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makeGaussianMatrix
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The makeGaussianMatrix function returns the M by M+1 system of linear equations
representing the coefficient derivative equations for the minimizing the least squares error.
The input argument {NumMatrix} must be an N by M+1 number matrix representing the original
independent variable observations with the dependent variable in the last column in the form of: The output argument will be an M by M+1 number matrix containing the dot products of
the column vectors of the original observation matrix XY, where:
G[r,c] = vectorDotProduct(colXY[r],colXY[c]). When to use The makeGaussianMatrix function is a non-destructive function useful when you
want to create a Gaussian matrix in preparation for primal form regression.
See Sedgewick[2] chap 38.
x x x x... y
x x x x... y
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x x x x... y
(makeGaussianMatrix NumMatrix ) A new number Matrix object containing the dot products of the column vectors of the
original observation matrix.
Here are a number of links to Lambda coding examples which contain this instruction in various use cases.
Example_NumMatrix_makeGaussianMatrix_001
Here are the links to the data types of the function arguments. Here are also a number of links to functions having arguments with any of these data types.
You can always talk with the AIS at aiserver.sourceforge.net.
Name
Description
AIS Types NumMatrix Matrix containing the original independent and dependent observations NumMatrix
Returns:
Examples
Argument Types
NumMatrix
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