def cluster(self, data, n_clusters):
n, d = shape(data)
locations = zeros((self.n_particles, n_clusters, d))
for i in range(self.n_particles):
for j in range(n_clusters):
locations[i, j, :] = copy(data[randint(n), :]) # Initialize cluster centers to random datapoints
bestlocations = copy(locations)
velocities = zeros((self.n_particles, n_clusters, d))
bestscores = [score(data, centroids=locations[i, :, :], norm=self.norm) for i in range(self.n_particles)]
sbestlocation = copy(locations[argmin(bestscores), :, :])
sbestscore = min(bestscores)
for i in range(self.n_iterations):
if i % self.printfreq == 0:
print "Particle swarm iteration", i, "best score:", sbestscore
for j in range(self.n_particles):
r = rand(n_clusters, d)
s = rand(n_clusters, d)
velocities[j, :, :] = (self.w * velocities[j, :, :]) + \
(self.c1 * r * (bestlocations[j, :, :] - locations[j, :, :])) + \
(self.c2 * s * (sbestlocation - locations[j, :, :]))
locations[j, :, :] = locations[j, :, :] + velocities[j, :, :]
currentscore = score(data, centroids=locations[j, :, :], norm=self.norm)
if currentscore < bestscores[j]:
bestscores[j] = currentscore
bestlocations[j, :, :] = locations[j, :, :]
if currentscore < sbestscore:
sbestscore = currentscore
sbestlocation = copy(locations[j, :, :])
return getlabels(data, centroids=sbestlocation, norm=self.norm)
def cluster(self, data, n_clusters):
n_samples, _ = shape(data)
assert self.n_ants < n_samples, "number of ants must be lower than number of samples"
bestscore = float('inf')
bestcentroids = None
bestweights = None
pheromone = self.t0 * ones((n_samples, n_clusters))
for it in range(self.n_iter):
for _ in range(self.n_ants):
# memory = -1 * ones(n_samples, dtype='int')
weights = zeros((n_samples, n_clusters), dtype='bool')
centroids = array([data[randint(n_samples), :] for _ in range(n_clusters)], copy=True)
for i in permutation(n_samples):
scores = self.centroidscore(data[i, :], centroids, pheromone[i, :], self.beta)
if randfloat() < self.q0:
j = argmax(scores) # exploit
else:
j = choice(n_clusters, p=(scores / sum(scores))) # explore
weights[i, j] = True
centroids[j, :] = average(data, axis=0, weights=weights[:, j])
currentscore = score(data, centroids=centroids, norm=self.beta)
if currentscore < bestscore:
bestscore = currentscore
bestcentroids = copy(centroids)
bestweights = copy(weights)
pheromone = (self.ro * pheromone) + ((1.0 / bestscore) * bestweights)
if it % self.printfreq == 0:
print "Ant Colony iteration", it, "best score:", bestscore
return getlabels(data, centroids=bestcentroids, norm=self.beta)
def cluster(self, data, n_clusters):
n, d = shape(data)
locations = zeros((self.n_bees, n_clusters, d))
for i in range(self.n_bees):
for j in range(n_clusters):
locations[i, j, :] = copy(data[randint(n), :]) # Initialize cluster centers to random datapoints
currentscore = array([score(data, centroids=locations[i, :, :], norm=self.norm) for i in range(self.n_bees)])
changecount = zeros(self.n_bees)
bestlocation = copy(locations[argmin(currentscore), :, :])
bestscore = min(currentscore)
for it in range(self.n_iter):
if it % self.printfreq == 0:
print "Artificial Bee iteration", it, "best score:", bestscore
for k in range(self.n_bees):
newcentroids, newscore = self.getnewcentroids(data, locations, k)
locations, currentscore, bestscore, bestlocation, changecount = self.update(
k, locations, newscore, newcentroids, currentscore, bestscore, bestlocation, changecount, True)
for _ in range(self.n_bees):
k = choice(self.n_bees, p=currentscore/sum(currentscore))
newcentroids, newscore = self.getnewcentroids(data, locations, k)
locations, currentscore, bestscore, bestlocation, changecount = self.update(
k, locations, newscore, newcentroids, currentscore, bestscore, bestlocation, changecount, False)
for k in nonzero(changecount >= self.limit):
newcentroids = array([data[randint(n), :] for _ in range(n_clusters)], copy=True)
newscore = score(data, centroids=newcentroids, norm=self.norm)
locations, currentscore, bestscore, bestlocation, changecount = self.update(
k, locations, newscore, newcentroids, currentscore, bestscore, bestlocation, changecount, False)
return getlabels(data, centroids=bestlocation, norm=self.norm)