示例#1
0
def mnist_digit_recognition():
    train_set, test_set = load_mnist_dataset()
    n_labels = 10  # 1,2,3,4,5,6,7,9,0
    n_features = 28 * 28

    draw_ex_images(5, 4, train_set[0].shape[0], train_set[0])

    mnist_model = GaussianNaiveBayes(n_labels, n_features)
    start = time.time()
    mnist_model.train(train_set[0], train_set[1])
    end = time.time()
    print(end - start)
    mnist_model.save_model()
    mean, var, pi = mnist_model.get_parameters()
    print(f"Model parameters: mean {mean}, var {var}, pi {pi}")

    test_data, labels = test_set
    limit = 150
    test_data, labels = test_data[:limit], labels[:limit]
    results = np.arange(limit, dtype=np.int)
    for n in range(limit):
        results[n] = mnist_model.classify(test_data[n])
        print(f"{n} : predicted {results[n]}, correct {labels[n]}")

    print("recognition rate: ", (results == labels).mean())
示例#2
0
def naive_bayes_test():
    n_labels, n_features = 2, 2
    nb = GaussianNaiveBayes(n_labels, n_features)

    # prepare sample training data
    data1 = np.random.multivariate_normal([1,4], [[2,0],[0,2]], size=100)
    data2 = np.random.multivariate_normal([5,7], [[3,0],[0,1]], size=100)
    data = np.concatenate((data1, data2), axis=0)

    # prepare training label data
    labels = np.concatenate((np.array([0]*100), np.array([1]*100)), axis=0)
    print "correct labels"
    print labels

    # nb.load()
    nb.train(data, labels)
    # nb.save()

    results = nb.classify(data)
    print "predicted labels"
    print results
    
    print "recognition rate: ", (results == labels).mean()
def mnist_digit_recognition():
    train_set, valid_set, test_set = load_mnist_dataset("mnist.pkl.gz")
    n_labels = 10 # 1,2,3,4,5,6,7,9,0
    n_features = 28*28
    mnist_model = GaussianNaiveBayes(n_labels, n_features)
    mnist_model.train(train_set[0], train_set[1])
    [mean, var], pi = mnist_model.get_parameters()

    # visualization of learned means
    create_2D_images_horizontal(mean, w=28, h=28)
    show()

    test_data, labels = test_set
    # slice
    #limit = len(test_data)
    limit = 50
    test_data, labels = test_data[:limit], labels[:limit]
    results = np.arange(limit, dtype=np.int)
    for n in range(limit):
        results[n] = mnist_model.classify(test_data[n])
        print "%d : predicted %s, correct %s" % (n, results[n], labels[n])
    # results = mnist_model.classify(test_data)

    print "recognition rate: ", (results == labels).mean()
def mnist_digit_recognition():
    train_set, valid_set, test_set = load_mnist_dataset("mnist.pkl.gz")
    n_labels = 10  # 1,2,3,4,5,6,7,9,0
    n_features = 28 * 28
    mnist_model = GaussianNaiveBayes(n_labels, n_features)
    mnist_model.train(train_set[0], train_set[1])
    [mean, var], pi = mnist_model.get_parameters()

    # visualization of learned means
    create_2D_images_horizontal(mean, w=28, h=28)
    show()

    test_data, labels = test_set
    # slice
    #limit = len(test_data)
    limit = 50
    test_data, labels = test_data[:limit], labels[:limit]
    results = np.arange(limit, dtype=np.int)
    for n in range(limit):
        results[n] = mnist_model.classify(test_data[n])
        print "%d : predicted %s, correct %s" % (n, results[n], labels[n])
    # results = mnist_model.classify(test_data)

    print "recognition rate: ", (results == labels).mean()