Statistics.Distribution.Poisson.Internal:probability from math-functions-0.1.5.2

Average Error: 0.0 → 0.0

Time: 7.0s

Precision: binary64

Cost: 13248

Math

\[e^{\left(x + y \cdot \log y\right) - z} \]

↓

\[e^{\left(x + y \cdot \log y\right) - z} \]

FPCore

(FPCore (x y z) :precision binary64 (exp (- (+ x (* y (log y))) z)))

↓

(FPCore (x y z) :precision binary64 (exp (- (+ x (* y (log y))) z)))

C

double code(double x, double y, double z) {
	return exp(((x + (y * log(y))) - z));
}

↓

double code(double x, double y, double z) {
	return exp(((x + (y * log(y))) - z));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = exp(((x + (y * log(y))) - z))
end function

↓

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = exp(((x + (y * log(y))) - z))
end function

Java

public static double code(double x, double y, double z) {
	return Math.exp(((x + (y * Math.log(y))) - z));
}

↓

public static double code(double x, double y, double z) {
	return Math.exp(((x + (y * Math.log(y))) - z));
}

Python

def code(x, y, z):
	return math.exp(((x + (y * math.log(y))) - z))

↓

def code(x, y, z):
	return math.exp(((x + (y * math.log(y))) - z))

Julia

function code(x, y, z)
	return exp(Float64(Float64(x + Float64(y * log(y))) - z))
end

↓

function code(x, y, z)
	return exp(Float64(Float64(x + Float64(y * log(y))) - z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = exp(((x + (y * log(y))) - z));
end

↓

function tmp = code(x, y, z)
	tmp = exp(((x + (y * log(y))) - z));
end

Wolfram

code[x_, y_, z_] := N[Exp[N[(N[(x + N[(y * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]], $MachinePrecision]

↓

code[x_, y_, z_] := N[Exp[N[(N[(x + N[(y * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]], $MachinePrecision]

Target

Original	0.0
Target	0.0
Herbie	0.0

\[e^{\left(x - z\right) + \log y \cdot y} \]

Derivation

1. Initial program 0.0

   \[e^{\left(x + y \cdot \log y\right) - z} \]

2. Final simplification 0.0

   \[\leadsto e^{\left(x + y \cdot \log y\right) - z} \]

Alternatives

Alternative 1
Error	1.5
Cost	6660

\[\begin{array}{l} \mathbf{if}\;x \leq -4.05 \cdot 10^{-22}:\\ \;\;\;\;e^{x}\\ \mathbf{else}:\\ \;\;\;\;e^{-z}\\ \end{array} \]

Alternative 2
Error	8.0
Cost	6596

\[\begin{array}{l} \mathbf{if}\;z \leq 2.2000348352314893 \cdot 10^{+149}:\\ \;\;\;\;e^{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{1 - z \cdot z} \cdot \left(1 - z\right)\\ \end{array} \]

Alternative 3
Error	0.6
Cost	6592

\[e^{x - z} \]

Alternative 4
Error	28.2
Cost	704

\[\frac{1}{1 - z \cdot z} \cdot \left(1 - z\right) \]

Alternative 5
Error	43.4
Cost	320

\[\frac{1}{z + 1} \]

Alternative 6
Error	44.3
Cost	64

\[1 \]

Reproduce

herbie shell --seed 2022308 
(FPCore (x y z)
  :name "Statistics.Distribution.Poisson.Internal:probability from math-functions-0.1.5.2"
  :precision binary64

  :herbie-target
  (exp (+ (- x z) (* (log y) y)))

  (exp (- (+ x (* y (log y))) z)))