Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, A

Average Error: 0.0 → 0.0

Time: 6.1s

Precision: binary64

Cost: 704

Math

\[\left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673 \]

↓

\[\left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673 \]

FPCore

(FPCore (x y)
 :precision binary64
 (+ (- (* x (- y 1.0)) (* y 0.5)) 0.918938533204673))

↓

(FPCore (x y)
 :precision binary64
 (+ (- (* x (- y 1.0)) (* y 0.5)) 0.918938533204673))

C

double code(double x, double y) {
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
}

↓

double code(double x, double y) {
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * (y - 1.0d0)) - (y * 0.5d0)) + 0.918938533204673d0
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * (y - 1.0d0)) - (y * 0.5d0)) + 0.918938533204673d0
end function

Java

public static double code(double x, double y) {
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
}

↓

public static double code(double x, double y) {
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
}

Python

def code(x, y):
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673

↓

def code(x, y):
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673

Julia

function code(x, y)
	return Float64(Float64(Float64(x * Float64(y - 1.0)) - Float64(y * 0.5)) + 0.918938533204673)
end

↓

function code(x, y)
	return Float64(Float64(Float64(x * Float64(y - 1.0)) - Float64(y * 0.5)) + 0.918938533204673)
end

MATLAB

function tmp = code(x, y)
	tmp = ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
end

↓

function tmp = code(x, y)
	tmp = ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
end

Wolfram

code[x_, y_] := N[(N[(N[(x * N[(y - 1.0), $MachinePrecision]), $MachinePrecision] - N[(y * 0.5), $MachinePrecision]), $MachinePrecision] + 0.918938533204673), $MachinePrecision]

↓

code[x_, y_] := N[(N[(N[(x * N[(y - 1.0), $MachinePrecision]), $MachinePrecision] - N[(y * 0.5), $MachinePrecision]), $MachinePrecision] + 0.918938533204673), $MachinePrecision]

Derivation

1. Initial program 0.0

   \[\left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673 \]

2. Final simplification 0.0

   \[\leadsto \left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673 \]

Alternatives

Alternative 1
Error	28.8
Cost	1380

\[\begin{array}{l} \mathbf{if}\;x \leq -0.92:\\ \;\;\;\;-x\\ \mathbf{elif}\;x \leq -4.2 \cdot 10^{-244}:\\ \;\;\;\;0.918938533204673\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{-277}:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{elif}\;x \leq 4 \cdot 10^{-149}:\\ \;\;\;\;0.918938533204673\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{-99}:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{-38}:\\ \;\;\;\;0.918938533204673\\ \mathbf{elif}\;x \leq 0.55:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{elif}\;x \leq 1.55 \cdot 10^{+55}:\\ \;\;\;\;-x\\ \mathbf{elif}\;x \leq 2.25 \cdot 10^{+116}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \]

Alternative 2
Error	28.4
Cost	1116

\[\begin{array}{l} \mathbf{if}\;x \leq -0.92:\\ \;\;\;\;-x\\ \mathbf{elif}\;x \leq -7 \cdot 10^{-244}:\\ \;\;\;\;0.918938533204673\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{-277}:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{elif}\;x \leq 7.3 \cdot 10^{-149}:\\ \;\;\;\;0.918938533204673\\ \mathbf{elif}\;x \leq 6.2 \cdot 10^{-99}:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{elif}\;x \leq 5.4 \cdot 10^{-39}:\\ \;\;\;\;0.918938533204673\\ \mathbf{elif}\;x \leq 0.56:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \]

Alternative 3
Error	1.2
Cost	840

\[\begin{array}{l} t_0 := \left(y - 1\right) \cdot x\\ \mathbf{if}\;x \leq -3 \cdot 10^{+18}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 200000000000:\\ \;\;\;\;\left(y \cdot x - y \cdot 0.5\right) + 0.918938533204673\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 4
Error	1.5
Cost	712

\[\begin{array}{l} \mathbf{if}\;y \leq -1.45:\\ \;\;\;\;y \cdot \left(x - 0.5\right)\\ \mathbf{elif}\;y \leq 1.3:\\ \;\;\;\;0.918938533204673 - x\\ \mathbf{else}:\\ \;\;\;\;-0.5 \cdot y + y \cdot x\\ \end{array} \]

Alternative 5
Error	11.1
Cost	588

\[\begin{array}{l} \mathbf{if}\;y \leq -1.25 \cdot 10^{+30}:\\ \;\;\;\;-0.5 \cdot y\\ \mathbf{elif}\;y \leq 1.25:\\ \;\;\;\;0.918938533204673 - x\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{+31}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;-0.5 \cdot y\\ \end{array} \]

Alternative 6
Error	1.5
Cost	584

\[\begin{array}{l} t_0 := y \cdot \left(x - 0.5\right)\\ \mathbf{if}\;y \leq -1.52:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;0.918938533204673 - x\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 7
Error	28.4
Cost	392

\[\begin{array}{l} \mathbf{if}\;x \leq -0.92:\\ \;\;\;\;-x\\ \mathbf{elif}\;x \leq 200000000000:\\ \;\;\;\;0.918938533204673\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \]

Alternative 8
Error	44.9
Cost	64

\[0.918938533204673 \]

Reproduce

herbie shell --seed 2023077 
(FPCore (x y)
  :name "Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, A"
  :precision binary64
  (+ (- (* x (- y 1.0)) (* y 0.5)) 0.918938533204673))