I need to calculate the mean and the variance of training set for anomaly detection but keep getting dimension mismatch error.
mean_train = 1/(num_train )* sum(Xtrain);
var_train = 1/(num_train )* sum(Xtrain - mean_train).^2;
First of all show all data, because we can't reproduce your mistake.
I suggest, you take this error because of this:
mean_train or num_train is a vector and then we can't simply multiply it or minus it with Xtrain.
But the answer for you:
just use build-in Matlab functions mean and var. This let you use it in this way:
mean(Xtrain)
avoiding using secondary variables like num_train.
I checked your code and found mistake:
Xtrain is an 100x2 array. And using sum on it returns 1x2 array! You can read about this in help:
If A is a matrix, then sum(A) returns a row vector containing the sum
of each column.
So your next action:
mean_train = 1/(num_train )* sum(Xtrain);
returns 1x2 array and matlab can't make this: Xtrain - mean_train.
Answer is easy:
use sum twice like this: sum(sum(Xtrain))
Related
I want to create a matrix by "zeros([numbers,3])"
syms x;
numbers=symsum(x,x,1,5);
zeros([numbers,3])
Here is the error: The size must be a number.
How to create a matrix like this "zeros([numbers,3])"
This is because numbers is of sym class. Convert it to double first. i.e. use XX=zeros(double(numbers),3); instead.
But still there is no need to use Symbolic Math Toolbox here. What you're doing can be done more simply as:
numbers = sum(1:5);
XX = zeros(numbers,3)
I'm trying to write code that will optimize a multivariate function using sklearn's optimize function, but it keeps returning an IndexError, and I'm not sure where to go from here.
The code is this:
revcoeff = coefficients[::-1]
xdot = np.zeros(0)
normfeat1 = normfeat1.reshape(-1,1)
xdot = np.append(normfeat1, normfeat2.reshape(-1,1), axis=1)
a = revcoeff[1:3]
b = xdot[0, :]
seed = np.zeros(5) #does seed need to be the coefficients? not sure
fun = lambda x: np.multiply((1/666), np.power(np.sum(np.dot(a, xdot[x, :])-medianv[x]),2)) #costfunction
optsol = optimize.minimize(fun, seed)
where there are two features I'm using in my nearest neighbors algorithm. Coefficients for the fitted regression model are given into the array "coefficients".
What I'm having trouble understanding is 1) why my code is throwing a "IndexError: arrays used as indicies must be of integer or boolean type"....and also partially I'm confused by the optimize.minimize function itself. It takes in two input values, the function and x0 (an ndarray with initial guesses). What should x0 be, the coefficients values? Or do I pick random values, and how many are necessary?
np.zeros() does not return integers by default. Try, for example, np.zeros(5, dtype=int) instead. It won't solve all of the problems with your code, though. You'll see some other error message.
Also, notice, that 1/666 returns 0 instead of 0.00150150. You probably want 1/666.0.
It would be helpful if you could clean up your code as half of it is of no use.
having a problem with my "new love", matlab: I wrote a function to calculate an integral using the trapz-method: `
function [L]=bogenlaenge_innen(schwingungen)
R = 1500; %Ablegeradius
OA = 1; %Amplitude
S = schwingungen; %Schwingungszahl
B = 3.175; %Tapebreite
phi = 0:2.*pi./10000:2.*pi;
BL = sqrt((R-B).^2+2.*(R-B).*OA.*sin(S.*phi)+OA.^2.*(sin(S.*phi)).^2+OA.^2.*S.^2.*(cos(S.*phi)).^2);
L = trapz(phi,BL)`
this works fine when i start it with one specific number out of the command-window. Now I want to plot the values of "L" for several S.
I did the following in a new *.m-file:
W = (0:1:1500);
T = bogenlaenge_innen(W);
plot(W,T)
And there it is:
Error using .*
Matrix dimensions must agree.
What is wrong? Is it just a dot somewhere? I am using matlab for the second day now, so please be patient.... ;) Thank you so much in advance!
PS: just ignore the german part of the code, it does not really matter :)
In your code, the arrays S and phi in the expression sin(S.*phi) should have same size or one of them should be a constant in order the code works
The error is most likely because you have made it so that the number of elements in schwingungen, i.e. W in your code, must be equal to the number of elements in phi. Since size(W) gives you a different result from size(0:2.*pi./10000:2.*pi), you get the error.
The way .* works is that is multiplies each corresponding elements of two matrices provided that they either have the same dimensions or that one of them is a scalar. So your code will work when schwingungen is a scalar, but not when it's a vector as chances are it has a different number of elements from the way you hard coded phi.
The simplest course of action (not necessarily the most Matlabesque though) for you is to loop through the different values of S:
W = (0:1:1500);
T = zeros(size(W); %Preallocate for speed)
for ii = 1:length(W)
T(ii) = bogenlaenge_innen(W(ii));
end
plot(W,T)
In your function you define phi as a vector of 10001 elements.
In this same function you do S.*phi, so if S is not the same length as phi, you will get the "dimensions must agree" error.
In your call to the function you are doing it with a vector of length 1501, so there is your error.
Regards
I am trying to model a vector data containing 200 sample points denoting a measurement.I want to see "goodness of fit" and after reading I found that this can be done by predicting the next set of values(I am not that confident though if this is the correct way).I am stuck at this since the following code gives an error and I am just unable to solve it.Can somebody please help in removing the error
Error using *
Inner matrix dimensions must agree.
Error in data_predict (line 27)
ypred(j) = ar_coeff' * y{i}(j-1:-1:j-p);
Also,can somebody tell me how to do the same thing i.e get the coefficients using nonlinear AR modelling,moving average and ARMA since using the command nlarx() did not return any model coefficients?
CODE
if ~iscell(y); y = {y}; end
model = ar(y, 2, 'yw');
%prediction
yresiduals=[];
nsegments=length(y);
ar_coeffs = model.a;
ar_coeff=[ar_coeffs(2) ar_coeffs(3)]
for i=1:nsegments
pred = zeros(length(y{i}),1);
for j=p+1:length(y{i})
ypred(j) = ar_coeff(:)' * y{i}(j-1:-1:j-p);
end
yresiduals = [yresiduals; y{i}(p+1:end) - ypred(p+1:end)];
end
In matlab, * is the matrix product between two matrices. That means that the number of columns in the first matrix must equal the number of rows in the second matrix. You might have intended to use .* element by element multiplication. EDIT: For element by element multiplication, the matrices must be the same size. Check the size of your matrices. If they don't fit either of these conditions, something needs to change.
I have a 3-dimensial matrix W of size 160x170x18 and I want to compute the difference
between each sucessive matrices inside W.
For example diff1 = W(:,:,1) - W(:,:,2) and diff2 = W(:,:,2) - W(:,:,3), etc ...
Next I want to select some special parts of the resulting matrices, For example:
NewDiff1 = [diff1(20:50,110:140); diff1(60:90,110:140)];
and the same thing for the other matrices.
finally I want to compute the mean of each matrix and the error as follow:
mean1 = mean(mean(NewDiff1));
er1 = 0.1-abs(mean1);
I succeeded to do this for each matrix alone, but prefer to do all at once in a for loop.
The expression
diff1 = diff(W,1,3)
will return, in your example, a 160*170*17 matrix where diffW(:,:,1) = W(:,:,2) - W(:,:,1), which isn't quite what you want. But
diff1 = (-1)*diff(W,1,3)
does, if my arithmetic is good, give you the differences you want. From there on you need something like:
newdiff1 = [diff1(20:50,110:140,:);diff1(60:90,110:140,:)];
and
means = mean(mean(newdiff1));
er1 = 0.1 - abs(mean1);
I haven't tested this thoroughly on matrices of the size you are working with, but it seems to work OK on smaller tests.
Store your matrices into a cell array and then just loop through the contents of the cell array and apply the same differencing logic to each thing. Be careful to use the {} syntax with a cell array to get its contents, rather than () which gives you the cell at a particular location.