I have a matrix tf.m
NxM and and data frame df
with N rows.
I want to assing row n
of the matrix to a column in the data frame, at the same row n
.
library("tm")
ftfidf <- function(text.d) {
txt <- VectorSource(text.d);
txt.corpus <- VCorpus(txt, readerControl = list(reader = readPlain, language = "en"));
revs <- tm_map(txt.corpus, content_transformer(tolower))
dtm <- DocumentTermMatrix(revs, control = list(weighting = function(x) weightTfIdf(x, normalize = T),stopwords = TRUE))
}
df<-data.frame(id=c("doc1", "doc2", "doc3"), text=c("hello world", "people people", "happy people"))
#id text
#1 doc1 hello world
#2 doc2 people people
#3 doc3 happy people
tf <- ftfidf(df$text) # a function that gets a DocumentTermMatrix
tf.m <- as.matrix(tf)
#Terms
#Docs happy hello people world
#1 0.0000000 0.7924813 0.0000000 0.7924813
#2 0.0000000 0.0000000 0.5849625 0.0000000
#3 0.7924813 0.0000000 0.2924813 0.0000000
If I run this, I get 4 more columns in the data frame
df$tf<-tf.m
#id text tf.happy tf.hello tf.people tf.world
#1 doc1 hello world 0.0000000 0.7924813 0.0000000 0.7924813
#2 doc2 people people 0.0000000 0.0000000 0.5849625 0.0000000
#3 doc3 happy people 0.7924813 0.0000000 0.2924813 0.0000000
I would like to have this:
#id text tf
#1 doc1 hello world happy hello people world
# 0.0000000 0.7924813 0.0000000 0.7924813
#2 doc2 people people happy hello people world
# 0.0000000 0.0000000 0.5849625 0.0000000
#2 doc3 happy people happy hello people world
# 0.7924813 0.0000000 0.2924813 0.0000000
to try to train a knn based on term frequency df$tf
(if possible)
knn_model <- knn(train = df$tf[1,], cl = df$id, k=3)
to query for the nearest-neighbors of a df$id
.
My goal is to run this 'like' python graphlab function in R:
knn_model = graphlab.nearest_neighbors.create(df,features=['tf'],label='id')