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()
print out_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
