from em.gmm import *

from sklearn import mixture

def checkResult():

X = np.loadtxt("./data/amix1-est.dat")

searchK = 4

epoch = 5

maxLogLikelihood = 0

maxResult = None

maxK = 0

alpha = None

for i in range(2, searchK):

k = i

for j in range(epoch):

model1 = GMM(k)

model1.fit(X)

if model1.loglike > maxLogLikelihood:

maxLogLikelihood = model1.loglike

maxResult = model1.predict(X)

maxK = k

alpha = model1.alpha

alpha, maxResult = changeLabel(alpha, maxResult)

print("my gmm k = %s, alpha = %s, maxloglike = %s"%(maxK,[round(a, 5) for a in alpha],maxLogLikelihood))

model2 = mixture.BayesianGaussianMixture(n_components=maxK,covariance_type='full')

result2 = model2.fit_predict(X)

alpha2, result2 = changeLabel(model2.weights_.tolist(), result2)

result = np.sum(maxResult==result2)

percent = np.mean(maxResult==result2)

print("sklearn gmm k = %s, alpha = %s, maxloglike = %s"%(maxK,[round(a, 5) for a in alpha2],model2.lower_bound_))

print("succ = %s/%s"%(result, len(result2)))

print("succ = %s"%(percent))

print(maxResult[:100])

print(result2[:100])

def changeLabel(alpha, predict):

alphaSorted = sorted(alpha, reverse=True)

labelOld = []

for i in predict:

if i not in labelOld:

labelOld.append(i)

if len(labelOld) == len(alpha):

break

labelNew = sorted(labelOld)

for i, old in enumerate(labelOld):

predict[predict == old] = labelNew[i] + 100

return alphaSorted, predict - 100

if __name__ == "__main__":

checkResult()