Data.Random.Distribution.Normal:doubleStdNormalZ from random-fu-0.2.6.2

Percentage Accurate: 100.0% → 100.0%

Time: 979.0ms

Alternatives: 3

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(x + x\right) - 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (+ x x) 1.0))

C

double code(double x) {
	return (x + x) - 1.0;
}

Fortran

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

Java

public static double code(double x) {
	return (x + x) - 1.0;
}

Python

def code(x):
	return (x + x) - 1.0

Julia

function code(x)
	return Float64(Float64(x + x) - 1.0)
end

MATLAB

function tmp = code(x)
	tmp = (x + x) - 1.0;
end

Wolfram

code[x_] := N[(N[(x + x), $MachinePrecision] - 1.0), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + x\right) - 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (+ x x) 1.0))

C

double code(double x) {
	return (x + x) - 1.0;
}

Fortran

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

Java

public static double code(double x) {
	return (x + x) - 1.0;
}

Python

def code(x):
	return (x + x) - 1.0

Julia

function code(x)
	return Float64(Float64(x + x) - 1.0)
end

MATLAB

function tmp = code(x)
	tmp = (x + x) - 1.0;
end

Wolfram

code[x_] := N[(N[(x + x), $MachinePrecision] - 1.0), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + x\right) - 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (+ x x) 1.0))

C

double code(double x) {
	return (x + x) - 1.0;
}

Fortran

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

Java

public static double code(double x) {
	return (x + x) - 1.0;
}

Python

def code(x):
	return (x + x) - 1.0

Julia

function code(x)
	return Float64(Float64(x + x) - 1.0)
end

MATLAB

function tmp = code(x)
	tmp = (x + x) - 1.0;
end

Wolfram

code[x_] := N[(N[(x + x), $MachinePrecision] - 1.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\left(x + x\right) - 1 \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 97.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x + x \leq -1 \cdot 10^{+14} \lor \neg \left(x + x \leq 10^{-7}\right):\\ \;\;\;\;x + x\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= (+ x x) -1e+14) (not (<= (+ x x) 1e-7))) (+ x x) -1.0))

C

double code(double x) {
	double tmp;
	if (((x + x) <= -1e+14) || !((x + x) <= 1e-7)) {
		tmp = x + x;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (((x + x) <= (-1d+14)) .or. (.not. ((x + x) <= 1d-7))) then
        tmp = x + x
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (((x + x) <= -1e+14) || !((x + x) <= 1e-7)) {
		tmp = x + x;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if ((x + x) <= -1e+14) or not ((x + x) <= 1e-7):
		tmp = x + x
	else:
		tmp = -1.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((Float64(x + x) <= -1e+14) || !(Float64(x + x) <= 1e-7))
		tmp = Float64(x + x);
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (((x + x) <= -1e+14) || ~(((x + x) <= 1e-7)))
		tmp = x + x;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[N[(x + x), $MachinePrecision], -1e+14], N[Not[LessEqual[N[(x + x), $MachinePrecision], 1e-7]], $MachinePrecision]], N[(x + x), $MachinePrecision], -1.0]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x x) < -1e14 or 9.9999999999999995e-8 < (+.f64 x x)

   1. Initial program 100.0%

      \[\left(x + x\right) - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-1} \]
   4. Step-by-step derivation

   5. Applied rewrites 3.0%

      \[\leadsto \color{blue}{-1} \]

   6. Taylor expanded in x around inf

      \[\leadsto \color{blue}{2 \cdot x} \]
   7. Step-by-step derivation

      1. lower-*.f64 99.1

         \[\leadsto \color{blue}{2 \cdot x} \]

   8. Applied rewrites 99.1%

      \[\leadsto \color{blue}{2 \cdot x} \]
   9. Step-by-step derivation

   10. Applied rewrites 99.1%

       \[\leadsto x + \color{blue}{x} \]

   if -1e14 < (+.f64 x x) < 9.9999999999999995e-8

   1. Initial program 100.0%

      \[\left(x + x\right) - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-1} \]
   4. Step-by-step derivation

   5. Applied rewrites 97.8%

      \[\leadsto \color{blue}{-1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x + x \leq -1 \cdot 10^{+14} \lor \neg \left(x + x \leq 10^{-7}\right):\\ \;\;\;\;x + x\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 49.9% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ -1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 -1.0)

C

double code(double x) {
	return -1.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = -1.0d0
end function

Java

public static double code(double x) {
	return -1.0;
}

Python

def code(x):
	return -1.0

Julia

function code(x)
	return -1.0
end

MATLAB

function tmp = code(x)
	tmp = -1.0;
end

Wolfram

code[x_] := -1.0

Derivation

1. Initial program 100.0%

   \[\left(x + x\right) - 1 \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{-1} \]
4. Step-by-step derivation

5. Applied rewrites 49.3%

   \[\leadsto \color{blue}{-1} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024320 
(FPCore (x)
  :name "Data.Random.Distribution.Normal:doubleStdNormalZ from random-fu-0.2.6.2"
  :precision binary64
  (- (+ x x) 1.0))