def main():
nr_person = 20
fpaths = get_training_data_fpaths()
X_train, y_train, X_test, y_test = datautil.read_data(fpaths, nr_person)
print("loading gmms ...")
gmmset = load_gmmset(y_train, nr_person)
# ubm = GMM.load(config.ubm_model_file)
# ubm = None
# gmmset = GMMSet(32,ubm=ubm, concurrency=8,
# verbosity=1, nr_iteration=100,
# threshold=1e-2)
# gmmset.fit(X_train, y_train)
print("predicting ...")
import time
start = time.time()
import cProfile
y_pred = gmmset.predict(X_test)
print(time.time() - start)
nr_total = len(y_test)
nr_correct = len(filter(lambda x: x[0] == x[1], zip(y_pred, y_test)))
print("{} {}/{}".format(float(nr_correct) / nr_total, nr_correct, nr_total))
print("nr_person: {}".format(nr_person))
def main():
nr_person = 20
fpaths = get_training_data_fpaths()
X_train, y_train, X_test, y_test = datautil.read_data(
fpaths, nr_person)
print "loading gmms ..."
gmmset = load_gmmset(y_train, nr_person)
# print "training ..."
# ubm = GMM.load(config.ubm_model_file)
# ubm = None
# gmmset = GMMSet(32,ubm=ubm, concurrency=8,
# verbosity=1, nr_iteration=100,
# threshold=1e-2)
# gmmset.fit(X_train, y_train)
print "predicting ..."
import time
start = time.time()
import cProfile
y_pred = gmmset.predict(X_test)
print time.time() - start
nr_total = len(y_test)
nr_correct = len(filter(lambda x: x[0] == x[1], zip(y_pred, y_test)))
print "{} {}/{}" . format(
float(nr_correct) / nr_total, nr_correct, nr_total)
print "nr_person: {}" . format(nr_person)
def main():
nr_person = 50
fpaths = get_training_data_fpaths()
X_train, y_train, X_test, y_test = datautil.read_data(fpaths, nr_person)
ubm = GMM.load("model/ubm-32.model")
for x, y in zip(X_train, y_train):
gmm = GMM(concurrency=8, threshold=0.01, nr_iteration=100, verbosity=1)
gmm.fit(x, ubm=ubm)
gmm.dump("model/" + y + ".32.model")
def main():
nr_person = 50
fpaths = get_training_data_fpaths()
X_train, y_train, X_test, y_test = datautil.read_data(fpaths, nr_person)
ubm = GMM.load('model/ubm-32.model')
for x, y in zip(X_train, y_train):
gmm = GMM(concurrency=8, threshold=0.01, nr_iteration=100, verbosity=1)
gmm.fit(x, ubm=ubm)
gmm.dump("model/" + y + ".32.model")
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"))
