Numeric.Histogram:binBounds from Chart-1.5.3

Percentage Accurate: 92.7% → 98.9%

Time: 9.4s

Alternatives: 13

Speedup: 1.0×

• 24.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x + \frac{\left(y - x\right) \cdot z}{t} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 92.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{\left(y - x\right) \cdot z}{t} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{z \cdot \left(y - x\right)}{t}\\ \mathbf{if}\;t_1 \leq -\infty \lor \neg \left(t_1 \leq 2 \cdot 10^{+291}\right):\\ \;\;\;\;x + z \cdot \frac{y - x}{t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ x (/ (* z (- y x)) t))))
   (if (or (<= t_1 (- INFINITY)) (not (<= t_1 2e+291)))
     (+ x (* z (/ (- y x) t)))
     t_1)))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + ((z * (y - x)) / t);
	double tmp;
	if ((t_1 <= -((double) INFINITY)) || !(t_1 <= 2e+291)) {
		tmp = x + (z * ((y - x) / t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x + ((z * (y - x)) / t);
	double tmp;
	if ((t_1 <= -Double.POSITIVE_INFINITY) || !(t_1 <= 2e+291)) {
		tmp = x + (z * ((y - x) / t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + ((z * (y - x)) / t)
	tmp = 0
	if (t_1 <= -math.inf) or not (t_1 <= 2e+291):
		tmp = x + (z * ((y - x) / t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(Float64(z * Float64(y - x)) / t))
	tmp = 0.0
	if ((t_1 <= Float64(-Inf)) || !(t_1 <= 2e+291))
		tmp = Float64(x + Float64(z * Float64(Float64(y - x) / t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + ((z * (y - x)) / t);
	tmp = 0.0;
	if ((t_1 <= -Inf) || ~((t_1 <= 2e+291)))
		tmp = x + (z * ((y - x) / t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(N[(z * N[(y - x), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, (-Infinity)], N[Not[LessEqual[t$95$1, 2e+291]], $MachinePrecision]], N[(x + N[(z * N[(N[(y - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x (/.f64 (*.f64 (-.f64 y x) z) t)) < -inf.0 or 1.9999999999999999e291 < (+.f64 x (/.f64 (*.f64 (-.f64 y x) z) t))

   1. Initial program 83.3%

      \[x + \frac{\left(y - x\right) \cdot z}{t} \]
   2. Step-by-step derivation

      1. associate-*l/ 100.0%

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

   3. Applied egg-rr 100.0%

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

   if -inf.0 < (+.f64 x (/.f64 (*.f64 (-.f64 y x) z) t)) < 1.9999999999999999e291

   1. Initial program 99.2%

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

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x + \frac{z \cdot \left(y - x\right)}{t} \leq -\infty \lor \neg \left(x + \frac{z \cdot \left(y - x\right)}{t} \leq 2 \cdot 10^{+291}\right):\\ \;\;\;\;x + z \cdot \frac{y - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z \cdot \left(y - x\right)}{t}\\ \end{array} \]

Alternative 2: 97.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{z}{t}, y - x, x\right) \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 93.5%

   \[x + \frac{\left(y - x\right) \cdot z}{t} \]
2. Step-by-step derivation

   1. +-commutative 93.5%

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

   2. *-commutative 93.5%

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

   3. associate-*l/ 98.6%

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

   4. fma-def 98.6%

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

3. Simplified 98.6%

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

4. Final simplification 98.6%

   \[\leadsto \mathsf{fma}\left(\frac{z}{t}, y - x, x\right) \]

Alternative 3: 54.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z}{t} \cdot y\\ t_2 := z \cdot \frac{x}{-t}\\ \mathbf{if}\;z \leq -3.4 \cdot 10^{+181}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{+158}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{+28}:\\ \;\;\;\;z \cdot \frac{y}{t}\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-20}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.3 \cdot 10^{+76}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+99}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 5 \cdot 10^{+221} \lor \neg \left(z \leq 8 \cdot 10^{+272}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (/ z t) y)) (t_2 (* z (/ x (- t)))))
   (if (<= z -3.4e+181)
     t_1
     (if (<= z -4.2e+158)
       t_2
       (if (<= z -4.8e+28)
         (* z (/ y t))
         (if (<= z 4.5e-20)
           x
           (if (<= z 4.3e+76)
             t_1
             (if (<= z 1.95e+99)
               x
               (if (or (<= z 5e+221) (not (<= z 8e+272))) t_1 t_2)))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double t_2 = z * (x / -t);
	double tmp;
	if (z <= -3.4e+181) {
		tmp = t_1;
	} else if (z <= -4.2e+158) {
		tmp = t_2;
	} else if (z <= -4.8e+28) {
		tmp = z * (y / t);
	} else if (z <= 4.5e-20) {
		tmp = x;
	} else if (z <= 4.3e+76) {
		tmp = t_1;
	} else if (z <= 1.95e+99) {
		tmp = x;
	} else if ((z <= 5e+221) || !(z <= 8e+272)) {
		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 = (z / t) * y
    t_2 = z * (x / -t)
    if (z <= (-3.4d+181)) then
        tmp = t_1
    else if (z <= (-4.2d+158)) then
        tmp = t_2
    else if (z <= (-4.8d+28)) then
        tmp = z * (y / t)
    else if (z <= 4.5d-20) then
        tmp = x
    else if (z <= 4.3d+76) then
        tmp = t_1
    else if (z <= 1.95d+99) then
        tmp = x
    else if ((z <= 5d+221) .or. (.not. (z <= 8d+272))) 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 = (z / t) * y;
	double t_2 = z * (x / -t);
	double tmp;
	if (z <= -3.4e+181) {
		tmp = t_1;
	} else if (z <= -4.2e+158) {
		tmp = t_2;
	} else if (z <= -4.8e+28) {
		tmp = z * (y / t);
	} else if (z <= 4.5e-20) {
		tmp = x;
	} else if (z <= 4.3e+76) {
		tmp = t_1;
	} else if (z <= 1.95e+99) {
		tmp = x;
	} else if ((z <= 5e+221) || !(z <= 8e+272)) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (z / t) * y
	t_2 = z * (x / -t)
	tmp = 0
	if z <= -3.4e+181:
		tmp = t_1
	elif z <= -4.2e+158:
		tmp = t_2
	elif z <= -4.8e+28:
		tmp = z * (y / t)
	elif z <= 4.5e-20:
		tmp = x
	elif z <= 4.3e+76:
		tmp = t_1
	elif z <= 1.95e+99:
		tmp = x
	elif (z <= 5e+221) or not (z <= 8e+272):
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(z / t) * y)
	t_2 = Float64(z * Float64(x / Float64(-t)))
	tmp = 0.0
	if (z <= -3.4e+181)
		tmp = t_1;
	elseif (z <= -4.2e+158)
		tmp = t_2;
	elseif (z <= -4.8e+28)
		tmp = Float64(z * Float64(y / t));
	elseif (z <= 4.5e-20)
		tmp = x;
	elseif (z <= 4.3e+76)
		tmp = t_1;
	elseif (z <= 1.95e+99)
		tmp = x;
	elseif ((z <= 5e+221) || !(z <= 8e+272))
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (z / t) * y;
	t_2 = z * (x / -t);
	tmp = 0.0;
	if (z <= -3.4e+181)
		tmp = t_1;
	elseif (z <= -4.2e+158)
		tmp = t_2;
	elseif (z <= -4.8e+28)
		tmp = z * (y / t);
	elseif (z <= 4.5e-20)
		tmp = x;
	elseif (z <= 4.3e+76)
		tmp = t_1;
	elseif (z <= 1.95e+99)
		tmp = x;
	elseif ((z <= 5e+221) || ~((z <= 8e+272)))
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(z * N[(x / (-t)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.4e+181], t$95$1, If[LessEqual[z, -4.2e+158], t$95$2, If[LessEqual[z, -4.8e+28], N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.5e-20], x, If[LessEqual[z, 4.3e+76], t$95$1, If[LessEqual[z, 1.95e+99], x, If[Or[LessEqual[z, 5e+221], N[Not[LessEqual[z, 8e+272]], $MachinePrecision]], t$95$1, t$95$2]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -3.40000000000000031e181 or 4.5000000000000001e-20 < z < 4.29999999999999978e76 or 1.94999999999999997e99 < z < 5.0000000000000002e221 or 8.0000000000000005e272 < z

   1. Initial program 88.0%

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

   2. Taylor expanded in t around 0 79.1%

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

   3. Taylor expanded in y around inf 58.2%

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

      1. associate-*r/ 68.8%

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

   5. Simplified 68.8%

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

   if -3.40000000000000031e181 < z < -4.1999999999999998e158 or 5.0000000000000002e221 < z < 8.0000000000000005e272

   1. Initial program 90.1%

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

   2. Taylor expanded in t around 0 90.1%

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

   3. Taylor expanded in y around 0 79.6%

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

      1. mul-1-neg 79.6%

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

      2. distribute-rgt-neg-out 79.6%

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

   5. Simplified 79.6%

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

      1. frac-2neg 79.6%

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

      2. div-inv 79.4%

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

      3. distribute-rgt-neg-out 79.4%

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

      4. remove-double-neg 79.4%

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

   7. Applied egg-rr 79.4%

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

      1. associate-*l* 88.6%

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

      2. associate-*r/ 88.6%

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

      3. *-rgt-identity 88.6%

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

   9. Simplified 88.6%

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

   if -4.1999999999999998e158 < z < -4.79999999999999962e28

   1. Initial program 86.1%

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

   2. Taylor expanded in t around 0 86.1%

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

   3. Taylor expanded in y around inf 72.0%

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

      1. associate-*l/ 85.8%

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

      2. *-commutative 85.8%

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

   5. Simplified 85.8%

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

   if -4.79999999999999962e28 < z < 4.5000000000000001e-20 or 4.29999999999999978e76 < z < 1.94999999999999997e99

   1. Initial program 98.4%

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

   2. Taylor expanded in z around 0 64.4%

      \[\leadsto \color{blue}{x} \]
3. Recombined 4 regimes into one program.

4. Final simplification 68.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.4 \cdot 10^{+181}:\\ \;\;\;\;\frac{z}{t} \cdot y\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{+158}:\\ \;\;\;\;z \cdot \frac{x}{-t}\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{+28}:\\ \;\;\;\;z \cdot \frac{y}{t}\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-20}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.3 \cdot 10^{+76}:\\ \;\;\;\;\frac{z}{t} \cdot y\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+99}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 5 \cdot 10^{+221} \lor \neg \left(z \leq 8 \cdot 10^{+272}\right):\\ \;\;\;\;\frac{z}{t} \cdot y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{x}{-t}\\ \end{array} \]

Alternative 4: 49.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z}{t} \cdot y\\ \mathbf{if}\;y \leq -4.1 \cdot 10^{+180}:\\ \;\;\;\;\frac{z \cdot y}{t}\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{+149}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{-193}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 92000000:\\ \;\;\;\;\frac{z}{t} \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (/ z t) y)))
   (if (<= y -4.1e+180)
     (/ (* z y) t)
     (if (<= y -5.8e+149)
       x
       (if (<= y -1.2e+17)
         t_1
         (if (<= y -2.4e-193)
           x
           (if (<= y 92000000.0) (* (/ z t) (- x)) t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double tmp;
	if (y <= -4.1e+180) {
		tmp = (z * y) / t;
	} else if (y <= -5.8e+149) {
		tmp = x;
	} else if (y <= -1.2e+17) {
		tmp = t_1;
	} else if (y <= -2.4e-193) {
		tmp = x;
	} else if (y <= 92000000.0) {
		tmp = (z / t) * -x;
	} else {
		tmp = t_1;
	}
	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 = (z / t) * y
    if (y <= (-4.1d+180)) then
        tmp = (z * y) / t
    else if (y <= (-5.8d+149)) then
        tmp = x
    else if (y <= (-1.2d+17)) then
        tmp = t_1
    else if (y <= (-2.4d-193)) then
        tmp = x
    else if (y <= 92000000.0d0) then
        tmp = (z / t) * -x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (z / t) * y;
	double tmp;
	if (y <= -4.1e+180) {
		tmp = (z * y) / t;
	} else if (y <= -5.8e+149) {
		tmp = x;
	} else if (y <= -1.2e+17) {
		tmp = t_1;
	} else if (y <= -2.4e-193) {
		tmp = x;
	} else if (y <= 92000000.0) {
		tmp = (z / t) * -x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (z / t) * y
	tmp = 0
	if y <= -4.1e+180:
		tmp = (z * y) / t
	elif y <= -5.8e+149:
		tmp = x
	elif y <= -1.2e+17:
		tmp = t_1
	elif y <= -2.4e-193:
		tmp = x
	elif y <= 92000000.0:
		tmp = (z / t) * -x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(z / t) * y)
	tmp = 0.0
	if (y <= -4.1e+180)
		tmp = Float64(Float64(z * y) / t);
	elseif (y <= -5.8e+149)
		tmp = x;
	elseif (y <= -1.2e+17)
		tmp = t_1;
	elseif (y <= -2.4e-193)
		tmp = x;
	elseif (y <= 92000000.0)
		tmp = Float64(Float64(z / t) * Float64(-x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (z / t) * y;
	tmp = 0.0;
	if (y <= -4.1e+180)
		tmp = (z * y) / t;
	elseif (y <= -5.8e+149)
		tmp = x;
	elseif (y <= -1.2e+17)
		tmp = t_1;
	elseif (y <= -2.4e-193)
		tmp = x;
	elseif (y <= 92000000.0)
		tmp = (z / t) * -x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -4.1e+180], N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision], If[LessEqual[y, -5.8e+149], x, If[LessEqual[y, -1.2e+17], t$95$1, If[LessEqual[y, -2.4e-193], x, If[LessEqual[y, 92000000.0], N[(N[(z / t), $MachinePrecision] * (-x)), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -4.1e180

   1. Initial program 93.1%

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

   2. Taylor expanded in t around 0 71.4%

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

   3. Taylor expanded in y around inf 68.1%

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

      1. *-commutative 68.1%

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

   5. Simplified 68.1%

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

   if -4.1e180 < y < -5.8000000000000004e149 or -1.2e17 < y < -2.4e-193

   1. Initial program 99.9%

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

   2. Taylor expanded in z around 0 65.8%

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

   if -5.8000000000000004e149 < y < -1.2e17 or 9.2e7 < y

   1. Initial program 90.0%

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

   2. Taylor expanded in t around 0 64.5%

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

   3. Taylor expanded in y around inf 61.8%

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

      1. associate-*r/ 67.6%

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

   5. Simplified 67.6%

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

   if -2.4e-193 < y < 9.2e7

   1. Initial program 93.5%

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

   2. Taylor expanded in t around 0 58.8%

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

   3. Taylor expanded in y around 0 55.1%

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

      1. associate-*r/ 55.1%

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

      2. mul-1-neg 55.1%

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

      3. distribute-rgt-neg-out 55.1%

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

      4. associate-*l/ 59.1%

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

   5. Simplified 59.1%

      \[\leadsto \color{blue}{\frac{z}{t} \cdot \left(-x\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 64.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.1 \cdot 10^{+180}:\\ \;\;\;\;\frac{z \cdot y}{t}\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{+149}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{+17}:\\ \;\;\;\;\frac{z}{t} \cdot y\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{-193}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 92000000:\\ \;\;\;\;\frac{z}{t} \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{t} \cdot y\\ \end{array} \]

Alternative 5: 74.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.8 \cdot 10^{+44} \lor \neg \left(x \leq -1.6 \cdot 10^{-5} \lor \neg \left(x \leq -1.9 \cdot 10^{-95}\right) \land x \leq 4.7 \cdot 10^{-91}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{t} \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -1.8e+44)
         (not (or (<= x -1.6e-5) (and (not (<= x -1.9e-95)) (<= x 4.7e-91)))))
   (* x (- 1.0 (/ z t)))
   (* (/ z t) y)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -1.8e+44) || !((x <= -1.6e-5) || (!(x <= -1.9e-95) && (x <= 4.7e-91)))) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = (z / t) * 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 <= (-1.8d+44)) .or. (.not. (x <= (-1.6d-5)) .or. (.not. (x <= (-1.9d-95))) .and. (x <= 4.7d-91))) then
        tmp = x * (1.0d0 - (z / t))
    else
        tmp = (z / t) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -1.8e+44) || !((x <= -1.6e-5) || (!(x <= -1.9e-95) && (x <= 4.7e-91)))) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = (z / t) * y;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -1.8e+44) or not ((x <= -1.6e-5) or (not (x <= -1.9e-95) and (x <= 4.7e-91))):
		tmp = x * (1.0 - (z / t))
	else:
		tmp = (z / t) * y
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -1.8e+44) || !((x <= -1.6e-5) || (!(x <= -1.9e-95) && (x <= 4.7e-91))))
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(Float64(z / t) * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -1.8e+44) || ~(((x <= -1.6e-5) || (~((x <= -1.9e-95)) && (x <= 4.7e-91)))))
		tmp = x * (1.0 - (z / t));
	else
		tmp = (z / t) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -1.8e+44], N[Not[Or[LessEqual[x, -1.6e-5], And[N[Not[LessEqual[x, -1.9e-95]], $MachinePrecision], LessEqual[x, 4.7e-91]]]], $MachinePrecision]], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.8e44 or -1.59999999999999993e-5 < x < -1.8999999999999999e-95 or 4.70000000000000006e-91 < x

   1. Initial program 94.0%

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

   2. Taylor expanded in x around inf 86.7%

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

      1. *-commutative 86.7%

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

      2. distribute-lft-in 86.7%

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

      3. *-rgt-identity 86.7%

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

      4. mul-1-neg 86.7%

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

      5. distribute-rgt-neg-in 86.7%

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

      6. unsub-neg 86.7%

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

   4. Simplified 86.7%

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

   5. Taylor expanded in x around 0 86.7%

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

   if -1.8e44 < x < -1.59999999999999993e-5 or -1.8999999999999999e-95 < x < 4.70000000000000006e-91

   1. Initial program 92.8%

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

   2. Taylor expanded in t around 0 71.8%

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

   3. Taylor expanded in y around inf 66.3%

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

      1. associate-*r/ 72.5%

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

   5. Simplified 72.5%

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

4. Final simplification 81.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.8 \cdot 10^{+44} \lor \neg \left(x \leq -1.6 \cdot 10^{-5} \lor \neg \left(x \leq -1.9 \cdot 10^{-95}\right) \land x \leq 4.7 \cdot 10^{-91}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{t} \cdot y\\ \end{array} \]

Alternative 6: 77.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.4 \cdot 10^{+44} \lor \neg \left(x \leq -2.6 \cdot 10^{-63} \lor \neg \left(x \leq -2.8 \cdot 10^{-81}\right) \land x \leq 5.8 \cdot 10^{-75}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{t} \cdot \left(y - x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -3.4e+44)
         (not (or (<= x -2.6e-63) (and (not (<= x -2.8e-81)) (<= x 5.8e-75)))))
   (* x (- 1.0 (/ z t)))
   (* (/ z t) (- y x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -3.4e+44) || !((x <= -2.6e-63) || (!(x <= -2.8e-81) && (x <= 5.8e-75)))) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = (z / t) * (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) :: tmp
    if ((x <= (-3.4d+44)) .or. (.not. (x <= (-2.6d-63)) .or. (.not. (x <= (-2.8d-81))) .and. (x <= 5.8d-75))) then
        tmp = x * (1.0d0 - (z / t))
    else
        tmp = (z / t) * (y - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -3.4e+44) || !((x <= -2.6e-63) || (!(x <= -2.8e-81) && (x <= 5.8e-75)))) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = (z / t) * (y - x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -3.4e+44) or not ((x <= -2.6e-63) or (not (x <= -2.8e-81) and (x <= 5.8e-75))):
		tmp = x * (1.0 - (z / t))
	else:
		tmp = (z / t) * (y - x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -3.4e+44) || !((x <= -2.6e-63) || (!(x <= -2.8e-81) && (x <= 5.8e-75))))
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(Float64(z / t) * Float64(y - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -3.4e+44) || ~(((x <= -2.6e-63) || (~((x <= -2.8e-81)) && (x <= 5.8e-75)))))
		tmp = x * (1.0 - (z / t));
	else
		tmp = (z / t) * (y - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -3.4e+44], N[Not[Or[LessEqual[x, -2.6e-63], And[N[Not[LessEqual[x, -2.8e-81]], $MachinePrecision], LessEqual[x, 5.8e-75]]]], $MachinePrecision]], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z / t), $MachinePrecision] * N[(y - x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.4e44 or -2.6000000000000001e-63 < x < -2.7999999999999999e-81 or 5.8000000000000003e-75 < x

   1. Initial program 93.1%

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

   2. Taylor expanded in x around inf 89.6%

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

      1. *-commutative 89.6%

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

      2. distribute-lft-in 89.5%

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

      3. *-rgt-identity 89.5%

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

      4. mul-1-neg 89.5%

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

      5. distribute-rgt-neg-in 89.5%

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

      6. unsub-neg 89.5%

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

   4. Simplified 89.5%

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

   5. Taylor expanded in x around 0 89.6%

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

   if -3.4e44 < x < -2.6000000000000001e-63 or -2.7999999999999999e-81 < x < 5.8000000000000003e-75

   1. Initial program 94.1%

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

   2. Taylor expanded in t around 0 72.2%

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

      1. *-commutative 72.2%

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

      2. associate-*l/ 75.5%

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

   4. Applied egg-rr 75.5%

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

4. Final simplification 83.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.4 \cdot 10^{+44} \lor \neg \left(x \leq -2.6 \cdot 10^{-63} \lor \neg \left(x \leq -2.8 \cdot 10^{-81}\right) \land x \leq 5.8 \cdot 10^{-75}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{t} \cdot \left(y - x\right)\\ \end{array} \]

Alternative 7: 54.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{+29} \lor \neg \left(z \leq 4.6 \cdot 10^{-20}\right) \land \left(z \leq 4.3 \cdot 10^{+76} \lor \neg \left(z \leq 1.95 \cdot 10^{+99}\right)\right):\\ \;\;\;\;\frac{z}{t} \cdot y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.5e+29)
         (and (not (<= z 4.6e-20)) (or (<= z 4.3e+76) (not (<= z 1.95e+99)))))
   (* (/ z t) y)
   x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.5e+29) || (!(z <= 4.6e-20) && ((z <= 4.3e+76) || !(z <= 1.95e+99)))) {
		tmp = (z / t) * y;
	} else {
		tmp = 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) :: tmp
    if ((z <= (-1.5d+29)) .or. (.not. (z <= 4.6d-20)) .and. (z <= 4.3d+76) .or. (.not. (z <= 1.95d+99))) then
        tmp = (z / t) * y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.5e+29) || (!(z <= 4.6e-20) && ((z <= 4.3e+76) || !(z <= 1.95e+99)))) {
		tmp = (z / t) * y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.5e+29) or (not (z <= 4.6e-20) and ((z <= 4.3e+76) or not (z <= 1.95e+99))):
		tmp = (z / t) * y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.5e+29) || (!(z <= 4.6e-20) && ((z <= 4.3e+76) || !(z <= 1.95e+99))))
		tmp = Float64(Float64(z / t) * y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.5e+29) || (~((z <= 4.6e-20)) && ((z <= 4.3e+76) || ~((z <= 1.95e+99)))))
		tmp = (z / t) * y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.5e+29], And[N[Not[LessEqual[z, 4.6e-20]], $MachinePrecision], Or[LessEqual[z, 4.3e+76], N[Not[LessEqual[z, 1.95e+99]], $MachinePrecision]]]], N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -1.5e29 or 4.5999999999999998e-20 < z < 4.29999999999999978e76 or 1.94999999999999997e99 < z

   1. Initial program 88.1%

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

   2. Taylor expanded in t around 0 81.6%

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

   3. Taylor expanded in y around inf 56.7%

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

      1. associate-*r/ 66.0%

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

   5. Simplified 66.0%

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

   if -1.5e29 < z < 4.5999999999999998e-20 or 4.29999999999999978e76 < z < 1.94999999999999997e99

   1. Initial program 98.4%

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

   2. Taylor expanded in z around 0 64.4%

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

4. Final simplification 65.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{+29} \lor \neg \left(z \leq 4.6 \cdot 10^{-20}\right) \land \left(z \leq 4.3 \cdot 10^{+76} \lor \neg \left(z \leq 1.95 \cdot 10^{+99}\right)\right):\\ \;\;\;\;\frac{z}{t} \cdot y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 8: 94.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{-101} \lor \neg \left(z \leq 6 \cdot 10^{-95}\right):\\ \;\;\;\;x + z \cdot \frac{y - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{1}{t} \cdot \left(z \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -6.5e-101) (not (<= z 6e-95)))
   (+ x (* z (/ (- y x) t)))
   (+ x (* (/ 1.0 t) (* z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -6.5e-101) || !(z <= 6e-95)) {
		tmp = x + (z * ((y - x) / t));
	} else {
		tmp = x + ((1.0 / 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 ((z <= (-6.5d-101)) .or. (.not. (z <= 6d-95))) then
        tmp = x + (z * ((y - x) / t))
    else
        tmp = x + ((1.0d0 / 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 ((z <= -6.5e-101) || !(z <= 6e-95)) {
		tmp = x + (z * ((y - x) / t));
	} else {
		tmp = x + ((1.0 / t) * (z * y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -6.5e-101) or not (z <= 6e-95):
		tmp = x + (z * ((y - x) / t))
	else:
		tmp = x + ((1.0 / t) * (z * y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -6.5e-101) || !(z <= 6e-95))
		tmp = Float64(x + Float64(z * Float64(Float64(y - x) / t)));
	else
		tmp = Float64(x + Float64(Float64(1.0 / t) * Float64(z * y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -6.5e-101) || ~((z <= 6e-95)))
		tmp = x + (z * ((y - x) / t));
	else
		tmp = x + ((1.0 / t) * (z * y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -6.5e-101], N[Not[LessEqual[z, 6e-95]], $MachinePrecision]], N[(x + N[(z * N[(N[(y - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(1.0 / t), $MachinePrecision] * N[(z * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -6.4999999999999996e-101 or 6e-95 < z

   1. Initial program 90.3%

      \[x + \frac{\left(y - x\right) \cdot z}{t} \]
   2. Step-by-step derivation

      1. associate-*l/ 98.7%

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

   3. Applied egg-rr 98.7%

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

   if -6.4999999999999996e-101 < z < 6e-95

   1. Initial program 98.8%

      \[x + \frac{\left(y - x\right) \cdot z}{t} \]
   2. Step-by-step derivation

      1. clear-num 98.8%

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

      2. associate-/r/ 98.9%

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

   3. Applied egg-rr 98.9%

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

   4. Taylor expanded in y around inf 92.0%

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

      1. *-commutative 23.5%

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

   6. Simplified 92.0%

      \[\leadsto x + \frac{1}{t} \cdot \color{blue}{\left(z \cdot y\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{-101} \lor \neg \left(z \leq 6 \cdot 10^{-95}\right):\\ \;\;\;\;x + z \cdot \frac{y - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{1}{t} \cdot \left(z \cdot y\right)\\ \end{array} \]

Alternative 9: 86.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-49} \lor \neg \left(y \leq 84000\right):\\ \;\;\;\;x + \frac{z}{t} \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -9.5e-49) (not (<= y 84000.0)))
   (+ x (* (/ z t) y))
   (* x (- 1.0 (/ z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.5e-49) || !(y <= 84000.0)) {
		tmp = x + ((z / t) * y);
	} else {
		tmp = x * (1.0 - (z / 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 <= (-9.5d-49)) .or. (.not. (y <= 84000.0d0))) then
        tmp = x + ((z / t) * y)
    else
        tmp = x * (1.0d0 - (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.5e-49) || !(y <= 84000.0)) {
		tmp = x + ((z / t) * y);
	} else {
		tmp = x * (1.0 - (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -9.5e-49) or not (y <= 84000.0):
		tmp = x + ((z / t) * y)
	else:
		tmp = x * (1.0 - (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -9.5e-49) || !(y <= 84000.0))
		tmp = Float64(x + Float64(Float64(z / t) * y));
	else
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -9.5e-49) || ~((y <= 84000.0)))
		tmp = x + ((z / t) * y);
	else
		tmp = x * (1.0 - (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -9.5e-49], N[Not[LessEqual[y, 84000.0]], $MachinePrecision]], N[(x + N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -9.50000000000000006e-49 or 84000 < y

   1. Initial program 92.3%

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

   2. Taylor expanded in y around inf 89.9%

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

      1. associate-*r/ 59.8%

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

   4. Simplified 93.2%

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

   if -9.50000000000000006e-49 < y < 84000

   1. Initial program 95.4%

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

   2. Taylor expanded in x around inf 93.7%

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

      1. *-commutative 93.7%

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

      2. distribute-lft-in 93.7%

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

      3. *-rgt-identity 93.7%

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

      4. mul-1-neg 93.7%

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

      5. distribute-rgt-neg-in 93.7%

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

      6. unsub-neg 93.7%

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

   4. Simplified 93.7%

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

   5. Taylor expanded in x around 0 93.7%

      \[\leadsto \color{blue}{\left(1 - \frac{z}{t}\right) \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 93.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-49} \lor \neg \left(y \leq 84000\right):\\ \;\;\;\;x + \frac{z}{t} \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \end{array} \]

Alternative 10: 86.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.95 \cdot 10^{-48}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;y \leq 11000:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z}{t} \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.95e-48)
   (+ x (/ y (/ t z)))
   (if (<= y 11000.0) (* x (- 1.0 (/ z t))) (+ x (* (/ z t) y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.95e-48) {
		tmp = x + (y / (t / z));
	} else if (y <= 11000.0) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = x + ((z / t) * 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 <= (-1.95d-48)) then
        tmp = x + (y / (t / z))
    else if (y <= 11000.0d0) then
        tmp = x * (1.0d0 - (z / t))
    else
        tmp = x + ((z / t) * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.95e-48) {
		tmp = x + (y / (t / z));
	} else if (y <= 11000.0) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = x + ((z / t) * y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.95e-48:
		tmp = x + (y / (t / z))
	elif y <= 11000.0:
		tmp = x * (1.0 - (z / t))
	else:
		tmp = x + ((z / t) * y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.95e-48)
		tmp = Float64(x + Float64(y / Float64(t / z)));
	elseif (y <= 11000.0)
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(x + Float64(Float64(z / t) * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.95e-48)
		tmp = x + (y / (t / z));
	elseif (y <= 11000.0)
		tmp = x * (1.0 - (z / t));
	else
		tmp = x + ((z / t) * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.95e-48], N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 11000.0], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.95e-48

   1. Initial program 89.9%

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

   2. Taylor expanded in y around inf 86.8%

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

      1. associate-*l/ 56.7%

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

      2. *-commutative 56.7%

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

   4. Simplified 89.3%

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

      1. associate-*r/ 86.8%

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

      2. *-commutative 86.8%

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

      3. associate-/l* 90.5%

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

   6. Applied egg-rr 90.5%

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

   if -1.95e-48 < y < 11000

   1. Initial program 95.4%

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

   2. Taylor expanded in x around inf 93.7%

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

      1. *-commutative 93.7%

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

      2. distribute-lft-in 93.7%

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

      3. *-rgt-identity 93.7%

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

      4. mul-1-neg 93.7%

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

      5. distribute-rgt-neg-in 93.7%

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

      6. unsub-neg 93.7%

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

   4. Simplified 93.7%

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

   5. Taylor expanded in x around 0 93.7%

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

   if 11000 < y

   1. Initial program 95.4%

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

   2. Taylor expanded in y around inf 93.8%

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

      1. associate-*r/ 61.5%

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

   4. Simplified 96.8%

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

4. Final simplification 93.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.95 \cdot 10^{-48}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;y \leq 11000:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z}{t} \cdot y\\ \end{array} \]

Alternative 11: 93.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 + (z / (t / (y - x)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 93.5%

   \[x + \frac{\left(y - x\right) \cdot z}{t} \]
2. Step-by-step derivation

   1. associate-*l/ 92.6%

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

3. Applied egg-rr 92.6%

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

   1. *-commutative 92.6%

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

   2. clear-num 92.1%

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

   3. un-div-inv 93.3%

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

5. Applied egg-rr 93.3%

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

6. Final simplification 93.3%

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

Alternative 12: 97.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 - x) / (t / z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 93.5%

   \[x + \frac{\left(y - x\right) \cdot z}{t} \]
2. Step-by-step derivation

   1. associate-/l* 98.6%

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

3. Simplified 98.6%

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

4. Final simplification 98.6%

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

Alternative 13: 38.5% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 93.5%

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

2. Taylor expanded in z around 0 39.2%

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

3. Final simplification 39.2%

   \[\leadsto x \]

Developer target: 97.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x < -9.025511195533005 \cdot 10^{-135}:\\ \;\;\;\;x - \frac{z}{t} \cdot \left(x - y\right)\\ \mathbf{elif}\;x < 4.275032163700715 \cdot 10^{-250}:\\ \;\;\;\;x + \frac{y - x}{t} \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y - x}{\frac{t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (< x -9.025511195533005e-135)
   (- x (* (/ z t) (- x y)))
   (if (< x 4.275032163700715e-250)
     (+ x (* (/ (- y x) t) z))
     (+ x (/ (- y x) (/ t z))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x < -9.025511195533005e-135) {
		tmp = x - ((z / t) * (x - y));
	} else if (x < 4.275032163700715e-250) {
		tmp = x + (((y - x) / t) * z);
	} else {
		tmp = x + ((y - x) / (t / 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 (x < (-9.025511195533005d-135)) then
        tmp = x - ((z / t) * (x - y))
    else if (x < 4.275032163700715d-250) then
        tmp = x + (((y - x) / t) * z)
    else
        tmp = x + ((y - x) / (t / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x < -9.025511195533005e-135) {
		tmp = x - ((z / t) * (x - y));
	} else if (x < 4.275032163700715e-250) {
		tmp = x + (((y - x) / t) * z);
	} else {
		tmp = x + ((y - x) / (t / z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x < -9.025511195533005e-135:
		tmp = x - ((z / t) * (x - y))
	elif x < 4.275032163700715e-250:
		tmp = x + (((y - x) / t) * z)
	else:
		tmp = x + ((y - x) / (t / z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x < -9.025511195533005e-135)
		tmp = Float64(x - Float64(Float64(z / t) * Float64(x - y)));
	elseif (x < 4.275032163700715e-250)
		tmp = Float64(x + Float64(Float64(Float64(y - x) / t) * z));
	else
		tmp = Float64(x + Float64(Float64(y - x) / Float64(t / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x < -9.025511195533005e-135)
		tmp = x - ((z / t) * (x - y));
	elseif (x < 4.275032163700715e-250)
		tmp = x + (((y - x) / t) * z);
	else
		tmp = x + ((y - x) / (t / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Less[x, -9.025511195533005e-135], N[(x - N[(N[(z / t), $MachinePrecision] * N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Less[x, 4.275032163700715e-250], N[(x + N[(N[(N[(y - x), $MachinePrecision] / t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - x), $MachinePrecision] / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Reproduce

herbie shell --seed 2023240 
(FPCore (x y z t)
  :name "Numeric.Histogram:binBounds from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (if (< x -9.025511195533005e-135) (- x (* (/ z t) (- x y))) (if (< x 4.275032163700715e-250) (+ x (* (/ (- y x) t) z)) (+ x (/ (- y x) (/ t z)))))

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