I have to create a function that takes as input a vector v and three scalars a, b and c. The function replaces every element of v that is equal to a with a two element array [b,c].
For example, given v = [1,2,3,4] and a = 2, b = 5, c = 5, the output would be:
out = [1,5,5,3,4]
My first attempt was to try this:
v = [1,2,3,4];
v(2) = [5,5];
However, I get an error, so I do not understand how to put two values in the place of one in a vector, i.e. shift all the following values one position to the right so that the new two values fit in the vector and, therefore, the size of the vector will increase in one. In addition, if there are several values of a that exist in v, I'm not sure how to replace them all at once.
How can I do this in MATLAB?
Here's a solution using cell arrays:
% remember the indices where a occurs
ind = (v == a);
% split array such that each element of a cell array contains one element
v = mat2cell(v, 1, ones(1, numel(v)));
% replace appropriate cells with two-element array
v(ind) = {[b c]};
% concatenate
v = cell2mat(v);
Like rayryeng's solution, it can replace multiple occurrences of a.
The problem mentioned by siliconwafer, that the array changes size, is here solved by intermediately keeping the partial arrays in cells of a cell array. Converting back to an array concenates these parts.
Something I would do is to first find the values of v that are equal to a which we will call ind. Then, create a new output vector that has the output size equal to numel(v) + numel(ind), as we are replacing each value of a that is in v with an additional value, then use indexing to place our new values in.
Assuming that you have created a row vector v, do the following:
%// Find all locations that are equal to a
ind = find(v == a);
%// Allocate output vector
out = zeros(1, numel(v) + numel(ind));
%// Determine locations in output vector that we need to
%// modify to place the value b in
indx = ind + (0:numel(ind)-1);
%// Determine locations in output vector that we need to
%// modify to place the value c in
indy = indx + 1;
%// Place values of b and c into the output
out(indx) = b;
out(indy) = c;
%// Get the rest of the values in v that are not equal to a
%// and place them in their corresponding spots.
rest = true(1,numel(out));
rest([indx,indy]) = false;
out(rest) = v(v ~= a);
The indx and indy statements are rather tricky, but certainly not hard to understand. For each index in v that is equal to a, what happens is that we need to shift the vector over by 1 for each index / location of v that is equal to a. The first value requires that we shift the vector over to the right by 1, then the next value requires that we shift to the right by 1 with respect to the previous shift, which means that we actually need to take the second index and shift by the right by 2 as this is with respect to the original index.
The next value requires that we shift to the right by 1 with respect to the second shift, or shifting to the right by 3 with respect to the original index and so on. These shifts define where we're going to place b. To place c, we simply take the indices generated for placing b and move them over to the right by 1.
What's left is to populate the output vector with those values that are not equal to a. We simply define a logical mask where the indices used to populate the output array have their locations set to false while the rest are set to true. We use this to index into the output and find those locations that are not equal to a to complete the assignment.
Example:
v = [1,2,3,4,5,4,4,5];
a = 4;
b = 10;
c = 11;
Using the above code, we get:
out =
1 2 3 10 11 5 10 11 10 11 5
This successfully replaces every value that is 4 in v with the tuple of [10,11].
I think that strrep deserves a mention here.
Although it's called string replacement and warns for non-char input, it still works perfectly fine for other numbers as well (including integers, doubles and even complex numbers).
v = [1,2,3,4]
a = 2, b = 5, c = 5
out = strrep(v, a, [b c])
Warning: Inputs must be character arrays or cell arrays of strings.
out =
1 5 5 3 4
You are not attempting to overwrite an existing value in the vector. You're attempting to change the size of the vector (meaning the number of rows or columns in the vector) because you're adding an element. This will always result in the vector being reallocated in memory.
Create a new vector, using the first and last half of v.
Let's say your index is stored in the variable index.
index = 2;
newValues = [5, 5];
x = [ v(1:index), newValues, v(index+1:end) ]
x =
1 2 5 5 3 4
Related
I'd like to implement a cellular automaton (CA) in Julia. Dimensions should be wrapped, this means: the left neighbor of the leftmost cell is the rightmost cell etc.
One crucial question is: how to get the neighbors of one cell to compute it's state in the next generation? As dimensions should be wrapped and Julia does not allow negative indices (as in Python) i had this idea:
Considered a 1D CA, one generation is a one-dimensional array:
0 0 1 0 0
What if we create a two dimensional Array, where the first row is shifted right and the third is shifted left, like this:
0 0 0 1 0
0 0 1 0 0
0 1 0 0 0
Now, the first column contain the states of the first cell and it's neighbors etc.
i think this can easily be generalized for two and more dimensions.
First question: do you think this is a good idea, or is this a wrong track?
EDIT: Answer to first question was no, second Question and code example discarded.
Second question: If the approach is basically ok, please have a look at the following sketch:
EDIT: Other approach, here is a stripped down version of a 1D CA, using mod1() for getting neighborhood-indices, as Bogumił Kamiński suggested.
for any cell:
- A array of all indices
- B array of all neighborhood states
- C states converted to one integer
- D lookup next state
function digits2int(digits, base=10)
int = 0
for digit in digits
int = int * base + digit
end
return int
end
gen = [0,0,0,0,0,1,0,0,0,0,0]
rule = [0,1,1,1,1,0,0,0]
function nextgen(gen, rule)
values = [mod1.(x .+ [-1,0,1], size(gen)) for x in 1:length(gen)] # A
values = [gen[value] for value in values] # B
values = [digits2int(value, 2) for value in values] # C
values = [rule[value+1] for value in values] # D
return values
end
for _ in 1:100
global gen
println(gen)
gen = nextgen(gen, rule)
end
Next step should be to extend it to two dimensions, will try it now...
The way I typically do it is to use mod1 function for wrapped indexing.
In this approach, no matter what dimensionality of your array a is then when you want to move from position x by delta dx it is enough to write mod1(x+dx, size(a, 1)) if x is the first dimension of an array.
Here is a simple example of a random walk on a 2D torus counting the number of times a given cell was visited (here I additionally use broadcasting to handle all dimensions in one expression):
function randomwalk()
a = zeros(Int, 8, 8)
pos = (1,1)
for _ in 1:10^6
# Von Neumann neighborhood
dpos = rand(((1,0), (-1,0), (0,1), (0,-1)))
pos = mod1.(pos .+ dpos, size(a))
a[pos...] += 1
end
a
end
Usually, if the CA has cells that are only dependent on the cells next to them, it's simpler just to "wrap" the vector by adding the last element to the front and the first element to the back, doing the simulation, and then "unwrap" by taking the first and last elements away again to get the result length the same as the starting array length. For the 1-D case:
const lines = 10
const start = ".........#........."
const rules = [90, 30, 14]
rule2poss(rule) = [rule & (1 << (i - 1)) != 0 for i in 1:8]
cells2bools(cells) = [cells[i] == '#' for i in 1:length(cells)]
bools2cells(bset) = prod([bset[i] ? "#" : "." for i in 1:length(bset)])
function transform(bset, ruleposs)
newbset = map(x->ruleposs[x],
[bset[i + 1] * 4 + bset[i] * 2 + bset[i - 1] + 1
for i in 2:length(bset)-1])
vcat(newbset[end], newbset, newbset[1])
end
const startset = cells2bools(start)
for rul in rules
println("\nUsing Rule $rul:")
bset = vcat(startset[end], startset, startset[1]) # wrap ends
rp = rule2poss(rul)
for _ in 1:lines
println(bools2cells(bset[2:end-1])) # unwrap ends
bset = transform(bset, rp)
end
end
As long as only the adjacent cells are used in the simulation for any given cell, this is correct.
If you extend this to a 2D matrix, you would also "wrap" the first and last rows as well as the first and last columns, and so forth.
I want to compare the pixel values of two images, which I have stored in arrays.
Suppose the arrays are A and B. I want to compare the elements one by one, and if A[l] == B[k], then I want to store the match as a key value-pair in a third array, C, like so: C[l] = k.
Since the arrays are naturally quite large, the solution needs to finish within a reasonable amount of time (minutes) on a Core 2 Duo system.
This seems to work in under a second for 1024*720 matrices:
A = randi(255,737280,1);
B = randi(255,737280,1);
C = zeros(size(A));
[b_vals, b_inds] = unique(B,'first');
for l = 1:numel(b_vals)
C(A == b_vals(l)) = b_inds(l);
end
First we find the unique values of B and the indices of the first occurrences of these values.
[b_vals, b_inds] = unique(B,'first');
We know that there can be no more than 256 unique values in a uint8 array, so we've reduced our loop from 1024*720 iterations to just 256 iterations.
We also know that for each occurrence of a particular value, say 209, in A, those locations in C will all have the same value: the location of the first occurrence of 209 in B, so we can set all of them at once. First we get locations of all of the occurrences of b_vals(l) in A:
A == b_vals(l)
then use that mask as a logical index into C.
C(A == b_vals(l))
All of these values will be equal to the corresponding index in B:
C(A == b_vals(l)) = b_inds(l);
Here is the updated code to consider all of the indices of a value in B (or at least as many as are necessary). If there are more occurrences of a value in A than in B, the indices wrap.
A = randi(255,737280,1);
B = randi(255,737280,1);
C = zeros(size(A));
b_vals = unique(B);
for l = 1:numel(b_vals)
b_inds = find(B==b_vals(l)); %// find the indices of each unique value in B
a_inds = find(A==b_vals(l)); %// find the indices of each unique value in A
%// in case the length of a_inds is greater than the length of b_inds
%// duplicate b_inds until it is larger (or equal)
b_inds = repmat(b_inds,[ceil(numel(a_inds)/numel(b_inds)),1]);
%// truncate b_inds to be the same length as a_inds (if necessary) and
%// put b_inds into the proper places in C
C(a_inds) = b_inds(1:numel(a_inds));
end
I haven't fully tested this code, but from my small samples it seems to work properly and on the full-size case, it only takes about twice as long as the previous code, or less than 2 seconds on my machine.
So, if I understand your question correctly, you want for each value of l=1:length(A) the (first) index k into B so that A(l) == B(k). Then:
C = arrayfun(#(val) find(B==val, 1, 'first'), A)
could give you your solution, as long as you're sure that every element will have a match. The above solution would fail otherwise, complaning that the function returned a non-scalar (because find would return [] if no match is found). You have two options:
Using a cell array to store the result instead of a numeric array. You would need to call arrayfun with 'UniformOutput', false at the end. Then, the values of A without matches in B would be those for which isempty(C{i}) is true.
Providing a default value for an index into A with no matches in B (e.g. 0 or NaN). I'm not sure about this one, but I think that you would need to add 'ErrorHandler', #(~,~) NaN to the arrayfun call. The error handler is a function that gets called when the function passed to arrayfun fails, and may either rethrow the error or compute a substitute value. Thus the #(~,~) NaN. I am not sure that it would work, however, since in this case the error is in arrayfun and not in the passed function, but you can try it.
If you have the images in arrays A & B
idx = A == B;
C = zeros(size(A));
C(idx) = A(idx);
Suppose I have an array of length N. I want to choose n positions randomly, make them zero and then add the existing elements to the next non-zero element.
For example, suppose r = (r1,r2,r3,r4,r5), N = 5. Let n = 2. And the randomly picked positions are 3rd and 4th. Then I want to transform r to
r_new = (r1, r2, 0, 0, r3+r4+r5).
Instead if the randomly selected positions were 1 and 3, then I want to have
r_new = (0, r1 + r2, 0, r3+r4, r5).
I am coding in MATLAB. Here is my current code.
u = randperm(T);
ind = sort(u(1:n(i)));
tmp = r(ind);
r(ind) = 0;
x = find( r );
I am not necessarily looking for MATLAB code. Pseudocode would be helpful enough.
I'm assuming the last position can never be selected, otherwise the intended behaviour is undefined. So you randomly select n positions uniformly distributed from 1 up to N-1 (not up to N).
Here's one approach:
Select n distinct random positions from 1 to N-1, and sort them. Call the resulting vector of positions pos. This can be easily done with randperm and sort.
For each value in pos, say p, accumulate r(p) into r(p+1), and set r(p) to zero. This is done with a for loop.
In step 2, if position p+1 happens to belong to pos too, the accumulated value will be moved further to the right in a subsequent iteration. This works because pos has been sorted, so the randomly selected positions are processed from left to right.
r = [3 5 4 3 7 2 8]; %// data
n = 2; %// number of positions
pos = sort(randperm(numel(r)-1,n)); %// randomly select positions, and sort them
for p = pos
r([p p+1]) = [0 r(p)+r(p+1)]; %// process position p
end
Assuming N, n and r are already generated, then we select random indexes:
inds = randi(N,n,1);
Then to achieve the desired results you can loop as follows:
inds = sort(inds);
for ii=1:numel(inds)
if(inds(ii)<N)
r(inds(ii)+1)=r(inds(ii)+1) +r(inds(ii));
r(inds)=0;
else
r(inds)=0;
end
end
This will create the desired outcome of adding the values to the next index that wasn't selected to be set to 0.
Note I had to assume an edge case where if the last index is set to 0, then its value is not added to anything.
i am looking for a way that i can add up elements in an array such that the first element of the first array is added to every element in the second array, then the second element in the first array is added to all every element in the second array and so on. The final vector will be length(a)*length(b) long
for example...
a=[1,2,3,4] b=[5,6,7]
answer =
[(1+5),(1+6),(1+7),(2+5),(2+6),(2+7),(3+5),(3+6),(3+7),(4+5),(4+6),(4+7)]
=[6,7,8,7,8,9,8,9,10,9,10,11]
Read up on bsxfun. It's very useful for this kind of things (and usually faster than arrayfun or for loops):
result = bsxfun(#plus, a(:).', b(:)); %'// matrix of size numel(b) x numel(a)
result = result(:).'; %'// linearize to a vector
Or, a little more freak: kron does what you want with products instead of sums. So:
result = log(kron(exp(a),exp(b)));
My first thought is to do this with arrayfun using an anonymous function that adds each scalar element of a to the full array in b. Then since you get a cell array result you can expand that cell array into the array you are looking for:
>> a=[1,2,3,4], b=[5,6,7]
>> result = arrayfun(#(x) x+b, a,'UniformOutput',false);
>> result = [result{:}]
result =
6 7 8 7 8 9 8 9 10 9 10 11
Use meshgrid to create matrices of a and b and use matrix addition to compute a+b
a=[1,2,3,4], b=[5,6,7]
[A_matrix,B_matrix] = meshgrid(a,b)
result = A_matrix + B_matrix
result = result(:)'
I have this loop which generates a vector "Diff". How do I place the values of Diff in an array that records all the Diff's generated? The problem is that the length of Diff should be a fixed length (36) which is the width of the table "CleanPrice". But because col_set varies in length (according to the number of NaNs in the data it is reading), then Diff also varies in length. What I need it to do is assign the answers generated according to their appropriate column number. i.e. row(i) of diff should contain col(i) where all other rows in Diff should be assigned a "0" or "NaN". Basically I need DiffArray to be a (nTrials x 36) array where each row is the (36 x 1) DiffArray generated. At the moment though, each time the length of col changes, I get the following error:
??? Subscripted assignment dimension mismatch.
Error in ==> NSSmodel
at 41 DiffMatrix(end+1,:)=Diff
This is my code:
DiffArray=[];
StartRow=2935;
EndRow=2940;
nTrials=EndRow-StartRow;
for row=StartRow:EndRow;
col_set=find(~isnan(gcm3.data.CleanPrice(row,1:end)));
col=col_set(:,2:end);
CleanPrices=transpose(gcm3.data.CleanPrice(row,col));
Maturity=gcm3.data.CouponandMaturity(col-1,2);
SettleDate=gcm3.data.CouponandMaturity(row,3);
Settle = repmat(SettleDate,[length(Maturity) 1]);
CleanPrices =transpose(gcm3.data.CleanPrice(row,col));
CouponRate = gcm3.data.CouponandMaturity(col-1,1);
Instruments = [Settle Maturity CleanPrices CouponRate];
PlottingPoints = gcm3.data.CouponandMaturity(1,2):gcm3.data.CouponandMaturity(36,2);
Yield = bndyield(CleanPrices,CouponRate,Settle,Maturity);
SvenssonModel = IRFunctionCurve.fitSvensson('Zero',SettleDate,Instruments)
ParYield=SvenssonModel.getParYields(Maturity);
[PriceActual, AccruedIntActual] = bndprice(Yield, CouponRate, Settle, Maturity);
[PriceNSS, AccruedIntNSS] = bndprice(ParYield, CouponRate, Settle, Maturity);
Diff=PriceActual-PriceNSS
DiffArray(end+1,:)=Diff
end
I looked at num2cell in this post but wasn't sure how to apply it correctly and started getting errors relating to that instead.
Is it correct to say you want to add an 'incomplete' row to DiffArray? If you know exactly where each element should go you could maybe do something like this:
indices = [1:7; 2:8; 3:9; [1 2 3 6 7 8 10]];
Diff = rand(4, 7);
DiffArray = zeros(4, 10) * NaN;
for row = 1:4
DiffArray(row, indices(row, :)) = Diff(row,:);
end
of course in your case you would be calculating Diff and Index (a row vector) inside the loop and not using preassigned arrays. The above is just to illustrate how to use an indexing vector to position a short row in a matrix.