I've got a big problem.
I've got a large raster (rows=180, columns=480, number of cells=86400)
At first I binarized it (so that there are only 1's and 0's) and then I labelled the clusters.(Cells that are 1 and connected to each other got the same label.)
Now I need to calculate all the distances between the cells, that are NOT 0.
There are quiet a lot and that's my big problem.
I did this to get the coordinates of the cells I'm interested in (get the positions (i.e. cell numbers) of the cells, that are not 0):
V=getValues(label)
Vu=c(1:max(V))
pos=which(V %in% Vu)
XY=xyFromCell(label,pos)
This works very well. So XY is a matrix, which contains all the coordinates (of cells that are not 0). But now I'm struggling. I need to calculate the distances between ALL of these coordinates. Then I have to put each one of them in one of 43 bins of distances. It's kind of like this (just an example):
0<x<0.2 bin 1
0.2<x<0.4 bin2
When I use this:
pD=pointDistance(XY,lonlat=FALSE)
R says it's not possible to allocate vector of this size. It's getting too large.
Then I thought I could do this (create an empty data frame df or something like that and let the function pointDistance run over every single value of XY):
for (i in 1:nrow(XY))
{pD=PointDistance(XY,XY[i,],lonlat=FALSE)
pDbin=as.matrix(table(cut(pD,breaks=seq(0,8.6,by=0.2),Labels=1:43)))
df=cbind(df,pDbin)
df=apply(df,1,FUN=function(x) sum(x))}
It is working when I try this with e.g. the first 50 values of XY.
But when I use that for the whole XY matrix it's taking too much time.(Sometimes this XY matrix contains 10000 xy-coordinates)
Does anyone have an idea how to do it faster?
I don't know if this will works fast or not. I recommend you try this:
Let say you have dataframe with value 0 or 1 in each cell. To find coordinates all you have to do is write the below code:
cord_matrix <- which(dataframe == 1, arr.ind = TRUE)
Now, you get the coordinate matrix with row index and column index.
To find the euclidean distance use dist() function. Go through it. It will look like this:
dist_vector <- dist(cord_matrix)
It will return lower triangular matrix. can be transformed into vector/symmetric matrix. Now all you have to do is calculating bins according to your requirement.
Let me know if this works within the specific memory space.
Related
I have a dataset that looks like the one shown in the code.
What I am guaranteed is that the "(var)x" (domain) of the variable is always between 0 and 1. The "(var)y" (co-domain) can vary but is also bounded, but within a larger range.
I am trying to get an average over the "(var)x" but over the different variables.
I would like some kind of selective averaging, not sure how to do this in R.
ax=c(0.11,0.22,0.33,0.44,0.55,0.68,0.89)
ay=c(0.2,0.4,0.5,0.42,0.5,0.43,0.6)
bx=c(0.14,0.23,0.46,0.51,0.78,0.91)
by=c(0.1,0.2,0.52,0.46,0.4,0.41)
qx=c(0.12,0.27,0.36,0.48,0.51,0.76,0.79,0.97)
qy=c(0.03,0.2,0.52,0.4,0.45,0.48,0.61,0.9)
a<-list(ax,ay)
b<-list(bx,by)
q<-list(qx,qy)
What I would like to have something like
avgd_x = c(0.12,0.27,0.36,0.48,0.51,0.76,0.79,0.97)
and
avgd_y would have contents that would
find the value of ay and by at 0.12 and find the mean with ay, by and qy.
Similarly and so forth for all the values in the vector with the largest number of elements.
How can I do this in R ?
P.S: This is a toy dataset, my dataset is spread over files and I am reading them with a custom function, but the raw data is available as shown in the code below.
Edit:
Some clarification:
avgd_y would have the length of the largest vector, for example, in the case above, avgd_y would be (ay'+by'+qy)/3 where ay' and by' would be vectors which have c(ay(qx(i))) and c(by(qx(i))) for i from 1 to length of qx, ay' and by' would have values interpolated at data points of qx
Assume X is a 5x5 matrix (which represents pixel luminosity from a picture):
I would like to select the element that fit within a given simple geometrical shape (e.g. square, circle, ovale) superposed to this matrix. In this example I would like to select the elements from the matrix that fall (even slightly) within the ovale.
Ultimately those elements position would be return into a vector logical vector elementInOvale on which I could perform a simple operation such as X[elementInOvale] <- 0
I have the feeling that this is a common problem which has already been solved, I just don't know how to formulate or where to find information about it, and to do with R.
I would like to create a function that looks at a column of values. from those values look at each value individually, and asses which of the other data points value is closest to that data point.
I'm guessing it could be done by checking the length of the data frame, making a list of the respective length in steps of 1. Then use that list to reference which cell is being analysed against the rest of the column. though I don't know how to implement that.
eg.
data:
20
17
29
33
1) is closest to 2)
2) is closest to 1)
3) is closest to 4)
4) is closest to 3)
I found this example which tests for similarity but id like to know what letter is assigns to.
x=c(1:100)
your.number=5.43
which(abs(x-your.number)==min(abs(x-your.number)))
Also if you know how I could do this, could you expain the parts of the code and what they mean?
I wrote a quick function that does the same thing as the code you provided.
The code you provided takes the absolute value of the difference between your number and each value in the vector, and compares that the minimum value from that vector. This is the same as the which.min function that I use below. I go through my steps below. Hope this helps.
Make up some data
a = 1:100
yourNumber = 6
Where Num is your number, and x is a vector
getClosest=function(x, Num){
return(which.min(abs(x-Num)))
}
Then if you run this command, it should return the index for the value of the vector that corresponds to the closest value to your specified number.
getClosest(x=a, Num=yourNumber)
I have a matrix, which includes 100 rows and 10 columns, here I want to compare the diversity between rows and sort them. And then, I want to select the 10 maximum dissimilarity rows from it, Which method can I use?
set.seed(123)
mat <- matrix(runif(100 * 10), nrow = 100, ncol = 10)
My initial method is to calculate the similarity (e.g. saying tanimoto coefficient or others: http://en.wikipedia.org/wiki/Jaccard_index ) between two rows, and dissimilairty = 1 - similarity, and then compare the dissimilarty values. At last I will sort all dissimilarity value, and select the 10 maximum dissimilarity values. But it seems that the result is a 100 * 100 matrix, maybe need efficient method to such calculation if there are a large number of rows. However, this is just my thought, maybe not right, so I need help.
[update]
After looking for some literatures. I find the one definition for the maximum dissimilarity method.
Maximum dissimilarity method: It begins by randomly choosing a data record as the first cluster center. The record maximally distant from the first point is selected as the next cluster center. The record maximally distant from both current points is selected after that . The process repeats itself until there is a sufficient number of cluster centers.
Here in my question, the sufficient number should be 10.
Thanks.
First of all, the Jacard Index is not right for you. From the wikipedia page
The Jaccard coefficient measures similarity between finite sample sets...
Your matrix has samples of floats, so you have a different problem (note that the Index in question is defined in terms of intersections; that should be a red flag right there :-).
So, you have to decide what you mean by dissimilarity. One natural interpretation would be to say row A is more dissimilar from the data set than row B if it has a greater Euclidean distance to the center of mass of the data set. You can think of the center of mass of the data set as the vector you get by taking the mean of each of the colums and putting them together (apply(mat, 2, mean)).
With this, you can take the distance of each row to that central vector, and then get an ordering on those distances. From that you can work back to the rows you desire from the original matrix.
All together:
center <- apply(mat, 2, mean)
# not quite the distances, actually, but their squares. That will work fine for us though, since the order
# will still be the same
dists <- apply(mat, 1, function(row) sum((row - center) ** 2))
# this gives us the row indices in order of least to greaest dissimiliarity
dist.order <- order(dists)
# Now we just grab the 10 most dissimilar of those
most.dissimilar.ids <- dist.order[91:100]
# and use them to get the corresponding rows of the matrix
most.dissimilar <- mat[most.dissimilar.ids,]
If I was actually writing this, I probably would have compressed the last three lines as most.dissimilar <- mat[order(dists)[91:100],], but hopefully having it broken up like this makes it a little easier to see what's going on.
Of course, if distance from the center of mass doesn't make sense as the best way of thinking of "dissimilarity" in your context, then you'll have to amend with something that does.
This questions comes after a calculation in GIS (ArcMap 10.1) takes over a month to calculate (and didn't finish yet). Now I am trying to find a faster solution in R.
I have a matrix of ~30,000 x 80,000 cells, where each cell represents a 5x5 meters square. I need to calculate the sum of values in cells that fall within a given radius (3000 meters) from each cell.
For the cells on the edge of the matrix I assume a value of 0 outside the matrix.
The question is how to define the cells that fall within the radius.
There must be a library that has this functionality, but I couldn't find any.
Any suggestions?
A quick method you can test, would be to use extract and set buffer to 3000m and then use sum in the fun argument. You can sequentially extract each cell number in your raster. But I still think this will take an inordinate amount of time. Let's assume your raster is called r....
# in the first instance I would set y to be smallish, like say 1:100 and see how long it takes
extract( r , y = 1:ncell(r) , buffer = 3000 , fun = sum )
Now, the raster package does have some parallelism built in, which with access to a large, large, large multi-core machine could speed up your operation a bit by running...
beginCluster()
extract( r , y = 1:ncell(r) , buffer = 3000 , fun = sum )
endCluster()
Don't forget to assign the output of extract to a variable.