Hi,
Thanks for your help.
I used the SparseM package
http://www.econ.uiuc.edu/~roger/research/sparse/SparseM.pdf
<http://www.econ.uiuc.edu/~roger/research/sparse/SparseM.pdf>
First of all, I create a class for sparse matrices stored in Compressed Sparse
Row (CSR) with as.matrix.csr(matrix).
After that, I plot the non-zero entries of a matrix of class matrix.csr with
image(m.csr)
This is my code:
library(SparseM)
data <- read.csv(pathCSV, header = FALSE, sep = ",")
numcol <- ncol(data)
dMatrix <- matrix(unlist(data), ncol = numcol, byrow = TRUE)
dMatrix.csr <- as.matrix.csr(dMatrix)
image(dMatrix.csr, col=c("white","blue"))
After clustering, I will have the same matrix but each row (vector) has a tag to
represent a cluster id. So, how could I plot my matrix to show a different color
for cluster id?
This is an example of my results:
213 0 0 0 0.213 0.3423
345 0 0 0.32 0 0
84 0 0.4 0 0.54 0
84 0.86 0 0 0 0
213 0 0.98 0 0 0.45
345 0 0.57 0 0 0.4
Cheers.
> On Jun 10, 2016, at 18:51, Amos Elberg <amos.elberg at gmail.com>
wrote:
>
> Sparse matrix visualization is a feature of my largeVis package:
https://github.com/elbamos/largeVis/tree/0.1.6
<https://github.com/elbamos/largeVis/tree/0.1.6>
>
>
>
> On Thu, Jun 9, 2016 at 6:27 PM, FRANCISCO XAVIER SUMBA TORAL
<xavier.sumba93 at ucuenca.ec <mailto:xavier.sumba93 at ucuenca.ec>>
wrote:
> Hi,
>
> First of all, sorry for my question it could be so basic for a common user
in R, but I am starting with this new environment.
>
> I have done a clustering job and I would like to visualize my vectors. I
have a matrix of TF-IDF weights of 4602 x 1817. I store the values in a CSV
file. How can I visualize my vectors in a 2D-space?
>
> After that, I execute a clustering algorithm and I got a label for each
cluster. How can I visualize my vectors resulting base on a color or figure for
each cluster?
>
> This is the code that I am having trying to accomplish my graphs:
>
> data <- read.csv(pathFile,header = FALSE, sep = ",?)
> dMatrix <- matrix(unlist(data), ncol = 4602, byrow = TRUE) # Use a
matrix to use melt.
> # Graph my data
> ggplot(melt(dMatrix), aes(Var1,Var2, fill=value)) + geom_raster() +
scale_fill_gradient2(low='red', high=?black', mid=?white') +
theme_bw() + xlab("x1") + ylab("x2")
>
>
> Cheers.
> [[alternative HTML version deleted]]
>
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