def classifier_qda_modular (fm_train_real=traindat, fm_test_real=testdat, label_train_twoclass=label_traindat, tolerance=1e-4, store_covs=False): from shogun.Features import RealFeatures, MulticlassLabels from shogun.Classifier import QDA feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = MulticlassLabels(label_train_twoclass) qda = QDA(feats_train, labels, tolerance, store_covs) qda.train() qda.apply(feats_test).get_labels() qda.set_features(feats_test) return qda, qda.apply().get_labels()
def classifier_qda_modular(fm_train_real=traindat,
fm_test_real=testdat,
label_train_twoclass=label_traindat,
tolerance=1e-4,
store_covs=False):
from shogun.Features import RealFeatures, Labels
from shogun.Classifier import QDA
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = Labels(label_train_twoclass)
qda = QDA(feats_train, labels, tolerance, store_covs)
qda.train()
qda.apply(feats_test).get_labels()
qda.set_features(feats_test)
return qda, qda.apply().get_labels()
plot(x_pos, y_pos, 'bo'); # negative examples mean_neg = [0, -3] cov_neg = [[100,50], [20, 3]] x_neg, y_neg = np.random.multivariate_normal(mean_neg, cov_neg, 500).T plot(x_neg, y_neg, 'ro'); # train qda labels = MulticlassLabels( np.concatenate([np.zeros(N), np.ones(N)]) ) pos = np.array([x_pos, y_pos]) neg = np.array([x_neg, y_neg]) features = RealFeatures( np.array(np.concatenate([pos, neg], 1)) ) qda = QDA() qda.set_labels(labels) qda.train(features) # compute output plot iso-lines xs = np.array(np.concatenate([x_pos, x_neg])) ys = np.array(np.concatenate([y_pos, y_neg])) x1_max = max(1.2*xs) x1_min = min(1.2*xs) x2_max = max(1.2*ys) x2_min = min(1.2*ys) x1 = np.linspace(x1_min, x1_max, size) x2 = np.linspace(x2_min, x2_max, size)
# Number of classes
M = 3
# Number of samples of each class
N = 300
# Dimension of the data
dim = 2
cols = ['blue', 'green', 'red']
fig = pylab.figure()
ax = fig.add_subplot(111)
pylab.title('Quadratic Discrimant Analysis')
X, y = gen_data()
labels = MulticlassLabels(y)
features = RealFeatures(X.T)
qda = QDA(features, labels, 1e-4, True)
qda.train()
ypred = qda.apply().get_labels()
plot_data(qda, X, y, ypred, ax)
for i in range(M):
plot_cov(ax, qda.get_mean(i), qda.get_cov(i), cols[i])
plot_regions(qda)
pylab.connect('key_press_event', util.quit)
pylab.show()
plot(x_pos, y_pos, 'bo') # negative examples mean_neg = [0, -3] cov_neg = [[100, 50], [20, 3]] x_neg, y_neg = np.random.multivariate_normal(mean_neg, cov_neg, 500).T plot(x_neg, y_neg, 'ro') # train qda labels = MulticlassLabels(np.concatenate([np.zeros(N), np.ones(N)])) pos = np.array([x_pos, y_pos]) neg = np.array([x_neg, y_neg]) features = RealFeatures(np.array(np.concatenate([pos, neg], 1))) qda = QDA() qda.set_labels(labels) qda.train(features) # compute output plot iso-lines xs = np.array(np.concatenate([x_pos, x_neg])) ys = np.array(np.concatenate([y_pos, y_neg])) x1_max = max(1.2 * xs) x1_min = min(1.2 * xs) x2_max = max(1.2 * ys) x2_min = min(1.2 * ys) x1 = np.linspace(x1_min, x1_max, size) x2 = np.linspace(x2_min, x2_max, size)
pylab.contour(xx, yy, Z, linewidths = 3, colors = 'k')
# Number of classes
M = 3
# Number of samples of each class
N = 300
# Dimension of the data
dim = 2
cols = ['blue', 'green', 'red']
fig = pylab.figure()
ax = fig.add_subplot(111)
pylab.title('Quadratic Discrimant Analysis')
X, y = gen_data()
labels = MulticlassLabels(y)
features = RealFeatures(X.T)
qda = QDA(features, labels, 1e-4, True)
qda.train()
ypred = qda.apply().get_labels()
plot_data(qda, X, y, ypred, ax)
for i in range(M):
plot_cov(ax, qda.get_mean(i), qda.get_cov(i), cols[i])
plot_regions(qda)
pylab.connect('key_press_event', util.quit)
pylab.show()
