yamaguchiyuto

import nltk

text_str = "I have written this book and these papers."

text = nltk.word_tokenize(text_str)

result = nltk.pos_tag(text)

nouns = [r[0] for r in result if r[1] == 'NN' or r[1] == 'NNS']

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

yamaguchiyuto

yamaguchiyuto

import numpy as np

class DPGMM:
    def __init__(self,alpha=1,mu0=0,rho0=1,a0=1,b0=1,max_iter=30):
        self.alpha = alpha
        self.mu0=mu0
        self.rho0=rho0
        self.a0=a0
        self.b0=b0
        self.max_iter=max_iter