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

Percentage Accurate: 97.3% → 97.6%

Time: 10.3s

Alternatives: 12

Speedup: 0.5×

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: 97.3% 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: 97.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x - y}{z - y}\\ \mathbf{if}\;t_1 \leq 10^{+117}:\\ \;\;\;\;t_1 \cdot t\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- x y) (- z y))))
   (if (<= t_1 1e+117) (* t_1 t) (/ (* x t) (- z y)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x - y) / (z - y);
	double tmp;
	if (t_1 <= 1e+117) {
		tmp = t_1 * t;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = (x - y) / (z - y)
    if (t_1 <= 1d+117) then
        tmp = t_1 * t
    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 t_1 = (x - y) / (z - y);
	double tmp;
	if (t_1 <= 1e+117) {
		tmp = t_1 * t;
	} else {
		tmp = (x * t) / (z - y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x - y) / (z - y)
	tmp = 0
	if t_1 <= 1e+117:
		tmp = t_1 * t
	else:
		tmp = (x * t) / (z - y)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x - y) / Float64(z - y))
	tmp = 0.0
	if (t_1 <= 1e+117)
		tmp = Float64(t_1 * t);
	else
		tmp = Float64(Float64(x * t) / Float64(z - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x - y) / (z - y);
	tmp = 0.0;
	if (t_1 <= 1e+117)
		tmp = t_1 * t;
	else
		tmp = (x * t) / (z - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x - y), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 1e+117], N[(t$95$1 * t), $MachinePrecision], N[(N[(x * t), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 x y) (-.f64 z y)) < 1.00000000000000005e117

   1. Initial program 97.5%

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

   if 1.00000000000000005e117 < (/.f64 (-.f64 x y) (-.f64 z y))

   1. Initial program 77.7%

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

      1. associate-*l/ 99.9%

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

      2. *-commutative 99.9%

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

      3. associate-*l/ 95.3%

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

   3. Simplified 95.3%

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

   4. Taylor expanded in x around inf 99.9%

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

4. Final simplification 97.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x - y}{z - y} \leq 10^{+117}:\\ \;\;\;\;\frac{x - y}{z - y} \cdot t\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \end{array} \]

Alternative 2: 90.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.75 \cdot 10^{+187}:\\ \;\;\;\;\frac{-t}{\frac{y}{x - y}}\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+163}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.75e+187)
   (/ (- t) (/ y (- x y)))
   (if (<= y 7.5e+163) (* (- x y) (/ t (- z y))) (/ t (- 1.0 (/ z y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.75e+187) {
		tmp = -t / (y / (x - y));
	} else if (y <= 7.5e+163) {
		tmp = (x - y) * (t / (z - y));
	} else {
		tmp = t / (1.0 - (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 <= (-1.75d+187)) then
        tmp = -t / (y / (x - y))
    else if (y <= 7.5d+163) then
        tmp = (x - y) * (t / (z - y))
    else
        tmp = t / (1.0d0 - (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 <= -1.75e+187) {
		tmp = -t / (y / (x - y));
	} else if (y <= 7.5e+163) {
		tmp = (x - y) * (t / (z - y));
	} else {
		tmp = t / (1.0 - (z / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.75e+187:
		tmp = -t / (y / (x - y))
	elif y <= 7.5e+163:
		tmp = (x - y) * (t / (z - y))
	else:
		tmp = t / (1.0 - (z / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.75e+187)
		tmp = Float64(Float64(-t) / Float64(y / Float64(x - y)));
	elseif (y <= 7.5e+163)
		tmp = Float64(Float64(x - y) * Float64(t / Float64(z - y)));
	else
		tmp = Float64(t / Float64(1.0 - Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.75e+187)
		tmp = -t / (y / (x - y));
	elseif (y <= 7.5e+163)
		tmp = (x - y) * (t / (z - y));
	else
		tmp = t / (1.0 - (z / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.75e+187], N[((-t) / N[(y / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.5e+163], N[(N[(x - y), $MachinePrecision] * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.7499999999999999e187

   1. Initial program 99.8%

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

      1. associate-*l/ 56.4%

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

      2. *-commutative 56.4%

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

      3. associate-*l/ 67.6%

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

   3. Simplified 67.6%

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

   4. Taylor expanded in z around 0 39.5%

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

      1. mul-1-neg 39.5%

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

      2. associate-/l* 83.0%

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

      3. distribute-neg-frac 83.0%

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

   6. Simplified 83.0%

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

   if -1.7499999999999999e187 < y < 7.50000000000000001e163

   1. Initial program 94.7%

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

      1. associate-*l/ 91.2%

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

      2. *-commutative 91.2%

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

      3. associate-*l/ 91.6%

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

   3. Simplified 91.6%

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

   if 7.50000000000000001e163 < y

   1. Initial program 99.9%

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

      1. associate-*l/ 49.6%

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

      2. *-commutative 49.6%

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

      3. associate-*l/ 66.5%

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

   3. Simplified 66.5%

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

   4. Taylor expanded in x around 0 43.2%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot y}{z - y}} \]
   5. Step-by-step derivation

      1. mul-1-neg 43.2%

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

      2. associate-/l* 86.4%

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

      3. distribute-neg-frac 86.4%

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

      4. div-sub 86.4%

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

      5. *-inverses 86.4%

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

   6. Simplified 86.4%

      \[\leadsto \color{blue}{\frac{-t}{\frac{z}{y} - 1}} \]
   7. Step-by-step derivation

      1. frac-2neg 86.4%

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

      2. div-inv 86.4%

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

      3. remove-double-neg 86.4%

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

      4. sub-neg 86.4%

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

      5. metadata-eval 86.4%

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

      6. distribute-neg-in 86.4%

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

      7. metadata-eval 86.4%

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

   8. Applied egg-rr 86.4%

      \[\leadsto \color{blue}{t \cdot \frac{1}{\left(-\frac{z}{y}\right) + 1}} \]
   9. Step-by-step derivation

      1. associate-*r/ 86.4%

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

      2. *-rgt-identity 86.4%

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

      3. +-commutative 86.4%

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

      4. unsub-neg 86.4%

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

   10. Simplified 86.4%

       \[\leadsto \color{blue}{\frac{t}{1 - \frac{z}{y}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 90.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.75 \cdot 10^{+187}:\\ \;\;\;\;\frac{-t}{\frac{y}{x - y}}\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+163}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \end{array} \]

Alternative 3: 74.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{-13}:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{-58}:\\ \;\;\;\;t \cdot \left(-\frac{y}{z - y}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.2e-13)
   (/ (* x t) (- z y))
   (if (<= x 1.8e-58) (* t (- (/ y (- z y)))) (* t (/ x (- z y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.2e-13) {
		tmp = (x * t) / (z - y);
	} else if (x <= 1.8e-58) {
		tmp = t * -(y / (z - 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 (x <= (-3.2d-13)) then
        tmp = (x * t) / (z - y)
    else if (x <= 1.8d-58) then
        tmp = t * -(y / (z - 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 (x <= -3.2e-13) {
		tmp = (x * t) / (z - y);
	} else if (x <= 1.8e-58) {
		tmp = t * -(y / (z - y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.2e-13:
		tmp = (x * t) / (z - y)
	elif x <= 1.8e-58:
		tmp = t * -(y / (z - y))
	else:
		tmp = t * (x / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.2e-13)
		tmp = Float64(Float64(x * t) / Float64(z - y));
	elseif (x <= 1.8e-58)
		tmp = Float64(t * Float64(-Float64(y / Float64(z - 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 (x <= -3.2e-13)
		tmp = (x * t) / (z - y);
	elseif (x <= 1.8e-58)
		tmp = t * -(y / (z - y));
	else
		tmp = t * (x / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.2e-13], N[(N[(x * t), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.8e-58], N[(t * (-N[(y / N[(z - y), $MachinePrecision]), $MachinePrecision])), $MachinePrecision], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.2e-13

   1. Initial program 93.4%

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

      1. associate-*l/ 91.0%

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

      2. *-commutative 91.0%

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

      3. associate-*l/ 87.3%

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

   3. Simplified 87.3%

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

   4. Taylor expanded in x around inf 75.4%

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

   if -3.2e-13 < x < 1.80000000000000005e-58

   1. Initial program 96.4%

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

   2. Taylor expanded in x around 0 87.5%

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

      1. neg-mul-1 87.5%

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

      2. distribute-neg-frac 87.5%

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

   4. Simplified 87.5%

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

   if 1.80000000000000005e-58 < x

   1. Initial program 97.5%

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

   2. Taylor expanded in x around inf 78.8%

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

4. Final simplification 81.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{-13}:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{-58}:\\ \;\;\;\;t \cdot \left(-\frac{y}{z - y}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \]

Alternative 4: 75.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{+26} \lor \neg \left(y \leq 1.7 \cdot 10^{+18}\right):\\ \;\;\;\;t - \frac{t}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -1.35e+26) (not (<= y 1.7e+18)))
   (- t (/ t (/ y x)))
   (* t (/ x (- z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.35e+26) || !(y <= 1.7e+18)) {
		tmp = t - (t / (y / x));
	} 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 <= (-1.35d+26)) .or. (.not. (y <= 1.7d+18))) then
        tmp = t - (t / (y / x))
    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 <= -1.35e+26) || !(y <= 1.7e+18)) {
		tmp = t - (t / (y / x));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -1.35e+26) or not (y <= 1.7e+18):
		tmp = t - (t / (y / x))
	else:
		tmp = t * (x / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -1.35e+26) || !(y <= 1.7e+18))
		tmp = Float64(t - Float64(t / Float64(y / x)));
	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 <= -1.35e+26) || ~((y <= 1.7e+18)))
		tmp = t - (t / (y / x));
	else
		tmp = t * (x / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -1.35e+26], N[Not[LessEqual[y, 1.7e+18]], $MachinePrecision]], N[(t - N[(t / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.35e26 or 1.7e18 < y

   1. Initial program 99.8%

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

      1. associate-*l/ 66.5%

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

      2. *-commutative 66.5%

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

      3. associate-*l/ 79.8%

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

   3. Simplified 79.8%

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

   4. Taylor expanded in z around 0 58.9%

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

      1. associate-*r/ 58.9%

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

      2. neg-mul-1 58.9%

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

   6. Simplified 58.9%

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

   7. Taylor expanded in y around 0 61.1%

      \[\leadsto \color{blue}{t + -1 \cdot \frac{t \cdot x}{y}} \]
   8. Step-by-step derivation

      1. mul-1-neg 61.1%

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

      2. associate-*r/ 70.5%

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

      3. unsub-neg 70.5%

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

      4. associate-*r/ 61.1%

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

      5. associate-/l* 70.4%

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

   9. Simplified 70.4%

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

   if -1.35e26 < y < 1.7e18

   1. Initial program 92.6%

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

   2. Taylor expanded in x around inf 78.1%

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

4. Final simplification 74.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{+26} \lor \neg \left(y \leq 1.7 \cdot 10^{+18}\right):\\ \;\;\;\;t - \frac{t}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \]

Alternative 5: 66.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+29}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 6.8 \cdot 10^{+100}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.8e+29) t (if (<= y 6.8e+100) (* (- x y) (/ t z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.8e+29) {
		tmp = t;
	} else if (y <= 6.8e+100) {
		tmp = (x - y) * (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 <= (-1.8d+29)) then
        tmp = t
    else if (y <= 6.8d+100) then
        tmp = (x - y) * (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 <= -1.8e+29) {
		tmp = t;
	} else if (y <= 6.8e+100) {
		tmp = (x - y) * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.8e+29:
		tmp = t
	elif y <= 6.8e+100:
		tmp = (x - y) * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.8e+29)
		tmp = t;
	elseif (y <= 6.8e+100)
		tmp = Float64(Float64(x - y) * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.8e+29)
		tmp = t;
	elseif (y <= 6.8e+100)
		tmp = (x - y) * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.8e+29], t, If[LessEqual[y, 6.8e+100], N[(N[(x - y), $MachinePrecision] * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -1.79999999999999988e29 or 6.79999999999999988e100 < y

   1. Initial program 99.8%

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

      1. associate-*l/ 64.2%

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

      2. *-commutative 64.2%

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

      3. associate-*l/ 76.1%

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

   3. Simplified 76.1%

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

   4. Taylor expanded in y around inf 58.8%

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

   if -1.79999999999999988e29 < y < 6.79999999999999988e100

   1. Initial program 93.4%

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

      1. associate-*l/ 93.8%

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

      2. *-commutative 93.8%

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

      3. associate-*l/ 92.2%

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

   3. Simplified 92.2%

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

   4. Taylor expanded in z around inf 64.5%

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

4. Final simplification 62.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+29}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 6.8 \cdot 10^{+100}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 6: 68.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+19}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+73}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.9e+19) t (if (<= y 7e+73) (* x (/ t (- z y))) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.9e+19) {
		tmp = t;
	} else if (y <= 7e+73) {
		tmp = x * (t / (z - y));
	} 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 <= (-1.9d+19)) then
        tmp = t
    else if (y <= 7d+73) then
        tmp = x * (t / (z - y))
    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 <= -1.9e+19) {
		tmp = t;
	} else if (y <= 7e+73) {
		tmp = x * (t / (z - y));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.9e+19:
		tmp = t
	elif y <= 7e+73:
		tmp = x * (t / (z - y))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.9e+19)
		tmp = t;
	elseif (y <= 7e+73)
		tmp = Float64(x * Float64(t / Float64(z - y)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.9e+19)
		tmp = t;
	elseif (y <= 7e+73)
		tmp = x * (t / (z - y));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.9e+19], t, If[LessEqual[y, 7e+73], N[(x * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -1.9e19 or 7.00000000000000004e73 < y

   1. Initial program 99.8%

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

      1. associate-*l/ 65.7%

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

      2. *-commutative 65.7%

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

      3. associate-*l/ 77.7%

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

   3. Simplified 77.7%

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

   4. Taylor expanded in y around inf 57.9%

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

   if -1.9e19 < y < 7.00000000000000004e73

   1. Initial program 93.1%

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

   2. Taylor expanded in x around inf 75.2%

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

      1. *-commutative 75.2%

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

      2. clear-num 75.1%

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

      3. un-div-inv 75.2%

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

   4. Applied egg-rr 75.2%

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

      1. associate-/r/ 73.1%

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

   6. Applied egg-rr 73.1%

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

4. Final simplification 66.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+19}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+73}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 7: 68.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+29}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+171}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.8e+29) t (if (<= y 9.5e+171) (* t (/ x (- z y))) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.8e+29) {
		tmp = t;
	} else if (y <= 9.5e+171) {
		tmp = t * (x / (z - y));
	} 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 <= (-1.8d+29)) then
        tmp = t
    else if (y <= 9.5d+171) then
        tmp = t * (x / (z - y))
    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 <= -1.8e+29) {
		tmp = t;
	} else if (y <= 9.5e+171) {
		tmp = t * (x / (z - y));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.8e+29:
		tmp = t
	elif y <= 9.5e+171:
		tmp = t * (x / (z - y))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.8e+29)
		tmp = t;
	elseif (y <= 9.5e+171)
		tmp = Float64(t * Float64(x / Float64(z - y)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.8e+29)
		tmp = t;
	elseif (y <= 9.5e+171)
		tmp = t * (x / (z - y));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.8e+29], t, If[LessEqual[y, 9.5e+171], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -1.79999999999999988e29 or 9.49999999999999924e171 < y

   1. Initial program 99.8%

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

      1. associate-*l/ 62.1%

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

      2. *-commutative 62.1%

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

      3. associate-*l/ 76.4%

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

   3. Simplified 76.4%

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

   4. Taylor expanded in y around inf 61.7%

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

   if -1.79999999999999988e29 < y < 9.49999999999999924e171

   1. Initial program 93.9%

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

   2. Taylor expanded in x around inf 71.9%

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

4. Final simplification 68.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+29}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+171}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 8: 74.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.05 \cdot 10^{-12}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \mathbf{elif}\;x \leq 1.08 \cdot 10^{-57}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.05e-12)
   (* x (/ t (- z y)))
   (if (<= x 1.08e-57) (/ t (- 1.0 (/ z y))) (* t (/ x (- z y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.05e-12) {
		tmp = x * (t / (z - y));
	} else if (x <= 1.08e-57) {
		tmp = t / (1.0 - (z / 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 (x <= (-1.05d-12)) then
        tmp = x * (t / (z - y))
    else if (x <= 1.08d-57) then
        tmp = t / (1.0d0 - (z / 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 (x <= -1.05e-12) {
		tmp = x * (t / (z - y));
	} else if (x <= 1.08e-57) {
		tmp = t / (1.0 - (z / y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.05e-12:
		tmp = x * (t / (z - y))
	elif x <= 1.08e-57:
		tmp = t / (1.0 - (z / y))
	else:
		tmp = t * (x / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.05e-12)
		tmp = Float64(x * Float64(t / Float64(z - y)));
	elseif (x <= 1.08e-57)
		tmp = Float64(t / Float64(1.0 - Float64(z / 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 (x <= -1.05e-12)
		tmp = x * (t / (z - y));
	elseif (x <= 1.08e-57)
		tmp = t / (1.0 - (z / y));
	else
		tmp = t * (x / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.05e-12], N[(x * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.08e-57], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.04999999999999997e-12

   1. Initial program 93.4%

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

   2. Taylor expanded in x around inf 73.0%

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

      1. *-commutative 73.0%

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

      2. clear-num 72.9%

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

      3. un-div-inv 73.0%

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

   4. Applied egg-rr 73.0%

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

      1. associate-/r/ 73.0%

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

   6. Applied egg-rr 73.0%

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

   if -1.04999999999999997e-12 < x < 1.08e-57

   1. Initial program 96.4%

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

      1. associate-*l/ 79.4%

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

      2. *-commutative 79.4%

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

      3. associate-*l/ 86.1%

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

   3. Simplified 86.1%

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

   4. Taylor expanded in x around 0 71.6%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot y}{z - y}} \]
   5. Step-by-step derivation

      1. mul-1-neg 71.6%

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

      2. associate-/l* 87.1%

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

      3. distribute-neg-frac 87.1%

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

      4. div-sub 87.1%

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

      5. *-inverses 87.1%

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

   6. Simplified 87.1%

      \[\leadsto \color{blue}{\frac{-t}{\frac{z}{y} - 1}} \]
   7. Step-by-step derivation

      1. frac-2neg 87.1%

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

      2. div-inv 87.1%

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

      3. remove-double-neg 87.1%

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

      4. sub-neg 87.1%

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

      5. metadata-eval 87.1%

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

      6. distribute-neg-in 87.1%

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

      7. metadata-eval 87.1%

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

   8. Applied egg-rr 87.1%

      \[\leadsto \color{blue}{t \cdot \frac{1}{\left(-\frac{z}{y}\right) + 1}} \]
   9. Step-by-step derivation

      1. associate-*r/ 87.1%

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

      2. *-rgt-identity 87.1%

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

      3. +-commutative 87.1%

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

      4. unsub-neg 87.1%

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

   10. Simplified 87.1%

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

   if 1.08e-57 < x

   1. Initial program 97.5%

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

   2. Taylor expanded in x around inf 78.8%

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

4. Final simplification 80.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.05 \cdot 10^{-12}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \mathbf{elif}\;x \leq 1.08 \cdot 10^{-57}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \]

Alternative 9: 73.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.85 \cdot 10^{-13}:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{-63}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.85e-13)
   (/ (* x t) (- z y))
   (if (<= x 8.5e-63) (/ t (- 1.0 (/ z y))) (* t (/ x (- z y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.85e-13) {
		tmp = (x * t) / (z - y);
	} else if (x <= 8.5e-63) {
		tmp = t / (1.0 - (z / 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 (x <= (-3.85d-13)) then
        tmp = (x * t) / (z - y)
    else if (x <= 8.5d-63) then
        tmp = t / (1.0d0 - (z / 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 (x <= -3.85e-13) {
		tmp = (x * t) / (z - y);
	} else if (x <= 8.5e-63) {
		tmp = t / (1.0 - (z / y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.85e-13:
		tmp = (x * t) / (z - y)
	elif x <= 8.5e-63:
		tmp = t / (1.0 - (z / y))
	else:
		tmp = t * (x / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.85e-13)
		tmp = Float64(Float64(x * t) / Float64(z - y));
	elseif (x <= 8.5e-63)
		tmp = Float64(t / Float64(1.0 - Float64(z / 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 (x <= -3.85e-13)
		tmp = (x * t) / (z - y);
	elseif (x <= 8.5e-63)
		tmp = t / (1.0 - (z / y));
	else
		tmp = t * (x / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.85e-13], N[(N[(x * t), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 8.5e-63], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.8499999999999998e-13

   1. Initial program 93.4%

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

      1. associate-*l/ 91.0%

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

      2. *-commutative 91.0%

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

      3. associate-*l/ 87.3%

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

   3. Simplified 87.3%

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

   4. Taylor expanded in x around inf 75.4%

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

   if -3.8499999999999998e-13 < x < 8.49999999999999969e-63

   1. Initial program 96.4%

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

      1. associate-*l/ 79.4%

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

      2. *-commutative 79.4%

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

      3. associate-*l/ 86.1%

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

   3. Simplified 86.1%

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

   4. Taylor expanded in x around 0 71.6%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot y}{z - y}} \]
   5. Step-by-step derivation

      1. mul-1-neg 71.6%

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

      2. associate-/l* 87.1%

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

      3. distribute-neg-frac 87.1%

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

      4. div-sub 87.1%

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

      5. *-inverses 87.1%

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

   6. Simplified 87.1%

      \[\leadsto \color{blue}{\frac{-t}{\frac{z}{y} - 1}} \]
   7. Step-by-step derivation

      1. frac-2neg 87.1%

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

      2. div-inv 87.1%

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

      3. remove-double-neg 87.1%

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

      4. sub-neg 87.1%

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

      5. metadata-eval 87.1%

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

      6. distribute-neg-in 87.1%

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

      7. metadata-eval 87.1%

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

   8. Applied egg-rr 87.1%

      \[\leadsto \color{blue}{t \cdot \frac{1}{\left(-\frac{z}{y}\right) + 1}} \]
   9. Step-by-step derivation

      1. associate-*r/ 87.1%

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

      2. *-rgt-identity 87.1%

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

      3. +-commutative 87.1%

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

      4. unsub-neg 87.1%

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

   10. Simplified 87.1%

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

   if 8.49999999999999969e-63 < x

   1. Initial program 97.5%

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

   2. Taylor expanded in x around inf 78.8%

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

4. Final simplification 81.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.85 \cdot 10^{-13}:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{-63}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \]

Alternative 10: 60.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.5 \cdot 10^{+14}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-21}:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -5.5e+14) t (if (<= y 1.35e-21) (* x (/ t z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -5.5e+14) {
		tmp = t;
	} else if (y <= 1.35e-21) {
		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 <= (-5.5d+14)) then
        tmp = t
    else if (y <= 1.35d-21) 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 <= -5.5e+14) {
		tmp = t;
	} else if (y <= 1.35e-21) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -5.5e+14:
		tmp = t
	elif y <= 1.35e-21:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -5.5e+14)
		tmp = t;
	elseif (y <= 1.35e-21)
		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 <= -5.5e+14)
		tmp = t;
	elseif (y <= 1.35e-21)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -5.5e+14], t, If[LessEqual[y, 1.35e-21], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -5.5e14 or 1.3500000000000001e-21 < y

   1. Initial program 99.8%

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

      1. associate-*l/ 67.6%

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

      2. *-commutative 67.6%

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

      3. associate-*l/ 80.4%

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

   3. Simplified 80.4%

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

   4. Taylor expanded in y around inf 54.9%

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

   if -5.5e14 < y < 1.3500000000000001e-21

   1. Initial program 92.4%

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

      1. associate-*l/ 95.5%

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

      2. *-commutative 95.5%

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

      3. associate-*l/ 91.0%

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

   3. Simplified 91.0%

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

      1. *-commutative 91.0%

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

      2. clear-num 91.0%

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

      3. div-inv 91.8%

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

      4. div-inv 91.7%

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

      5. associate-/r* 92.2%

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

   5. Applied egg-rr 92.2%

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

   6. Taylor expanded in y around 0 59.7%

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

      1. associate-*l/ 57.8%

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

   8. Simplified 57.8%

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

4. Final simplification 56.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.5 \cdot 10^{+14}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-21}:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 11: 61.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+17}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-21}:\\ \;\;\;\;t \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.5e+17) t (if (<= y 1.35e-21) (* t (/ x z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.5e+17) {
		tmp = t;
	} else if (y <= 1.35e-21) {
		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 <= (-6.5d+17)) then
        tmp = t
    else if (y <= 1.35d-21) 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 <= -6.5e+17) {
		tmp = t;
	} else if (y <= 1.35e-21) {
		tmp = t * (x / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -6.5e+17:
		tmp = t
	elif y <= 1.35e-21:
		tmp = t * (x / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.5e+17)
		tmp = t;
	elseif (y <= 1.35e-21)
		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 <= -6.5e+17)
		tmp = t;
	elseif (y <= 1.35e-21)
		tmp = t * (x / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.5e+17], t, If[LessEqual[y, 1.35e-21], N[(t * N[(x / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if y < -6.5e17 or 1.3500000000000001e-21 < y

   1. Initial program 99.8%

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

      1. associate-*l/ 67.6%

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

      2. *-commutative 67.6%

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

      3. associate-*l/ 80.4%

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

   3. Simplified 80.4%

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

   4. Taylor expanded in y around inf 54.9%

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

   if -6.5e17 < y < 1.3500000000000001e-21

   1. Initial program 92.4%

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

   2. Taylor expanded in y around 0 61.0%

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

4. Final simplification 58.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+17}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-21}:\\ \;\;\;\;t \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 12: 35.1% 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 95.8%

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

   1. associate-*l/ 82.5%

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

   2. *-commutative 82.5%

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

   3. associate-*l/ 86.1%

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

3. Simplified 86.1%

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

4. Taylor expanded in y around inf 29.5%

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

5. Final simplification 29.5%

   \[\leadsto t \]

Developer target: 97.3% 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 2023308 
(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))