How do I create a vector like this:
a = [a_1;a_2;...,a_n];
aNew = [a;a.^2;a.^3;...;a.^T].
Is it possible to create aNew without a loop?
So you want different powers of a, all strung out into a vector? I would create an array, where each column of the array is a different power of a. Then string it out into a vector. Something like this...
aNew = bsxfun(#power,a,1:T);
aNew = aNew(:);
This does what you want, in a simple, efficient way. bsxfun is a more efficient way of writing the expansion than are other methods, such as repmat, ndgrid and meshgrid.
The code I wrote does assume that a is a column vector, as you have constructed it.
The idea is to use meshgrid to create two arrays of size n x T:
[n_mesh, t_mesh] = meshgrid(a, 1:T);
Now n_mesh is an array where each row is a duplicate of a, and t_mesh is an array where each column is 1:T.
Now you can use an element-wise operation on them to create what you need:
aNew = n_mesh .^ t_mesh;
Related
I want to retrieve all the elements along the last dimension of an N-dimensional array A. That is, if idx is an (N-1) dimensional tuple, I want A[idx...,:]. I've figured out how to use CartesianRange for this, and it works as shown below
A = rand(2,3,4)
for idx in CartesianRange(size(A)[1:end-1])
i = zeros(Int, length(idx))
[i[bdx] = idx[bdx] for bdx in 1:length(idx)]
#show(A[i...,:])
end
However, there must be an easier way to create the index i shown above . Splatting idx does not work - what am I doing wrong?
You can just index directly with the CartesianIndex that gets generated from the CartesianRange!
julia> for idx in CartesianRange(size(A)[1:end-1])
#show(A[idx,:])
end
A[idx,:] = [0.0334735,0.216738,0.941401,0.973918]
A[idx,:] = [0.842384,0.236736,0.103348,0.729471]
A[idx,:] = [0.056548,0.283617,0.504253,0.718918]
A[idx,:] = [0.551649,0.55043,0.126092,0.259216]
A[idx,:] = [0.65623,0.738998,0.781989,0.160111]
A[idx,:] = [0.177955,0.971617,0.942002,0.210386]
The other recommendation I'd have here is to use the un-exported Base.front function to extract the leading dimensions from size(A) instead of indexing into it. Working with tuples in a type-stable way like this can be a little tricky, but they're really fast once you get the hang of it.
It's also worth noting that Julia's arrays are column-major, so accessing the trailing dimension like this is going to be much slower than grabbing the columns.
I have the following matrix A = [1.00 2.00; 3.00 4.00] and I need to convert it into a vector of Vectors as follows:
A1 = [1.00; 3.00]
A2 = [2.00; 4.00]
Any ideas?
tl;dr
This can be very elegantly created with a list comprehension:
A = [A[:,i] for i in 1:size(A,2)]
Explanation:
This essentially converts A from something that would be indexed as A[1,2] to something that would be indexed as A[2][1], which is what you asked.
Here I'm assigning directly back to A, which seems to me what you had in mind. But, only do this if the code is unambiguous! It's generally not a good idea to have same-named variables that represent different things at different points in the code.
NOTE: if this reversal of row / column order in the indexing isn't the order you had in mind, and you'd prefer A[1,2] to be indexed as A[1][2], then perform your list comprehension 'per row' instead, i.e.
A = [A[i,:] for i in 1:size(A,1)]
It would be much better simply to use slices of your matrix i.e. instead of A1 use
A[:,1]
and instead of A2 use
A[:,2]
If you really need them to be "seperate" objections you could try creating a cell array like so:
myfirstcell = cell(size(A,2))
for i in 1:size(A,2)
myfirstcell[i] = A[:,i]
end
See http://docs.julialang.org/en/release-0.4/stdlib/arrays/#Base.cell
(Cell arrays allow several different types of object to be stored in the same array)
Another option is B = [eachcol(A)...]. This returns an variable with type Vector{SubArray} which might be fine depending on what you want to do. To get a Vector{Vector{Float64}} try,
B = Vector{eltype(A)}[eachcol(A)...]
In Julia, is there a good way to "choose to loop over an arbitrary dimension" d? For example, I want to apply a diffusion filter to a 2D x I want to do
for j = 1:size(x,2)
for i = 2:size(x,1)-1
x2[i,j] = x[i-1,j] - 2x[i,j] + x[i+1,j]
end
end
But I want to write a function diffFilter(x2,x,d) where x can be an arbitrary dimension array and d is any dimension less than ndims(x), and it applies this x[i-1] + 2x[i] - x[i+1] filter along the dimension d (into x2 without allocating). Any idea how to do the indexing such that I can use that d to have that special part of the loop be the dth index?
You'll want to look at the pair of blog posts that Tim Holy has written on the subject:
http://julialang.org/blog/2016/02/iteration
http://julialang.org/blog/2016/03/arrays-iteration
That should give you a start on the subject.
The standard library function mapslices does this. You can write a function that applies the filter to a vector, and mapslices will take care of applying it to a particular dimension.
This is my problem:
There is a predefined list named gamma with three entries: gamma$'2' is 2x2 matrix gamma$'3' a 3x3 matrix and gamma$'4' a 4x4 matrix. I would like to have function that returns the matrix I need:
GiveMatrix <- function(n) {
gamma.list <- #init the list of matrices
gamma.list$n # return the list entry named n
Since n is not a character, the last line does not work. I tried gamma.list$paste(n)and gamma.list$as.character(n)but both did not work. Is there a function that converts nto the right format? Or is there maybe a much better way? I know, I am not really good in R.
You need to use:
gamma.list[[as.character(n)]]
In your example, R is looking for a entry in the list called n. When using [[, the contents of n is used, which is what you need.
I've found it!
gamma.list[as.character(n)] is the solution I needed.
I have assignment using R and have a little problem. In the assignment several matrices have to be generated with random number of rows and later used for various calculations. Everything works perfect, unless number of rows is 1.
In the calculations I use nrow(matrix) in different ways, for example if (i <= nrow(matrix) ) {action} and also statements like matrix[,4] and so on.
So in case number of rows is 1 (I know it is actually vector) R give errors, definitely because nrow(1-dimensional matrix)=NULL. Is there simple way to deal with this? Otherwise probably whole code have to be rewritten, but I'm very short in time :(
It is not that single-row/col matrices in R have ncol/nrow set to NULL -- in R everything is a 1D vector which can behave like matrix (i.e. show as a matrix, accept matrix indexing, etc.) when it has a dim attribute set. It seems otherwise because simple indexing a matrix to a single row or column drops dim and leaves the data in its default (1D vector) state.
Thus you can accomplish your goal either by directly recreating dim attribute of a vector (say it is called x):
dim(x)<-c(length(x),1)
x #Now a single column matrix
dim(x)<-c(1,length(x))
x #Now a single row matrix
OR by preventing [] operator from dropping dim by adding drop=FALSE argument:
x<-matrix(1:12,3,4)
x #OK, matrix
x[,3] #Boo, vector
x[,3,drop=FALSE] #Matrixicity saved!
Let's call your vector x. Try using matrix(x) or t(matrix(x)) to convert it into a proper (2D) matrix.