com.linkedin.photon.ml.hyperparameter.estimators.kernels
Kernel
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package kernels
Applies the kernel functions to the two sets of points
Applies the kernel functions to the two sets of points
the matrix containing the first set of points, where each of the m rows is a point in the space
the matrix containing the second set of points, where each of the p rows is a point in the space
the m x p covariance matrix
Applies the kernel function to the given points
Applies the kernel function to the given points
the matrix of points, where each of the m rows is a point in the space
the m x m covariance matrix
Builds a kernel with initial settings, based on the observations
Builds a kernel with initial settings, based on the observations
the observed features
the observed labels
the initial kernel
Returns the kernel parameters as a vector
Returns the kernel parameters as a vector
the kernel parameters
Computes the log likelihood of the kernel parameters
Computes the log likelihood of the kernel parameters
the observed features
the observed labels
the log likelihood
Creates a new kernel function of the same type, with the given parameters
Creates a new kernel function of the same type, with the given parameters
the parameter vector for the new kernel function
the new kernel function
If only one parameter value has been specified, builds a new vector with the single value repeated to fill all dimensions
If only one parameter value has been specified, builds a new vector with the single value repeated to fill all dimensions
the initial parameters
the dimensions of the final vector
the vector with all dimensions specified
Base trait for covariance kernel functions
In Gaussian processes estimators and models, the covariance kernel determines the similarity between points in the space. We assume that similarity in domain entails similarity in range, hence the kernel also encodes our prior assumptions about how the function behaves.
"Gaussian Processes for Machine Learning" (GPML), http://www.gaussianprocess.org/gpml/, Chapter 4