vene

"""
(C) August 2013, Mathieu Blondel
# License: BSD 3 clause

Custom group support by Vlad Niculae (vlad@vene.ro)

This is a Numba-based reimplementation of the block coordinate descent solver
(without line search) described in the paper:

    Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass

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vene

# (C) Mathieu Blondel 2012

import numpy as np
from scipy.optimize import fmin_l_bfgs_b

from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils.extmath import safe_sparse_dot


class LbfgsNNLS(BaseEstimator, RegressorMixin):

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import numpy as np
from sklearn.metrics import zero_one_score

# Input data corresponds to 4 words:
# - descalcarea (des-cal-ca-rea, predicted: de-s-cal-ca-rea)
# - somnolezi   (som-no-lezi,    predicted: som-no-lezi)
# - salandere   (sa-lan-de-re,   predicted: sa-lan-de-re)

y_pred = np.array(
    [False,  True,  True, False, False,  True, False,  True, False,

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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

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# 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'):