def k_means(x, k):
#max_v = max([v for xi in x for v in xi])
#min_v = min([v for xi in x for v in xi])
miu = [x[0], x[1], x[2], x[6], x[8]]
#miu = []
#for i in range(k):
#miu.append([random.randint(min_v,max_v) for j in range(len(x[0]))])
n = 0
while n < 50:
w = [None] * k
for i in range(len(x)):
dists = [(cos_similarity(x[i], miu[j]), j) for j in range(k)]
min_center = max(dists, key=lambda x: x[0])[1]
if w[min_center]:
w[min_center].append(i)
else:
l = []
l.append(i)
w[min_center] = l
for m in range(k):
if w[m]:
centroid = numpy.array([0.0] * len(x[0]))
for v in w[m]:
centroid += x[v]
miu[m] = centroid / len(w[m])
#else:
#print 'wrong'
#sys.exit()
n = n + 1
return miu, w
def k_means(x,k):
#max_v = max([v for xi in x for v in xi])
#min_v = min([v for xi in x for v in xi])
miu = [x[0],x[1],x[2],x[6],x[8]]
#miu = []
#for i in range(k):
#miu.append([random.randint(min_v,max_v) for j in range(len(x[0]))])
n = 0
while n < 50:
w = [None]*k
for i in range(len(x)):
dists = [(cos_similarity(x[i],miu[j]),j) for j in range(k)]
min_center = max(dists,key=lambda x:x[0])[1]
if w[min_center]:
w[min_center].append(i)
else:
l = []
l.append(i)
w[min_center] = l
for m in range(k):
if w[m]:
centroid = numpy.array([0.0]*len(x[0]))
for v in w[m]:
centroid += x[v]
miu[m] = centroid/len(w[m])
#else:
#print 'wrong'
#sys.exit()
n = n + 1
return miu,w
def k_means(x,k):
miu = [x[i] for i in range(k)]
n = 0
while n < 1000:
w = [None]*k
for i in range(len(x)):
dists = [(cos_similarity(x[i],miu[j]),j) for j in range(k)]
min_center = min(dists,key=lambda x:x[0])[1]
if w[min_center]:
w[min_center].append(i)
else:
l = []
l.append(i)
w[min_center] = l
for m in range(k):
if w[m] and len(w[m]):
centroid = numpy.array([0]*len(x[0]))
for v in w[m]:
centroid = centroid + x[v]
miu[m] = centroid/len(w[m])
#else:
#print 'wrong'
#sys.exit()
n = n + 1
#print miu,w
return miu,w
def state_probability(probabiliy_matrix):
dimension = probabiliy_matrix.shape[0]
start_states = numpy.array([1.0/dimension]*dimension)
old_states = start_states
while True:
states = numpy.mat(start_states) * probabiliy_matrix
if math.acos(cos_similarity(states,old_states)) < (math.pi/180):
break
else:
old_states = states
print states
return states
