def classifier_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 Perceptron 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=Perceptron(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_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, BinaryLabels from shogun.Classifier import Perceptron feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=BinaryLabels(label_train_twoclass) perceptron=Perceptron(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_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 Perceptron feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=Labels(label_train_twoclass) perceptron=Perceptron(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_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 Perceptron
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 = Perceptron(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