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kernel.clj
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(ns fastmath.kernel
"Various kernel functions.
* RBF (double -> double functions)
* vector kernels (vector x vector -> double function; may be positive definite, conditional positive definite, positive semi-definite, mercer)
* density estimation
* some kernel operations"
(:require [fastmath.core :as m]
[fastmath.distance :as d]
[fastmath.vector :as v])
(:import [smile.math.rbf RadialBasisFunction]
[smile.math.kernel MercerKernel]
[smile.stat.distribution KernelDensity]
[clojure.lang IFn]
[org.apache.commons.math3.distribution NormalDistribution]))
(set! *unchecked-math* :warn-on-boxed)
(m/use-primitive-operators)
;;
;; RBF kernels
;; https://www.math.unipd.it/~demarchi/RBF/LectureNotes.pdf
;; http://evoq-eval.siam.org/Portals/0/Publications/SIURO/Vol4/Choosing_Basis_Functions_and_Shape_Parameters.pdf
(defmulti rbf
"RBF kernel creator. RBF is double->double function.
Parameters:
All kernels accept `scale` parameter (as last parameter).
Following kernels also accept `beta`: `:multiquadratic`, `:inverse-multiquadratic`, `:truncated-power`, `:radial-powers` and `:thin-plate`."
(fn [k & _] k))
(defmethod rbf :linear
([_] (rbf :linear 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
xs))))
(defmethod rbf :gaussian
([_] (rbf :gaussian 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(m/exp (- (* xs xs)))))))
(defmethod rbf :multiquadratic
([_] (rbf :multiquadratic 1.0))
([_ scale] (rbf :multiquadratic 0.5 scale))
([_ ^double beta ^double scale] (let [s2 (* scale scale)]
(cond
(== beta 1.0) (fn [^double x] (+ s2 (* x x)))
(== beta 0.5) (fn [^double x] (m/sqrt (+ s2 (* x x))))
(== beta 2.0) (fn [^double x] (m/sq (+ s2 (* x x))))
(== beta 3.0) (fn [^double x] (m/cb (+ s2 (* x x))))
:else (fn [^double x] (m/pow (+ s2 (* x x)) beta))))))
(defmethod rbf :inverse-multiquadratic
([_] (rbf :inverse-multiquadratic 1.0))
([_ scale] (rbf :inverse-multiquadratic 0.5 scale))
([_ ^double beta ^double scale] (let [s2 (* scale scale)
beta- (- beta)]
(cond
(== beta 1.0) (fn [^double x] (/ (+ s2 (* x x))))
(== beta 0.5) (fn [^double x] (/ (m/sqrt (+ s2 (* x x)))))
(== beta 2.0) (fn [^double x] (/ (m/sq (+ s2 (* x x)))))
(== beta 3.0) (fn [^double x] (/ (m/cb (+ s2 (* x x)))))
:else (fn [^double x] (m/pow (+ s2 (* x x)) beta-))))))
(defmethod rbf :truncated-power
([_] (rbf :truncated-power 1.0))
([_ scale] (rbf :truncated-power 1.0 scale))
([_ ^double k ^double scale] (cond
(== k 0.5) (fn [^double x] (let [xs (/ (m/abs x) scale)]
(if (<= xs 1.0) (m/sqrt (- 1.0 xs)) 0.0)))
(== k 1.0) (fn [^double x] (let [xs (/ (m/abs x) scale)]
(if (<= xs 1.0) (- 1.0 xs) 0.0)))
(== k 2.0) (fn [^double x] (let [xs (/ (m/abs x) scale)]
(if (<= xs 1.0) (m/sq (- 1.0 xs)) 0.0)))
(== k 3.0) (fn [^double x] (let [xs (/ (m/abs x) scale)]
(if (<= xs 1.0) (m/cb (- 1.0 xs)) 0.0)))
:else (fn [^double x] (let [xs (/ (m/abs x) scale)]
(if (<= xs 1.0) (m/pow (- 1.0 xs) k) 0.0))))))
;; https://www.math.unipd.it/~demarchi/RBF/LectureNotes.pdf
;; page 33
(defmethod rbf :gaussians-laguerre-11
([_] (rbf :gaussian-laguerre-11 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)
x2 (* xs xs)]
(* (- 1.5 x2) (m/exp (- x2)))))))
(defmethod rbf :gaussians-laguerre-12
([_] (rbf :gaussian-laguerre-12 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)
x2 (* xs xs)]
(* (+ 1.875 (* -2.5 x2) (* 0.5 x2 x2)) (m/exp (- x2)))))))
(defmethod rbf :gaussians-laguerre-21
([_] (rbf :gaussian-laguerre-21 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)
x2 (* xs xs)]
(* (- 2.0 x2) (m/exp (- x2)))))))
(defmethod rbf :gaussians-laguerre-22
([_] (rbf :gaussian-laguerre-22 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)
x2 (* xs xs)]
(* (+ 3.0 (* -3.0 x2) (* 0.5 x2 x2)) (m/exp (- x2)))))))
;; page 34
(defmethod rbf :poisson-2
([_] (rbf :poisson-2 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(m/bessel-j 0 xs)))))
(defmethod rbf :poisson-3
([_] (rbf :poisson-3 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(* 0.7978845608028654 (m/sinc xs))))))
(defmethod rbf :poisson-4
([_] (rbf :poisson-4 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (m/delta-eq xs 0.0 1.0e-7) 0.5 (/ (m/bessel-j 1 xs) xs))))))
;; page 35
;; also http://evoq-eval.siam.org/Portals/0/Publications/SIURO/Vol4/Choosing_Basis_Functions_and_Shape_Parameters.pdf
;; page 193
(defmethod rbf :mattern-c0
([_] (rbf :mattern-c0 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(m/exp (- xs))))))
(defmethod rbf :mattern-c2
([_] (rbf :mattern-c2 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(* (inc xs) (m/exp (- xs)))))))
(defmethod rbf :mattern-c4
([_] (rbf :mattern-c4 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(* (+ 3.0 (* 3.0 xs) (* xs xs)) (m/exp (- xs)))))))
;; page 37
(defmethod rbf :whittaker-02
([_] (rbf :whittaker-02 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(+ 1.0 (- xs) (* xs (m/exp (/ -1.0 xs))))))))
(defmethod rbf :whittaker-12
([_] (rbf :whittaker-12 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(+ 1.0 (* -2.0 xs) (* (inc (* 2.0 xs)) (m/exp (/ -1.0 xs))))))))
(defmethod rbf :whittaker-03
([_] (rbf :whittaker-03 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)
x2 (* 2.0 xs xs)]
(+ 1.0 (* -2.0 xs) x2 (* (- x2) (m/exp (/ -1.0 xs))))))))
(defmethod rbf :whittaker-13
([_] (rbf :whittaker-13 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)
x2 (* 6.0 xs xs)]
(+ 1.0 (* -4.0 xs) x2 (* (- (+ xs xs x2)) (m/exp (/ -1.0 xs))))))))
;; page 43
(defmethod rbf :radial-powers
([_] (rbf :radial-powers 1.0))
([_ scale] (rbf :radial-powers 1.0 scale))
([_ ^double beta ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(m/pow xs beta)))))
(defmethod rbf :thin-plate
([_] (rbf :thin-plate 1.0))
([_ scale] (rbf :thin-plate 1.0 scale))
([_ ^double beta ^double scale] (if (== beta 1.0)
(fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if-not (pos? xs) 0.0 (* xs xs (m/log xs)))))
(fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if-not (pos? xs) 0.0 (* (m/pow (* xs xs) beta) (m/log xs))))))))
;; https://www.researchgate.net/profile/Zongmin_Wu/publication/246909840_Multivariate_compactly_supported_positive_definite_radial_functions/links/542247e30cf26120b7a0209b/Multivariate-compactly-supported-positive-definite-radial-functions.pdf
;; page 9
(defmethod rbf :wu-00
([_] (rbf :wu-00 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (- 1.0 xs) 0.0)))))
(defmethod rbf :wu-10
([_] (rbf :wu-10 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)]
(* r- r- r- (inc (* xs (+ 3.0 xs))))) 0.0)))))
(defmethod rbf :wu-11
([_] (rbf :wu-11 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)]
(* r- r- (+ 2.0 xs))) 0.0)))))
(defmethod rbf :wu-20
([_] (rbf :wu-20 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)
xs2 (* xs xs)]
(* r- r2- r2- (inc (+ (* 5 xs)
(* 9 xs2)
(* 5 xs xs2)
(* xs2 xs2))))) 0.0)))))
(defmethod rbf :wu-21
([_] (rbf :wu-21 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)
xs2 (* xs xs)]
(* r2- r2- (+ 4.0
(* 16.0 xs)
(* 12.0 xs2)
(* 3.0 xs xs2)))) 0.0)))))
(defmethod rbf :wu-22
([_] (rbf :wu-22 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)]
(* r- r2- (+ 8.0
(* 9.0 xs)
(* 3.0 xs xs)))) 0.0)))))
(defmethod rbf :wu-30
([_] (rbf :wu-30 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r3- (* r- r- r-)
xs2 (* xs xs)
xs3 (* xs2 xs)]
(* r- r3- r3- (+ 5.0
(* 35.0 xs)
(* 101.0 xs2)
(* 147.0 xs xs2)
(* 101.0 xs2 xs2)
(* 35.0 xs3 xs2)
(* 5.0 xs3 xs3)))) 0.0)))))
(defmethod rbf :wu-31
([_] (rbf :wu-31 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r3- (* r- r- r-)
xs2 (* xs xs)
xs4 (* xs2 xs2)]
(* r3- r3- (+ 6.0
(* 36.0 xs)
(* 82.0 xs2)
(* 72.0 xs xs2)
(* 30.0 xs4)
(* 5.0 xs xs4)))) 0.0)))))
(defmethod rbf :wu-32
([_] (rbf :wu-32 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)
xs2 (* xs xs)]
(* r- r2- r2- (+ 8.0
(* 40.0 xs)
(* 48.0 xs2)
(* 25.0 xs xs2)
(* 5.0 xs2 xs2)))) 0.0)))))
(defmethod rbf :wu-33
([_] (rbf :wu-33 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)
xs2 (* xs xs)]
(* r2- r2- (+ 16.0
(* 29.0 xs)
(* 20.0 xs2)
(* 5.0 xs xs2)))) 0.0)))))
;; wendland
(defmethod rbf :wendland-10
([_] (rbf :wendland-10 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (- 1.0 xs) 0.0)))))
(defmethod rbf :wendland-21
([_] (rbf :wendland-21 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)]
(* r- r2- (inc (* 3.0 xs)))) 0.0)))))
(defmethod rbf :wendland-32
([_] (rbf :wendland-32 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)]
(* r- r2- r2- (inc (+ (* 8.0 xs xs)
(* 5.0 xs))))) 0.0)))))
(defmethod rbf :wendland-20
([_] (rbf :wendland-20 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (m/sq (- 1.0 xs)) 0.0)))))
(defmethod rbf :wendland-31
([_] (rbf :wendland-31 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)]
(* r2- r2- (inc (* 4.0 xs)))) 0.0)))))
(defmethod rbf :wendland-42
([_] (rbf :wendland-42 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)]
(* r2- r2- r2- (+ (* 35.0 xs xs)
(* 18.0 xs)
3.0))) 0.0)))))
(defmethod rbf :wendland-53
([_] (rbf :wendland-53 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)
r4- (* r2- r2-)
xs2 (* xs xs)]
(* r4- r4- (inc (+ (* 32.0 xs2 xs)
(* 25.0 xs2)
(* 8.0 xs))))) 0.0)))))
(defmethod rbf :wendland-30
([_] (rbf :wendland-30 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (m/pow3 (- 1.0 xs)) 0.0)))))
(defmethod rbf :wendland-41
([_] (rbf :wendland-41 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r2- (* r- r-)]
(* r- r2- r2- (inc (* 5.0 xs)))) 0.0)))))
(defmethod rbf :wendland-52
([_] (rbf :wendland-52 1.0))
([_ ^double scale] (fn [^double x]
(let [xs (/ (m/abs x) scale)]
(if (< xs 1.0) (let [r- (- 1.0 xs)
r3- (* r- r- r-)]
(* r- r3- r3- (inc (+ (* 16.0 xs xs)
(* 7.0 xs))))) 0.0)))))
(def rbf-list ^{:doc "List of RBF kernels"} (sort (keys (methods rbf))))
(defn smile-rbf
"Create RBF Smile object.
Used to pass to Smile constructors/functions."
[rbf-fn]
(reify
RadialBasisFunction (^double f [_ ^double x] (rbf-fn x))
IFn (invoke ^double [_ x] (rbf-fn x))))
;;;;;;;;;;;;;;;;;;;;;
;; Various kernels
(defn rbf->kernel
"Treat RBF kernel as vector kernel using distance function (default [[euclidean]]."
([rbf-kernel] (rbf->kernel rbf-kernel d/euclidean))
([rbf-kernel distance]
(fn [x y] (rbf-kernel (distance x y)))))
;; http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/
;; Marc G. Genton, Classes of Kernels for Machine Learning: A Statistics Perspective
;; http://www.jmlr.org/papers/volume2/genton01a/genton01a.pdf
(defmulti kernel
"Crated vector kernel.
Kernels can be Mercer, positive definite, conditional positive definite, positive semi-definite or other.
Optional parameters:
For `:gaussian`, `:exponential`, `:laplacian`, `:rational-quadratic`, `:multiquadratic`, `:inverse-multiquadratic`, `:circular`, `:spherical`, `:wave`, `:power`, `:log`, `:cauchy`, `:generalized-t-student`, `:hyperbolic-secant`, `:thin-plate`, `:mattern-12`, `:mattern-32`, `:mattern-52` and `::hyperbolic-secant` you can provide scaling parameter and `distance` (see [[fastmath.distance]], default is [[euclidean]]).
Others:
* `:linear` - `alpha`, scaling parameter
* `:polynomial` - `alpha` (scaling), `c` (shift) and `d` (power)
* `:anova` - `sigma` (scaling), `k` and `d` (power)
* `:hyperbolic-tangent` - `alpha` (scaling), `c` (shift)
* `:periodic` - `sigma` (scaling), `periodicity` and `distance`
* `:bessel` - `sigma` (scaling), `n` and `v` (power factors) and `distance`
* `:generalized-histogram` - `alpha` and `beta` (power factors)
* `:dirichlet` - `N`
* `:pearson` - `sigma` (scaling) and `omega` (power)
Additionally there are two special kernels build from funcitons:
* `:scalar-functions` - provide one or two double->double functions
* `:variance-function` - provide any variance function (smooth, vector->double type)
The rest of the kernels do not require parameters."
(fn [k & _] k))
(defmethod kernel :linear
([_] (fn [x y] (v/dot x y)))
([_ ^double alpha] (fn [x y] (* alpha (v/dot x y)))))
(defmethod kernel :polynomial
([_] (fn [x y] (m/sq (v/dot x y))))
([_ ^double alpha ^double c ^double d] (fn [x y] (m/pow (+ c (* alpha (v/dot x y))) d))))
(defmethod kernel :gaussian
([_] (kernel :gaussian 1.0))
([_ sigma] (kernel :gaussian sigma d/euclidean))
([_ ^double sigma distance]
(let [s2r (/ (* sigma sigma))]
(fn [x y] (m/exp (* -0.5 s2r (m/sq (distance x y))))))))
(defmethod kernel :exponential
([_] (kernel :exponential 1.0))
([_ sigma] (kernel :exponential sigma d/euclidean))
([_ ^double sigma distance]
(let [s2r (/ (* sigma sigma))]
(fn [x y] (m/exp (* -0.5 s2r ^double (distance x y)))))))
(defmethod kernel :laplacian
([_] (kernel :laplacian 1.0))
([_ sigma] (kernel :laplacian sigma d/euclidean))
([_ ^double sigma distance] (fn [x y] (m/exp (- (/ ^double (distance x y) sigma))))))
(defmethod kernel :anova
([_] (kernel :anova 1.0))
([_ ^double sigma] (kernel :anova sigma 1.0 1.0))
([_ ^double sigma ^double k ^double d]
(let [powk #(m/pow ^double % k)
powd #(m/pow ^double % d)]
(fn [x y]
(let [xk (v/fmap x powk)
yk (v/fmap y powk)]
(-> (v/sub xk yk)
(v/sq)
(v/mult (- sigma))
(v/exp)
(v/fmap powd)
(v/sum)))))))
(defmethod kernel :hyperbolic-tangent
([_] (kernel :hyperbolic-tangent 1.0))
([_ ^double alpha] (kernel :hyperbolic-tangent alpha 0.0))
([_ ^double alpha ^double c]
(fn [x y] (m/tanh (+ c (* alpha (v/dot x y)))))))
(defmethod kernel :rational-quadratic
([_] (kernel :rational-quadratic 1.0))
([_ ^double c] (kernel :rational-quadratic c d/euclidean))
([_ ^double c distance]
(fn [x y] (let [d (m/sq (distance x y))]
(- 1.0 (/ d (+ d c)))))))
(defmethod kernel :multiquadratic
([_] (kernel :multiquadratic 1.0))
([_ ^double c] (kernel :multiquadratic c d/euclidean))
([_ ^double c distance]
(fn [x y] (let [d (m/sq (distance x y))]
(m/sqrt (+ d (* c c)))))))
(defmethod kernel :inverse-multiquadratic
([_] (kernel :inverse-multiquadratic 1.0))
([_ ^double c] (kernel :inverse-multiquadratic c d/euclidean))
([_ ^double c distance]
(fn [x y] (let [d (m/sq (distance x y))]
(/ (m/sqrt (+ d (* c c))))))))
(defmethod kernel :circular
([_] (kernel :circular 1.0))
([_ ^double sigma] (kernel :circular sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (let [^double d (distance x y)]
(if (>= d sigma) 0.0
(let [ds (/ d sigma)]
(* 0.6366197723675814 (- (m/acos ds)
(* ds (m/sqrt (- 1.0 (* ds ds))))))))))))
(defmethod kernel :spherical
([_] (kernel :spherical 1.0))
([_ ^double sigma] (kernel :spherical sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (let [^double d (distance x y)]
(if (>= d sigma) 0.0
(let [ds (/ d sigma)]
(+ (- 1.0 (* 1.5 ds)) (* 0.5 ds ds ds))))))))
(defmethod kernel :wave
([_] (kernel :wave 1.0))
([_ ^double sigma] (kernel :wave sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (let [^double d (distance x y)]
(if (zero? d) 1.0
(* (/ sigma d) (m/sin (/ d sigma))))))))
(defmethod kernel :periodic
([_] (kernel :periodic 1.0))
([_ sigma] (kernel :periodic sigma 1.0))
([_ sigma periodicity] (kernel :periodic sigma periodicity d/euclidean))
([_ ^double sigma ^double periodicity distance]
(let [p (/ m/PI periodicity)
s2 (* sigma sigma)]
(fn [x y] (let [^double d (distance x y)]
(m/exp (/ (* -2.0 (m/sq (m/sin (* p d)))) s2)))))))
(defmethod kernel :power
([_] (kernel :power 2.0))
([_ d] (kernel :power d d/euclidean))
([_ ^double d distance]
(fn [x y] (- (m/pow (distance x y) d)))))
(defmethod kernel :log
([_] (kernel :log 2.0))
([_ d] (kernel :log d d/euclidean))
([_ ^double d distance]
(fn [x y] (- (m/log1p (m/pow (distance x y) d))))))
(defmethod kernel :spline
[_] (fn [x y]
(reduce #(* ^double %1 ^double %2) 1.0 (map (fn [^double xi ^double yi]
(let [xiyi (* xi yi)
m (min xi yi)
m2 (* m m)]
(inc (+ xiyi
(* xiyi m)
(* -0.5 (+ xi yi) m2)
(* m/THIRD m2 m))))) x y))))
(defmethod kernel :bessel
([_] (kernel :bessel 2.0))
([_ sigma] (kernel :bessel sigma 2.0))
([_ sigma n] (kernel :bessel sigma n -1.0))
([_ sigma n v] (kernel :bessel sigma n v d/euclidean))
([_ sigma n v distance]
(fn [x y] (let [^double d (distance x y)
v+ (inc ^double v)]
(/ (m/bessel-j v+ (* ^double sigma d))
(m/pow d (- (* ^double n v+))))))))
(defmethod kernel :cauchy
([_] (kernel :cauchy 1.0))
([_ ^double sigma] (kernel :cauchy sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (/ (inc (m/sq (/ ^double (distance x y) sigma)))))))
(defmethod kernel :chi-square-pd
[_] (fn [x y] (reduce #(+ ^double %1 ^double %2) 0.0 (map (fn [^double xi ^double yi]
(/ (* 2.0 xi yi)
(+ xi yi))) x y))))
(defmethod kernel :chi-square-cpd
[_] (fn [x y] (- 1.0 ^double (reduce #(+ ^double %1 ^double %2) 0.0 (map (fn [^double xi ^double yi]
(/ (m/sq (- xi yi))
(* 0.5 (+ xi yi)))) x y)))))
(defmethod kernel :histogram
[_] (fn [x y] (reduce #(+ ^double %1 ^double %2) 0.0 (map (fn [^double xi ^double yi]
(min xi yi)) x y))))
(defmethod kernel :generalized-histogram
([_] (kernel :generalized-histogram 1.0 1.0))
([_ ^double alpha ^double beta]
(fn [x y] (reduce #(+ ^double %1 ^double %2) 0.0 (map (fn [^double xi ^double yi]
(min (m/pow (m/abs xi) alpha)
(m/pow (m/abs yi) beta))) x y)))))
(defmethod kernel :generalized-t-student
([_] (kernel :generalized-t-student 1.0))
([_ ^double d] (kernel :generalized-t-student d d/euclidean))
([_ ^double d distance]
(fn [x y] (/ (inc (m/pow (distance x y) d))))))
(defmethod kernel :dirichlet
([_] (kernel :dirichlet 1.0))
([_ ^double N]
(let [N5 (+ 0.5 N)]
(fn [x y] (reduce #(* ^double %1 ^double %2) 1.0 (map (fn [^double xi ^double yi]
(let [delta (- xi yi)
num (m/sin (* N5 delta))
den (* 2.0 (m/sin (* 0.5 delta)))]
(/ num den))) x y))))))
(defmethod kernel :hellinger
([_] (fn [x y] (v/dot (v/safe-sqrt x)
(v/safe-sqrt y)))))
(defmethod kernel :pearson
([_] (kernel :pearson 1.0 1.0))
([_ ^double sigma ^double omega]
(let [c (/ (* 2.0 (m/sqrt (dec (m/pow 2.0 (/ omega))))) sigma)]
(fn [x y] (let [xx (v/sum (v/sq x))
yy (v/sum (v/sq y))
xy (v/sum (v/emult x y))
m (* c (m/sqrt (+ (* -2.0 xy) xx yy)))]
(/ (m/pow (inc (* m m)) omega)))))))
(defmethod kernel :thin-plate
([_] (kernel :thin-plate 1.0))
([_ sigma] (kernel :thin-plate sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y]
(let [ds (/ ^double (distance x y) sigma)]
(if-not (pos? ds) 0.0 (* (m/sq ds) (m/log ds)))))))
(defmethod kernel :mattern-12
([_] (kernel :mattern-12 1.0))
([_ ^double sigma] (kernel :mattern-12 sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (m/exp (- (/ ^double (distance x y) sigma))))))
(defmethod kernel :mattern-32
([_] (kernel :mattern-32 1.0))
([_ ^double sigma] (kernel :mattern-32 sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (let [d (/ (* m/SQRT3 ^double (distance x y)) sigma)]
(* (inc d) (m/exp (- d)))))))
(defmethod kernel :mattern-52
([_] (kernel :mattern-52 1.0))
([_ ^double sigma] (kernel :mattern-52 sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (let [d (/ (* m/SQRT5 ^double (distance x y)) sigma)]
(* (inc (+ d (* d d m/THIRD))) (m/exp (- d)))))))
(defmethod kernel :hyperbolic-secant
([_] (kernel :hyperbolic-secant 1.0))
([_ ^double sigma] (kernel :hyperbolic-secant sigma d/euclidean))
([_ ^double sigma distance]
(fn [x y] (let [d (* sigma ^double (distance x y))]
(+ (/ 2.0 (m/exp d)) (m/exp (- d)))))))
(defmethod kernel :scalar-functions
([_] (kernel :scalar-functions v/mag))
([_ f] (kernel :scalar-functions f f))
([_ f1 f2] (fn [x y] (* ^double (f1 x) ^double (f2 y)))))
(defmethod kernel :variance-function
([_] (kernel :variance-function v/mag))
([_ h] (fn [x y] (* 0.25 (- ^double (h (v/add x y)) ^double (h (v/sub x y)))))))
(def kernels-list ^{:doc "List of available vector kernels."} (sort (keys (methods kernel))))
(defn smile-mercer
"Create Smile Mercer Kernel object
Used to pass to Smile constructors/functions."
[k]
(reify
MercerKernel (k [_ x y] (k x y))
IFn (invoke ^double [_ x y] (k x y))))
;; kernel manipulation functions
(defn kernel->rbf
"Convert vector kernel to RBF kernel. `center` is fixed `y` vector (default contains [[EPSILON]] values)."
([k] (kernel->rbf k m/EPSILON))
([k center]
(let [c (vector center)]
(fn [x] (k [x] c)))))
(defn exp
"Kernel wraper. exp of kernel `k` with optional scaling value `t`."
([k] (exp k 1.0))
([k ^double t]
(fn [x y] (m/exp (* t ^double (k x y))))))
(defn approx
"Kernel wrapper. Round value returned by kernel using [[fastmath.core/approx]] function."
([k precision] (comp #(m/approx % precision) k))
([k] (comp m/approx k)))
(defn scale
"Kernel wrapper. Scale kernel result."
[k ^double scale]
(fn [x y] (* scale ^double (k x y))))
(defn mult
"Kernel wrapper. Multiply two or more kernels."
([k1] k1)
([k1 k2] (fn [x y] (* ^double (k1 x y) ^double (k2 x y))))
([k1 k2 k3] (fn [x y] (* ^double (k1 x y) ^double (k2 x y) ^double (k3 x y))))
([k1 k2 k3 & r]
(let [k (mult k1 k2 k3)]
(if-not (seq r) k
(apply mult k r)))))
(defn wadd
"Kernel wrapper. Add kernels (weighted)."
([kernels] (wadd (repeat (count kernels) 1.0) kernels))
([weights kernels]
(fn [x y] (reduce #(+ ^double %1 ^double %2)
(map (fn [^double w k] (* w ^double (k x y))) weights kernels)))))
(defn fields
"Kernel wrapper. Apply vector field for each input before applying kernel function."
([k f] (fields k f f))
([k f1 f2]
(fn [x y] (k (f1 x) (f2 y)))))
(defn- zero-vec [c] (vec (repeat c 0.0)))
(def ^:private zero-vec-m (memoize zero-vec))
;; doesn't work well
(defn cpd->pd
"Convert conditionally positive definite kernel into positive definite.
Formula is based on this [SO answer](https://stats.stackexchange.com/questions/149889/prove-that-a-kernel-is-conditionally-positive-definite). `x0` is equal `0`.
Doesn't work well."
[k]
(fn [x y] (let [zero (zero-vec-m (count x))]
(float (* 0.5 (+ ^double (k zero zero)
(- ^double (k x y)
^double (k x zero)
^double (k zero y))))))))
;;;;;;;;; density
(def ^{:const true :private true :tag 'double} gaussian-factor (/ (m/sqrt m/TWO_PI)))
(defn uniform-density-kernel ^double [^double x] (if (<= (m/abs x) 1.0) 0.5 0.0))
(defn gaussian-density-kernel ^double [^double x] (* gaussian-factor (m/exp (* -0.5 x x))))
(defn triangular-density-kernel ^double [^double x] (let [absx (m/abs x)]
(if (<= absx 1.0) (- 1.0 absx) 0.0)))
(defn epanechnikov-density-kernel ^double [^double x] (if (<= (m/abs x) 1.0) (* 0.75 (- 1.0 (* x x))) 0.0))
(defn quartic-density-kernel ^double [^double x] (if (<= (m/abs x) 1.0) (* 0.9375 (m/sq (- 1.0 (* x x)))) 0.0))
(defn triweight-density-kernel
^double [^double x]
(if (<= (m/abs x) 1.0)
(let [v (- 1.0 (* x x))]
(* 1.09375 v v v)) 0.0))
(defn tricube-density-kernel
^double [^double x]
(let [absx (m/abs x)]
(if (<= absx 1.0)
(let [v (- 1.0 (* absx absx absx))]
(* 0.8875 v v v)) 0.0)))
(defn cosine-density-kernel ^double [^double x] (if (<= (m/abs x) 1.0) (* m/QUARTER_PI (m/cos (* m/HALF_PI x))) 0.0))
(defn logistic-density-kernel ^double [^double x] (/ (+ 2.0 (m/exp x) (m/exp (- x)))))
(defn sigmoid-density-kernel ^double [^double x] (/ (/ 2.0 (+ (m/exp x) (m/exp (- x)))) m/PI))
(defn silverman-density-kernel
^double [^double x]
(let [xx (/ (m/abs x) m/SQRT2)]
(* 0.5 (m/exp (- xx)) (m/sin (+ xx m/QUARTER_PI)))))
;; experimental
(defn laplace-density-kernel ^double [^double x] (* 0.5 (m/exp (- (m/abs x)))))
(defn wigner-density-kernel ^double [^double x] (if (<= (m/abs x) 1.0) (/ (* 2.0 (m/sqrt (- 1.0 (* x x)))) m/PI) 0.0))
(defn cauchy-density-kernel ^double [^double x] (/ (* m/HALF_PI (inc (m/sq (/ x 0.5))))))
;;
(defn- nrd
^double [data]
(let [adata (m/seq->double-array data)
sd (smile.math.MathEx/sd adata)
iqr (- (smile.math.MathEx/q3 adata)
(smile.math.MathEx/q1 adata))
res (double (cond
(and (pos? sd) (pos? iqr)) (min sd (/ iqr 1.34))
(pos? sd) sd
(pos? iqr) (/ iqr 1.34)
:else 1.0))]
(* 1.06 res (m/pow (alength ^doubles adata) -0.2))))
(defn- kde
"Return kernel density estimation function"
([data k] (kde data k nil))
([data k h]
(let [data (let [a (m/seq->double-array data)] (java.util.Arrays/sort a) a)
last-idx (dec (alength data))
h (double (or h (nrd data)))
hrev (/ h)
span (* 6.0 h)
factor (/ (* (alength data) h))
mn (aget data 0)
mx (aget data last-idx)]
[(fn [^double x]
(let [start (java.util.Arrays/binarySearch data (- x span))
start (long (if (neg? start) (dec (- start)) start))
end (java.util.Arrays/binarySearch data (+ x span))
end (min last-idx (long (if (neg? end) (dec (- end)) end)))]
(loop [i start
sum 0.0]
(if (<= i end)
(recur (inc i) (+ sum ^double (k (* hrev (- x (aget data i))))))
(* factor sum)))))
factor h (- mn span) (+ mx span)])))
(defonce ^:private kde-integral
{:uniform 0.5
:triangular m/TWO_THIRD
:epanechnikov 0.6
:quartic (/ 5.0 7.0)
:triweight (/ 350.0 429.0)
:tricube (/ 175.0 247.0)
:gaussian (* 0.5 (/ m/SQRTPI))
:cosine (* 0.0625 m/PI m/PI)
:logistic m/SIXTH
:sigmoid (/ 2.0 (* m/PI m/PI))
:silverman (* 0.0625 3.0 m/SQRT2)
:wigner (/ 16.0 (* 3 m/PI m/PI))
:cauchy m/M_1_PI
:laplace 0.25})
(defmulti kernel-density
"Create kernel density estimator.
Parameters:
* kernel name, see [[kernel-density-list]].
* sequence of data values
* optional: bandwidth (by default, bandwidth is estimated using nrd method)"
(fn [k & _] k))
(defmacro ^:private make-kernel-density-fns
[lst]
`(do ~@(for [v (eval lst)
:let [n (symbol (str (name v) "-density-kernel"))]]
`(defmethod kernel-density ~v
([k# vs#] (first (kde vs# ~n)))
([k# vs# h#] (first (kde vs# ~n h#)))
([k# vs# h# all?#] (let [kded# (kde vs# ~n h#)]
(if all?# kded# (first kded#))))))))
(make-kernel-density-fns (keys kde-integral))
(defmethod kernel-density :smile
([_ vs h] (if h
(let [^KernelDensity k (KernelDensity. (m/seq->double-array vs) h)]
(fn [x] (.p k x)))
(kernel-density :smile vs)))
([_ vs] (let [^KernelDensity k (KernelDensity. (m/seq->double-array vs))]
(fn [x] (.p k x)))))
(defmethod kernel-density :default [_ & r] (apply kernel-density :gaussian r))
(def kernel-density-list ^{:doc "List of available density kernels."} (sort (keys (methods kernel-density))))
(defn kernel-density-ci
"Create function which returns confidence intervals for given kde method.
Check 6.1.5 http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xlghtmlnode33.html
Parameters:
* `method` - kernel name
* `data` - sequence of data values
* `bandwidth`
* `alpha` - confidence level parameter
Returns three values: density, lower confidence, upper confidence"
([method data] (kernel-density-ci method data nil))
([method data bandwidth] (kernel-density-ci method data bandwidth 0.05))
([method data bandwidth ^double alpha]
(if (contains? kde-integral method)
(let [^NormalDistribution local-normal (NormalDistribution.)
za (.inverseCumulativeProbability local-normal (- 1.0 (* 0.5 (or alpha 0.05))))
[kde-f ^double factor] (kernel-density method data bandwidth true)]
(fn [^double x]
(let [^double fx (kde-f x)
band (* za (m/sqrt (* factor ^double (kde-integral method) fx)))]
[fx (- fx band) (+ fx band)])))
(let [kde-f (kernel-density method data bandwidth)]
(fn [x] (let [fx (kde-f x)]
[fx fx fx]))))))