Applying a function to each row vector of a matrix

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Sameer Karim
Sameer Karim 2018-4-7
评论： Sameer Karim 2018-4-7
I have a function that I want to apply to each row of a 7800x784 matrix. The function returns a scalar and puts it into a 7800x1 matrix. So, each row of the first matrix is computed one by one and the output is sent to the second matrix. s and Ns are both 1x26 matrix. Following is my function:
function [ind] = naive_bayes(X, s, Ns)
prob = zeros(1,26);
x = 1;
y = 1800;
for i = 1:size(s,2)
like = sum(X(x:y,:))/s(1,i);
prob(1,i) = Ns(1,i) * prod((like.^X).* (1-like).^(1-X));
x = y + 1;
y = y +1800;
end
[~,ind] = max(prob);
end
Thank you!

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回答（1 个）

Walter Roberson
Walter Roberson 2018-4-7
编辑：Walter Roberson 2018-4-7
result = arrayfun(@(ROWIDX) naive_bayes(YourArray(ROWIDX,:)), (1:size(YourArray,1)).');
However, your function has the difficulty that it uses x and y and s without those having been defined.
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Walter Roberson
Walter Roberson 2018-4-7
result = arrayfun(@(ROWIDX) naive_bayes(YourArray(ROWIDX,:),s,Ns), (1:size(YourArray,1)).');
Sameer Karim
Sameer Karim 2018-4-7
I think i messed up the transistion as naive_bayes was actually a part of my main code. I want to reorganise my question so this is the code that i initially had.
prob = zeros(1,26);
for jj = 1:N
for i = 1:size(s,2)
like = sum(X(x:y,:))/s(1,i);
prob(1,i) = Ns(1,i) * prod((like.^Xtst(jj,:)).* (1-like).^(1- Xtst(jj,:)));
x = y + 1;
y = y +1800;
end
x = 1;
y = 1800;
[~,ind] = max(prob);
Cpreds(jj,1) = ind;
end
Here Xtst is my 7800x784 array with N being 7800. X is a 48600x784 array, so sum forms a 1x784 matrix. prob is processed size(s, 2)xN times, x and y resets to its initial values after one row of Xtst is processed. Now Cpreds is my Nx1 matrix with i want to finally produce. So the previous functions was actually being applied on each row of Xtst. Since the matrix size is huge I wanted to implement it using vectorisation. Thanks in advance!

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