yamaguchiyuto

import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import grid_search
from oreore_ridge import RidgeRegression

def psi(xlist,M):
    """ make a design matrix """
    ret = []
    for x in xlist:

yamaguchiyuto

import sys
import numpy as np
from sklearn import cross_validation
from oreore_ridge import RidgeRegression

def psi(xlist,M):
    """ make a design matrix """
    ret = []
    for x in xlist:
        ret.append([x**i for i in range(0,M+1)])

yamaguchiyuto

import re

def remove_usernames_and_urls(text):
    username_removed_text = re.sub('@\w+', '', text) # remove usernames
    return re.sub('(https?|ftp)(:\/\/[-_.!~*\'()a-zA-Z0-9;\/?:\@&=+\$,%#]+)', '', username_removed_text) # remove urls

yamaguchiyuto

import numpy as np
from scipy import linalg,sparse,random

class RESCAL:
    def __init__(self,r,lamb_A,lamb_R):
        self.r = r
        self.lamb_A = lamb_A
        self.lamb_R = lamb_R

    def fit(self,X,niter=30):

yamaguchiyuto

import sys
import pickle
import numpy as np
import pandas as pd
from transe import TRANSE


modelfilepath = sys.argv[1]
h = sys.argv[2]
r = sys.argv[3]

yamaguchiyuto

yamaguchiyuto

import numpy as np

class CPALS:
    def __init__(self,k,lamb,max_iter):
        self.k=k
        self.lamb=lamb
        self.max_iter=max_iter

    def _calc_data_shape(self,X):
        max_i = -1

yamaguchiyuto

yamaguchiyuto

yamaguchiyuto

import numpy as np

class Tucker:
    def __init__(self,R,S,T,max_iter):
        self.latent_size = (R,S,T)
        self.max_iter=max_iter

    def _calc_data_shape(self,X):
        max_i = -1
        max_j = -1