Optimisation.CirclePacking:place from circle-packing-0.1.0.4, F

Percentage Accurate: 92.7% → 97.4%

Time: 9.3s

Alternatives: 10

Speedup: 1.0×

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

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 92.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 97.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ x - \frac{1}{\frac{\frac{a}{y}}{z - t}} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (- x (/ 1.0 (/ (/ a y) (- z t)))))

C

double code(double x, double y, double z, double t, double a) {
	return x - (1.0 / ((a / y) / (z - t)));
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = x - (1.0d0 / ((a / y) / (z - t)))
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return x - (1.0 / ((a / y) / (z - t)));
}

Python

def code(x, y, z, t, a):
	return x - (1.0 / ((a / y) / (z - t)))

Julia

function code(x, y, z, t, a)
	return Float64(x - Float64(1.0 / Float64(Float64(a / y) / Float64(z - t))))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = x - (1.0 / ((a / y) / (z - t)));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(x - N[(1.0 / N[(N[(a / y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 92.8%

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

   1. associate-*r/ 94.0%

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

   2. *-commutative 94.0%

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

   3. div-inv 93.9%

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

   4. associate-*l* 96.9%

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

4. Applied egg-rr 96.9%

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

   1. *-commutative 96.9%

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

   2. associate-*l* 92.7%

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

   3. add-sqr-sqrt 49.7%

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

   4. unpow2 49.7%

      \[\leadsto x - \frac{1}{a} \cdot \color{blue}{{\left(\sqrt{y \cdot \left(z - t\right)}\right)}^{2}} \]

   5. associate-/r/ 49.7%

      \[\leadsto x - \color{blue}{\frac{1}{\frac{a}{{\left(\sqrt{y \cdot \left(z - t\right)}\right)}^{2}}}} \]

   6. unpow2 49.7%

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

   7. add-sqr-sqrt 92.7%

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

   8. associate-/r* 97.2%

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

6. Applied egg-rr 97.2%

   \[\leadsto x - \color{blue}{\frac{1}{\frac{\frac{a}{y}}{z - t}}} \]
7. Add Preprocessing

Alternative 2: 78.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(\frac{1}{a} \cdot \left(t - z\right)\right)\\ \mathbf{if}\;y \leq -8 \cdot 10^{-45}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{+29}:\\ \;\;\;\;x - \frac{y}{a} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (* (/ 1.0 a) (- t z)))))
   (if (<= y -8e-45) t_1 (if (<= y 1.5e+29) (- x (* (/ y a) z)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * ((1.0 / a) * (t - z));
	double tmp;
	if (y <= -8e-45) {
		tmp = t_1;
	} else if (y <= 1.5e+29) {
		tmp = x - ((y / a) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * ((1.0d0 / a) * (t - z))
    if (y <= (-8d-45)) then
        tmp = t_1
    else if (y <= 1.5d+29) then
        tmp = x - ((y / a) * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * ((1.0 / a) * (t - z));
	double tmp;
	if (y <= -8e-45) {
		tmp = t_1;
	} else if (y <= 1.5e+29) {
		tmp = x - ((y / a) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * ((1.0 / a) * (t - z))
	tmp = 0
	if y <= -8e-45:
		tmp = t_1
	elif y <= 1.5e+29:
		tmp = x - ((y / a) * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(Float64(1.0 / a) * Float64(t - z)))
	tmp = 0.0
	if (y <= -8e-45)
		tmp = t_1;
	elseif (y <= 1.5e+29)
		tmp = Float64(x - Float64(Float64(y / a) * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * ((1.0 / a) * (t - z));
	tmp = 0.0;
	if (y <= -8e-45)
		tmp = t_1;
	elseif (y <= 1.5e+29)
		tmp = x - ((y / a) * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(N[(1.0 / a), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -8e-45], t$95$1, If[LessEqual[y, 1.5e+29], N[(x - N[(N[(y / a), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -7.99999999999999987e-45 or 1.5e29 < y

   1. Initial program 88.1%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 80.8%

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

      1. div-inv 80.7%

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

      2. div-inv 80.7%

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

      3. distribute-rgt-out-- 84.4%

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

   7. Applied egg-rr 84.4%

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

   if -7.99999999999999987e-45 < y < 1.5e29

   1. Initial program 98.1%

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

      1. associate-/l* 87.4%

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

   3. Simplified 87.4%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 75.2%

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

      1. clear-num 75.2%

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

      2. un-div-inv 75.7%

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

   7. Applied egg-rr 75.7%

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

      1. associate-/r/ 80.3%

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

   9. Applied egg-rr 80.3%

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

Alternative 3: 69.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{a} \cdot \left(t - z\right)\\ \mathbf{if}\;y \leq -2.25 \cdot 10^{-105}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-78}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (/ y a) (- t z))))
   (if (<= y -2.25e-105) t_1 (if (<= y 8e-78) x t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y / a) * (t - z);
	double tmp;
	if (y <= -2.25e-105) {
		tmp = t_1;
	} else if (y <= 8e-78) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (y / a) * (t - z)
    if (y <= (-2.25d-105)) then
        tmp = t_1
    else if (y <= 8d-78) then
        tmp = 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 a) {
	double t_1 = (y / a) * (t - z);
	double tmp;
	if (y <= -2.25e-105) {
		tmp = t_1;
	} else if (y <= 8e-78) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y / a) * (t - z)
	tmp = 0
	if y <= -2.25e-105:
		tmp = t_1
	elif y <= 8e-78:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y / a) * Float64(t - z))
	tmp = 0.0
	if (y <= -2.25e-105)
		tmp = t_1;
	elseif (y <= 8e-78)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y / a) * (t - z);
	tmp = 0.0;
	if (y <= -2.25e-105)
		tmp = t_1;
	elseif (y <= 8e-78)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y / a), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.25e-105], t$95$1, If[LessEqual[y, 8e-78], x, t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.2499999999999999e-105 or 7.99999999999999999e-78 < y

   1. Initial program 89.5%

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

      1. associate-/l* 98.7%

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

   3. Simplified 98.7%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 74.5%

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

      1. *-commutative 74.5%

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

      2. sub-div 78.1%

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

      3. associate-*l/ 70.7%

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

   7. Applied egg-rr 70.7%

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

      1. associate-/l* 77.0%

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

      2. *-commutative 77.0%

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

   9. Applied egg-rr 77.0%

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

   if -2.2499999999999999e-105 < y < 7.99999999999999999e-78

   1. Initial program 98.7%

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

      1. associate-/l* 85.4%

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

   3. Simplified 85.4%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 68.5%

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

Alternative 4: 75.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - y \cdot \frac{z}{a}\\ \mathbf{if}\;x \leq -1.42 \cdot 10^{-157}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 2300:\\ \;\;\;\;\frac{y}{a} \cdot \left(t - z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* y (/ z a)))))
   (if (<= x -1.42e-157) t_1 (if (<= x 2300.0) (* (/ y a) (- t z)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (y * (z / a));
	double tmp;
	if (x <= -1.42e-157) {
		tmp = t_1;
	} else if (x <= 2300.0) {
		tmp = (y / a) * (t - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x - (y * (z / a))
    if (x <= (-1.42d-157)) then
        tmp = t_1
    else if (x <= 2300.0d0) then
        tmp = (y / a) * (t - z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (y * (z / a));
	double tmp;
	if (x <= -1.42e-157) {
		tmp = t_1;
	} else if (x <= 2300.0) {
		tmp = (y / a) * (t - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - (y * (z / a))
	tmp = 0
	if x <= -1.42e-157:
		tmp = t_1
	elif x <= 2300.0:
		tmp = (y / a) * (t - z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(y * Float64(z / a)))
	tmp = 0.0
	if (x <= -1.42e-157)
		tmp = t_1;
	elseif (x <= 2300.0)
		tmp = Float64(Float64(y / a) * Float64(t - z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - (y * (z / a));
	tmp = 0.0;
	if (x <= -1.42e-157)
		tmp = t_1;
	elseif (x <= 2300.0)
		tmp = (y / a) * (t - z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.42e-157], t$95$1, If[LessEqual[x, 2300.0], N[(N[(y / a), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.42000000000000001e-157 or 2300 < x

   1. Initial program 94.9%

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

      1. associate-/l* 94.5%

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

   3. Simplified 94.5%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 77.0%

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

   if -1.42000000000000001e-157 < x < 2300

   1. Initial program 89.3%

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

      1. associate-/l* 93.1%

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

   3. Simplified 93.1%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 79.3%

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

      1. *-commutative 79.3%

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

      2. sub-div 81.3%

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

      3. associate-*l/ 78.3%

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

   7. Applied egg-rr 78.3%

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

      1. associate-/l* 84.1%

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

      2. *-commutative 84.1%

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

   9. Applied egg-rr 84.1%

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

Alternative 5: 77.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \frac{y}{a} \cdot z\\ \mathbf{if}\;x \leq -1.42 \cdot 10^{-157}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 1850:\\ \;\;\;\;\frac{y}{a} \cdot \left(t - z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* (/ y a) z))))
   (if (<= x -1.42e-157) t_1 (if (<= x 1850.0) (* (/ y a) (- t z)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y / a) * z);
	double tmp;
	if (x <= -1.42e-157) {
		tmp = t_1;
	} else if (x <= 1850.0) {
		tmp = (y / a) * (t - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x - ((y / a) * z)
    if (x <= (-1.42d-157)) then
        tmp = t_1
    else if (x <= 1850.0d0) then
        tmp = (y / a) * (t - z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y / a) * z);
	double tmp;
	if (x <= -1.42e-157) {
		tmp = t_1;
	} else if (x <= 1850.0) {
		tmp = (y / a) * (t - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - ((y / a) * z)
	tmp = 0
	if x <= -1.42e-157:
		tmp = t_1
	elif x <= 1850.0:
		tmp = (y / a) * (t - z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(Float64(y / a) * z))
	tmp = 0.0
	if (x <= -1.42e-157)
		tmp = t_1;
	elseif (x <= 1850.0)
		tmp = Float64(Float64(y / a) * Float64(t - z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - ((y / a) * z);
	tmp = 0.0;
	if (x <= -1.42e-157)
		tmp = t_1;
	elseif (x <= 1850.0)
		tmp = (y / a) * (t - z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(N[(y / a), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.42e-157], t$95$1, If[LessEqual[x, 1850.0], N[(N[(y / a), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.42000000000000001e-157 or 1850 < x

   1. Initial program 94.9%

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

      1. associate-/l* 94.5%

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

   3. Simplified 94.5%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 77.0%

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

      1. clear-num 77.0%

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

      2. un-div-inv 77.4%

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

   7. Applied egg-rr 77.4%

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

      1. associate-/r/ 80.4%

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

   9. Applied egg-rr 80.4%

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

   if -1.42000000000000001e-157 < x < 1850

   1. Initial program 89.3%

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

      1. associate-/l* 93.1%

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

   3. Simplified 93.1%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 79.3%

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

      1. *-commutative 79.3%

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

      2. sub-div 81.3%

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

      3. associate-*l/ 78.3%

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

   7. Applied egg-rr 78.3%

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

      1. associate-/l* 84.1%

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

      2. *-commutative 84.1%

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

   9. Applied egg-rr 84.1%

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

Alternative 6: 49.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{t}{a}\\ \mathbf{if}\;t \leq -5.6 \cdot 10^{-58}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 4.5 \cdot 10^{+47}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ t a))))
   (if (<= t -5.6e-58) t_1 (if (<= t 4.5e+47) x t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (t / a);
	double tmp;
	if (t <= -5.6e-58) {
		tmp = t_1;
	} else if (t <= 4.5e+47) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (t / a)
    if (t <= (-5.6d-58)) then
        tmp = t_1
    else if (t <= 4.5d+47) then
        tmp = 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 a) {
	double t_1 = y * (t / a);
	double tmp;
	if (t <= -5.6e-58) {
		tmp = t_1;
	} else if (t <= 4.5e+47) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (t / a)
	tmp = 0
	if t <= -5.6e-58:
		tmp = t_1
	elif t <= 4.5e+47:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(t / a))
	tmp = 0.0
	if (t <= -5.6e-58)
		tmp = t_1;
	elseif (t <= 4.5e+47)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (t / a);
	tmp = 0.0;
	if (t <= -5.6e-58)
		tmp = t_1;
	elseif (t <= 4.5e+47)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.6e-58], t$95$1, If[LessEqual[t, 4.5e+47], x, t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -5.6000000000000001e-58 or 4.49999999999999979e47 < t

   1. Initial program 91.9%

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

      1. associate-/l* 91.8%

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

   3. Simplified 91.8%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 65.0%

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

   6. Taylor expanded in t around inf 57.8%

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

   if -5.6000000000000001e-58 < t < 4.49999999999999979e47

   1. Initial program 93.7%

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

      1. associate-/l* 96.2%

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

   3. Simplified 96.2%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 51.7%

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

Alternative 7: 51.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{a} \cdot t\\ \mathbf{if}\;y \leq -7.4 \cdot 10^{-45}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 10^{-77}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (/ y a) t))) (if (<= y -7.4e-45) t_1 (if (<= y 1e-77) x t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y / a) * t;
	double tmp;
	if (y <= -7.4e-45) {
		tmp = t_1;
	} else if (y <= 1e-77) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (y / a) * t
    if (y <= (-7.4d-45)) then
        tmp = t_1
    else if (y <= 1d-77) then
        tmp = 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 a) {
	double t_1 = (y / a) * t;
	double tmp;
	if (y <= -7.4e-45) {
		tmp = t_1;
	} else if (y <= 1e-77) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y / a) * t
	tmp = 0
	if y <= -7.4e-45:
		tmp = t_1
	elif y <= 1e-77:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y / a) * t)
	tmp = 0.0
	if (y <= -7.4e-45)
		tmp = t_1;
	elseif (y <= 1e-77)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y / a) * t;
	tmp = 0.0;
	if (y <= -7.4e-45)
		tmp = t_1;
	elseif (y <= 1e-77)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y / a), $MachinePrecision] * t), $MachinePrecision]}, If[LessEqual[y, -7.4e-45], t$95$1, If[LessEqual[y, 1e-77], x, t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -7.4e-45 or 9.9999999999999993e-78 < y

   1. Initial program 88.8%

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

      1. associate-/l* 99.2%

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

   3. Simplified 99.2%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 76.9%

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

   6. Taylor expanded in t around inf 47.2%

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

      1. clear-num 47.2%

         \[\leadsto y \cdot \color{blue}{\frac{1}{\frac{a}{t}}} \]

      2. un-div-inv 47.3%

         \[\leadsto \color{blue}{\frac{y}{\frac{a}{t}}} \]

   8. Applied egg-rr 47.3%

      \[\leadsto \color{blue}{\frac{y}{\frac{a}{t}}} \]
   9. Step-by-step derivation

      1. associate-/r/ 49.7%

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

   10. Applied egg-rr 49.7%

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

   if -7.4e-45 < y < 9.9999999999999993e-78

   1. Initial program 98.8%

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

      1. associate-/l* 86.0%

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

   3. Simplified 86.0%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 66.2%

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

Alternative 8: 93.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.7 \cdot 10^{+190}:\\ \;\;\;\;x - \frac{y}{a} \cdot z\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{z - t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.7e+190) (- x (* (/ y a) z)) (- x (* y (/ (- z t) a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.7e+190) {
		tmp = x - ((y / a) * z);
	} else {
		tmp = x - (y * ((z - t) / a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-2.7d+190)) then
        tmp = x - ((y / a) * z)
    else
        tmp = x - (y * ((z - t) / a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.7e+190) {
		tmp = x - ((y / a) * z);
	} else {
		tmp = x - (y * ((z - t) / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.7e+190:
		tmp = x - ((y / a) * z)
	else:
		tmp = x - (y * ((z - t) / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.7e+190)
		tmp = Float64(x - Float64(Float64(y / a) * z));
	else
		tmp = Float64(x - Float64(y * Float64(Float64(z - t) / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.7e+190)
		tmp = x - ((y / a) * z);
	else
		tmp = x - (y * ((z - t) / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.7e+190], N[(x - N[(N[(y / a), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.70000000000000004e190

   1. Initial program 80.5%

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

      1. associate-/l* 83.5%

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

   3. Simplified 83.5%

      \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 83.5%

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

      1. clear-num 83.3%

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

      2. un-div-inv 83.1%

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

   7. Applied egg-rr 83.1%

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

      1. associate-/r/ 96.1%

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

   9. Applied egg-rr 96.1%

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

   if -2.70000000000000004e190 < z

   1. Initial program 94.7%

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

      1. associate-/l* 95.6%

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

   3. Simplified 95.6%

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

Alternative 9: 97.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.8%

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

   1. *-commutative 92.8%

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

   2. associate-/l* 96.9%

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

4. Applied egg-rr 96.9%

   \[\leadsto x - \color{blue}{\left(z - t\right) \cdot \frac{y}{a}} \]
5. Add Preprocessing

Alternative 10: 39.8% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.8%

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

   1. associate-/l* 94.0%

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

3. Simplified 94.0%

   \[\leadsto \color{blue}{x - y \cdot \frac{z - t}{a}} \]
4. Add Preprocessing

5. Taylor expanded in x around inf 38.5%

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

Developer target: 99.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{a}{z - t}\\ \mathbf{if}\;y < -1.0761266216389975 \cdot 10^{-10}:\\ \;\;\;\;x - \frac{1}{\frac{t\_1}{y}}\\ \mathbf{elif}\;y < 2.894426862792089 \cdot 10^{-49}:\\ \;\;\;\;x - \frac{y \cdot \left(z - t\right)}{a}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{y}{t\_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ a (- z t))))
   (if (< y -1.0761266216389975e-10)
     (- x (/ 1.0 (/ t_1 y)))
     (if (< y 2.894426862792089e-49)
       (- x (/ (* y (- z t)) a))
       (- x (/ y t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = a / (z - t);
	double tmp;
	if (y < -1.0761266216389975e-10) {
		tmp = x - (1.0 / (t_1 / y));
	} else if (y < 2.894426862792089e-49) {
		tmp = x - ((y * (z - t)) / a);
	} else {
		tmp = x - (y / t_1);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a / (z - t)
    if (y < (-1.0761266216389975d-10)) then
        tmp = x - (1.0d0 / (t_1 / y))
    else if (y < 2.894426862792089d-49) then
        tmp = x - ((y * (z - t)) / a)
    else
        tmp = x - (y / t_1)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = a / (z - t);
	double tmp;
	if (y < -1.0761266216389975e-10) {
		tmp = x - (1.0 / (t_1 / y));
	} else if (y < 2.894426862792089e-49) {
		tmp = x - ((y * (z - t)) / a);
	} else {
		tmp = x - (y / t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = a / (z - t)
	tmp = 0
	if y < -1.0761266216389975e-10:
		tmp = x - (1.0 / (t_1 / y))
	elif y < 2.894426862792089e-49:
		tmp = x - ((y * (z - t)) / a)
	else:
		tmp = x - (y / t_1)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(a / Float64(z - t))
	tmp = 0.0
	if (y < -1.0761266216389975e-10)
		tmp = Float64(x - Float64(1.0 / Float64(t_1 / y)));
	elseif (y < 2.894426862792089e-49)
		tmp = Float64(x - Float64(Float64(y * Float64(z - t)) / a));
	else
		tmp = Float64(x - Float64(y / t_1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = a / (z - t);
	tmp = 0.0;
	if (y < -1.0761266216389975e-10)
		tmp = x - (1.0 / (t_1 / y));
	elseif (y < 2.894426862792089e-49)
		tmp = x - ((y * (z - t)) / a);
	else
		tmp = x - (y / t_1);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(a / N[(z - t), $MachinePrecision]), $MachinePrecision]}, If[Less[y, -1.0761266216389975e-10], N[(x - N[(1.0 / N[(t$95$1 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Less[y, 2.894426862792089e-49], N[(x - N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], N[(x - N[(y / t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Reproduce

herbie shell --seed 2024076 -o generate:simplify
(FPCore (x y z t a)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, F"
  :precision binary64

  :alt
  (if (< y -1.0761266216389975e-10) (- x (/ 1.0 (/ (/ a (- z t)) y))) (if (< y 2.894426862792089e-49) (- x (/ (* y (- z t)) a)) (- x (/ y (/ a (- z t))))))

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