def distance_sparseeuclidean_modular(train_fname=traindat, test_fname=testdat):
from modshogun import RealFeatures, SparseRealFeatures, SparseEuclideanDistance, CSVFile
realfeat = RealFeatures(CSVFile(train_fname))
feats_train = SparseRealFeatures()
feats_train.obtain_from_simple(realfeat)
realfeat = RealFeatures(CSVFile(test_fname))
feats_test = SparseRealFeatures()
feats_test.obtain_from_simple(realfeat)
distance = SparseEuclideanDistance(feats_train, feats_train)
dm_train = distance.get_distance_matrix()
distance.init(feats_train, feats_test)
dm_test = distance.get_distance_matrix()
return distance, dm_train, dm_test
def distance_sparseeuclidean_modular (train_fname=traindat,test_fname=testdat): from modshogun import RealFeatures, SparseRealFeatures, SparseEuclideanDistance, CSVFile realfeat=RealFeatures(CSVFile(train_fname)) feats_train=SparseRealFeatures() feats_train.obtain_from_simple(realfeat) realfeat=RealFeatures(CSVFile(test_fname)) feats_test=SparseRealFeatures() feats_test.obtain_from_simple(realfeat) distance=SparseEuclideanDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def compute_output_plot_isolines(classifier,
kernel=None,
train=None,
sparse=False,
pos=None,
neg=None,
regression=False):
size = 100
if pos is not None and neg is not None:
x1_max = max(1.2 * pos[0, :])
x1_min = min(1.2 * neg[0, :])
x2_min = min(1.2 * neg[1, :])
x2_max = max(1.2 * pos[1, :])
x1 = linspace(x1_min, x1_max, size)
x2 = linspace(x2_min, x2_max, size)
else:
x1 = linspace(-5, 5, size)
x2 = linspace(-5, 5, size)
x, y = meshgrid(x1, x2)
dense = RealFeatures(array((ravel(x), ravel(y))))
if sparse:
test = SparseRealFeatures()
test.obtain_from_simple(dense)
else:
test = dense
if kernel and train:
kernel.init(train, test)
else:
classifier.set_features(test)
labels = None
if regression:
labels = classifier.apply().get_labels()
else:
labels = classifier.apply().get_values()
z = labels.reshape((size, size))
return x, y, z
def compute_output_plot_isolines(
classifier, kernel=None, train=None, sparse=False, pos=None, neg=None, regression=False
):
size = 100
if pos is not None and neg is not None:
x1_max = max(1.2 * pos[0, :])
x1_min = min(1.2 * neg[0, :])
x2_min = min(1.2 * neg[1, :])
x2_max = max(1.2 * pos[1, :])
x1 = linspace(x1_min, x1_max, size)
x2 = linspace(x2_min, x2_max, size)
else:
x1 = linspace(-5, 5, size)
x2 = linspace(-5, 5, size)
x, y = meshgrid(x1, x2)
dense = RealFeatures(array((ravel(x), ravel(y))))
if sparse:
test = SparseRealFeatures()
test.obtain_from_simple(dense)
else:
test = dense
if kernel and train:
kernel.init(train, test)
else:
classifier.set_features(test)
labels = None
if regression:
labels = classifier.apply().get_labels()
else:
labels = classifier.apply().get_values()
z = labels.reshape((size, size))
return x, y, z
