kernel.functions | R Documentation |
Compute similarities between feature vectors according to a specific kernel function
cauchy.kernel(W, sigma = 1) laplacian.kernel(W, sigma = 1) gaussian.kernel(W, sigma = 1) inv.multiquadric.kernel(W, v = 1) identity.kernel(W, a = 1) linear.kernel(W, a = 1) poly.kernel(W, degree = 2, scale = -1, v = 0)
W |
a numeric matrix, Rows are examples and columns are features |
sigma |
a real value representing the sigma parameter (def. 1) of the Cauchy, Gaussian and Laplacian kernel |
v |
constant factor (def. 1) of the inverse multiquadric kernel and of the polynomail kernel; for the inverse multiquadric kernel v must be larger than 0. |
a |
unused parameter, maintained for compatibility reasons . |
degree |
integer corresponding to a degree of the polynomial (def. 2) |
scale |
double: scaling factor of the polynomial kernel. If scale=-1 (def) scale is set to 1/ncol(W); |
All the kernel matrices are computed by calling C code to speed-up the computation.
cauchy.kernel
computes the Cauchy kernel.
laplacian.kernel
computes the Lapalacian kernel.
gaussian.kernel
computes the Gaussian kernel.
inv.multiquadric.kernel
computes the inverse multiquadric kernel.
identity.kernel
computes the identity kernel. In this case the input W represents a similarity square matrix (obtained i.e. through the Pearson correlation) between examples.
linear.kernel
computes the linear kernel.
A kernel matrix representing the similarities between the examples (rows of W), according to a specific kernel function.
rw.kernel-methods
# computing kernels on the Tanimoto chemical structure similarity matrix library(bionetdata); data(DD.chem.data); K <- identity.kernel(DD.chem.data); K <- linear.kernel(DD.chem.data); K <- gaussian.kernel(DD.chem.data); K <- inv.multiquadric.kernel(DD.chem.data); K <- poly.kernel(DD.chem.data);
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