def main(datapath, ubmpath, gmmPath):
fpaths = get_training_data_fpaths(datapath)
# print "The fpath is :",fpaths
X_train, y_train = datautil.read_data(fpaths)
ubm = GMM.load(ubmpath)
for x, y in zip(X_train, y_train):
gmm = GMM(concurrency=8, threshold=0.01, nr_iteration=100, verbosity=1)
start = time.time()
gmm.fit(x, ubm=ubm)
# score = gmm.score(X_train[0])
# print(gmm.weights_)
# score_ubm = ubm.score(X_train[0])
# print(sum(score))
# print(sum(score_ubm))
# score_all = gmm.score_all(X_train[6])
# score_all_ubm = ubm.score_all(X_train[6])
# print(str(score_all) + " score_all")
# print(str(score_all_ubm) + " score_all")
# print(str(score_all/score_all_ubm) + " score_all")
end = time.time()
print(str(end - start) + " seconds")
gmm.dump(os.path.join(gmmPath, y + ".model"))
print(os.path.join(gmmPath, y + ".model"))
def fit_new(self, x, label):
self.y.append(label)
gmm = GMM(self.gmm_order, **self.kwargs)
gmm.fit(x, self.ubm)
self.gmms.append(gmm)
def fit_new(self, x, label):
self.y.append(label)
gmm = GMM(self.gmm_order)
gmm.fit(x)
self.gmms.append(gmm)
def fit_new(self, x, label):
self.y.append(label)
gmm = GMM(self.gmm_order)
gmm.fit(x)
self.gmms.append(gmm)
def fit_new(self, x, label):
self.y.append(label)
gmm = GMM(self.gmm_order, **self.kwargs)
gmm.fit(x, self.ubm)
self.gmms.append(gmm)