I am stuck trying to figure this out. I have an array:
a = [ 1 1 1 2 1 1 1 3 2 1 1 2 1 1 1]
I want to add the values in the array so that it equal to 10. Once the added value reaches 10, I want the array to start adding the value again until it reaches 10. There is two problem that I face here,
1) How can I add the array so that the sum = 10 everytime. Notice that in the array, there is 3. If I add all the value before 3, I get 8 and I only need 2 from 3. I need to make sure that the remainder, which is 1 is added to the next array to get the sum 10.
2) How do I break the loop once it reaches 10 and ask it continue the summation to next value to get another 10?
I created a loop but it only works for the first part of the array. I have no idea how to make it continue. The code is as follow:
a = [ 1 1 1 2 1 1 1 3 2 1 1 2 1 1 1];
c = 0;
for i = 1:length(a)
while c < 10
c = c + a(i);
break
end
end
Please help. Thank you
This can be done using cumsum, mod, diff and find as follows:
temp = cumsum(a);
required = find([0 diff(mod(temp,10))] <0)
cumsum returns the cumulative sum which then is rescaled using mod. diff determines where the sum gets greater than or equal to 10 and finally find determines those indexes.
Edit: Above solution works if a doesn't have negative elements. If a can have negative elements then:
temp1=cumsum(a); %Commulative Sum
temp2=[0 diff(mod(temp1,10))];%Indexes where sum >=10 (indicated by negative values)
temp2(temp1<0)=0; %Removing false indexes which may come if `a` has -ve values
required = find(temp2 <0) %Required indexes
This should do what you are trying. It displays the index at which each time the sum equals 10. Check this with your testcases. rem stores the residual sum in each iteration which is carried forward in the next iteration. The rest of the code is similar to what you were doing.
a = [ 1 1 1 2 1 1 1 3 2 1 1 2 1 1 1];
c = 0;
rem = 0;
i = 1;
length(a);
while(i <= length(a))
c = rem;
while (c < 10 && i <= length(a))
c = c + a(i);
i = i + 1;
if(c >= 10)
rem = c - 10;
break
end
end
if(c >= 10)
disp(i-1)
end
use cumsum instead of your while loop:
a = [ 1 1 1 2 1 1 1 3 2 1 1 2 1 1 1];
a_ = a;
endidxlist = false(size(a));
startidxlist = false(size(a));
startidxlist(1) = true;
while any(a_) && (sum(a_) >= 10)
b = cumsum(a_);
idx = find(b >= 10,1);
endidxlist(idx) = true;
% move residual to the next sequence
a_(idx) = b(idx) - 10;
if a_(idx) > 0
startidxlist(idx) = idx;
elseif (idx+1) <= numel(a)
startidxlist(idx+1) = true;
end
a_(1:idx-1) = 0;
end
if (idx+1) <= numel(a)
startidxlist(idx+1) = false;
end
endidxlist gives you the end-indexes of each sequence and startidxlist the start-indexes
I'm a beginner in MATLAB. I have my function testnetwork:
function result = TestNetwork(network, input)
result = input;
b= [-1 -1 -1 -1 ];
% Iterate over all the couches
for i=1:length(network.couches)
result = network.activation(matrix_multiplication_same_dimension(network.couches{i} , vertcat ( result , b)));
end
end
and this is my main script:
% initialis a cell of zeros for example output = zeros_quat(zeros(1, 2)) is %equal to [0 0 0 0] [0 0 0 0]
output = zeros_quat(zeros(10, size(testset,2)));
%
for i = 1:size(testset, 2)
%testset is a cell of arrays size (81 * 180)
output {:,i} = TestNetwork(network, testset{:,i});
end
end
I get the error too many input arguments. I don't know what the problem is.
This line is the problem:
output {:,i} = TestNetwork(network, testset{:,i});
When you de-reference the cell array testset using curly braces {} with multiple entries, it takes the individual cells of the cell array and returns all of them as separate arguments:
a = { [ 1 2], [3 4] };
a{1}
ans =
1 2
a{1,:}
ans =
1 2
ans =
3 4
Note the two instances of ans in the second evaluation. I suspect that what you really want is a reference to a single cell on both sides of the equation:
output{i} = TestNetwork(network, testset{i});
I have an array (say of 1s and 0s) and I want to find the index, i, for the first location where 1 appears n times in a row.
For example,
x = [0 0 1 0 1 1 1 0 0 0] ;
i = 5, for n = 3, as this is the first time '1' appears three times in a row.
Note: I want to find where 1 appears n times in a row so
i = find(x,n,'first');
is incorrect as this would give me the index of the first n 1s.
It is essentially a string search? eg findstr but with a vector.
You can do it with convolution as follows:
x = [0 0 1 0 1 1 1 0 0 0];
N = 3;
result = find(conv(x, ones(1,N), 'valid')==N, 1)
How it works
Convolve x with a vector of N ones and find the first time the result equals N. Convolution is computed with the 'valid' flag to avoid edge effects and thus obtain the correct value for the index.
Another answer that I have is to generate a buffer matrix where each row of this matrix is a neighbourhood of overlapping n elements of the array. Once you create this, index into your array and find the first row that has all 1s:
x = [0 0 1 0 1 1 1 0 0 0]; %// Example data
n = 3; %// How many times we look for duplication
%// Solution
ind = bsxfun(#plus, (1:numel(x)-n+1).', 0:n-1); %'
out = find(all(x(ind),2), 1);
The first line is a bit tricky. We use bsxfun to generate a matrix of size m x n where m is the total number of overlapping neighbourhoods while n is the size of the window you are searching for. This generates a matrix where the first row is enumerated from 1 to n, the second row is enumerated from 2 to n+1, up until the very end which is from numel(x)-n+1 to numel(x). Given n = 3, we have:
>> ind
ind =
1 2 3
2 3 4
3 4 5
4 5 6
5 6 7
6 7 8
7 8 9
8 9 10
These are indices which we will use to index into our array x, and for your example it generates the following buffer matrix when we directly index into x:
>> x = [0 0 1 0 1 1 1 0 0 0];
>> x(ind)
ans =
0 0 1
0 1 0
1 0 1
0 1 1
1 1 1
1 1 0
1 0 0
0 0 0
Each row is an overlapping neighbourhood of n elements. We finally end by searching for the first row that gives us all 1s. This is done by using all and searching over every row independently with the 2 as the second parameter. all produces true if every element in a row is non-zero, or 1 in our case. We then combine with find to determine the first non-zero location that satisfies this constraint... and so:
>> out = find(all(x(ind), 2), 1)
out =
5
This tells us that the fifth location of x is where the beginning of this duplication occurs n times.
Based on Rayryeng's approach you can loop this as well. This will definitely be slower for short array sizes, but for very large array sizes this doesn't calculate every possibility, but stops as soon as the first match is found and thus will be faster. You could even use an if statement based on the initial array length to choose whether to use the bsxfun or the for loop. Note also that for loops are rather fast since the latest MATLAB engine update.
x = [0 0 1 0 1 1 1 0 0 0]; %// Example data
n = 3; %// How many times we look for duplication
for idx = 1:numel(x)-n
if all(x(idx:idx+n-1))
break
end
end
Additionally, this can be used to find the a first occurrences:
x = [0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0]; %// Example data
n = 3; %// How many times we look for duplication
a = 2; %// number of desired matches
collect(1,a)=0; %// initialise output
kk = 1; %// initialise counter
for idx = 1:numel(x)-n
if all(x(idx:idx+n-1))
collect(kk) = idx;
if kk == a
break
end
kk = kk+1;
end
end
Which does the same but shuts down after a matches have been found. Again, this approach is only useful if your array is large.
Seeing you commented whether you can find the last occurrence: yes. Same trick as before, just run the loop backwards:
for idx = numel(x)-n:-1:1
if all(x(idx:idx+n-1))
break
end
end
One possibility with looping:
i = 0;
n = 3;
for idx = n : length(x)
idx_true = 1;
for sub_idx = (idx - n + 1) : idx
idx_true = idx_true & (x(sub_idx));
end
if(idx_true)
i = idx - n + 1;
break
end
end
if (i == 0)
disp('No index found.')
else
disp(i)
end
In Matlab, say I have the following matrix, which represents a population of 10 individuals:
pop = [0 0 0 0 0; 1 1 1 0 0; 1 1 1 1 1; 1 1 1 0 0; 0 0 0 0 0; 0 0 0 0 0; 1 0 0 0 0; 1 1 1 1 1; 0 0 0 0 0; 0 0 0 0 0];
Where rows of ones and zeros define 6 different 'types' of individuals.
a = [0 0 0 0 0];
b = [1 0 0 0 0];
c = [1 1 0 0 0];
d = [1 1 1 0 0];
e = [1 1 1 1 0];
f = [1 1 1 1 1];
I want to define the proportion/frequency of a, b, c, d, e and f in pop.
I want to end up with the following list:
a = 0.5;
b = 0.1;
c = 0;
d = 0.2;
e = 0;
f = 0.2;
One way I can think of is by summing the rows, then counting the number of times each appears, and then sorting and indexing
sum_pop = sum(pop')';
x = unique(sum_pop);
N = numel(x);
count = zeros(N,1);
for l = 1:N
count(l) = sum(sum_pop==x(l));
end
pop_frequency = [x(:) count/10];
But this doesn't quite get me what I want (i.e. when frequency = 0) and it seems there must be a faster way?
You can use pdist2 (Statistics Toolbox) to get all frequencies:
indiv = [a;b;c;d;e;f]; %// matrix with all individuals
result = mean(pdist2(pop, indiv)==0, 1);
This gives, in your example,
result =
0.5000 0.1000 0 0.2000 0 0.2000
Equivalently, you can use bsxfun to manually compute pdist2(pop, indiv)==0, as in Divakar's answer.
For the specific individuals in your example (that can be identified by the number of ones) you could also do
result = histc(sum(pop, 2), 0:size(pop,2)) / size(pop,1);
There is some functionality in unique that can be used for this. If
[q,w,e] = unique(pop,'rows');
q is the matrix of unique rows, w is the index of the row first appears in the matrix. The third element e contains indices of q so that pop = q(e,:). Armed with this, the rest of the problem should be straight forward. The probability of a value in e should be the probability that this row appears in pop.
The counting can be done with histc
histc(e,1:max(e))/length(e)
and the non occuring rows can be found with
ismember(a,q,'rows')
There is of course other ways as well, maybe (probably) faster ways, or oneliners. Why I post this is because it provides a way that is easy to understand, readable and that does not require any special toolboxes.
EDIT
This example gives expected output
a = [0,0,0,0,0;1,0,0,0,0;1,1,0,0,0;1,1,1,0,0;1,1,1,1,0;1,1,1,1,1]; % catenated a-f
[q,w,e] = unique(pop,'rows');
prob = histc(e,1:max(e))/length(e);
out = zeros(size(a,1),1);
out(ismember(a,q,'rows')) = prob;
Approach #1
With bsxfun -
A = cat(1,a,b,c,d,e,f)
out = squeeze(sum(all(bsxfun(#eq,pop,permute(A,[3 2 1])),2),1))/size(pop,1)
Output -
out =
0.5000
0.1000
0
0.2000
0
0.2000
Approach #2
If those elements are binary numbers, you can convert them into decimal format.
Thus, decimal format for pop becomes -
>> bi2de(pop)
ans =
0
7
31
7
0
0
1
31
0
0
And that of the concatenated array, A becomes -
>> bi2de(A)
ans =
0
1
3
7
15
31
Finally, you need to count the decimal formatted numbers from A in that of pop, which you can do with histc. Here's the code -
A = cat(1,a,b,c,d,e,f)
out = histc(bi2de(pop),bi2de(A))/size(pop,1)
Output -
out =
0.5000
0.1000
0
0.2000
0
0.2000
I think ismember is the most direct and general way to do this. If your groups were more complicated, this would be the way to go:
population = [0,0,0,0,0; 1,1,1,0,0; 1,1,1,1,1; 1,1,1,0,0; 0,0,0,0,0; 0,0,0,0,0; 1,0,0,0,0; 1,1,1,1,1; 0,0,0,0,0; 0,0,0,0,0];
groups = [0,0,0,0,0; 1,0,0,0,0; 1,1,0,0,0; 1,1,1,0,0; 1,1,1,1,0; 1,1,1,1,1];
[~, whichGroup] = ismember(population, groups, 'rows');
freqOfGroup = accumarray(whichGroup, 1)/size(groups, 1);
In your special case the groups can be represented by their sums, so if this generic solution is not fast enough, use the sum-histc simplification Luis used.