Statistics.Sample:$swelfordMean from math-functions-0.1.5.2

Percentage Accurate: 100.0% → 100.0%

Time: 4.0s

Alternatives: 6

Speedup: 1.0×

• 20.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x + \frac{y - x}{z} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ x (/ (- y x) z)))

C

double code(double x, double y, double z) {
	return x + ((y - x) / 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 = x + ((y - x) / z)
end function

Java

public static double code(double x, double y, double z) {
	return x + ((y - x) / z);
}

Python

def code(x, y, z):
	return x + ((y - x) / z)

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(y - x) / z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + ((y - x) / z);
end

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y - x}{z} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ x (/ (- y x) z)))

C

double code(double x, double y, double z) {
	return x + ((y - x) / 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 = x + ((y - x) / z)
end function

Java

public static double code(double x, double y, double z) {
	return x + ((y - x) / z);
}

Python

def code(x, y, z):
	return x + ((y - x) / z)

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(y - x) / z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + ((y - x) / z);
end

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y - x}{z} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ x (/ (- y x) z)))

C

double code(double x, double y, double z) {
	return x + ((y - x) / 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 = x + ((y - x) / z)
end function

Java

public static double code(double x, double y, double z) {
	return x + ((y - x) / z);
}

Python

def code(x, y, z):
	return x + ((y - x) / z)

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(y - x) / z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + ((y - x) / z);
end

Wolfram

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

Derivation

1. Initial program 100.0%

   \[x + \frac{y - x}{z} \]

2. Final simplification 100.0%

   \[\leadsto x + \frac{y - x}{z} \]

Alternative 2: 60.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-x}{z}\\ \mathbf{if}\;z \leq -4 \cdot 10^{+84}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.2 \cdot 10^{-16}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-89}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -4.4 \cdot 10^{-270}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 1:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- x) z)))
   (if (<= z -4e+84)
     x
     (if (<= z -3.2e-16)
       (/ y z)
       (if (<= z -1.15e-89)
         t_0
         (if (<= z -4.4e-270) (/ y z) (if (<= z 1.0) t_0 x)))))))

C

double code(double x, double y, double z) {
	double t_0 = -x / z;
	double tmp;
	if (z <= -4e+84) {
		tmp = x;
	} else if (z <= -3.2e-16) {
		tmp = y / z;
	} else if (z <= -1.15e-89) {
		tmp = t_0;
	} else if (z <= -4.4e-270) {
		tmp = y / z;
	} else if (z <= 1.0) {
		tmp = t_0;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = -x / z
    if (z <= (-4d+84)) then
        tmp = x
    else if (z <= (-3.2d-16)) then
        tmp = y / z
    else if (z <= (-1.15d-89)) then
        tmp = t_0
    else if (z <= (-4.4d-270)) then
        tmp = y / z
    else if (z <= 1.0d0) then
        tmp = t_0
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -x / z;
	double tmp;
	if (z <= -4e+84) {
		tmp = x;
	} else if (z <= -3.2e-16) {
		tmp = y / z;
	} else if (z <= -1.15e-89) {
		tmp = t_0;
	} else if (z <= -4.4e-270) {
		tmp = y / z;
	} else if (z <= 1.0) {
		tmp = t_0;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -x / z
	tmp = 0
	if z <= -4e+84:
		tmp = x
	elif z <= -3.2e-16:
		tmp = y / z
	elif z <= -1.15e-89:
		tmp = t_0
	elif z <= -4.4e-270:
		tmp = y / z
	elif z <= 1.0:
		tmp = t_0
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(-x) / z)
	tmp = 0.0
	if (z <= -4e+84)
		tmp = x;
	elseif (z <= -3.2e-16)
		tmp = Float64(y / z);
	elseif (z <= -1.15e-89)
		tmp = t_0;
	elseif (z <= -4.4e-270)
		tmp = Float64(y / z);
	elseif (z <= 1.0)
		tmp = t_0;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -x / z;
	tmp = 0.0;
	if (z <= -4e+84)
		tmp = x;
	elseif (z <= -3.2e-16)
		tmp = y / z;
	elseif (z <= -1.15e-89)
		tmp = t_0;
	elseif (z <= -4.4e-270)
		tmp = y / z;
	elseif (z <= 1.0)
		tmp = t_0;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[((-x) / z), $MachinePrecision]}, If[LessEqual[z, -4e+84], x, If[LessEqual[z, -3.2e-16], N[(y / z), $MachinePrecision], If[LessEqual[z, -1.15e-89], t$95$0, If[LessEqual[z, -4.4e-270], N[(y / z), $MachinePrecision], If[LessEqual[z, 1.0], t$95$0, x]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -4.00000000000000023e84 or 1 < z

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in z around inf 73.5%

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

   if -4.00000000000000023e84 < z < -3.20000000000000023e-16 or -1.15e-89 < z < -4.3999999999999997e-270

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in x around 0 65.3%

      \[\leadsto \color{blue}{\frac{y}{z}} \]

   if -3.20000000000000023e-16 < z < -1.15e-89 or -4.3999999999999997e-270 < z < 1

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in x around inf 62.5%

      \[\leadsto \color{blue}{\left(1 - \frac{1}{z}\right) \cdot x} \]
   3. Step-by-step derivation

      1. *-commutative 62.5%

         \[\leadsto \color{blue}{x \cdot \left(1 - \frac{1}{z}\right)} \]

      2. sub-neg 62.5%

         \[\leadsto x \cdot \color{blue}{\left(1 + \left(-\frac{1}{z}\right)\right)} \]

      3. distribute-lft-in 62.5%

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(-\frac{1}{z}\right)} \]

      4. *-rgt-identity 62.5%

         \[\leadsto \color{blue}{x} + x \cdot \left(-\frac{1}{z}\right) \]

      5. distribute-rgt-neg-out 62.5%

         \[\leadsto x + \color{blue}{\left(-x \cdot \frac{1}{z}\right)} \]

      6. associate-*r/ 62.5%

         \[\leadsto x + \left(-\color{blue}{\frac{x \cdot 1}{z}}\right) \]

      7. *-rgt-identity 62.5%

         \[\leadsto x + \left(-\frac{\color{blue}{x}}{z}\right) \]

      8. sub-neg 62.5%

         \[\leadsto \color{blue}{x - \frac{x}{z}} \]

   4. Simplified 62.5%

      \[\leadsto \color{blue}{x - \frac{x}{z}} \]

   5. Taylor expanded in z around 0 61.4%

      \[\leadsto \color{blue}{-1 \cdot \frac{x}{z}} \]
   6. Step-by-step derivation

      1. mul-1-neg 61.4%

         \[\leadsto \color{blue}{-\frac{x}{z}} \]

      2. distribute-frac-neg 61.4%

         \[\leadsto \color{blue}{\frac{-x}{z}} \]

   7. Simplified 61.4%

      \[\leadsto \color{blue}{\frac{-x}{z}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 67.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4 \cdot 10^{+84}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.2 \cdot 10^{-16}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-89}:\\ \;\;\;\;\frac{-x}{z}\\ \mathbf{elif}\;z \leq -4.4 \cdot 10^{-270}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 1:\\ \;\;\;\;\frac{-x}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 75.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.5 \cdot 10^{-130} \lor \neg \left(x \leq 1.4 \cdot 10^{-54}\right):\\ \;\;\;\;x - \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -6.5e-130) (not (<= x 1.4e-54))) (- x (/ x z)) (/ y z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -6.5e-130) || !(x <= 1.4e-54)) {
		tmp = x - (x / z);
	} else {
		tmp = y / z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-6.5d-130)) .or. (.not. (x <= 1.4d-54))) then
        tmp = x - (x / z)
    else
        tmp = y / z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -6.5e-130) || !(x <= 1.4e-54)) {
		tmp = x - (x / z);
	} else {
		tmp = y / z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -6.5e-130) or not (x <= 1.4e-54):
		tmp = x - (x / z)
	else:
		tmp = y / z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -6.5e-130) || !(x <= 1.4e-54))
		tmp = Float64(x - Float64(x / z));
	else
		tmp = Float64(y / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -6.5e-130) || ~((x <= 1.4e-54)))
		tmp = x - (x / z);
	else
		tmp = y / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -6.5e-130], N[Not[LessEqual[x, 1.4e-54]], $MachinePrecision]], N[(x - N[(x / z), $MachinePrecision]), $MachinePrecision], N[(y / z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -6.5000000000000002e-130 or 1.4000000000000001e-54 < x

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in x around inf 79.0%

      \[\leadsto \color{blue}{\left(1 - \frac{1}{z}\right) \cdot x} \]
   3. Step-by-step derivation

      1. *-commutative 79.0%

         \[\leadsto \color{blue}{x \cdot \left(1 - \frac{1}{z}\right)} \]

      2. sub-neg 79.0%

         \[\leadsto x \cdot \color{blue}{\left(1 + \left(-\frac{1}{z}\right)\right)} \]

      3. distribute-lft-in 79.0%

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(-\frac{1}{z}\right)} \]

      4. *-rgt-identity 79.0%

         \[\leadsto \color{blue}{x} + x \cdot \left(-\frac{1}{z}\right) \]

      5. distribute-rgt-neg-out 79.0%

         \[\leadsto x + \color{blue}{\left(-x \cdot \frac{1}{z}\right)} \]

      6. associate-*r/ 79.0%

         \[\leadsto x + \left(-\color{blue}{\frac{x \cdot 1}{z}}\right) \]

      7. *-rgt-identity 79.0%

         \[\leadsto x + \left(-\frac{\color{blue}{x}}{z}\right) \]

      8. sub-neg 79.0%

         \[\leadsto \color{blue}{x - \frac{x}{z}} \]

   4. Simplified 79.0%

      \[\leadsto \color{blue}{x - \frac{x}{z}} \]

   if -6.5000000000000002e-130 < x < 1.4000000000000001e-54

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in x around 0 70.8%

      \[\leadsto \color{blue}{\frac{y}{z}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 76.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6.5 \cdot 10^{-130} \lor \neg \left(x \leq 1.4 \cdot 10^{-54}\right):\\ \;\;\;\;x - \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z}\\ \end{array} \]

Alternative 4: 84.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.95 \cdot 10^{+83}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 16500000000:\\ \;\;\;\;\frac{y - x}{z}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.95e+83) x (if (<= z 16500000000.0) (/ (- y x) z) (- x (/ x z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.95e+83) {
		tmp = x;
	} else if (z <= 16500000000.0) {
		tmp = (y - x) / z;
	} else {
		tmp = x - (x / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-1.95d+83)) then
        tmp = x
    else if (z <= 16500000000.0d0) then
        tmp = (y - x) / z
    else
        tmp = x - (x / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.95e+83) {
		tmp = x;
	} else if (z <= 16500000000.0) {
		tmp = (y - x) / z;
	} else {
		tmp = x - (x / z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1.95e+83:
		tmp = x
	elif z <= 16500000000.0:
		tmp = (y - x) / z
	else:
		tmp = x - (x / z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.95e+83)
		tmp = x;
	elseif (z <= 16500000000.0)
		tmp = Float64(Float64(y - x) / z);
	else
		tmp = Float64(x - Float64(x / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1.95e+83)
		tmp = x;
	elseif (z <= 16500000000.0)
		tmp = (y - x) / z;
	else
		tmp = x - (x / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.95e+83], x, If[LessEqual[z, 16500000000.0], N[(N[(y - x), $MachinePrecision] / z), $MachinePrecision], N[(x - N[(x / z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.9500000000000001e83

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in z around inf 85.5%

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

   if -1.9500000000000001e83 < z < 1.65e10

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in z around 0 94.3%

      \[\leadsto \color{blue}{\frac{y - x}{z}} \]

   if 1.65e10 < z

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in x around inf 68.8%

      \[\leadsto \color{blue}{\left(1 - \frac{1}{z}\right) \cdot x} \]
   3. Step-by-step derivation

      1. *-commutative 68.8%

         \[\leadsto \color{blue}{x \cdot \left(1 - \frac{1}{z}\right)} \]

      2. sub-neg 68.8%

         \[\leadsto x \cdot \color{blue}{\left(1 + \left(-\frac{1}{z}\right)\right)} \]

      3. distribute-lft-in 68.8%

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(-\frac{1}{z}\right)} \]

      4. *-rgt-identity 68.8%

         \[\leadsto \color{blue}{x} + x \cdot \left(-\frac{1}{z}\right) \]

      5. distribute-rgt-neg-out 68.8%

         \[\leadsto x + \color{blue}{\left(-x \cdot \frac{1}{z}\right)} \]

      6. associate-*r/ 68.8%

         \[\leadsto x + \left(-\color{blue}{\frac{x \cdot 1}{z}}\right) \]

      7. *-rgt-identity 68.8%

         \[\leadsto x + \left(-\frac{\color{blue}{x}}{z}\right) \]

      8. sub-neg 68.8%

         \[\leadsto \color{blue}{x - \frac{x}{z}} \]

   4. Simplified 68.8%

      \[\leadsto \color{blue}{x - \frac{x}{z}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 86.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.95 \cdot 10^{+83}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 16500000000:\\ \;\;\;\;\frac{y - x}{z}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{z}\\ \end{array} \]

Alternative 5: 60.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.2 \cdot 10^{+85}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{+15}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -8.2e+85) x (if (<= z 1.9e+15) (/ y z) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -8.2e+85) {
		tmp = x;
	} else if (z <= 1.9e+15) {
		tmp = y / z;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-8.2d+85)) then
        tmp = x
    else if (z <= 1.9d+15) then
        tmp = y / z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -8.2e+85) {
		tmp = x;
	} else if (z <= 1.9e+15) {
		tmp = y / z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -8.2e+85:
		tmp = x
	elif z <= 1.9e+15:
		tmp = y / z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -8.2e+85)
		tmp = x;
	elseif (z <= 1.9e+15)
		tmp = Float64(y / z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -8.2e+85)
		tmp = x;
	elseif (z <= 1.9e+15)
		tmp = y / z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -8.2e+85], x, If[LessEqual[z, 1.9e+15], N[(y / z), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if z < -8.19999999999999957e85 or 1.9e15 < z

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in z around inf 76.5%

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

   if -8.19999999999999957e85 < z < 1.9e15

   1. Initial program 100.0%

      \[x + \frac{y - x}{z} \]

   2. Taylor expanded in x around 0 50.9%

      \[\leadsto \color{blue}{\frac{y}{z}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 62.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8.2 \cdot 10^{+85}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{+15}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6: 36.0% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 x)

C

double code(double x, double y, double z) {
	return x;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x
end function

Java

public static double code(double x, double y, double z) {
	return x;
}

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

function tmp = code(x, y, z)
	tmp = x;
end

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 100.0%

   \[x + \frac{y - x}{z} \]

2. Taylor expanded in z around inf 37.5%

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

3. Final simplification 37.5%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023174 
(FPCore (x y z)
  :name "Statistics.Sample:$swelfordMean from math-functions-0.1.5.2"
  :precision binary64
  (+ x (/ (- y x) z)))