github.com/gopherd/gonum@v0.0.4/stat/distuv/triangle.go

// Copyright ©2017 The Gonum Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package distuv

import (
	"math"

	"math/rand"
)

// Triangle represents a triangle distribution (https://en.wikipedia.org/wiki/Triangular_distribution).
type Triangle struct {
	a, b, c float64
	src     rand.Source
}

// NewTriangle constructs a new triangle distribution with lower limit a, upper limit b, and mode c.
// Constraints are a < b and a ≤ c ≤ b.
// This distribution is uncommon in nature, but may be useful for simulation.
func NewTriangle(a, b, c float64, src rand.Source) Triangle {
	checkTriangleParameters(a, b, c)
	return Triangle{a: a, b: b, c: c, src: src}
}

func checkTriangleParameters(a, b, c float64) {
	if a >= b {
		panic("triangle: constraint of a < b violated")
	}
	if a > c {
		panic("triangle: constraint of a <= c violated")
	}
	if c > b {
		panic("triangle: constraint of c <= b violated")
	}
}

// CDF computes the value of the cumulative density function at x.
func (t Triangle) CDF(x float64) float64 {
	switch {
	case x <= t.a:
		return 0
	case x <= t.c:
		d := x - t.a
		return (d * d) / ((t.b - t.a) * (t.c - t.a))
	case x < t.b:
		d := t.b - x
		return 1 - (d*d)/((t.b-t.a)*(t.b-t.c))
	default:
		return 1
	}
}

// Entropy returns the entropy of the distribution.
func (t Triangle) Entropy() float64 {
	return 0.5 + math.Log(t.b-t.a) - math.Ln2
}

// ExKurtosis returns the excess kurtosis of the distribution.
func (Triangle) ExKurtosis() float64 {
	return -3.0 / 5.0
}

// Fit is not appropriate for Triangle, because the distribution is generally used when there is little data.

// LogProb computes the natural logarithm of the value of the probability density function at x.
func (t Triangle) LogProb(x float64) float64 {
	return math.Log(t.Prob(x))
}

// Mean returns the mean of the probability distribution.
func (t Triangle) Mean() float64 {
	return (t.a + t.b + t.c) / 3
}

// Median returns the median of the probability distribution.
func (t Triangle) Median() float64 {
	if t.c >= (t.a+t.b)/2 {
		return t.a + math.Sqrt((t.b-t.a)*(t.c-t.a)/2)
	}
	return t.b - math.Sqrt((t.b-t.a)*(t.b-t.c)/2)
}

// Mode returns the mode of the probability distribution.
func (t Triangle) Mode() float64 {
	return t.c
}

// NumParameters returns the number of parameters in the distribution.
func (Triangle) NumParameters() int {
	return 3
}

// Prob computes the value of the probability density function at x.
func (t Triangle) Prob(x float64) float64 {
	switch {
	case x < t.a:
		return 0
	case x < t.c:
		return 2 * (x - t.a) / ((t.b - t.a) * (t.c - t.a))
	case x == t.c:
		return 2 / (t.b - t.a)
	case x <= t.b:
		return 2 * (t.b - x) / ((t.b - t.a) * (t.b - t.c))
	default:
		return 0
	}
}

// Quantile returns the inverse of the cumulative probability distribution.
func (t Triangle) Quantile(p float64) float64 {
	if p < 0 || p > 1 {
		panic(badPercentile)
	}

	f := (t.c - t.a) / (t.b - t.a)

	if p < f {
		return t.a + math.Sqrt(p*(t.b-t.a)*(t.c-t.a))
	}
	return t.b - math.Sqrt((1-p)*(t.b-t.a)*(t.b-t.c))
}

// Rand returns a random sample drawn from the distribution.
func (t Triangle) Rand() float64 {
	var rnd float64
	if t.src == nil {
		rnd = rand.Float64()
	} else {
		rnd = rand.New(t.src).Float64()
	}

	return t.Quantile(rnd)
}

// Score returns the score function with respect to the parameters of the
// distribution at the input location x. The score function is the derivative
// of the log-likelihood at x with respect to the parameters
//  (∂/∂θ) log(p(x;θ))
// If deriv is non-nil, len(deriv) must equal the number of parameters otherwise
// Score will panic, and the derivative is stored in-place into deriv. If deriv
// is nil a new slice will be allocated and returned.
//
// The order is [∂LogProb / ∂Mu, ∂LogProb / ∂Sigma].
//
// For more information, see https://en.wikipedia.org/wiki/Score_%28statistics%29.
func (t Triangle) Score(deriv []float64, x float64) []float64 {
	if deriv == nil {
		deriv = make([]float64, t.NumParameters())
	}
	if len(deriv) != t.NumParameters() {
		panic(badLength)
	}
	if (x < t.a) || (x > t.b) {
		deriv[0] = math.NaN()
		deriv[1] = math.NaN()
		deriv[2] = math.NaN()
	} else {
		invBA := 1 / (t.b - t.a)
		invCA := 1 / (t.c - t.a)
		invBC := 1 / (t.b - t.c)
		switch {
		case x < t.c:
			deriv[0] = -1/(x-t.a) + invBA + invCA
			deriv[1] = -invBA
			deriv[2] = -invCA
		case x > t.c:
			deriv[0] = invBA
			deriv[1] = 1/(t.b-x) - invBA - invBC
			deriv[2] = invBC
		default:
			deriv[0] = invBA
			deriv[1] = -invBA
			deriv[2] = 0
		}
		switch {
		case x == t.a:
			deriv[0] = math.NaN()
		case x == t.b:
			deriv[1] = math.NaN()
		case x == t.c:
			deriv[2] = math.NaN()
		}
		switch {
		case t.a == t.c:
			deriv[0] = math.NaN()
			deriv[2] = math.NaN()
		case t.b == t.c:
			deriv[1] = math.NaN()
			deriv[2] = math.NaN()
		}
	}
	return deriv
}

// ScoreInput returns the score function with respect to the input of the
// distribution at the input location specified by x. The score function is the
// derivative of the log-likelihood
//  (d/dx) log(p(x)) .
// Special cases (c is the mode of the distribution):
//  ScoreInput(c) = NaN
//  ScoreInput(x) = NaN for x not in (a, b)
func (t Triangle) ScoreInput(x float64) float64 {
	if (x <= t.a) || (x >= t.b) || (x == t.c) {
		return math.NaN()
	}
	if x < t.c {
		return 1 / (x - t.a)
	}
	return 1 / (x - t.b)
}

// Skewness returns the skewness of the distribution.
func (t Triangle) Skewness() float64 {
	n := math.Sqrt2 * (t.a + t.b - 2*t.c) * (2*t.a - t.b - t.c) * (t.a - 2*t.b + t.c)
	d := 5 * math.Pow(t.a*t.a+t.b*t.b+t.c*t.c-t.a*t.b-t.a*t.c-t.b*t.c, 3.0/2.0)

	return n / d
}

// StdDev returns the standard deviation of the probability distribution.
func (t Triangle) StdDev() float64 {
	return math.Sqrt(t.Variance())
}

// Survival returns the survival function (complementary CDF) at x.
func (t Triangle) Survival(x float64) float64 {
	return 1 - t.CDF(x)
}

// parameters returns the parameters of the distribution.
func (t Triangle) parameters(p []Parameter) []Parameter {
	nParam := t.NumParameters()
	if p == nil {
		p = make([]Parameter, nParam)
	} else if len(p) != nParam {
		panic("triangle: improper parameter length")
	}
	p[0].Name = "A"
	p[0].Value = t.a
	p[1].Name = "B"
	p[1].Value = t.b
	p[2].Name = "C"
	p[2].Value = t.c
	return p
}

// setParameters modifies the parameters of the distribution.
func (t *Triangle) setParameters(p []Parameter) {
	if len(p) != t.NumParameters() {
		panic("triangle: incorrect number of parameters to set")
	}
	if p[0].Name != "A" {
		panic("triangle: " + panicNameMismatch)
	}
	if p[1].Name != "B" {
		panic("triangle: " + panicNameMismatch)
	}
	if p[2].Name != "C" {
		panic("triangle: " + panicNameMismatch)
	}

	checkTriangleParameters(p[0].Value, p[1].Value, p[2].Value)

	t.a = p[0].Value
	t.b = p[1].Value
	t.c = p[2].Value
}

// Variance returns the variance of the probability distribution.
func (t Triangle) Variance() float64 {
	return (t.a*t.a + t.b*t.b + t.c*t.c - t.a*t.b - t.a*t.c - t.b*t.c) / 18
}