I have a set of boolean equations, i.e.
var1 = x AND y
var2 = x OR z
var3 = ...
var4 = ...
And the constraint that every output vari should equal 1.
I want every corresponding combination of input variables (x, y ,z ...) which satisfies these equations.
For example, the first two equations would allow [x y z] = [1 1 0] or [1 1 1] as solutions.
You can do this pretty easily if you don't have too many variables.
This method will stall if you do have many variables, because it uses use a matrix of size K*(2^K), where K is the number of variables, and combvec gets pretty slow for large K too.
Whilst you have to be wary of the number of variables, this method is pretty capable of handling many logical 'equations' with little overhead.
In the x, y, z example:
% Get all combinations of x/y/z, where each is true or false
opts = repmat( {[true, false]}, 1, 3 );
xyz = combvec( opts{:} )
% Assign each row to a named variable
x = xyz(1,:); y = xyz(2,:); z = xyz(3,:);
% Get the combinations which satisfy your conditions
results = xyz( :, (x & y) & (x | z) );
% Each column of results is a solution
>> results
results =
1 1
1 1
1 0
Written more generally, it might look something like this:
K = 3; % K variables (previously x, y and z so K = 3)
% Create all true/false combinations
opts = repmat( {[true, false]}, 1, K );
combs = combvec( opts{:} );
% Shorthand so we can write in(i) not combs(i,:)
in = #(k) combs(k,:);
% Apply conditions
results = combs( :, (in(1) & in(2)) ...
& (in(1) | in(3)) );
Note: if you don't have the Neural Network Toolbox, you won't have combvec. There are many alternatives for getting all the combinations.
Related
Imagine you have the following formula:
a=4*b*c^2
Is there any way in Matlab to program this is in a way that the if 2 of 3 variables are provided, Matlab will solve and provide the missing one?
Because the only alternative I am seeing is using switch-case and solving the equation myself.
if isempty(a)
switchVar=1
elseif isempty(b)
switchVar=2;
else
switchVar=3;
end
switch switchVar
case 1
a=4*b*c^2;
case 2
b=a/4/c^2;
case 3
c=sqrt(a/4/b);
end
Thanky you very much in advance!
For a numeric (rather than symbolic) solution...
You can do this with some faffing around and anonymous functions. See the comments for details:
% For the target function: 0 = 4*b*c - a
% Let x = [a,b,c]
% Define the values we know about, i.e. not "c"
% You could put any values in for the known variables, and NaN for the unknown.
x0 = [5, 10, NaN];
% Define an index for the unknown, and then clear any NaNs
idx = isnan(x0);
x0(idx) = 0;
% Make sure we have 1 unknown
assert( nnz( idx ) == 1 );
% Define a function which handles which elements of "x"
% actually influence the equation
X = #(x,ii) ( x*idx(ii) + x0(ii)*(~idx(ii)) );
% Define the function to solve, 0 = 4*b*c - a = 4*x(2)*x(3)^2 - x(1) = ...
f = #(x) 4 * X(x,2) * X(x,3).^2 - X(x,1);
% Solve using fzero. Note this requires an initial guess
x0(idx) = fzero( f, 1 );
We can check these results are correct by plotting the function for a range of c values, and checking the intersection with the x-axis aligns to the output x0(3):
c = -1:0.01:1;
y = 4*x(2)*c.^2-x(1);
figure
hold on
plot(t,y);
plot(t,y.*0,'r');
plot(x0(3),0,'ok','markersize',10,'linewidth',2)
Note that there were 2 valid solutions, since this is a quadratic. The initial condition provided to fzero will largely dictate which solution is found.
Edit:
You can condense this down a bit with some tweaks to my earlier syntax:
% Define all initial conditions. This includes known variable values
% i.e. a = 5, b = 10
% As well as the initial guess for unknown variable values
% i.e. c = 1 (maybe? ish?)
x0 = [5, 10, 1];
% Specify the index of the unknown variable in x
idx = 3;
% Define the helper function which handles the influence of each variable
X = #(x,ii) x*(ii==idx) + x0(ii)*(ii~=idx);
% Define the function to solve, as before
f = #(x) 4 * X(x,2) * X(x,3).^2 - X(x,1);
% Solve
x0(idx) = fzero( f, x0(idx) )
This approach has the benefit that you can just change idx (and re-run the definition steps for X and f) to switch the variable of choice!
First, specify the given known variables
Then rewrite the equation as 0 = 4*b*c - a
Finally use solve to find the missing value
Code is as follows
syms a b c
% define known variable
a = 2; c = 5;
% equation rewritten
f = 4*b*c^2 - a == 0;
missing_value = solve(f);
I am trying to implement a simplex algorithm following the rules I was given at my optimization course. The problem is
min c'*x s.t.
Ax = b
x >= 0
All vectors are assumes to be columns, ' denotes the transpose. The algorithm should also return the solution to dual LP. The rules to follow are:
Here, A_J denotes columns from A with indices in J and x_J, x_K denotes elements of vector x with indices in J or K respectively. Vector a_s is column s of matrix A.
Now I do not understand how this algorithm takes care of condition x >= 0, but I decided to give it a try and follow it step by step. I used Matlab for this and got the following code.
X = zeros(n, 1);
Y = zeros(m, 1);
% i. Choose starting basis J and K = {1,2,...,n} \ J
J = [4 5 6] % for our problem
K = setdiff(1:n, J)
% this while is for goto
while 1
% ii. Solve system A_J*\bar{x}_J = b.
xbar = A(:,J) \ b
% iii. Calculate value of criterion function with respect to current x_J.
fval = c(J)' * xbar
% iv. Calculate dual solution y from A_J^T*y = c_J.
y = A(:,J)' \ c(J)
% v. Calculate \bar{c}^T = c_K^T - u^T A_K. If \bar{c}^T >= 0, we have
% found the optimal solution. If not, select the smallest s \in K, such
% that c_s < 0. Variable x_s enters basis.
cbar = c(K)' - c(J)' * inv(A(:,J)) * A(:,K)
cbar = cbar'
tmp = findnegative(cbar)
if tmp == -1 % we have found the optimal solution since cbar >= 0
X(J) = xbar;
Y = y;
FVAL = fval;
return
end
s = findnegative(c, K) %x_s enters basis
% vi. Solve system A_J*\bar{a} = a_s. If \bar{a} <= 0, then the problem is
% unbounded.
abar = A(:,J) \ A(:,s)
if findpositive(abar) == -1 % we failed to find positive number
disp('The problem is unbounded.')
return;
end
% vii. Calculate v = \bar{x}_J / \bar{a} and find the smallest rho \in J,
% such that v_rho > 0. Variable x_rho exits basis.
v = xbar ./ abar
rho = J(findpositive(v))
% viii. Update J and K and goto ii.
J = setdiff(J, rho)
J = union(J, s)
K = setdiff(K, s)
K = union(K, rho)
end
Functions findpositive(x) and findnegative(x, S) return the first index of positive or negative value in x. S is the set of indices, over which we look at. If S is omitted, whole vector is checked. Semicolons are omitted for debugging purposes.
The problem I tested this code on is
c = [-3 -1 -3 zeros(1,3)];
A = [2 1 1; 1 2 3; 2 2 1];
A = [A eye(3)];
b = [2; 5; 6];
The reason for zeros(1,3) and eye(3) is that the problem is inequalities and we need slack variables. I have set starting basis to [4 5 6] because the notes say that starting basis should be set to slack variables.
Now, what happens during execution is that on first run of while, variable with index 1 enters basis (in Matlab, indices go from 1 on) and 4 exits it and that is reasonable. On the second run, 2 enters the basis (since it is the smallest index such that c(idx) < 0 and 1 leaves it. But now on the next iteration, 1 enters basis again and I understand why it enters, because it is the smallest index, such that c(idx) < 0. But here the looping starts. I assume that should not have happened, but following the rules I cannot see how to prevent this.
I guess that there has to be something wrong with my interpretation of the notes but I just cannot see where I am wrong. I also remember that when we solved LP on the paper, we were updating our subjective function on each go, since when a variable entered basis, we removed it from the subjective function and expressed that variable in subj. function with the expression from one of the equalities, but I assume that is different algorithm.
Any remarks or help will be highly appreciated.
The problem has been solved. Turned out that the point 7 in the notes was wrong. Instead, point 7 should be
Here is a question about whether we can use vectorization type of operation in matlab to avoid writing for loop.
I have a vector
Q = [0.1,0.3,0.6,1.0]
I generate a uniformly distributed random vector over [0,1)
X = [0.11,0.72,0.32,0.94]
I want to know whether each entry of X is between [0,0.1) or [0.1,0.3) or [0.3,0.6), or [0.6,1.0) and I want to return a vector which contains the index of the maximum element in Q that each entry of X is less than.
I could write a for loop
Y = zeros(length(X),1)
for i = 1:1:length(X)
Y(i) = find(X(i)<Q, 1);
end
Expected result for this example:
Y = [2,4,3,4]
But I wonder if there is a way to avoid writing for loop? (I see many very good answers to my question. Thank you so much! Now if we go one step further, what if my Q is a matrix, such that I want check whether )
Y = zeros(length(X),1)
for i = 1:1:length(X)
Y(i) = find(X(i)<Q(i), 1);
end
Use the second output of max, which acts as a sort of "vectorized find":
[~, Y] = max(bsxfun(#lt, X(:).', Q(:)), [], 1);
How this works:
For each element of X, test if it is less than each element of Q. This is done with bsxfun(#lt, X(:).', Q(:)). Note each column in the result corresponds to an element of X, and each row to an element of Q.
Then, for each element of X, get the index of the first element of Q for which that comparison is true. This is done with [~, Y] = max(..., [], 1). Note that the second output of max returns the index of the first maximizer (along the specified dimension), so in this case it gives the index of the first true in each column.
For your example values,
Q = [0.1, 0.3, 0.6, 1.0];
X = [0.11, 0.72, 0.32, 0.94];
[~, Y] = max(bsxfun(#lt, X(:).', Q(:)), [], 1);
gives
Y =
2 4 3 4
Using bsxfun will help accomplish this. You'll need to read about it. I also added a Q = 0 at the beginning to handle the small X case
X = [0.11,0.72,0.32,0.94 0.01];
Q = [0.1,0.3,0.6,1.0];
Q_extra = [0 Q];
Diff = bsxfun(#minus,X(:)',Q_extra (:)); %vectorized subtraction
logical_matrix = diff(Diff < 0); %find the transition from neg to positive
[X_categories,~] = find(logical_matrix == true); % get indices
% output is 2 4 3 4 1
EDIT: How long does each method take?
I got curious about the difference between each solution:
Test Code Below:
Q = [0,0.1,0.3,0.6,1.0];
X = rand(1,1e3);
tic
Y = zeros(length(X),1);
for i = 1:1:length(X)
Y(i) = find(X(i)<Q, 1);
end
toc
tic
result = arrayfun(#(x)find(x < Q, 1), X);
toc
tic
Q = [0 Q];
Diff = bsxfun(#minus,X(:)',Q(:)); %vectorized subtraction
logical_matrix = diff(Diff < 0); %find the transition from neg to positive
[X_categories,~] = find(logical_matrix == true); % get indices
toc
Run it for yourself, I found that when the size of X was 1e6, bsxfun was much faster, while for smaller arrays the differences were varying and negligible.
SAMPLE: when size X was 1e3
Elapsed time is 0.001582 seconds. % for loop
Elapsed time is 0.007324 seconds. % anonymous function
Elapsed time is 0.000785 seconds. % bsxfun
Octave has a function lookup to do exactly that. It takes a lookup table of sorted values and an array, and returns an array with indices for values in the lookup table.
octave> Q = [0.1 0.3 0.6 1.0];
octave> x = [0.11 0.72 0.32 0.94];
octave> lookup (Q, X)
ans =
1 3 2 3
The only issue is that your lookup table has an implicit zero which be fixed easily with:
octave> lookup ([0 Q], X) # alternatively, just add 1 at the results
ans =
2 4 3 4
You can create an anonymous function to perform the comparison, then apply it to each member of X using arrayfun:
compareFunc = #(x)find(x < Q, 1);
result = arrayfun(compareFunc, X, 'UniformOutput', 1);
The Q array will be stored in the anonymous function ( compareFunc ) when the anonymous function is created.
Or, as one line (Uniform Output is the default behavior of arrayfun):
result = arrayfun(#(x)find(x < Q, 1), X);
Octave does a neat auto-vectorization trick for you if the vectors you have are along different dimensions. If you make Q a column vector, you can do this:
X = [0.11, 0.72, 0.32, 0.94];
Q = [0.1; 0.3; 0.6; 1.0; 2.0; 3.0];
X <= Q
The result is a 6x4 matrix indicating which elements of Q each element of X is less than. I made Q a different length than X just to illustrate this:
0 0 0 0
1 0 0 0
1 0 1 0
1 1 1 1
1 1 1 1
1 1 1 1
Going back to the original example you have, you can do
length(Q) - sum(X <= Q) + 1
to get
2 4 3 4
Notice that I have semicolons instead of commas in the definition of Q. If you want to make it a column vector after defining it, do something like this instead:
length(Q) - sum(X <= Q') + 1
The reason that this works is that Octave implicitly applies bsxfun to an operation on a row and column vector. MATLAB will not do this until R2016b according to #excaza's comment, so in MATLAB you can do this:
length(Q) - sum(bsxfun(#le, X, Q)) + 1
You can play around with this example in IDEOne here.
Inspired by the solution posted by #Mad Physicist, here is my solution.
Q = [0.1,0.3,0.6,1.0]
X = [0.11,0.72,0.32,0.94]
Temp = repmat(X',1,4)<repmat(Q,4,1)
[~, ind]= max( Temp~=0, [], 2 );
The idea is that make the X and Q into the "same shape", then use element wise comparison, then we obtain a logical matrix whose row tells whether a given element in X is less than each of the element in Q, then return the first non-zero index of each row of this logical matrix. I haven't tested how fast this method is comparing to other methods
I'm basicaly trying to find the product of an expression that goes like this:
(x-(N-1)/2).....(x+(N-1)/2) for even value of N
x is a value that I will set at the beginning that changes too but that is a different problem...
let's say for the sake of argument that for now x is a constant (ex x=1)
example for N=6
(x-5/2)(x-3/2)(x-1/2)(x+1/2)(x+3/2)*(x+5/2)
the idea was to create a row vector every element of which is each individual term (P(1)=x-5/2) (P(2)=x-3/2)...etc and then calculate its product
N=6;
x=1;
P=ones(1,N);
for k=(-N-1)/2:(N-1)/2
for n=1:N
P(n)=(x-k);
end
end
y=prod(P);
instead this creates a vector that takes only the first value of the epxression and then
repeats the same value at each cell.
there is obviously a fundamental problem with my loop but I just can't see it.
So if anyone can help with that OR suggest a better way to calculate the product I would be grateful.
Use vectorized commands
Why use a loop when you can use vectorized commands like prod?
y = prod(2 * x + [-N + 1 : 2 : N - 1]) / 2;
For convenience, you may want to define an anonymous function for it:
f = #(N,x) reshape(prod(bsxfun(#plus, 2 * x(:), -N + 1 : 2 : N - 1) / 2, 2), size(x));
Note that the function is compatible with a (row or column) vector input x.
Tests in MATLAB's Command Window
>> f(6, [2,2]')
ans =
-14.7656
4.9219
-3.5156
4.9219
-14.7656
>> f(6, [2,2])
ans =
-14.7656 4.9219 -3.5156 4.9219 -14.7656
Benchmark
Here is a comparison of rayreng's approach versus mine. The former emerges as the clear winner... :'( ...at least as N increases.
Varying N, fixed x
Fixed N (= 10), vector x of varying length
Fixed N (= 100), vector x of varying length
Benchmark code
function benchmark
% varying N, fixed x
clear all
n = logspace(2,4,20)';
x = rand(1000,1);
tr = zeros(size(n));
tj = tr;
for k = 1 : numel(n)
% rayreng's approach (poly/polyval)
fr = #() rayreng(n(k), x);
tr(k) = timeit(fr);
% Jubobs's approach (prod/reshape/bsxfun)
fj = #() jubobs(n(k), x);
tj(k) = timeit(fj);
end
figure
hold on
plot(n, tr, 'bo')
plot(n, tj, 'ro')
hold off
xlabel('N')
ylabel('time (s)')
legend('rayreng', 'jubobs')
end
function y = jubobs(N,x)
y = reshape(prod(bsxfun(#plus,...
2 * x(:),...
-N + 1 : 2 : N - 1) / 2,...
2),...
size(x));
end
function y = rayreng(N, x)
p = poly(linspace(-(N-1)/2, (N-1)/2, N));
y = polyval(p, x);
end
function benchmark2
% fixed N, varying x
clear all
n = 100;
nx = round(logspace(2,4,20));
tr = zeros(size(n));
tj = tr;
for k = 1 : numel(nx)
disp(k)
x = rand(nx(k), 1);
% rayreng's approach (poly/polyval)
fr = #() rayreng(n, x);
tr(k) = timeit(fr);
% Jubobs's approach (prod/reshape/bsxfun)
fj = #() jubobs(n, x);
tj(k) = timeit(fj);
end
figure
hold on
plot(nx, tr, 'bo')
plot(nx, tj, 'ro')
hold off
xlabel('number of elements in vector x')
ylabel('time (s)')
legend('rayreng', 'jubobs')
title(['n = ' num2str(n)])
end
function y = jubobs(N,x)
y = reshape(prod(bsxfun(#plus,...
2 * x(:),...
-N + 1 : 2 : N - 1) / 2,...
2),...
size(x));
end
function y = rayreng(N, x)
p = poly(linspace(-(N-1)/2, (N-1)/2, N));
y = polyval(p, x);
end
An alternative
Alternatively, because the terms in your product form an arithmetic progression (each term is greater than the previous one by 1/2), you can use the formula for the product of an arithmetic progression.
I agree with #Jubobs in that you should avoid using for loops for this kind of computation. There are cases where for loops perform fast, but for something as simple as this, avoid using loops if possible.
An alternative approach to what Jubobs has suggested is that you can consider that polynomial equation to be in factored form where each factor denotes a root located at that particular location. You can use poly to convert these factors into a polynomial equation, then use polyval to evaluate the expression at the point you want. First, generate your roots by linspace where the points vary from -(N-1)/2 to (N-1)/2 and there are N of them, then plug this into poly. Finally, for any values of x, put this into polyval with the output of poly. The advantage of this approach is that you can evaluate multiple points of x in a single sweep.
Going with what you have, you would simply do this:
p = poly(linspace(-(N-1)/2, (N-1)/2, N));
out = polyval(p, x);
With your example, supposing that N = 6, this would be the output of the first line:
p =
1.0000 0 -8.7500 0 16.1875 0 -3.5156
As such, this is saying that when we expand out (x-5/2)(x-3/2)(x-1/2)(x+1/2)(x+3/2)(x+5/2), we get:
x^6 - 8.75x^4 + 16.1875x^2 - 3.5156
If we take a look at the roots of this equation, this is what we get:
r = roots(p)
r =
-2.5000
2.5000
-1.5000
1.5000
-0.5000
0.5000
As you can see, each term corresponds to one factor in your polynomial equation, so we do have the right mindset here. Now, all you have to do is use p with your values of x into polyval to obtain your results. For example, if I wanted to evaluate that polynomial from -2 <= x <= 2 where x is an integer, this is the result I get:
polyval(p, -2:2)
ans =
-14.7656 4.9219 -3.5156 4.9219 -14.7656
Therefore, when x = -2, the result is -14.7656 and so on.
Though I would recommend the solution by #Jubobs, it is also good to check what the issue is with your loop.
The first indication that something is wrong, is that you have a nested loop over 2 variables, and only index with one of them to store the result. Probably you just need a single loop.
Here is a loop that you may be interested in that should do roughly what you need:
N=6;
x=1;
k=(-N-1)/2:(N-1)/2
P = ones(size(k));
for n=1:numel(k)
P(n)=(x-k(n));
end
y=prod(P);
I tried to keep the code close to the original, so hopefully it is easy to understand.
I have some Cluster Centers and some Data Points. I want to calculate the distances as below (norm is for Euclidean distance):
costsTmp = zeros(NObjects,NClusters);
lambda = zeros(NObjects,NClusters);
for clustclust = 1:NClusters
for objobj = 1:NObjects
costsTmp(objobj,clustclust) = norm(curCenters(clustclust,:)-curPartData(objobj,:),'fro');
lambda(objobj,clustclust) = (costsTmp(objobj,clustclust) - log(si1(clustclust,objobj)))/log(si2(objobj,clustclust));
end
end
How can I vectorize this snippet?
Thanks
Try this:
Difference = zeros(NObjects,NClusters);
costsTmp = zeros(NObjects,NClusters);
lambda = zeros(NObjects,NClusters);
for clustclust = 1:NClusters
repeated_curCenter = repmat(curCenter(clustclust,:), NObjects, 1);
% ^^ This creates a repeated matrix of 1 cluster center but with NObject
% rows. Now, dimensions of repeated_curCenter equals that of curPartData
Difference(:,clustclust) = repeated_curCenter - curPartData;
costsTmp(:,clustclust) = sqrt(sum(abs(costsTmp(:,clustclust)).^2, 1)); %Euclidean norm
end
The approach is to try and make the matrices of equal dimensions. You could eliminate the present for loop also by extending this concept by making 2 3D arrays like this:
costsTmp = zeros(NObjects,NClusters);
lambda = zeros(NObjects,NClusters);
%Assume that number of dimensions for data = n
%curCenter's dimensions = NClusters x n
repeated_curCenter = repmat(curCenter, 1, 1, NObjects);
%repeated_curCenter's dimensions = NClusters x n x NObjects
%curPartData's dimensions = NObject x n
repeated_curPartData = repmat(curPartData, 1, 1, NClusters);
%repeated_curPartData's dimensions = NObjects x n x NClusters
%Alligning the matrices along similar dimensions. After this, both matrices
%have dimensions of NObjects x n x NClusters
new_repeated_curCenter = permute(repeated_curCenter, [3, 2, 1]);
Difference = new_repeated_curCenter - repeated_curPartData;
Norm = sqrt(sum(abs(Difference)).^2, 2); %sums along the 2nd dimensions i.e. n
%Norm's dimensions are now NObjects x 1 x NClusters.
Norm = permute(Norm, [1, 3, 2]);
Here, Norm is kinda like costsTmp, just with an extra dimensions. I havent provided the code for lambda. I dont know what lambda is in the question's code too.
This vectorization can be done very elegantly (if I may say so) using bsxfun. No need for any repmats
costsTemp = bsxfun( #minus, permute( curCenters, [1 3 2] ), ...
permute( curPartData, [3 1 2] ) );
% I am not sure why you use Frobenius norm, this is the same as Euclidean norm for vector
costsTemp = sqrt( sum( costsTemp.^2, 3 ) ); % now we have the norms
lambda = costsTmp -reallog(si1)./reallog(si2);
you might need to play a bit with the order of the permute dimensions vector to get the output exactly the same (in terms of transposing it).