def reset(self, **kwargs):
IterativeAlgorithm.reset(self)
# memberships = [self.normalize([random() for j in range(self.n_clusters)]) for i in range(len(self.sample))]
# self.model = FuzzyCMeansClusterer(self.sample, memberships)
if self.initializer:
self.n_clusters = len(self.initializer)
centroids = self.initializer
data = array([elem.pattern for elem in self.sample])
memberships = [self.normalize([random() for j in range(self.n_clusters)]) for i in range(len(self.sample))]
for j in range(len(centroids)):
for i in range(len(data)):
den = sum([(linalg.norm(data[i] - centroids[j])/
linalg.norm(data[i]-centroids[k]))**(2/(self.m-1))
for k in range(self.n_clusters)])
memberships[i][j] = 1/den
else:
memberships = [self.normalize([random() for j in range(self.n_clusters)]) for i in range(len(self.sample))]
self.model = FuzzyCMeansClusterer(self.sample, memberships)
# 10 means simply a big value, so that we don't run the risk of erronously stop
# if we check convergence before first iteration
self.memberships_old = [[10 for j in range(self.n_clusters)] for i in range(len(self.sample))]
