def gmmEM(data, K, it,show=False,usekmeans=True):
#data += finfo(float128).eps*100
centroid = kmeans2(data, K)[0] if usekmeans else ((max(data) - min(data))*random_sample((K,data.shape[1])) + min(data))
N = data.shape[0]
gmm = GaussianMM(centroid)
if show: gmm.draw(data)
while it > 0:
print it," iterations remaining"
it = it - 1
# e-step
gausses = zeros((K, N), dtype = data.dtype)
for k in range(0, K):
gausses[k] = gmm.c[k]*mulnormpdf(data, gmm.mean[k], gmm.covm[k])
sums = sum(gausses, axis=0)
if count_nonzero(sums) != sums.size:
raise "Divide by Zero"
gausses /= sums
# m step
sg = sum(gausses, axis=1)
if count_nonzero(sg) != sg.size:
raise "Divide by Zero"
gmm.c = ones(sg.shape) / N * sg
for k in range(0, K):
gmm.mean[k] = sum(data * gausses[k].reshape((-1,1)), axis=0) / sg[k]
d = data - gmm.mean[k]
d1 = d.transpose()*gausses[k]
gmm.covm[k]=dot(d1,d)/sg[k]
if show: gmm.draw(data)
return gmm
def getP(self,sample):
p = zeros(sample.shape[0])
for k in range(0,self.N):
p += self.c[k]*mulnormpdf(sample, self.mean[k], self.covm[k])
return p.reshape((-1,1))
