I have a data set (called A) which contains positive integer numbers.
I want to find numbers in x and y axis of the histogram of A in two different vectors. I want a vector of unique values and a vector with the count for each values.
To obtain a vector x of unique values and a vector y of their occurrence counts:
x = unique(A(:)).';
y = sum(bsxfun(#eq,A(:),x),1);
Or, alternatively,
x = unique(A(:)).';
y = histcounts(A, [x inf]);
Related
I am a beginner in matlab and I have a particular z matrix of size m×1 with values 0,1,3,5,2 etc..with above values repeating. Now I have 4 other column matrix x1,x2,x3 and y and I want to do regression.
I have used lm = LinearModel.fit(x,y,'linear') specifying columns.Now I want to do regression only for values in matrix x1,x2,x3 and y for those corresponding to z matrix with value of 1 and neglect the other rows.How do I do it?
That's very simple. I'm going to assume that your matrix of predictor variables and outputs are also of size m (number of samples). All you have to do is find the locations within z that are 1, subset your 3 column matrix of x1,x2,x3 and y, then use LinearModel.fit to fit your data. Assuming your matrix of predictors is stored in X, and your outputs are stored in y, you would do this:
ind = z == 1;
xOut = X(ind,:);
yOut = y(ind);
lm1 = LinearModel.fit(xOut, yOut, 'linear');
BTW, these are very simple subsetting operations in MATLAB. Suggest you read a tutorial before asking any further questions here.
Which of the following statements will find the minimum difference between any pair of elements (a,b) where a is from the vector A and b is from the vector B.
A. [X,Y] = meshgrid(A,B);
min(abs(X-Y))
B. [X,Y] = meshgrid(A,B);
min(abs(min(Y-X)))
C. min(abs(A-B))
D. [X,Y] = meshgrid(A,B);
min(min(abs(X-Y)))
Can someone please explain to me?
By saying "minimum difference between any pair of elements(a,b)", I presume you mean that you are treating A and B as sets and you intend to find the absolute difference in any possible pair of elements from these two sets. So in this case you should use your option D
[X,Y] = meshgrid(A,B);
min(min(abs(X-Y)))
Explanation: Meshgrid turns a pair of 1-D vectors into 2-D grids. This link can explain what I mean to say:
http://www.mathworks.com/help/matlab/ref/meshgrid.html?s_tid=gn_loc_drop
Hence (X-Y) will give the difference in all possible pairs (a,b) such that a belongs to A and b belongs to B. Note that this will be a 2-D matrix.
abs(X-Y) would return the absolute values of all elements in this matrix (the absolute difference in each pair).
To find the smallest element in this matrix you will have to use min(min(abs(X-Y))). This is because if Z is a matrix, min(Z) treats the columns of Z as vectors, returning a row vector containing the minimum element from each column. So a single min command will give a row vector with each element being the min of the elements of that column. Using min for a second time returns the min of this row vector. This would be the smallest element in the entire matrix.
This can help:
http://www.mathworks.com/help/matlab/ref/min.html?searchHighlight=min
Options C is correct if you treat A and B as vectors and not sets. In this case you won't be considering all possible pairs. You'll end up finding the minimum of (a-b) where a,b are both in the same position in their corresponding vectors (pair-wise difference).
D. [X,Y] = meshgrid(A,B);
min(min(abs(X-Y)))
meshgrid will generate two grids - X and Y - from the vectors, which are arranged so that X-Y will generate all combinations of ax-bx where ax is in a and bx is in b.
The rest of the expression just gets the minimum absolute value from the array resulting from the subtraction, which is the value you want.
CORRECT ANSWER IS D
Let m = size(A) and n = size(B)
You want to subtract each pair of (a,b) such that a is from vector A and b is from vector B.
meshgrid(A,B) creates two matrices X Y both of size nxm where X have rows sames have vector A while Yhas columns same as vector B .
Hence , Z = X-Y will give you a matrix with n*m values corresponding to the difference between each pair of values taken from A and B . Now all you have to do is to find the absolute minimum among all values of Z.
You can do that by
req_min = min(min(abs(z)))
The whole code is
[X Y ] = meshgrid(A,B);
Z= X-Y;
Z = abs(Z);
req_min = min(min(Z));
You could also use bsxfun instead of meshgrid:
min(min(abs(bsxfun(#minus, A(:), B(:).'))))
Or use pdist2:
min(min(pdist2(A(:),B(:))))
I have a file with data arranged in three columns. I am trying to make 2D contour plot of these values, where the values in the third column (Z) is projected on the space formed by values in the first (X) and second column (Y). But usual matlab commands like 'contour' and 'imagesc' take the Z-values in the matrix format. Is there a way out in Matlab to plot these values in a 2D-plane?
Contour usually works with two vectors (X and Y) and a matrix (Z). So for each elements of the two vectors (X(i) , Y(i)), there should be a value in the matrix (Z(i,j)). Thus the size of the matrix Z should be equal to the size of the first vector (X) multiplied by the size of the second vector (Y).
if the x,y,z have the same size then you can do something like this:
[X,Y,Z] = meshgrid(x,y,z);
contour(X,Y,Z)
on the other hand if you manage to make the sizes correct then you can do something like this example:
x = linspace(-2*pi,2*pi);
y = linspace(0,4*pi);
[X,Y] = meshgrid(x,y);
Z = sin(X)+cos(Y);
figure
contour(X,Y,Z)
I am charting the following data:
a=[...
0.1, 0.7, 0.00284643369242828;...
0.1, 0.71, 0.00284643369242828;...]
such that column 1 never surpasses approximately 10
also such that column 2 goes from .7 to 1.
Column 3 seems ok
When i chart my surface using surf(a) it looks like this:
it appears not to be properly considering what should be x and y.
anything seem weird there?
I think you need to try one of two things: either break out your height column into its own rectangular matrix Z and use surf(Z) to plot each point relative to its location in the matrix (so your x- and y-axes will not be scaled the way you want), or you can put your desired x- and y-coordinates in their own vectors, and plot the matrix Z (defined at every point (xi, yj) for all i in N and j in M where x is N elements long and y is M elements long) with surf(x,y,Z).
x = 0.1:0.1:10; % or whatever increment you need
y = 0.7:0.01:1; % or whatever increment you need
Z = zeros(length(x),length(y); % initialized to the correct size, fill with data
I think you are going to have to regenerate your Z-data so that it is in a rectangular matrix that is (elements in x) by (elements in y) in dimension.
EDIT: You do not need to recreate your data. If you know that you have n unique elements in x and m unique elements in y, then you can use:
X = reshape(data(:,1),m,n);
Y = reshape(data(:,2),m,n);
Z = reshape(data(:,3),m,n);
surf(X,Y,Z);
And that should give you what you are looking for.
I am trying to find the sum of certain coordinates in a matrix.
I have a N x M matrix. I have a vector which contains 2xM values. Every pair of values in the vector is a coordinate in the matrix. Hence their are M number of coordinates. I want to find the sum of all of the coordinates without using a for loop.
Is there a matrix operation I can use to get this?
Thanks
As I understand it the vector contains the (row,column) coordinates to the matrix elements. You can just transform them into matrix element number indices. This example shows how to do it. I'm assuming that your coordinate vector looks like this:
[n-coordinate1 m-coordinate1 n-coordinate2 m-coordinate2 ...]
n = 5; % number of rows
m = 5; % number of columns
matrix = round(10*rand(n,m)); % An n by m example matrix
% A vector with 2*m elements. Element 1 is the n coordinate,
% Element 2 the m coordinate, and so on. Indexes into the matrix:
vector = ceil(rand(1,2*m)*5);
% turn the (n,m) coordinates into the element number index:
matrixIndices = vector(1:2:end) + (vector(2:2:end)-1)*n);
sumOfMatrixElements = sum(matrix(matrixIndices)); % sums the values of the indexed matrix elements
If you want to find the centroid of your 2xM array coords, then you can simply write
centroid = mean(coords,2)
If you want to find the weighted centroid, where each coordinate pair is weighted by the corresponding entry in the MxN array A, you can use sub2ind like this:
idx = sub2ind(size(A),coords(1,:)',coords(2,:)');
weights = A(idx);
weightedCentroid = sum( bsxfun( #times, coords', weights), 1 ) / sum(weights);
If all you want is the sum of all the entries to which the coordinates point, you can do the above and simply sum the weights:
idx = sub2ind(size(A),coords(1,:)',coords(2,:)');
weights = A(idx);
sumOfValues = sum(weights);