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import sys
import numpy as np
import sklearn
import lightning

print("python", sys.version)
print("numpy", np.__version__)
print("scikit-learn", sklearn.__version__)
print("lightning", lightning.__version__)

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from sklearn.utils.metaestimators import if_delegate_has_method
from sklearn.utils.fixes import signature


class Test(object):
    def hi(self, what):
        return 1 + what


class Kid(object):

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# low-rank bilinear regression using theano (supports sparse inputs)

# predicts f(x_left, x_right) = x_left' UV' x_right
# Reference:
#  Generalised Bilinear Regression
#  K. Ruben Gabriel
#  Source: Biometrika, Vol. 85, No. 3 (Sep., 1998), pp. 689-700
#  Stable URL: http://www.jstor.org/stable/2337396

# Author: Vlad Niculae <[email protected]>

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# -*- coding: utf8 -*-
"""Convex factorization machines

Implements the solver by: Mathieu Blondel, Akinori Fujino, Naonori Ueda.
"Convex factorization machines". Proc. of ECML-PKDD 2015
http://www.mblondel.org/publications/mblondel-ecmlpkdd2015.pdf
"""

# Author: Vlad Niculae <[email protected]>
# License: Simplified BSD

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""" Poisson-loss Factorization Machines with Numba

Follows the vanilla FM model from:
Steffen Rendle (2012): Factorization Machines with libFM.
In: ACM Trans. Intell. Syst. Technol., 3(3), May.
http://doi.acm.org/10.1145/2168752.2168771

See also: https://github.com/coreylynch/pyFM
"""

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from collections import Counter
import numpy as np
from sklearn.metrics import euclidean_distances
from pyemd import emd as pyemd


def word_movers_distance(a, b, embeddings):
    """Word Mover's Distance.
    
    A measure of text similarity: earth mover's distance in embedding metric space.