trjhtr

This is my implementation of Fuzzy c-Means in Python. In the main section of the code, I compared the time it takes with the sklearn implementation of kMeans.

import time
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.cluster import KMeans


def fcm(data, n_clusters=1, n_init=30, m=2, max_iter=300, tol=1e-16):

 min_cost = np.inf
 for iter_init in range(n_init):

 # Randomly initialize centers
 centers = data[np.random.choice(
 data.shape[0], size=n_clusters, replace=False
 ), :]

 # Compute initial distances
 # Zeros are replaced by eps to avoid division issues
 dist = np.fmax(
 cdist(centers, data, metric='sqeuclidean'),
 np.finfo(np.float64).eps
 )

 for iter1 in range(max_iter):

 # Compute memberships 
 u = (1 / dist) ** (1 / (m-1))
 um = (u / u.sum(axis=0))**m

 # Recompute centers
 prev_centers = centers
 centers = um.dot(data) / um.sum(axis=1)[:, None]

 dist = cdist(centers, data, metric='sqeuclidean')

 if np.linalg.norm(centers - prev_centers) < tol:
 break

 # Compute cost
 cost = np.sum(um * dist)
 if cost < min_cost:
 min_cost = cost
 min_centers = centers
 mem = um.argmax(axis=0)

 return min_centers, mem


if __name__ == '__main__':
 data = np.loadtxt('grid5_edit.txt')
 labels = data[:, -1]
 k = len(np.unique(labels))
 data = data[:, 0:-1]

 repeats = 10

 # Time This
 fcm_time = 0
 for iter1 in range(repeats):
 fcm_start = time.time()
 centers, mem = fcm(
 data, n_clusters=k, n_init=30, m=2, max_iter=300, tol=1e-16
 )
 fcm_time += (time.time() - fcm_start)
 print('Average FCM time =', fcm_time/repeats)

 # Time This (as well)
 km_time = 0
 for iter1 in range(repeats):
 km_start = time.time()
 km1 = KMeans(
 n_clusters=k, n_init=30, max_iter=300, tol=1e-16
 ).fit(data)
 km_time += (time.time() - km_start)
 print('Average kMeans time =', km_time/repeats)

 print('Ratio of time =', fcm_time / km_time)

It seems the code is around 15 to 16 times slower than kMeans.

Average FCM time = 3.7663435697555543
Average kMeans time = 0.24237003326416015
Ratio of time = 15.539642087892112

Other than a code review, I'm also hoping for any suggestions to make the code faster.