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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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# (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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{
 "metadata": {
  "name": "EnglishDating"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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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 <vlad@vene.ro>

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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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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__)