for label in range(3):
    ax0.plot(xpts[labels == label], ypts[labels == label], '.',
             color=colors[label])
ax0.set_title('Test data: 200 points x3 clusters.')

# Set up the loop and plot
fig1, axes1 = plt.subplots(3, 3, figsize=(8, 8))
alldata = np.vstack((xpts, ypts)).T

for ncenters, ax in enumerate(axes1.reshape(-1), 2):
    cntr, U, T, obj_fcn = pfcm(alldata, ncenters)

    # Plot assigned clusters, for each data point in training set
    cluster_membership = np.argmax(U, axis=0)
    for j in range(ncenters):
        ax.plot(xpts[cluster_membership == j],
                ypts[cluster_membership == j], '.', color=colors[j])

    # Mark the center of each fuzzy cluster
    for pt in cntr:
        ax.plot(pt[0], pt[1], 'rs')

    ax.set_title('Centers = {0}'.format(ncenters))
    ax.axis('off')

fig1.tight_layout()

# Regenerate fuzzy model with 3 cluster centers - note that center ordering
# is random in this clustering algorithm, so the centers may change places
cntr, U, T, obj_fcn = pfcm(alldata, 3)

# Show 3-cluster model
fig2, ax2 = plt.subplots()
ax2.set_title('Trained model')
for j in range(3):
    ax2.plot(alldata.T[0, np.argmax(U, axis=0) == j],
             alldata.T[1, np.argmax(U, axis=0) == j], 'o',
             label='series ' + str(j))
ax2.legend()

# Generate uniformly sampled data spread across the range [0, 10] in x and y
newdata = np.random.uniform(0, 1, (1100, 2)) * 10

# Predict new cluster membership with `pfcm_predict` as well as
# `new_cntr` from the 3-cluster model
new_cntr, U, T, obj_fcn = pfcm_predict(newdata, cntr)

# Plot the classified uniform data. Note for visualization the maximum
# membership value has been taken at each point (i.e. these are hardened,
# not fuzzy results visualized) but the full fuzzy result is the output
# from pfcm_predict.
cluster_membership = np.argmax(U, axis=0)  # Hardening for visualization

fig3, ax3 = plt.subplots()
ax3.set_title('Random points classifed according to known centers')
for j in range(3):
    ax3.plot(newdata[cluster_membership == j, 0],
             newdata[cluster_membership == j, 1], 'o',
             label='series ' + str(j))
for pt in cntr:
    ax3.plot(pt[0], pt[1], 'rs')
# for pt in new_cntr:
    # ax3.plot(pt[0], pt[1], 'bs')
ax3.legend()

plt.show()