wdevazelhes

import numpy as np
from sklearn.utils.extmath import softmax
from sklearn.kernel_approximation import RBFSampler
from sklearn_extra.kernel_approximation import Fastfood

seed = 42
rng = np.random.RandomState(seed)

D = 20

wdevazelhes

import numpy as np
from scipy.sparse.csgraph import laplacian
from sklearn.utils import check_random_state
from scipy.sparse import coo_matrix
from numpy.testing import assert_allclose

RNG = check_random_state(0)

def test_loss_sdml_uncertainties():
    n_samples = 10

wdevazelhes

from metric_learn import MLKR
from sklearn.utils import check_random_state
import numpy as np
from losses import _loss_non_optimized, _loss_optimized
from collections import defaultdict
from sklearn.datasets import make_regression


for n_features in [5, 100]:
    print('n_features={}'.format(n_features))

wdevazelhes

import numpy as np
from scipy.sparse.csgraph import laplacian
from sklearn.utils import check_random_state
from scipy.sparse import coo_matrix
from numpy.testing import assert_array_almost_equal

RNG = check_random_state(0)

def test_loss_sdml():
    n_samples = 10