I am stuck at creating a matrix of a matrix (vector in this case)
What I have so far
index = zeros(size(A)) // This is some matrix but isn't important to the question
indexIndex = 1;
for rows=1:length(R)
for columns=1:length(K)
if(A(rows,columns)==x)
V=[rows columns]; // I create a vector holding the row + column
index(indexIndex) = V(1,2) // I want to store all these vectors
indexIndex = indexIndex + 1
end
end
end
I have tried various ways of getting the information out of V (such as V(1:2)) but nothing seems to work correctly.
In other words, I'm trying to get an array of points.
Thanks in advance
I do not understand your question exactly. What is the size of A? What is x, K and R? But under some assumptions,
Using list
You could use a list
// Create some matrix A
A = zeros(8,8)
//initialize the list
index = list();
// Get the dimensions of A
rows = size(A,1);
cols = size(A,2);
x = 0;
for row=1:rows
for col=1:cols
if(A(row,col)==x)
// Create a vector holding row and col
V=[row col];
// Append it to list using $ (last index) + 1
index($+1) = V
end
end
end
Single indexed matrices
Another approach would be to make use of the fact an multi-dimensional matrix can also be indexed by a single value.
For instance create a random matrix named a:
-->a = rand(3,3)
a =
0.6212882 0.5211472 0.0881335
0.3454984 0.2870401 0.4498763
0.7064868 0.6502795 0.7227253
Access the first value:
-->a(1)
ans =
0.6212882
-->a(1,1)
ans =
0.6212882
Access the second value:
-->a(2)
ans =
0.3454984
-->a(2,1)
ans =
0.3454984
So that proves how the single indexing works. Now to apply it to your problem and knocking out a for-loop.
// Create some matrix A
A = zeros(8,8)
//initialize the array of indices
index = [];
// Get the dimensions of A
rows = size(A,1);
cols = size(A,2);
x = 0;
for i=1:length(A)
if(A(i)==x)
// Append it to list using $ (last index) + 1
index($+1) = i;
end
end
Without for-loop
If you just need the values that adhere to a certain condition you could also do something like this
values = A(A==x);
Be carefull when comparing doubles, these are not always (un)equal when you expect.
Related
In the Help Documentation of Scilab 6.0.2, I can read the following instruction on the Overloading entry, regarding the last operation code "iext" showed in this entry's table:
"The 6 char code may be used for some complex insertion algorithm like x.b(2) = 33 where b field is not defined in the structure x. The insertion is automatically decomposed into temp = x.b; temp(2) = 33; x.b = temp. The 6 char code is used for the first step of this algorithm. The 6 overloading function is very similar to the e's one."
But I can't find a complete example on how to use this "char 6 code" to overload a function. I'm trying to use it, without success. Does anyone have an example on how to do this?
The code bellow creates a normal "mlist" as a example. Which needs overloading functions
A = rand(5,3)
names = ["colA" "colB" "colC"]
units = ["ft" "in" "lb"]
M = mlist(["Mlog" "names" "units" names],names,units,A(:,1),A(:,2),A(:,3))
Following are the overload functions:
//define display
function %Mlog_p(M)
n = size(M.names,"*")
formatStr = strcat(repmat("%10s ",1,n)) + "\n"
formatNum = strcat(repmat("%0.10f ",1,n)) + "\n"
mprintf(formatStr,M.names)
mprintf(formatStr,M.units)
disp([M(M.names(1)),M(M.names(2)),M(M.names(3))])
end
//define extraction operation
function [Mat]=%Mlog_e(varargin)
M = varargin($)
cols = [1:size(M.names,"*")] // This will also work
cols = cols(varargin($-1)) // when varargin($-1) = 1:1:$
Mat = []
if length(varargin)==3 then
for i = M.names(cols)
Mat = [Mat M(i)(varargin(1))]
end
else
for i=1:size(M.names(cols),"*")
Mat(i).name = M.names(cols(i))
Mat(i).unit = M.units(cols(i))
Mat(i).data = M(:,cols(i))
end
end
endfunction
//define insertion operations (a regular matrix into a Mlog matrix)
function ML=%s_i_Mlog(i,j,V,M)
names = M.names
units = M.units
A = M(:,:) // uses function above
A(i,j) = V
ML = mlist(["Mlog" "names" "units" names],names,units,A(:,1),A(:,2),A(:,3))
endfunction
//insertion operation with structures (the subject of the question)
function temp = %Mlog_6(j,M)
temp = M(j) // uses function %Mlog_e
endfunction
function M = %st_i_Mlog(j,st,M)
A = M(:,:) // uses function %Mlog_e
M.names(j) = st.name // uses function above
M.units(j) = st.unit // uses function above
A(:,j) = st.data // uses function above
names = M.names
units = M.units
M = mlist(["Mlog" "names" "units" names],names,units,A(:,1),A(:,2),A(:,3))
endfunction
The first overload (displays mlist) will show the matrix in the form of the following table:
--> M
M =
colA colB colC
ft in lb
0.4720517 0.6719395 0.5628382
0.0623731 0.1360619 0.5531093
0.0854401 0.2119744 0.0768984
0.0134564 0.4015942 0.5360758
0.3543002 0.4036219 0.0900212
The next overloads (extraction and insertion) Will allow the table to be access as a simple matrix M(i,j).
The extraction function Will also allow M to be access by column, which returns a structure, for instance:
--> M(2)
ans =
name: "colB"
unit: "in"
data: [5x1 constant]
The last two functions are the overloads mentioned in the question. They allow the column metadata to be changed in a structure form.
--> M(2).name = "length"
M =
colA length colC
ft in lb
0.4720517 0.6719395 0.5628382
0.0623731 0.1360619 0.5531093
0.0854401 0.2119744 0.0768984
0.0134564 0.4015942 0.5360758
0.3543002 0.4036219 0.0900212
I am trying to save the results of loop in a new array, then plot them.
But now I can only save the last value comes from the loop. How can I save all the results from the loop?
for j=1,200 do begin
h = where(o eq j,ct3)
if (ct3 ne 0) then begin
mag = a1[h].imag
bcg = min(mag)
deltay = pqq[plu2[j]]
bcg1 = float(bcg)
u = where(bcg1*deltay ne 0)
bcg2 = bcg1[u]
deltay1 = deltay[u]
print,deltay1,bcg2
plot,bcg2,deltay1,psym=5
endif
endfor
To store a variable number of values each time through your loop, I would use a list and then the toArray method when you want the final array to plot.
For example, at the beginning of your code create a list to store the results in:
deltay_list = list()
Then in your loop, add elements to your list:
deltay_list->add, deltay1, /extract
The EXTRACT keyword indicates that you should add the individual elements of deltay1, not add deltay as a single element of the list. When you want to plot, then do:
deltay_array = deltay_list->toArray()
obj_destroy, deltay_list
plot, deltay_array
I need to identify the rows (/columns) that have defined values in a large sparse Boolean Matrix. I want to use this to 1. slice (actually view) the Matrix by those rows/columns; and 2. slice (/view) vectors and matrices that have the same dimensions as the margins of a Matrix. I.e. the result should probably be a Vector of indices / Bools or (preferably) an iterator.
I've tried the obvious:
a = sprand(10000, 10000, 0.01)
cols = unique(a.colptr)
rows = unique(a.rowvals)
but each of these take like 20ms on my machine, probably because they allocate about 1MB (at least they allocate cols and rows). This is inside a performance-critical function, so I'd like the code to be optimized. The Base code seems to have an nzrange iterator for sparse matrices, but it is not easy for me to see how to apply that to my case.
Is there a suggested way of doing this?
Second question: I'd need to also perform this operation on views of my sparse Matrix - would that be something like x = view(a,:,:); cols = unique(x.parent.colptr[x.indices[:,2]]) or is there specialized functionality for this? Views of sparse matrices appear to be tricky (cf https://discourse.julialang.org/t/slow-arithmetic-on-views-of-sparse-matrices/3644 – not a cross-post)
Thanks a lot!
Regarding getting the non-zero rows and columns of a sparse matrix, the following functions should be pretty efficient:
nzcols(a::SparseMatrixCSC) = collect(i
for i in 1:a.n if a.colptr[i]<a.colptr[i+1])
function nzrows(a::SparseMatrixCSC)
active = falses(a.m)
for r in a.rowval
active[r] = true
end
return find(active)
end
For a 10_000x10_000 matrix with 0.1 density it takes 0.2ms and 2.9ms for cols and rows, respectively. It should also be quicker than method in question (apart from the correctness issue as well).
Regarding views of sparse matrices, a quick solution would be to turn view into a sparse matrix (e.g. using b = sparse(view(a,100:199,100:199))) and use functions above. In code:
nzcols(b::SubArray{T,2,P}) where {T,P<:AbstractSparseArray} = nzcols(sparse(b))
nzrows(b::SubArray{T,2,P}) where {T,P<:AbstractSparseArray} = nzrows(sparse(b))
A better solution would be to customize the functions according to view. For example, when the view uses UnitRanges for both rows and columns:
# utility predicate returning true if element of sorted v in range r
inrange(v,r) = searchsortedlast(v,last(r))>=searchsortedfirst(v,first(r))
function nzcols(b::SubArray{T,2,P,Tuple{UnitRange{Int64},UnitRange{Int64}}}
) where {T,P<:SparseMatrixCSC}
return collect(i+1-start(b.indexes[2])
for i in b.indexes[2]
if b.parent.colptr[i]<b.parent.colptr[i+1] &&
inrange(b.parent.rowval[nzrange(b.parent,i)],b.indexes[1]))
end
function nzrows(b::SubArray{T,2,P,Tuple{UnitRange{Int64},UnitRange{Int64}}}
) where {T,P<:SparseMatrixCSC}
active = falses(length(b.indexes[1]))
for c in b.indexes[2]
for r in nzrange(b.parent,c)
if b.parent.rowval[r] in b.indexes[1]
active[b.parent.rowval[r]+1-start(b.indexes[1])] = true
end
end
end
return find(active)
end
which work faster than the versions for the full matrices (for 100x100 submatrix of above 10,000x10,000 matrix cols and rows take 16μs and 12μs, respectively on my machine, but these are unstable results).
A proper benchmark would use fixed matrices (or at least fix the random seed). I'll edit this line with such a benchmark if I do it.
In case the indices are not ranges, the fallback to converting to a sparse matrix works, but here are versions for indices which are Vectors. If the indices are mixed, yet another set of versions needs to be made. Quite repetitive, but this is the strength of Julia, when the versions are done, the code will choose optimized methods correctly using the types in the caller without too much effort.
function sortedintersecting(v1, v2)
i,j = start(v1), start(v2)
while i <= length(v1) && j <= length(v2)
if v1[i] == v2[j] return true
elseif v1[i] > v2[j] j += 1
else i += 1
end
end
return false
end
function nzcols(b::SubArray{T,2,P,Tuple{Vector{Int64},Vector{Int64}}}
) where {T,P<:SparseMatrixCSC}
brows = sort(unique(b.indexes[1]))
return [k
for (k,i) in enumerate(b.indexes[2])
if b.parent.colptr[i]<b.parent.colptr[i+1] &&
sortedintersecting(brows,b.parent.rowval[nzrange(b.parent,i)])]
end
function nzrows(b::SubArray{T,2,P,Tuple{Vector{Int64},Vector{Int64}}}
) where {T,P<:SparseMatrixCSC}
active = falses(length(b.indexes[1]))
for c in b.indexes[2]
active[findin(b.indexes[1],b.parent.rowval[nzrange(b.parent,c)])] = true
end
return find(active)
end
-- ADDENDUM --
Since it was noted nzrows for Vector{Int} indices is a bit slow, this is an attempt to improve its speed by replacing findin with a version exploiting sortedness:
function findin2(inds,v,w)
i,j = start(v),start(w)
res = Vector{Int}()
while i<=length(v) && j<=length(w)
if v[i]==w[j]
push!(res,inds[i])
i += 1
elseif (v[i]<w[j]) i += 1
else j += 1
end
end
return res
end
function nzrows(b::SubArray{T,2,P,Tuple{Vector{Int64},Vector{Int64}}}
) where {T,P<:SparseMatrixCSC}
active = falses(length(b.indexes[1]))
inds = sortperm(b.indexes[1])
brows = (b.indexes[1])[inds]
for c in b.indexes[2]
active[findin2(inds,brows,b.parent.rowval[nzrange(b.parent,c)])] = true
end
return find(active)
end
I am having trouble figuring out how to get the length of a matrix within a matrix within a matrix (nested depth of 3). So what the code is doing in short is... looks to see if the publisher is already in the array, then it either adds a new column in the array with a new publisher and the corresponding system, or adds the new system to the existing array publisher
output[k][1] is the publisher array
output[k][2][l] is the system
where the first [] is the amount of different publishers
and the second [] is the amount of different systems within the same publisher
So how would I find out what the length of the third deep array is?
function reviewPubCount()
local output = {}
local k = 0
for i = 1, #keys do
if string.find(tostring(keys[i]), '_') then
key = Split(tostring(keys[i]), '_')
for j = 1, #reviewer_code do
if key[1] == reviewer_code[j] and key[1] ~= '' then
k = k + 1
output[k] = {}
-- output[k] = reviewer_code[j]
for l = 1, k do
if output[l][1] == reviewer_code[j] then
ltable = output[l][2]
temp = table.getn(ltable)
output[l][2][temp+1] = key[2]
else
output[k][1] = reviewer_code[j]
output[k][2][1] = key[2]
end
end
end
end
end
end
return output
end
The code has been fixed here for future reference: http://codepad.org/3di3BOD2#output
You should be able to replace table.getn(t) with #t (it's deprecated in Lua 5.1 and removed in Lua 5.2); instead of this:
ltable = output[l][2]
temp = table.getn(ltable)
output[l][2][temp+1] = key[2]
try this:
output[l][2][#output[l][2]+1] = key[2]
or this:
table.insert(output[l][2], key[2])
I am working in classic ASP; using getRows to get multidimension array of rows and column.
while iterating a row; I want to pass that single row into another function to build the column layout.
with C# I can do this:
obj[][] multiDimArray = FunctionCall_To_InitializeArray_4X16();
for (int rowId = 0 ; rowId < 4 ; rowId++)
{
FunctionCall_to_ProcessSingleRow(multiDimArray[rowId][]);
//this function only accept single dimension array
}
How can I do this is asp classic/vbscript:
1. I have a function that accept single dimension array as parameter.
2. Call that function and pass 1 part of 2 dimension array.
Thank you
I think you will need to populate a new array or dictionary object with the single dimension you want to process.
here a piece from working code, should get you going..
aResults = oRst.Getrows
oRst.Close
Set oRst = Nothing
Call SubCloseDatabaseOracle
iRows = UBound(aResults, 2)
iCols = UBound(aResults, 1)
row = 1 'first row
line = ""
separator = ""
FOR col = 0 TO iCols
line = line & separator & cStr(aResults(col,row))
separator = ";"
NEXT
aSingleDimensionArray = split(line,";")