vene

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/* Modifications by Vlad Niculae <[email protected]>

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Changes:

vene

# Author: Vlad Niculae <[email protected]>
# License: 2-clause BSD

"""2D implementation of the robust Siegel Repeated Median slope estimator

This estimator tolerates corruption of up to 50% of the input points in either
the X or the Y dimension.

Vectorized implementation, and a naive implementation for sanity-check.
"""

vene

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vene

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

deaths = [596577, 142942, 73831, 41374, 39518, 21176, 7683, 6849]
money = [54.1, 7, 4.2, 257.85, 3.2, 147, 14, 22.9]
names = ["Heart disease", "COPS", "Diabetes", "Breast cancer",
         "Suicide", "Prostate cancer", "HIV/AIDS", "Motor neuron disease"]

sns.set_style("white")

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The full tagger software package is licensed as GPL version 2.

src/ -- All original code we've written -- the files in src/ with one
exception below -- we license under the Apache License version 2.0. However,
we have several GPL'd dependencies that we include in this package, which,
as we understand it, force the full package to be GPL.

src/cmu/arktweetnlp/impl/OWLQN.java -- is licensed GPL, originally from the
Stanford POS Tagger version 2010-05-26.

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#!/usr/bin/env perl

# Lemmatize CONLL-style (tabular) POS-tagged file using Treex
# Prerequisites: cpan -i -f Treex::Tool::EnglishMorpho::Lemmatizer
#   (I think the -f is needed because some tests are failing)
# Usage example:
#  $ echo "1\tgoes\t_\tVBZ\n" > example
#  $ <example ./lemmatize.pl
#  1    goes    go    VBZ
#

vene

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

from __future__ import division, print_function
import numpy as np
from sklearn.utils import check_random_state


class SquaredLoss(object):
    def loss(self, y, pred):

vene

"""l-bfgs-b L1-Logistic Regression solver"""

# Author: Vlad Niculae <[email protected]>
# Suggested by Mathieu Blondel

from __future__ import division, print_function

import numpy as np
from scipy.optimize import fmin_l_bfgs_b

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from __future__ import print_function

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.dummy import DummyClassifier
from sklearn.cross_validation import LeaveOneOut

docs = ["the cat lives in the hat", "the quick brown fox jumps over a dog",
        "a clockwork orange"]

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import re
from collections import OrderedDict
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin

class LexicalSetVectorizer(BaseEstimator, TransformerMixin):
    def __init__(self, word_sets=None, normalize=False, lower=False,
                 token_pattern=ur'(?u)\b\w\w+\b'):
        self.word_sets = word_sets
        self.normalize = normalize