Various vector based kernels
Various vector based kernels
(anova)
(anova {:keys [sigma k d] :or {sigma 1.0 k 1.0 d 1.0}})
Anova kernel.
Parameters:
:sigma
- multiplier (default: 1.0):k
and :d
- exponents (default: 1.0)Anova kernel. Parameters: * `:sigma` - multiplier (default: 1.0) * `:k` and `:d` - exponents (default: 1.0)
(b-spline)
(b-spline {:keys [n] :or {n 2.0}})
B-spline kernel with degree :n
(default: 2.0).
B-spline kernel with degree `:n` (default: 2.0).
(bessel)
(bessel {:keys [sigma n v distance]
:or {sigma 1.0 n 2.0 v -1.0 distance v/dist}})
Bessel (of the first kind) kernel
Parameters:
:sigma
- shape (default: 1.0):n
- exponent factor (default: 2.0):v
- Bessel J order - 1 (default: -1.0):distance
- distance function (default: euclidean)Bessel (of the first kind) kernel Parameters: * `:sigma` - shape (default: 1.0) * `:n` - exponent factor (default: 2.0) * `:v` - Bessel J order - 1 (default: -1.0) * `:distance` - distance function (default: euclidean)
(bessel2)
(bessel2 {:keys [sigma order degree distance]
:or {sigma 1.0 order 0.0 degree 1.0 distance v/dist}})
Bessel (of the first kind) kernel, R kernlab implementation.
Parameters:
:sigma
- shape (default: 1.0):degree
- exponent (default: 1.0):order
- Bessel J order (default: 0.0):distance
- distance function (default: euclidean)Bessel (of the first kind) kernel, R kernlab implementation. Parameters: * `:sigma` - shape (default: 1.0) * `:degree` - exponent (default: 1.0) * `:order` - Bessel J order (default: 0.0) * `:distance` - distance function (default: euclidean)
(cauchy)
(cauchy {:keys [sigma distance] :or {sigma 1.0 distance v/dist}})
Cauchy kernel.
Parameters:
sigma
- scale (default: 1.0):distance
- distance function (default: euclidean)Cauchy kernel. Parameters: * `sigma` - scale (default: 1.0) * `:distance` - distance function (default: euclidean)
(chi-square2)
(chi-square2 _)
Chi-square kernel, second version
Chi-square kernel, second version
(dirichlet)
(dirichlet {:keys [n] :or {n 1.0}})
Dirichlet kernel with :n
dimensionality (default: 1.0).
Dirichlet kernel with `:n` dimensionality (default: 1.0).
(exponential)
(exponential {:keys [sigma distance] :or {sigma 1.0 distance v/dist}})
Exponential kernel.
Parameters:
:sigma
- shape of the kernel (default: 1.0):distance
- distance function (default: euclidean)Exponential kernel. Parameters: * `:sigma` - shape of the kernel (default: 1.0) * `:distance` - distance function (default: euclidean)
(gaussian)
(gaussian {:keys [sigma distance] :or {sigma 1.0 distance v/dist}})
Gaussian kernel.
Parameters:
:sigma
- shape of the kernel (default: 1.0):distance
- distance function (default: euclidean)Gaussian kernel. Parameters: * `:sigma` - shape of the kernel (default: 1.0) * `:distance` - distance function (default: euclidean)
(generalized-histogram)
(generalized-histogram {:keys [p] :or {p 2.0}})
Generalized histogram with :p
exponent (default: 2.0).
Generalized histogram with `:p` exponent (default: 2.0).
(generalized-t-student)
(generalized-t-student {:keys [p distance]})
Generalized t-student.
Parameters:
:p
- exponent:distance
- distance function (default: euclidean)Generalized t-student. Parameters: * `:p` - exponent * `:distance` - distance function (default: euclidean)
(geometric)
(geometric {:keys [n r distance] :or {r 1.0 distance v/dist}})
Geometric Compactly Supported kernel
Parameters:
:n
- dimension:r
- shape (default: 1.0):distance
- distance function (default: euclidean)Specific kernel names for :n
:
Geometric Compactly Supported kernel Parameters: * `:n` - dimension * `:r` - shape (default: 1.0) * `:distance` - distance function (default: euclidean) Specific kernel names for `:n`: * 1 - triangular * 2 - circular * 3 - spherical
(hyperbolic-secant)
(hyperbolic-secant {:keys [a distance] :or {a 1.0 distance v/dist}})
Hyperbolic secant kernel.
Parameters:
:a
scaling factor (default: 1.0):distance
- distance function (default: euclidean)Hyperbolic secant kernel. Parameters: * `:a` scaling factor (default: 1.0) * `:distance` - distance function (default: euclidean)
(hyperbolic-tangent)
(hyperbolic-tangent {:keys [alpha c] :or {alpha 1.0 c 0.0}})
Hyperbolic tangent of the dot product.
Parameters:
:alpha
- dot product multiplier (default: 1.0):c
- shift (default: 0.0)Hyperbolic tangent of the dot product. Parameters: * `:alpha` - dot product multiplier (default: 1.0) * `:c` - shift (default: 0.0)
(inverse-multiquadratic)
(inverse-multiquadratic {:keys [c distance] :or {c 1.0 distance v/dist}})
Inverse multiquadratic kernel.
Parameters:
:c
- shift (default: 1.0)
:distance
- distance function (default: euclidean)
Inverse multiquadratic kernel. Parameters: `:c` - shift (default: 1.0) `:distance` - distance function (default: euclidean)
(laplacian)
(laplacian {:keys [sigma distance] :or {sigma 1.0 distance v/dist}})
Laplacian kernel.
Parameters:
:sigma
- shape of the kernel (default: 1.0):distance
- distance function (default: euclidean)Laplacian kernel. Parameters: * `:sigma` - shape of the kernel (default: 1.0) * `:distance` - distance function (default: euclidean)
(log)
(log {:keys [p distance]})
Logarithmic.
Parameters:
:p
- exponent (default: 2.0):distance
- distance function (default: euclidean)Logarithmic. Parameters: * `:p` - exponent (default: 2.0) * `:distance` - distance function (default: euclidean)
(matern)
(matern {:keys [order theta distance] :or {order 1 theta 1.0 distance v/dist}})
Matern kernel.
Parameters:
:order
- order of the kernel, should be odd (default: 1).:theta
- shape (default: 1.0):distance
- distance function (default: euclidean)Order of the Bessel K function is a half of :order
parameter. For example to get Matern 5/2 kernel, call (matern 5)
.
Matern kernel. Parameters: * `:order` - order of the kernel, should be odd (default: 1). * `:theta` - shape (default: 1.0) * `:distance` - distance function (default: euclidean) Order of the Bessel K function is a half of `:order` parameter. For example to get Matern 5/2 kernel, call `(matern 5)`.
(multiquadratic)
(multiquadratic {:keys [c distance] :or {c 1.0 distance v/dist}})
Multiquadratic kernel.
Parameters:
:c
- shift (default: 1.0)
:distance
- distance function (default: euclidean)
Multiquadratic kernel. Parameters: `:c` - shift (default: 1.0) `:distance` - distance function (default: euclidean)
(pearson)
(pearson {:keys [sigma omega distance]
:or {sigma 1.0 omega 1.0 distance v/dist}})
Pearson VII kernel
Parameters:
:sigma
- scale (default: 1.0):omega
- exponent (default: 1.0):distance
- distance function (default: euclidean)Pearson VII kernel Parameters: * `:sigma` - scale (default: 1.0) * `:omega` - exponent (default: 1.0) * `:distance` - distance function (default: euclidean)
(periodic)
(periodic {:keys [sigma periodicity distance]
:or {sigma 1.0 periodicity 1.0 distance v/dist}})
Periodic kernel.
Parameters:
:sigma
- scale (default: 1.0):periodicity
- periodicity (default: 1.0):distance
- distance function (default: euclidean)Periodic kernel. Parameters: * `:sigma` - scale (default: 1.0) * `:periodicity` - periodicity (default: 1.0) * `:distance` - distance function (default: euclidean)
(polynomial)
(polynomial {:keys [alpha c p] :or {alpha 1.0 c 0.0 p 2.0}})
Polynomial (power of dot product).
Parameters:
:alpha
- dot product multiplier (default: 1.0):c
- shift (default: 0.0):p
- exponent (default: 2.0)Polynomial (power of dot product). Parameters: * `:alpha` - dot product multiplier (default: 1.0) * `:c` - shift (default: 0.0) * `:p` - exponent (default: 2.0)
(power)
(power {:keys [p distance] :or {p 2.0 distance v/dist}})
Power (negative) of distance.
Parameters:
:p
- exponent (default: 2.0):distance
- distance function (default: euclidean)Power (negative) of distance. Parameters: * `:p` - exponent (default: 2.0) * `:distance` - distance function (default: euclidean)
(rational-quadratic)
(rational-quadratic {:keys [c distance] :or {c 1.0 distance v/dist}})
Rational quadratic kernel.
Parameters:
:c
- shift (default: 1.0)
:distance
- distance function (default: euclidean)
Rational quadratic kernel. Parameters: `:c` - shift (default: 1.0) `:distance` - distance function (default: euclidean)
(rbf->kernel rbf-kernel)
(rbf->kernel rbf-kernel distance)
Convert RBF kernel as vector kernel using a distance
function (default: euclidean).
Convert RBF kernel as vector kernel using a `distance` function (default: euclidean).
(wave)
(wave {:keys [sigma distance] :or {sigma 1.0 distance v/dist}})
Wave (sinc) kernel.
Parameters:
:sigma
- scale (default: 1.0):distance
- distance function (default: euclidean)Wave (sinc) kernel. Parameters: * `:sigma` - scale (default: 1.0) * `:distance` - distance function (default: euclidean)
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