diff --git a/classification/fcm.py b/classification/fcm.py
new file mode 100644
index 0000000..79a3ee9
--- /dev/null
+++ b/classification/fcm.py
@@ -0,0 +1,57 @@
+from tools import *
+
+# https://en.wikipedia.org/wiki/Fuzzy_clustering
+
+
+class FuzzyCMeans:
+ def __init__(self, n_clusters, initial_centers, data, max_iter=250, m=2, error=1e-5):
+ assert m > 1
+ #assert initial_centers.shape[0] == n_clusters
+ self.U = None
+ self.centers = initial_centers
+ self.max_iter = max_iter
+ self.m = m
+ self.error = error
+ self.data = data
+
+ def membership(self, data, centers):
+ U_temp = cdist(data, centers, 'euclidean')
+ U_temp = numpy.power(U_temp, 2/(self.m - 1))
+ denominator_ = U_temp.reshape(
+ (data.shape[0], 1, -1)).repeat(U_temp.shape[-1], axis=1)
+ denominator_ = U_temp[:, :, numpy.newaxis] / denominator_
+ return 1 / denominator_.sum(2)
+
+ def Centers(self, data, U):
+ um = U ** self.m
+ return (data.T @ um / numpy.sum(um, axis=0)).T
+
+ def newImage(self, U, centers, im):
+ best = numpy.argmax(self.U, axis=-1)
+ # print(best)
+ # numpy.round()
+ image = im.astype(int)
+ for i in range(256):
+ image = numpy.where(image == float(i), centers[best[i]][0], image)
+ return image
+
+ def compute(self):
+ self.U = self.membership(self.data, self.centers)
+
+ past_U = numpy.copy(self.U)
+ begin_time = datetime.datetime.now()
+ for i in range(self.max_iter):
+
+ self.centers = self.Centers(self.data, self.U)
+ self.U = self.membership(self.data, self.centers)
+
+ if norm(self.U - past_U) < self.error:
+ break
+ past_U = numpy.copy(self.U)
+ x = datetime.datetime.now() - begin_time
+ return self.centers, self.U, x
+
+# that's how you run it, data being your data, and the other parameters being the basic FCM parameters such as numbe rof cluseters, degree of fuzziness and so on
+# f = FuzzyCMeans(n_clusters=C, initial_centers=Initial_centers,
+# data=data m=2, max_iter=1000, error=1e-5)
+# centers, U, time = f.compute()
diff --git a/classification/tools.py b/classification/tools.py
new file mode 100644
index 0000000..682268a
--- /dev/null
+++ b/classification/tools.py
@@ -0,0 +1,20 @@
+from matplotlib.image import imread
+import matplotlib.pyplot as plt
+from math import sqrt
+import math
+import random
+import numpy
+import operator
+from scipy.spatial.distance import cdist
+from scipy.linalg import norm
+import datetime
+
+
+def Histogram(path):
+ image = imread(path)
+ if len(image.shape) != 2:
+ def gray(rgb): return numpy.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])
+ gray = gray(image)
+ image = gray
+ hist, bins = numpy.histogram(image.ravel(), 256, [0, 256])
+ return adapt(hist)
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