Numeric.Signal.Multichannel:$cput from hsignal-0.2.7.1

Percentage Accurate: 96.7% → 96.7%

Time: 9.5s

Alternatives: 11

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (* (/ (- x y) (- z y)) t))

C

double code(double x, double y, double z, double t) {
	return ((x - y) / (z - y)) * t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x - y) / (z - y)) * t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 96.7% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (* (/ (- x y) (- z y)) t))

C

double code(double x, double y, double z, double t) {
	return ((x - y) / (z - y)) * t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x - y) / (z - y)) * t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 96.7% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (* (/ (- x y) (- z y)) t))

C

double code(double x, double y, double z, double t) {
	return ((x - y) / (z - y)) * t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x - y) / (z - y)) * t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.3%

   \[\frac{x - y}{z - y} \cdot t \]

2. Final simplification 98.3%

   \[\leadsto \frac{x - y}{z - y} \cdot t \]

Alternative 2: 73.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{x - y}{z}\\ t_2 := t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{if}\;y \leq -1.15 \cdot 10^{+41}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{-215}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{-74}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{+49}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* t (/ (- x y) z))) (t_2 (* t (- 1.0 (/ x y)))))
   (if (<= y -1.15e+41)
     t_2
     (if (<= y 8.2e-215)
       t_1
       (if (<= y 1.25e-74) (* t (/ x (- z y))) (if (<= y 3.2e+49) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t * ((x - y) / z);
	double t_2 = t * (1.0 - (x / y));
	double tmp;
	if (y <= -1.15e+41) {
		tmp = t_2;
	} else if (y <= 8.2e-215) {
		tmp = t_1;
	} else if (y <= 1.25e-74) {
		tmp = t * (x / (z - y));
	} else if (y <= 3.2e+49) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t * ((x - y) / z)
    t_2 = t * (1.0d0 - (x / y))
    if (y <= (-1.15d+41)) then
        tmp = t_2
    else if (y <= 8.2d-215) then
        tmp = t_1
    else if (y <= 1.25d-74) then
        tmp = t * (x / (z - y))
    else if (y <= 3.2d+49) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = t * ((x - y) / z);
	double t_2 = t * (1.0 - (x / y));
	double tmp;
	if (y <= -1.15e+41) {
		tmp = t_2;
	} else if (y <= 8.2e-215) {
		tmp = t_1;
	} else if (y <= 1.25e-74) {
		tmp = t * (x / (z - y));
	} else if (y <= 3.2e+49) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t * ((x - y) / z)
	t_2 = t * (1.0 - (x / y))
	tmp = 0
	if y <= -1.15e+41:
		tmp = t_2
	elif y <= 8.2e-215:
		tmp = t_1
	elif y <= 1.25e-74:
		tmp = t * (x / (z - y))
	elif y <= 3.2e+49:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t * Float64(Float64(x - y) / z))
	t_2 = Float64(t * Float64(1.0 - Float64(x / y)))
	tmp = 0.0
	if (y <= -1.15e+41)
		tmp = t_2;
	elseif (y <= 8.2e-215)
		tmp = t_1;
	elseif (y <= 1.25e-74)
		tmp = Float64(t * Float64(x / Float64(z - y)));
	elseif (y <= 3.2e+49)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t * ((x - y) / z);
	t_2 = t * (1.0 - (x / y));
	tmp = 0.0;
	if (y <= -1.15e+41)
		tmp = t_2;
	elseif (y <= 8.2e-215)
		tmp = t_1;
	elseif (y <= 1.25e-74)
		tmp = t * (x / (z - y));
	elseif (y <= 3.2e+49)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.15e+41], t$95$2, If[LessEqual[y, 8.2e-215], t$95$1, If[LessEqual[y, 1.25e-74], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.2e+49], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.1499999999999999e41 or 3.20000000000000014e49 < y

   1. Initial program 99.9%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. frac-2neg 99.9%

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

      2. neg-sub0 99.9%

         \[\leadsto \frac{\color{blue}{0 - \left(x - y\right)}}{-\left(z - y\right)} \cdot t \]

      3. associate-+l- 99.9%

         \[\leadsto \frac{\color{blue}{\left(0 - x\right) + y}}{-\left(z - y\right)} \cdot t \]

      4. neg-sub0 99.9%

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

      5. +-commutative 99.9%

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

      6. sub-neg 99.9%

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

      7. neg-sub0 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{0 - \left(z - y\right)}} \cdot t \]

      8. associate-+l- 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{\left(0 - z\right) + y}} \cdot t \]

      9. neg-sub0 99.9%

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

      10. +-commutative 99.9%

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

      11. sub-neg 99.9%

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

      12. div-inv 99.6%

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

      13. *-commutative 99.6%

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

   3. Applied egg-rr 99.6%

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

   4. Taylor expanded in z around 0 83.4%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \cdot t \]
   5. Step-by-step derivation

      1. div-sub 83.4%

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

      2. *-inverses 83.4%

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

   6. Simplified 83.4%

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

   if -1.1499999999999999e41 < y < 8.1999999999999997e-215 or 1.25e-74 < y < 3.20000000000000014e49

   1. Initial program 96.5%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in z around inf 78.5%

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

   if 8.1999999999999997e-215 < y < 1.25e-74

   1. Initial program 99.7%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 93.6%

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

4. Final simplification 82.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.15 \cdot 10^{+41}:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{-215}:\\ \;\;\;\;t \cdot \frac{x - y}{z}\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{-74}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{+49}:\\ \;\;\;\;t \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \end{array} \]

Alternative 3: 73.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{\frac{y - z}{y}}\\ t_2 := t \cdot \frac{x}{z - y}\\ \mathbf{if}\;x \leq -0.25:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 1.55 \cdot 10^{-106}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 5.8 \cdot 10^{-6}:\\ \;\;\;\;t \cdot \frac{x - y}{z}\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{+90}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ t (/ (- y z) y))) (t_2 (* t (/ x (- z y)))))
   (if (<= x -0.25)
     t_2
     (if (<= x 1.55e-106)
       t_1
       (if (<= x 5.8e-6) (* t (/ (- x y) z)) (if (<= x 4.5e+90) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t / ((y - z) / y);
	double t_2 = t * (x / (z - y));
	double tmp;
	if (x <= -0.25) {
		tmp = t_2;
	} else if (x <= 1.55e-106) {
		tmp = t_1;
	} else if (x <= 5.8e-6) {
		tmp = t * ((x - y) / z);
	} else if (x <= 4.5e+90) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t / ((y - z) / y)
    t_2 = t * (x / (z - y))
    if (x <= (-0.25d0)) then
        tmp = t_2
    else if (x <= 1.55d-106) then
        tmp = t_1
    else if (x <= 5.8d-6) then
        tmp = t * ((x - y) / z)
    else if (x <= 4.5d+90) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = t / ((y - z) / y);
	double t_2 = t * (x / (z - y));
	double tmp;
	if (x <= -0.25) {
		tmp = t_2;
	} else if (x <= 1.55e-106) {
		tmp = t_1;
	} else if (x <= 5.8e-6) {
		tmp = t * ((x - y) / z);
	} else if (x <= 4.5e+90) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t / ((y - z) / y)
	t_2 = t * (x / (z - y))
	tmp = 0
	if x <= -0.25:
		tmp = t_2
	elif x <= 1.55e-106:
		tmp = t_1
	elif x <= 5.8e-6:
		tmp = t * ((x - y) / z)
	elif x <= 4.5e+90:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t / Float64(Float64(y - z) / y))
	t_2 = Float64(t * Float64(x / Float64(z - y)))
	tmp = 0.0
	if (x <= -0.25)
		tmp = t_2;
	elseif (x <= 1.55e-106)
		tmp = t_1;
	elseif (x <= 5.8e-6)
		tmp = Float64(t * Float64(Float64(x - y) / z));
	elseif (x <= 4.5e+90)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t / ((y - z) / y);
	t_2 = t * (x / (z - y));
	tmp = 0.0;
	if (x <= -0.25)
		tmp = t_2;
	elseif (x <= 1.55e-106)
		tmp = t_1;
	elseif (x <= 5.8e-6)
		tmp = t * ((x - y) / z);
	elseif (x <= 4.5e+90)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t / N[(N[(y - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -0.25], t$95$2, If[LessEqual[x, 1.55e-106], t$95$1, If[LessEqual[x, 5.8e-6], N[(t * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.5e+90], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.25 or 4.5e90 < x

   1. Initial program 97.3%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 83.4%

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

   if -0.25 < x < 1.54999999999999993e-106 or 5.8000000000000004e-6 < x < 4.5e90

   1. Initial program 99.1%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. associate-*l/ 84.5%

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

      2. *-commutative 84.5%

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

      3. associate-/l* 98.2%

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

      4. sub-neg 98.2%

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

      5. remove-double-neg 98.2%

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

      6. distribute-neg-in 98.2%

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

      7. +-commutative 98.2%

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

      8. sub-neg 98.2%

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

      9. mul-1-neg 98.2%

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

      10. associate-/r* 98.2%

          \[\leadsto \frac{t}{\color{blue}{\frac{\frac{z - y}{-1}}{y - x}}} \]

   3. Simplified 98.2%

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

   4. Taylor expanded in x around 0 72.1%

      \[\leadsto \color{blue}{\frac{t \cdot y}{y - z}} \]
   5. Step-by-step derivation

      1. associate-/l* 86.3%

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

   6. Simplified 86.3%

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

   if 1.54999999999999993e-106 < x < 5.8000000000000004e-6

   1. Initial program 99.8%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in z around inf 77.0%

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

4. Final simplification 84.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.25:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;x \leq 1.55 \cdot 10^{-106}:\\ \;\;\;\;\frac{t}{\frac{y - z}{y}}\\ \mathbf{elif}\;x \leq 5.8 \cdot 10^{-6}:\\ \;\;\;\;t \cdot \frac{x - y}{z}\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{+90}:\\ \;\;\;\;\frac{t}{\frac{y - z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \]

Alternative 4: 73.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{\frac{y - z}{y}}\\ \mathbf{if}\;x \leq -5.8 \cdot 10^{-8}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{-106}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{-7}:\\ \;\;\;\;t \cdot \frac{x - y}{z}\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{+94}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{\frac{z - y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ t (/ (- y z) y))))
   (if (<= x -5.8e-8)
     (* t (/ x (- z y)))
     (if (<= x 3.5e-106)
       t_1
       (if (<= x 2.75e-7)
         (* t (/ (- x y) z))
         (if (<= x 2.6e+94) t_1 (/ t (/ (- z y) x))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t / ((y - z) / y);
	double tmp;
	if (x <= -5.8e-8) {
		tmp = t * (x / (z - y));
	} else if (x <= 3.5e-106) {
		tmp = t_1;
	} else if (x <= 2.75e-7) {
		tmp = t * ((x - y) / z);
	} else if (x <= 2.6e+94) {
		tmp = t_1;
	} else {
		tmp = t / ((z - y) / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t / ((y - z) / y)
    if (x <= (-5.8d-8)) then
        tmp = t * (x / (z - y))
    else if (x <= 3.5d-106) then
        tmp = t_1
    else if (x <= 2.75d-7) then
        tmp = t * ((x - y) / z)
    else if (x <= 2.6d+94) then
        tmp = t_1
    else
        tmp = t / ((z - y) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = t / ((y - z) / y);
	double tmp;
	if (x <= -5.8e-8) {
		tmp = t * (x / (z - y));
	} else if (x <= 3.5e-106) {
		tmp = t_1;
	} else if (x <= 2.75e-7) {
		tmp = t * ((x - y) / z);
	} else if (x <= 2.6e+94) {
		tmp = t_1;
	} else {
		tmp = t / ((z - y) / x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t / ((y - z) / y)
	tmp = 0
	if x <= -5.8e-8:
		tmp = t * (x / (z - y))
	elif x <= 3.5e-106:
		tmp = t_1
	elif x <= 2.75e-7:
		tmp = t * ((x - y) / z)
	elif x <= 2.6e+94:
		tmp = t_1
	else:
		tmp = t / ((z - y) / x)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t / Float64(Float64(y - z) / y))
	tmp = 0.0
	if (x <= -5.8e-8)
		tmp = Float64(t * Float64(x / Float64(z - y)));
	elseif (x <= 3.5e-106)
		tmp = t_1;
	elseif (x <= 2.75e-7)
		tmp = Float64(t * Float64(Float64(x - y) / z));
	elseif (x <= 2.6e+94)
		tmp = t_1;
	else
		tmp = Float64(t / Float64(Float64(z - y) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t / ((y - z) / y);
	tmp = 0.0;
	if (x <= -5.8e-8)
		tmp = t * (x / (z - y));
	elseif (x <= 3.5e-106)
		tmp = t_1;
	elseif (x <= 2.75e-7)
		tmp = t * ((x - y) / z);
	elseif (x <= 2.6e+94)
		tmp = t_1;
	else
		tmp = t / ((z - y) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t / N[(N[(y - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -5.8e-8], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.5e-106], t$95$1, If[LessEqual[x, 2.75e-7], N[(t * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.6e+94], t$95$1, N[(t / N[(N[(z - y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -5.8000000000000003e-8

   1. Initial program 97.2%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 80.9%

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

   if -5.8000000000000003e-8 < x < 3.5e-106 or 2.7500000000000001e-7 < x < 2.5999999999999999e94

   1. Initial program 99.1%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. associate-*l/ 84.5%

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

      2. *-commutative 84.5%

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

      3. associate-/l* 98.2%

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

      4. sub-neg 98.2%

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

      5. remove-double-neg 98.2%

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

      6. distribute-neg-in 98.2%

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

      7. +-commutative 98.2%

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

      8. sub-neg 98.2%

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

      9. mul-1-neg 98.2%

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

      10. associate-/r* 98.2%

          \[\leadsto \frac{t}{\color{blue}{\frac{\frac{z - y}{-1}}{y - x}}} \]

   3. Simplified 98.2%

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

   4. Taylor expanded in x around 0 72.1%

      \[\leadsto \color{blue}{\frac{t \cdot y}{y - z}} \]
   5. Step-by-step derivation

      1. associate-/l* 86.3%

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

   6. Simplified 86.3%

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

   if 3.5e-106 < x < 2.7500000000000001e-7

   1. Initial program 99.8%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in z around inf 77.0%

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

   if 2.5999999999999999e94 < x

   1. Initial program 97.4%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 72.3%

      \[\leadsto \color{blue}{\frac{t \cdot x}{z - y}} \]
   3. Step-by-step derivation

      1. associate-/l* 87.8%

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

   4. Simplified 87.8%

      \[\leadsto \color{blue}{\frac{t}{\frac{z - y}{x}}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 84.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5.8 \cdot 10^{-8}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{-106}:\\ \;\;\;\;\frac{t}{\frac{y - z}{y}}\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{-7}:\\ \;\;\;\;t \cdot \frac{x - y}{z}\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{+94}:\\ \;\;\;\;\frac{t}{\frac{y - z}{y}}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{\frac{z - y}{x}}\\ \end{array} \]

Alternative 5: 89.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8 \cdot 10^{+111}:\\ \;\;\;\;\frac{t}{\frac{y}{y - x}}\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+202}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -8e+111)
   (/ t (/ y (- y x)))
   (if (<= y 1.85e+202) (* (- x y) (/ t (- z y))) (* t (- 1.0 (/ x y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -8e+111) {
		tmp = t / (y / (y - x));
	} else if (y <= 1.85e+202) {
		tmp = (x - y) * (t / (z - y));
	} else {
		tmp = t * (1.0 - (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-8d+111)) then
        tmp = t / (y / (y - x))
    else if (y <= 1.85d+202) then
        tmp = (x - y) * (t / (z - y))
    else
        tmp = t * (1.0d0 - (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -8e+111) {
		tmp = t / (y / (y - x));
	} else if (y <= 1.85e+202) {
		tmp = (x - y) * (t / (z - y));
	} else {
		tmp = t * (1.0 - (x / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -8e+111:
		tmp = t / (y / (y - x))
	elif y <= 1.85e+202:
		tmp = (x - y) * (t / (z - y))
	else:
		tmp = t * (1.0 - (x / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -8e+111)
		tmp = Float64(t / Float64(y / Float64(y - x)));
	elseif (y <= 1.85e+202)
		tmp = Float64(Float64(x - y) * Float64(t / Float64(z - y)));
	else
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -8e+111)
		tmp = t / (y / (y - x));
	elseif (y <= 1.85e+202)
		tmp = (x - y) * (t / (z - y));
	else
		tmp = t * (1.0 - (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -8e+111], N[(t / N[(y / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.85e+202], N[(N[(x - y), $MachinePrecision] * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -7.99999999999999965e111

   1. Initial program 99.8%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. associate-*l/ 73.3%

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

      2. *-commutative 73.3%

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

      3. associate-/l* 99.8%

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

      4. sub-neg 99.8%

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

      5. remove-double-neg 99.8%

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

      6. distribute-neg-in 99.8%

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

      7. +-commutative 99.8%

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

      8. sub-neg 99.8%

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

      9. mul-1-neg 99.8%

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

      10. associate-/r* 99.8%

          \[\leadsto \frac{t}{\color{blue}{\frac{\frac{z - y}{-1}}{y - x}}} \]

   3. Simplified 99.8%

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

   4. Taylor expanded in z around 0 63.5%

      \[\leadsto \color{blue}{\frac{t \cdot \left(y - x\right)}{y}} \]
   5. Step-by-step derivation

      1. associate-/l* 86.0%

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

   6. Simplified 86.0%

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

   if -7.99999999999999965e111 < y < 1.8499999999999999e202

   1. Initial program 97.8%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. associate-*l/ 89.8%

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

      2. *-commutative 89.8%

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

      3. associate-/l* 97.2%

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

      4. sub-neg 97.2%

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

      5. remove-double-neg 97.2%

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

      6. distribute-neg-in 97.2%

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

      7. +-commutative 97.2%

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

      8. sub-neg 97.2%

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

      9. mul-1-neg 97.2%

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

      10. associate-/r* 97.2%

          \[\leadsto \frac{t}{\color{blue}{\frac{\frac{z - y}{-1}}{y - x}}} \]

   3. Simplified 97.2%

      \[\leadsto \color{blue}{\frac{t}{\frac{y - z}{y - x}}} \]
   4. Step-by-step derivation

      1. associate-/l* 89.8%

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

      2. *-commutative 89.8%

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

      3. sub-neg 89.8%

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

      4. +-commutative 89.8%

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

      5. neg-sub0 89.8%

         \[\leadsto \frac{\left(\color{blue}{\left(0 - x\right)} + y\right) \cdot t}{y - z} \]

      6. associate-+l- 89.8%

         \[\leadsto \frac{\color{blue}{\left(0 - \left(x - y\right)\right)} \cdot t}{y - z} \]

      7. neg-sub0 89.8%

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

      8. sub-neg 89.8%

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

      9. +-commutative 89.8%

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

      10. neg-sub0 89.8%

          \[\leadsto \frac{\left(-\left(x - y\right)\right) \cdot t}{\color{blue}{\left(0 - z\right)} + y} \]

      11. associate-+l- 89.8%

          \[\leadsto \frac{\left(-\left(x - y\right)\right) \cdot t}{\color{blue}{0 - \left(z - y\right)}} \]

      12. neg-sub0 89.8%

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

      13. associate-*l/ 97.8%

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

      14. frac-2neg 97.8%

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

      15. associate-*l/ 89.8%

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

      16. *-commutative 89.8%

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

      17. associate-*l/ 89.1%

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

   5. Applied egg-rr 89.1%

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

   if 1.8499999999999999e202 < y

   1. Initial program 100.0%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. frac-2neg 100.0%

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

      2. neg-sub0 100.0%

         \[\leadsto \frac{\color{blue}{0 - \left(x - y\right)}}{-\left(z - y\right)} \cdot t \]

      3. associate-+l- 100.0%

         \[\leadsto \frac{\color{blue}{\left(0 - x\right) + y}}{-\left(z - y\right)} \cdot t \]

      4. neg-sub0 100.0%

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

      5. +-commutative 100.0%

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

      6. sub-neg 100.0%

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

      7. neg-sub0 100.0%

         \[\leadsto \frac{y - x}{\color{blue}{0 - \left(z - y\right)}} \cdot t \]

      8. associate-+l- 100.0%

         \[\leadsto \frac{y - x}{\color{blue}{\left(0 - z\right) + y}} \cdot t \]

      9. neg-sub0 100.0%

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

      10. +-commutative 100.0%

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

      11. sub-neg 100.0%

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

      12. div-inv 99.6%

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

      13. *-commutative 99.6%

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

   3. Applied egg-rr 99.6%

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

   4. Taylor expanded in z around 0 95.9%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \cdot t \]
   5. Step-by-step derivation

      1. div-sub 95.9%

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

      2. *-inverses 95.9%

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

   6. Simplified 95.9%

      \[\leadsto \color{blue}{\left(1 - \frac{x}{y}\right)} \cdot t \]
3. Recombined 3 regimes into one program.

4. Final simplification 89.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8 \cdot 10^{+111}:\\ \;\;\;\;\frac{t}{\frac{y}{y - x}}\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+202}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \end{array} \]

Alternative 6: 67.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3 \cdot 10^{+40} \lor \neg \left(y \leq 1.25 \cdot 10^{+48}\right):\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -3e+40) (not (<= y 1.25e+48)))
   (* t (- 1.0 (/ x y)))
   (* t (/ x z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -3e+40) || !(y <= 1.25e+48)) {
		tmp = t * (1.0 - (x / y));
	} else {
		tmp = t * (x / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-3d+40)) .or. (.not. (y <= 1.25d+48))) then
        tmp = t * (1.0d0 - (x / y))
    else
        tmp = t * (x / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -3e+40) || !(y <= 1.25e+48)) {
		tmp = t * (1.0 - (x / y));
	} else {
		tmp = t * (x / z);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -3e+40) or not (y <= 1.25e+48):
		tmp = t * (1.0 - (x / y))
	else:
		tmp = t * (x / z)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -3e+40) || !(y <= 1.25e+48))
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	else
		tmp = Float64(t * Float64(x / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -3e+40) || ~((y <= 1.25e+48)))
		tmp = t * (1.0 - (x / y));
	else
		tmp = t * (x / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -3e+40], N[Not[LessEqual[y, 1.25e+48]], $MachinePrecision]], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(x / z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.0000000000000002e40 or 1.24999999999999993e48 < y

   1. Initial program 99.9%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. frac-2neg 99.9%

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

      2. neg-sub0 99.9%

         \[\leadsto \frac{\color{blue}{0 - \left(x - y\right)}}{-\left(z - y\right)} \cdot t \]

      3. associate-+l- 99.9%

         \[\leadsto \frac{\color{blue}{\left(0 - x\right) + y}}{-\left(z - y\right)} \cdot t \]

      4. neg-sub0 99.9%

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

      5. +-commutative 99.9%

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

      6. sub-neg 99.9%

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

      7. neg-sub0 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{0 - \left(z - y\right)}} \cdot t \]

      8. associate-+l- 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{\left(0 - z\right) + y}} \cdot t \]

      9. neg-sub0 99.9%

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

      10. +-commutative 99.9%

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

      11. sub-neg 99.9%

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

      12. div-inv 99.6%

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

      13. *-commutative 99.6%

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

   3. Applied egg-rr 99.6%

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

   4. Taylor expanded in z around 0 83.4%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \cdot t \]
   5. Step-by-step derivation

      1. div-sub 83.4%

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

      2. *-inverses 83.4%

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

   6. Simplified 83.4%

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

   if -3.0000000000000002e40 < y < 1.24999999999999993e48

   1. Initial program 97.2%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in y around 0 68.5%

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

4. Final simplification 74.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3 \cdot 10^{+40} \lor \neg \left(y \leq 1.25 \cdot 10^{+48}\right):\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z}\\ \end{array} \]

Alternative 7: 75.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{+43} \lor \neg \left(y \leq 6.2 \cdot 10^{-34}\right):\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -1.1e+43) (not (<= y 6.2e-34)))
   (* t (- 1.0 (/ x y)))
   (* x (/ t (- z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.1e+43) || !(y <= 6.2e-34)) {
		tmp = t * (1.0 - (x / y));
	} else {
		tmp = x * (t / (z - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-1.1d+43)) .or. (.not. (y <= 6.2d-34))) then
        tmp = t * (1.0d0 - (x / y))
    else
        tmp = x * (t / (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.1e+43) || !(y <= 6.2e-34)) {
		tmp = t * (1.0 - (x / y));
	} else {
		tmp = x * (t / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -1.1e+43) or not (y <= 6.2e-34):
		tmp = t * (1.0 - (x / y))
	else:
		tmp = x * (t / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -1.1e+43) || !(y <= 6.2e-34))
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	else
		tmp = Float64(x * Float64(t / Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -1.1e+43) || ~((y <= 6.2e-34)))
		tmp = t * (1.0 - (x / y));
	else
		tmp = x * (t / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -1.1e+43], N[Not[LessEqual[y, 6.2e-34]], $MachinePrecision]], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.1e43 or 6.1999999999999996e-34 < y

   1. Initial program 99.9%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. frac-2neg 99.9%

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

      2. neg-sub0 99.9%

         \[\leadsto \frac{\color{blue}{0 - \left(x - y\right)}}{-\left(z - y\right)} \cdot t \]

      3. associate-+l- 99.9%

         \[\leadsto \frac{\color{blue}{\left(0 - x\right) + y}}{-\left(z - y\right)} \cdot t \]

      4. neg-sub0 99.9%

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

      5. +-commutative 99.9%

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

      6. sub-neg 99.9%

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

      7. neg-sub0 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{0 - \left(z - y\right)}} \cdot t \]

      8. associate-+l- 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{\left(0 - z\right) + y}} \cdot t \]

      9. neg-sub0 99.9%

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

      10. +-commutative 99.9%

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

      11. sub-neg 99.9%

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

      12. div-inv 99.7%

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

      13. *-commutative 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in z around 0 77.6%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \cdot t \]
   5. Step-by-step derivation

      1. div-sub 77.6%

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

      2. *-inverses 77.6%

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

   6. Simplified 77.6%

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

   if -1.1e43 < y < 6.1999999999999996e-34

   1. Initial program 96.9%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 77.6%

      \[\leadsto \color{blue}{\frac{t \cdot x}{z - y}} \]
   3. Step-by-step derivation

      1. associate-*l/ 73.4%

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

   4. Applied egg-rr 73.4%

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

4. Final simplification 75.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{+43} \lor \neg \left(y \leq 6.2 \cdot 10^{-34}\right):\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \end{array} \]

Alternative 8: 75.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.4 \cdot 10^{+45} \lor \neg \left(y \leq 5.7 \cdot 10^{+48}\right):\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -7.4e+45) (not (<= y 5.7e+48)))
   (* t (- 1.0 (/ x y)))
   (* t (/ x (- z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -7.4e+45) || !(y <= 5.7e+48)) {
		tmp = t * (1.0 - (x / y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-7.4d+45)) .or. (.not. (y <= 5.7d+48))) then
        tmp = t * (1.0d0 - (x / y))
    else
        tmp = t * (x / (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -7.4e+45) || !(y <= 5.7e+48)) {
		tmp = t * (1.0 - (x / y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -7.4e+45) or not (y <= 5.7e+48):
		tmp = t * (1.0 - (x / y))
	else:
		tmp = t * (x / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -7.4e+45) || !(y <= 5.7e+48))
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	else
		tmp = Float64(t * Float64(x / Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -7.4e+45) || ~((y <= 5.7e+48)))
		tmp = t * (1.0 - (x / y));
	else
		tmp = t * (x / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -7.4e+45], N[Not[LessEqual[y, 5.7e+48]], $MachinePrecision]], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -7.39999999999999954e45 or 5.69999999999999968e48 < y

   1. Initial program 99.9%

      \[\frac{x - y}{z - y} \cdot t \]
   2. Step-by-step derivation

      1. frac-2neg 99.9%

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

      2. neg-sub0 99.9%

         \[\leadsto \frac{\color{blue}{0 - \left(x - y\right)}}{-\left(z - y\right)} \cdot t \]

      3. associate-+l- 99.9%

         \[\leadsto \frac{\color{blue}{\left(0 - x\right) + y}}{-\left(z - y\right)} \cdot t \]

      4. neg-sub0 99.9%

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

      5. +-commutative 99.9%

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

      6. sub-neg 99.9%

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

      7. neg-sub0 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{0 - \left(z - y\right)}} \cdot t \]

      8. associate-+l- 99.9%

         \[\leadsto \frac{y - x}{\color{blue}{\left(0 - z\right) + y}} \cdot t \]

      9. neg-sub0 99.9%

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

      10. +-commutative 99.9%

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

      11. sub-neg 99.9%

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

      12. div-inv 99.6%

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

      13. *-commutative 99.6%

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

   3. Applied egg-rr 99.6%

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

   4. Taylor expanded in z around 0 83.2%

      \[\leadsto \color{blue}{\frac{y - x}{y}} \cdot t \]
   5. Step-by-step derivation

      1. div-sub 83.2%

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

      2. *-inverses 83.2%

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

   6. Simplified 83.2%

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

   if -7.39999999999999954e45 < y < 5.69999999999999968e48

   1. Initial program 97.2%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 76.2%

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

4. Final simplification 79.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7.4 \cdot 10^{+45} \lor \neg \left(y \leq 5.7 \cdot 10^{+48}\right):\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \]

Alternative 9: 59.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+103}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 3.05 \cdot 10^{+41}:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.2e+103) t (if (<= y 3.05e+41) (* x (/ t z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.2e+103) {
		tmp = t;
	} else if (y <= 3.05e+41) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-1.2d+103)) then
        tmp = t
    else if (y <= 3.05d+41) then
        tmp = x * (t / z)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.2e+103) {
		tmp = t;
	} else if (y <= 3.05e+41) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.2e+103:
		tmp = t
	elif y <= 3.05e+41:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.2e+103)
		tmp = t;
	elseif (y <= 3.05e+41)
		tmp = Float64(x * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.2e+103)
		tmp = t;
	elseif (y <= 3.05e+41)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.2e+103], t, If[LessEqual[y, 3.05e+41], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -1.1999999999999999e103 or 3.04999999999999999e41 < y

   1. Initial program 99.9%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in y around inf 62.4%

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

   if -1.1999999999999999e103 < y < 3.04999999999999999e41

   1. Initial program 97.4%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in x around inf 72.8%

      \[\leadsto \color{blue}{\frac{t \cdot x}{z - y}} \]
   3. Step-by-step derivation

      1. associate-*l/ 69.8%

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

   4. Applied egg-rr 69.8%

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

   5. Taylor expanded in z around inf 58.8%

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

4. Final simplification 60.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+103}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 3.05 \cdot 10^{+41}:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 10: 60.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+103}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 5.7 \cdot 10^{+49}:\\ \;\;\;\;t \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.2e+103) t (if (<= y 5.7e+49) (* t (/ x z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.2e+103) {
		tmp = t;
	} else if (y <= 5.7e+49) {
		tmp = t * (x / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-1.2d+103)) then
        tmp = t
    else if (y <= 5.7d+49) then
        tmp = t * (x / z)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.2e+103) {
		tmp = t;
	} else if (y <= 5.7e+49) {
		tmp = t * (x / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.2e+103:
		tmp = t
	elif y <= 5.7e+49:
		tmp = t * (x / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.2e+103)
		tmp = t;
	elseif (y <= 5.7e+49)
		tmp = Float64(t * Float64(x / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.2e+103)
		tmp = t;
	elseif (y <= 5.7e+49)
		tmp = t * (x / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.2e+103], t, If[LessEqual[y, 5.7e+49], N[(t * N[(x / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -1.1999999999999999e103 or 5.6999999999999998e49 < y

   1. Initial program 99.9%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in y around inf 63.5%

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

   if -1.1999999999999999e103 < y < 5.6999999999999998e49

   1. Initial program 97.4%

      \[\frac{x - y}{z - y} \cdot t \]

   2. Taylor expanded in y around 0 66.5%

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

4. Final simplification 65.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+103}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 5.7 \cdot 10^{+49}:\\ \;\;\;\;t \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 11: 35.2% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := t

Derivation

1. Initial program 98.3%

   \[\frac{x - y}{z - y} \cdot t \]

2. Taylor expanded in y around inf 32.6%

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

3. Final simplification 32.6%

   \[\leadsto t \]

Developer target: 96.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023297 
(FPCore (x y z t)
  :name "Numeric.Signal.Multichannel:$cput from hsignal-0.2.7.1"
  :precision binary64

  :herbie-target
  (/ t (/ (- z y) (- x y)))

  (* (/ (- x y) (- z y)) t))