aesuli

import numpy as np
from scipy.sparse import csr_matrix
import torch

__author__ = 'Andrea Esuli'

Acsr = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
print('Acsr',Acsr)

Acoo = Acsr.tocoo()

jakevdp

# Author: Jake VanderPlas
# LICENSE: MIT

from __future__ import division
import numpy as np


def convolution_matrix(x, N=None, mode='full'):
    """Compute the Convolution Matrix

lampts

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

ttezel

#A Collection of NLP notes

##N-grams

###Calculating unigram probabilities:

P( w_i ) = count ( w_i ) ) / count ( total number of words )

In english..

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np