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java.lang.Object com.gregdennis.drej.Regression
public final class Regression
A least-squares regression, also known as a regularized least squares classification.
Method Summary | |
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static GMatrix |
kernelMatrix(GMatrix data,
Kernel kernel)
Returns a kernel matrix for the specified data matrix (each column contains a data point) and the specified kernel. |
static Representer |
solve(GMatrix data,
GVector values,
Kernel kernel,
double lambda)
Performs a least squares regression for the specified data matrix (one data point in each column), and returns a representer function fit to the data. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Method Detail |
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public static Representer solve(GMatrix data, GVector values, Kernel kernel, double lambda)
Given the kernel matrix K, the identity matrix I, and the values vector y, the returned representer function has the following vector c of coefficients:
c = (K - λI)-1y
public static GMatrix kernelMatrix(GMatrix data, Kernel kernel)
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