covariance_function.go

package gp

import (
    "math"
    "hector/core"
)

type CovFunc func(*core.Vector, *core.Vector)float64

func CovMatrix(X []*core.RealSample, cov_func CovFunc) (*core.Matrix) {
    l := int64(len(X))
    ret := core.NewMatrix()
    for i := int64(0); i < l; i++ {
        for j := i; j < l; j++ {
            c := cov_func(X[i].GetFeatureVector(), X[j].GetFeatureVector())
            ret.SetValue(i, j, c)
            ret.SetValue(j, i, c)
        }
    }
    return ret
}

func CovVector(X []*core.RealSample, y *core.RealSample, cov_func CovFunc) (*core.Vector) {
    l := int64(len(X))
    ret := core.NewVector()
    for i := int64(0); i < l; i++ {
        ret.SetValue(i, cov_func(X[i].GetFeatureVector(), y.GetFeatureVector()))
    }
    return ret
}

/* 
 Squared error covariance function
 ARD = auto relevance detection, and here indicates there is a scaling/radius factor per dimension
 */
type CovSEARD struct {
    Radiuses *core.Vector // dim -> radius
    Amp float64
}

func (cov_func *CovSEARD) Init(radiuses *core.Vector, amp float64) {
    cov_func.Radiuses = radiuses
    cov_func.Amp = amp
}

func (cov_func *CovSEARD) Cov(x1 *core.Vector, x2 *core.Vector) float64 {
    ret := 0.0
    tmp := 0.0
    for key, r := range cov_func.Radiuses.Data {
        v1 := x1.GetValue(key)
        v2 := x2.GetValue(key)
        tmp = (v1-v2)/r
        ret += tmp * tmp
    }
    ret = cov_func.Amp * math.Exp(-ret)
    return ret
}