Question

我有2个尺寸为6的向量。

a=c("HDa","2Pb","2","BxU","BuQ","Bve")

b=c("HCK","2Pb","2","09","F","G")

任何人都可以解释我应该做什么吗？

Answer 1

使用lsa包和此包的手册

# create some files
library('lsa')
td = tempfile()
dir.create(td)
write( c("HDa","2Pb","2","BxU","BuQ","Bve"), file=paste(td, "D1", sep="/"))
write( c("HCK","2Pb","2","09","F","G"), file=paste(td, "D2", sep="/"))

# read files into a document-term matrix
myMatrix = textmatrix(td, minWordLength=1)

编辑：显示mymatrix对象

是怎样的

myMatrix
#myMatrix
#       docs
#  terms D1 D2
#    2    1  1
#    2pb  1  1
#    buq  1  0
#    bve  1  0
#    bxu  1  0
#    hda  1  0
#    09   0  1
#    f    0  1
#    g    0  1
#    hck  0  1

# Calculate cosine similarity
res <- lsa::cosine(myMatrix[,1], myMatrix[,2])
res
#0.3333

Answer 2

首先需要一个可能术语的字典，然后将你的矢量转换为二进制矢量，相应术语的位置为1，其他位置为0。如果您为新向量a2和b2命名，则可以使用cor(a2, b2)类似地计算余弦，但请注意余弦类似于介于-1和1之间。您可以将其映射到[0 ，1]有这样的事情：0.5*cor(a2, b2) + 0.5

Answer 3

CSString_vector <- c("Hi Hello","Hello");
corp <- tm::VCorpus(VectorSource(CSString_vector));
controlForMatrix <- list(removePunctuation = TRUE,wordLengths = c(1, Inf), weighting = weightTf)
dtm <- DocumentTermMatrix(corp,control = controlForMatrix);
matrix_of_vector = as.matrix(dtm);
res <- lsa::cosine(matrix_of_vector[1,], matrix_of_vector[2,]);

对于较大的数据集，

可能是更好的数据集。

Answer 4

高级形式的嵌入可能会帮助您获得更好的输出。请检查以下代码。它是一种通用句子编码模型，使用基于转换器的架构生成句子嵌入。

from absl import logging
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns

module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
print ("module %s loaded" % module_url)
def embed(input):
  return model([input])

paragraph = [
    "Universal Sentence Encoder embeddings also support short paragraphs. ",
    "Universal Sentence Encoder support paragraphs"]
messages = [paragraph]

print(np.inner( embed(paragraph[0]) , embed(paragraph[1])))

如何计算两个字符串向量之间的余弦相似度

4 个答案: