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vq = interp1( x , v , xq ) returns interpolated values of a 1-D function at specific query points using linear interpolation. Vector x contains the sample points, and v contains the corresponding values, v(x). Vector xq contains the coordinates of the query points.
S = sparse( m,n ) generates an m -by- n all zero sparse matrix. S = sparse( i,j , v ) generates a sparse matrix S from the triplets i , j , and v such that S(i(k),j(k)) = v(k) . The max(i) -by- max(j) output matrix has space allotted for length(v) nonzero elements.
The griddata function interpolates the surface at the query points specified by (xq,yq) and returns the interpolated values, vq . The surface always passes through the data points defined by x and y . vq = griddata( x , y , z , v , xq , yq , zq ) fits a hypersurface of the form v = f(x,y,z).
If I have a matrix like this
A = [1 2; 3 4];
I can use interp2 to interpolate it like this
newA = interp2(A,2);
and I get a 5x5 interpolated matrix.
But what if I have a matrix like this:
B = zeros(20);
B(3,2) = 5;
B(17,4) = 3;
B(16, 19) = 2.3;
B(5, 18) = 4.5;
How would I interpolate (or fill-in the blanks) this matrix. I've looked into interp2 as well as TriScatteredInterp but neither of these seem to fit my needs exactly.
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