def classifier_averaged_perceptron_modular(
    n=100, dim=2, distance=5, learn_rate=1.0, max_iter=1000, num_threads=1, seed=1
):
    from shogun.Features import RealFeatures, BinaryLabels
    from shogun.Classifier import AveragedPerceptron

    random.seed(seed)

    # produce some (probably) linearly separable training data by hand
    # Two Gaussians at a far enough distance
    X = array(random.randn(dim, n)) + distance
    Y = array(random.randn(dim, n)) - distance
    X_test = array(random.randn(dim, n)) + distance
    Y_test = array(random.randn(dim, n)) - distance
    label_train_twoclass = hstack((ones(n), -ones(n)))

    # plot(X[0,:], X[1,:], 'x', Y[0,:], Y[1,:], 'o')
    fm_train_real = hstack((X, Y))
    fm_test_real = hstack((X_test, Y_test))

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)

    labels = BinaryLabels(label_train_twoclass)

    perceptron = AveragedPerceptron(feats_train, labels)
    perceptron.set_learn_rate(learn_rate)
    perceptron.set_max_iter(max_iter)
    # only guaranteed to converge for separable data
    perceptron.train()

    perceptron.set_features(feats_test)
    out_labels = perceptron.apply().get_labels()
    return perceptron, out_labels
def classifier_averaged_perceptron_modular(
    fm_train_real=traindat,
    fm_test_real=testdat,
    label_train_twoclass=label_traindat,
    learn_rate=1.0,
    max_iter=1000,
    num_threads=1,
):
    from shogun.Features import RealFeatures, BinaryLabels
    from shogun.Classifier import AveragedPerceptron

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)

    labels = BinaryLabels(label_train_twoclass)

    perceptron = AveragedPerceptron(feats_train, labels)
    perceptron.set_learn_rate(learn_rate)
    perceptron.set_max_iter(max_iter)
    # only guaranteed to converge for separable data
    perceptron.train()

    perceptron.set_features(feats_test)
    out_labels = perceptron.apply().get_labels()
    # print(out_labels)
    return perceptron, out_labels
def classifier_averaged_perceptron_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,learn_rate=1.,max_iter=1000,num_threads=1):
	from shogun.Features import RealFeatures, Labels
	from shogun.Classifier import AveragedPerceptron

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)

	labels=Labels(label_train_twoclass)

	perceptron=AveragedPerceptron(feats_train, labels)
	perceptron.set_learn_rate(learn_rate)
	perceptron.set_max_iter(max_iter)
	# only guaranteed to converge for separable data
	perceptron.train()

	perceptron.set_features(feats_test)
	out_labels = perceptron.apply().get_labels()
	return perceptron, out_labels
def classifier_averaged_perceptron_modular(n=100,
                                           dim=2,
                                           distance=5,
                                           learn_rate=1.,
                                           max_iter=1000,
                                           num_threads=1,
                                           seed=1):
    from shogun.Features import RealFeatures, BinaryLabels
    from shogun.Classifier import AveragedPerceptron

    random.seed(seed)

    # produce some (probably) linearly separable training data by hand
    # Two Gaussians at a far enough distance
    X = array(random.randn(dim, n)) + distance
    Y = array(random.randn(dim, n)) - distance
    X_test = array(random.randn(dim, n)) + distance
    Y_test = array(random.randn(dim, n)) - distance
    label_train_twoclass = hstack((ones(n), -ones(n)))

    #plot(X[0,:], X[1,:], 'x', Y[0,:], Y[1,:], 'o')
    fm_train_real = hstack((X, Y))
    fm_test_real = hstack((X_test, Y_test))

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)

    labels = BinaryLabels(label_train_twoclass)

    perceptron = AveragedPerceptron(feats_train, labels)
    perceptron.set_learn_rate(learn_rate)
    perceptron.set_max_iter(max_iter)
    # only guaranteed to converge for separable data
    perceptron.train()

    perceptron.set_features(feats_test)
    out_labels = perceptron.apply().get_labels()
    return perceptron, out_labels