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# -*- coding: utf-8 -*-
"""
Newton interpolation and numerical differentiation
Created on Sun Apr 10 01:22:46 2011

@author: vene
"""
from __future__ import division
from copy import copy

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# -*- coding: utf-8 -*-
"""
http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

parametrization by error is still in progress
"""
from time import time
import numpy as np
from scipy import linalg
import matplotlib.pyplot as pl

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import os

import numpy as np
import matplotlib.pylab as pl
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error

X = np.array([[13.],    # This is dataset no. 3 from Anscombe's quartet.
              [10.],    # I moved the outlier to the first position for
              [8.],     # prettier code. This toy dataset illustrates

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# Author: Vlad Niculae <[email protected]>
# 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'):

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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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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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