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"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""

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from pylab import *

def string_kernel(s1,s2,n=inf):
    k1=[]
    k2=[]
    A=set()
    for k in xrange(1,min(n,len(s1)+1)):
        for i in xrange(0,len(s1)-(k-1)):
            A.add(s1[i:i+k])
    for k in xrange(1,min(n,len(s2)+1)):

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def intersected(a,b,c,d):
    x1=b[0]-a[0]
    x2=c[0]-d[0]
    x3=b[1]-a[1]
    x4=c[1]-d[1]
    d=float(x1*x4-x2*x3)
    if d==0: return False
    c1=c[0]-a[0]
    c2=c[1]-a[1]
    s=(c1*x4-c2*x2)/d

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def dijkstra(M,s,SELF=0,NOTCONNECTED=-1):
    """
    M: adjacency matrix
    s: starting point
    """
    n=len(M)
    table=[float("inf") for i in xrange(n)]
    done=set()
    heap=[]
    heappush(heap,[0,s])

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from sklearn.base import BaseEstimatior
class KernelRidgeClassifier(BaseEstimator):
    def __init__(self,alpha=0.1,**kwds):
        self.alpha=alpha
        self.kwds=kwds

    def fit(self,X,y):
        n=len(y)
        K=array([[self.kwds["kernel"](xi,xj) for xj in X] for xi in X])
        self.theta=pinv(K+self.alpha*identity(n)).dot(c_[y])

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def WMF(R,k=2,alpha=0.1):
    m,n=R.shape
    X=rand(m,k)
    Y=rand(n,k)
    for itr in xrange(10000):
        Xold=X.copy()
        Yold=Y.copy()
        for i in xrange(m):
            ri=R[i]
            C=(ri!=0)*1

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from multiprocessing import Pool
import requests

def urlopen(url):
    try: return requests.get(url).content
    except: return None

def urlsopen(urls):
    pool = Pool(processes=8)
    return pool.map(urlopen, urls)

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import numpy
from numpy import random
from numpy import linalg

def WMF(R,k=10,alpha=0.1,beta=10.,max_iteration=100000,stop_criterion=1e-6):
    """
    R: feedback matrix like R_ij represents the number that user_i clicks item_j
    k: dimensionality of latent factor vector
    alpha: regularization parameter
    beta: confidence parameter

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import numpy
from numpy import random
from matplotlib import pyplot
from sklearn import datasets
from sklearn import base
from sklearn import metrics
from sklearn import grid_search

class NonParametricKNN(base.BaseEstimator):
    def __init__(self,sigma=0.1):

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

import logging
import sys
import os.path
import bz2

from gensim.corpora import WikiCorpus
from gensim.corpora.wikicorpus import filter_wiki