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")
Beispiel #4
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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")
Beispiel #5
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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"))