makeGaussianMatrix
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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]). Usage 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
....
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.
Expression:
Arguments
Name
Type
Description Argument: NumMatrix NumMatrix
Matrix containing the original independent and dependent observations
Returns:
Here are a number of links to Lambda coding examples which contain this instruction in various use cases.
Here are the links to the data types of the function arguments.
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Here are also a number of links to functions having arguments with any of these data types.
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Analytic Information Server (AIS)AIS Component Systems
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