vene

class Base:

    def say(self, val):
        print("base says", val)


class A(Base):

    def say(self, val):
        print("say A")

vene

# Author: Vlad Niculae <vlad@vene.ro>
# License: Simplified BSD

import numpy as np

try:
    from numba import jit
except ImportError:
    print("numba not available")
    def jit(nopython):

vene

# Author: Vlad Niculae <vlad@vene.ro>
# Makes use of memory_profiler from Fabian Pedregosa
# available at https://github.com/fabianp/memory_profiler

from IPython.core.magic import Magics, line_magic, magics_class


class MemMagics(Magics):
    @line_magic
    def memit(self, line='', setup='pass'):

vene

import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse import issparse

from sklearn.utils import atleast2d_or_csr
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.metrics.pairwise import check_pairwise_arrays, euclidean_distances
from sklearn.metrics.euclidean_fast import dense_euclidean_distances, sparse_euclidean_distances

vene

# author: vlad niculae <vlad@vene.ro>
# license: mit

import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors

from entmax import sparsemax, entmax15
from entmax.losses import sparsemax_loss, entmax15_loss

vene

# author: vn

import numpy as np
from scipy.optimize import root_scalar
import torch
import matplotlib.pyplot as plt


def entropy(y, a, b):

vene

# Author: vlad niculae <vlad@vene.ro>
# License: 3-clause BSD

import numpy as np
import cvxpy as cx

from copt.constraint import SimplexConstraint
from copt.splitting import minimize_primal_dual

vene

# Density estimation with energy-based models
# Langevin sampling, contrastive divergence training.
# Author: Vlad Niculae <vlad@vene.ro>
# License: MIT

import numpy as np
import torch
from sklearn import datasets
import matplotlib.pyplot as plt

vene

# Geometric intepretation of the gradient of the mapping:
# f : (0, inf) x Sphere(k-1) -> R^k
# f(r, u) -> r*u
# The *catch*: R can vary on (0, inf) but u may only vary on the
# k-1--dimensional tangent plane!

import numpy as np

def main():

vene

# linearity of expectation under mixture model
# license: mit
# author: vlad niculae

from scipy.stats import norm

import numpy as np

def main():
    rng = np.random.RandomState(42)