nkt1546789

import numpy as np
import scipy.sparse.linalg as la
import bs4
from scipy import sparse

def get_text_elements(elem):
    if isinstance(elem, bs4.NavigableString):
        if type(elem) not in (bs4.Comment, bs4.Declaration) and elem.strip():
            yield elem
    elif elem.name not in ('script', 'style'):

nkt1546789

import numpy as np
from scipy import sparse
from sklearn import preprocessing
from graphranker import GraphRanker

class TextRank(GraphRanker):
    def fit(self, texts):
        self.texts = texts
        dictionary = {}
        data = []

nkt1546789

import numpy as np
from copy import deepcopy

class GraphRanker(object):
    def __init__(self, d=0.85, tol=1e-6, max_iters=200):
        self.d = d
        self.tol = tol
        self.max_iters = max_iters

    def fit(self, A):

nkt1546789

import numpy as np
from scipy import sparse
from sklearn import preprocessing
from graphranker import GraphRanker

class TokenRank(GraphRanker):
    def __init__(self, window=10, **kwds):
        self.window = window
        super(TokenRank, self).__init__(**kwds)

nkt1546789

"""
A demo code for ranking SVM
The data used in this code comes from http://download.joachims.org/svm_light/examples/example3.tar.gz
"""

import numpy as np
import itertools
from sklearn.linear_model import SGDClassifier
from sklearn.grid_search import GridSearchCV
np.random.seed(0)

nkt1546789

import numpy as np

def fft(X,D,k):
    """
    X: input vectors (n_samples by dimensionality)
    D: distance matrix (n_samples by n_samples)
    k: number of centroids
    out: indices of centroids
    """
    n=X.shape[0]

nkt1546789

import numpy as np
import matplotlib.pyplot as plt
from numpy import random
import seaborn as sns
from sklearn import metrics
from puwrapper import PUWrapper
from sklearn.linear_model import LogisticRegression,LogisticRegressionCV
sns.set_style("white")
random.seed(0)

nkt1546789

ブルーベリー is-a 果物
動物 has-a モルモット
動物 has-a ワタボウシタマリン
スポーツ is-a スポーツ
登山 is-a スポーツ
ロデオ is-a スポーツ
動物 has-a ユーラシアカワウソ
スポーツ has-a フリーダイビング
競馬 is-a スポーツ
スポーツ has-a ゴルフ

nkt1546789

# coding: utf-8
data=[[u"スポーツ",u"競走"],
[u"スポーツ",u"跳躍"],
[u"スポーツ",u"投てき"],
[u"スポーツ",u"混成"],
[u"スポーツ",u"トライアスロン"],
[u"スポーツ",u"バイアスロン"],
[u"スポーツ",u"近代五種"],
[u"スポーツ",u"水泳"],
[u"スポーツ",u"競泳"],

nkt1546789

import numpy as np

class RbfModelWrapper(object):
    def __init__(self,model,gamma=1.,**kwds):
        self._model=model
        self.gamma=gamma

    def fit(self,X,y):
        X2=np.c_[np.sum(X**2,1)]
        Phi=np.exp(-self.gamma*(X2+X2.T-2*X.dot(X.T)))