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

Percentage Accurate: 92.5% → 98.2%

Time: 8.0s

Alternatives: 10

Speedup: 1.0×

• 24.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 92.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 50.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{-x}{t}\\ \mathbf{if}\;t \leq -2.1 \cdot 10^{+31}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq -8.2 \cdot 10^{-131}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;t \leq 1.55 \cdot 10^{-301}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 7.5 \cdot 10^{-268}:\\ \;\;\;\;\frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq 5.8 \cdot 10^{-137}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ (- x) t))))
   (if (<= t -2.1e+31)
     x
     (if (<= t -8.2e-131)
       (* y (/ z t))
       (if (<= t 1.55e-301)
         t_1
         (if (<= t 7.5e-268) (/ y (/ t z)) (if (<= t 5.8e-137) t_1 x)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (-x / t);
	double tmp;
	if (t <= -2.1e+31) {
		tmp = x;
	} else if (t <= -8.2e-131) {
		tmp = y * (z / t);
	} else if (t <= 1.55e-301) {
		tmp = t_1;
	} else if (t <= 7.5e-268) {
		tmp = y / (t / z);
	} else if (t <= 5.8e-137) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = y * (-x / t)
    if (t <= (-2.1d+31)) then
        tmp = x
    else if (t <= (-8.2d-131)) then
        tmp = y * (z / t)
    else if (t <= 1.55d-301) then
        tmp = t_1
    else if (t <= 7.5d-268) then
        tmp = y / (t / z)
    else if (t <= 5.8d-137) then
        tmp = t_1
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (-x / t);
	double tmp;
	if (t <= -2.1e+31) {
		tmp = x;
	} else if (t <= -8.2e-131) {
		tmp = y * (z / t);
	} else if (t <= 1.55e-301) {
		tmp = t_1;
	} else if (t <= 7.5e-268) {
		tmp = y / (t / z);
	} else if (t <= 5.8e-137) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (-x / t)
	tmp = 0
	if t <= -2.1e+31:
		tmp = x
	elif t <= -8.2e-131:
		tmp = y * (z / t)
	elif t <= 1.55e-301:
		tmp = t_1
	elif t <= 7.5e-268:
		tmp = y / (t / z)
	elif t <= 5.8e-137:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(-x) / t))
	tmp = 0.0
	if (t <= -2.1e+31)
		tmp = x;
	elseif (t <= -8.2e-131)
		tmp = Float64(y * Float64(z / t));
	elseif (t <= 1.55e-301)
		tmp = t_1;
	elseif (t <= 7.5e-268)
		tmp = Float64(y / Float64(t / z));
	elseif (t <= 5.8e-137)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (-x / t);
	tmp = 0.0;
	if (t <= -2.1e+31)
		tmp = x;
	elseif (t <= -8.2e-131)
		tmp = y * (z / t);
	elseif (t <= 1.55e-301)
		tmp = t_1;
	elseif (t <= 7.5e-268)
		tmp = y / (t / z);
	elseif (t <= 5.8e-137)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[((-x) / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.1e+31], x, If[LessEqual[t, -8.2e-131], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.55e-301], t$95$1, If[LessEqual[t, 7.5e-268], N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 5.8e-137], t$95$1, x]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 50.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{\frac{-t}{x}}\\ \mathbf{if}\;t \leq -1 \cdot 10^{+32}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq -8.2 \cdot 10^{-131}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;t \leq 3.35 \cdot 10^{-301}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.2 \cdot 10^{-268}:\\ \;\;\;\;\frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq 7.8 \cdot 10^{-135}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ y (/ (- t) x))))
   (if (<= t -1e+32)
     x
     (if (<= t -8.2e-131)
       (* y (/ z t))
       (if (<= t 3.35e-301)
         t_1
         (if (<= t 4.2e-268) (/ y (/ t z)) (if (<= t 7.8e-135) t_1 x)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y / (-t / x);
	double tmp;
	if (t <= -1e+32) {
		tmp = x;
	} else if (t <= -8.2e-131) {
		tmp = y * (z / t);
	} else if (t <= 3.35e-301) {
		tmp = t_1;
	} else if (t <= 4.2e-268) {
		tmp = y / (t / z);
	} else if (t <= 7.8e-135) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = y / (-t / x)
    if (t <= (-1d+32)) then
        tmp = x
    else if (t <= (-8.2d-131)) then
        tmp = y * (z / t)
    else if (t <= 3.35d-301) then
        tmp = t_1
    else if (t <= 4.2d-268) then
        tmp = y / (t / z)
    else if (t <= 7.8d-135) then
        tmp = t_1
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y / (-t / x);
	double tmp;
	if (t <= -1e+32) {
		tmp = x;
	} else if (t <= -8.2e-131) {
		tmp = y * (z / t);
	} else if (t <= 3.35e-301) {
		tmp = t_1;
	} else if (t <= 4.2e-268) {
		tmp = y / (t / z);
	} else if (t <= 7.8e-135) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y / (-t / x)
	tmp = 0
	if t <= -1e+32:
		tmp = x
	elif t <= -8.2e-131:
		tmp = y * (z / t)
	elif t <= 3.35e-301:
		tmp = t_1
	elif t <= 4.2e-268:
		tmp = y / (t / z)
	elif t <= 7.8e-135:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y / Float64(Float64(-t) / x))
	tmp = 0.0
	if (t <= -1e+32)
		tmp = x;
	elseif (t <= -8.2e-131)
		tmp = Float64(y * Float64(z / t));
	elseif (t <= 3.35e-301)
		tmp = t_1;
	elseif (t <= 4.2e-268)
		tmp = Float64(y / Float64(t / z));
	elseif (t <= 7.8e-135)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y / (-t / x);
	tmp = 0.0;
	if (t <= -1e+32)
		tmp = x;
	elseif (t <= -8.2e-131)
		tmp = y * (z / t);
	elseif (t <= 3.35e-301)
		tmp = t_1;
	elseif (t <= 4.2e-268)
		tmp = y / (t / z);
	elseif (t <= 7.8e-135)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y / N[((-t) / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1e+32], x, If[LessEqual[t, -8.2e-131], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.35e-301], t$95$1, If[LessEqual[t, 4.2e-268], N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 7.8e-135], t$95$1, x]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 51.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8.5 \cdot 10^{+30}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq -2.6 \cdot 10^{-130}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;t \leq 2.6 \cdot 10^{-302}:\\ \;\;\;\;\frac{y}{\frac{-t}{x}}\\ \mathbf{elif}\;t \leq 4 \cdot 10^{-268}:\\ \;\;\;\;\frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq 3.9 \cdot 10^{-134}:\\ \;\;\;\;\frac{x \cdot y}{-t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -8.5e+30)
   x
   (if (<= t -2.6e-130)
     (* y (/ z t))
     (if (<= t 2.6e-302)
       (/ y (/ (- t) x))
       (if (<= t 4e-268)
         (/ y (/ t z))
         (if (<= t 3.9e-134) (/ (* x y) (- t)) x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -8.5e+30) {
		tmp = x;
	} else if (t <= -2.6e-130) {
		tmp = y * (z / t);
	} else if (t <= 2.6e-302) {
		tmp = y / (-t / x);
	} else if (t <= 4e-268) {
		tmp = y / (t / z);
	} else if (t <= 3.9e-134) {
		tmp = (x * y) / -t;
	} 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 (t <= (-8.5d+30)) then
        tmp = x
    else if (t <= (-2.6d-130)) then
        tmp = y * (z / t)
    else if (t <= 2.6d-302) then
        tmp = y / (-t / x)
    else if (t <= 4d-268) then
        tmp = y / (t / z)
    else if (t <= 3.9d-134) then
        tmp = (x * y) / -t
    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 (t <= -8.5e+30) {
		tmp = x;
	} else if (t <= -2.6e-130) {
		tmp = y * (z / t);
	} else if (t <= 2.6e-302) {
		tmp = y / (-t / x);
	} else if (t <= 4e-268) {
		tmp = y / (t / z);
	} else if (t <= 3.9e-134) {
		tmp = (x * y) / -t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -8.5e+30:
		tmp = x
	elif t <= -2.6e-130:
		tmp = y * (z / t)
	elif t <= 2.6e-302:
		tmp = y / (-t / x)
	elif t <= 4e-268:
		tmp = y / (t / z)
	elif t <= 3.9e-134:
		tmp = (x * y) / -t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -8.5e+30)
		tmp = x;
	elseif (t <= -2.6e-130)
		tmp = Float64(y * Float64(z / t));
	elseif (t <= 2.6e-302)
		tmp = Float64(y / Float64(Float64(-t) / x));
	elseif (t <= 4e-268)
		tmp = Float64(y / Float64(t / z));
	elseif (t <= 3.9e-134)
		tmp = Float64(Float64(x * y) / Float64(-t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -8.5e+30)
		tmp = x;
	elseif (t <= -2.6e-130)
		tmp = y * (z / t);
	elseif (t <= 2.6e-302)
		tmp = y / (-t / x);
	elseif (t <= 4e-268)
		tmp = y / (t / z);
	elseif (t <= 3.9e-134)
		tmp = (x * y) / -t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -8.5e+30], x, If[LessEqual[t, -2.6e-130], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.6e-302], N[(y / N[((-t) / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4e-268], N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.9e-134], N[(N[(x * y), $MachinePrecision] / (-t)), $MachinePrecision], x]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 73.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{-93} \lor \neg \left(x \leq 2.15 \cdot 10^{-165}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -4e-93) (not (<= x 2.15e-165)))
   (* x (- 1.0 (/ y t)))
   (* y (/ z t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4e-93) || !(x <= 2.15e-165)) {
		tmp = x * (1.0 - (y / t));
	} else {
		tmp = y * (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 ((x <= (-4d-93)) .or. (.not. (x <= 2.15d-165))) then
        tmp = x * (1.0d0 - (y / t))
    else
        tmp = y * (z / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4e-93) || !(x <= 2.15e-165)) {
		tmp = x * (1.0 - (y / t));
	} else {
		tmp = y * (z / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -4e-93) or not (x <= 2.15e-165):
		tmp = x * (1.0 - (y / t))
	else:
		tmp = y * (z / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -4e-93) || !(x <= 2.15e-165))
		tmp = Float64(x * Float64(1.0 - Float64(y / t)));
	else
		tmp = Float64(y * Float64(z / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -4e-93) || ~((x <= 2.15e-165)))
		tmp = x * (1.0 - (y / t));
	else
		tmp = y * (z / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -4e-93], N[Not[LessEqual[x, 2.15e-165]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 75.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{-80} \lor \neg \left(x \leq 1.55 \cdot 10^{-154}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -5e-80) (not (<= x 1.55e-154)))
   (* x (- 1.0 (/ y t)))
   (* y (/ (- z x) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -5e-80) || !(x <= 1.55e-154)) {
		tmp = x * (1.0 - (y / t));
	} else {
		tmp = y * ((z - x) / 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 ((x <= (-5d-80)) .or. (.not. (x <= 1.55d-154))) then
        tmp = x * (1.0d0 - (y / t))
    else
        tmp = y * ((z - x) / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -5e-80) || !(x <= 1.55e-154)) {
		tmp = x * (1.0 - (y / t));
	} else {
		tmp = y * ((z - x) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -5e-80) or not (x <= 1.55e-154):
		tmp = x * (1.0 - (y / t))
	else:
		tmp = y * ((z - x) / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -5e-80) || !(x <= 1.55e-154))
		tmp = Float64(x * Float64(1.0 - Float64(y / t)));
	else
		tmp = Float64(y * Float64(Float64(z - x) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -5e-80) || ~((x <= 1.55e-154)))
		tmp = x * (1.0 - (y / t));
	else
		tmp = y * ((z - x) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -5e-80], N[Not[LessEqual[x, 1.55e-154]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(N[(z - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 85.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.8 \cdot 10^{-18} \lor \neg \left(x \leq 6 \cdot 10^{+134}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -5.8e-18) (not (<= x 6e+134)))
   (* x (- 1.0 (/ y t)))
   (+ x (* z (/ y t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -5.8e-18) || !(x <= 6e+134)) {
		tmp = x * (1.0 - (y / t));
	} else {
		tmp = x + (z * (y / 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 ((x <= (-5.8d-18)) .or. (.not. (x <= 6d+134))) then
        tmp = x * (1.0d0 - (y / t))
    else
        tmp = x + (z * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -5.8e-18) || !(x <= 6e+134)) {
		tmp = x * (1.0 - (y / t));
	} else {
		tmp = x + (z * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -5.8e-18) or not (x <= 6e+134):
		tmp = x * (1.0 - (y / t))
	else:
		tmp = x + (z * (y / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -5.8e-18) || !(x <= 6e+134))
		tmp = Float64(x * Float64(1.0 - Float64(y / t)));
	else
		tmp = Float64(x + Float64(z * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -5.8e-18) || ~((x <= 6e+134)))
		tmp = x * (1.0 - (y / t));
	else
		tmp = x + (z * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -5.8e-18], N[Not[LessEqual[x, 6e+134]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 49.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{-93}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.4 \cdot 10^{-150}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -6.6e-93) x (if (<= x 1.4e-150) (* y (/ z t)) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -6.6e-93) {
		tmp = x;
	} else if (x <= 1.4e-150) {
		tmp = y * (z / t);
	} 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 (x <= (-6.6d-93)) then
        tmp = x
    else if (x <= 1.4d-150) then
        tmp = y * (z / t)
    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 (x <= -6.6e-93) {
		tmp = x;
	} else if (x <= 1.4e-150) {
		tmp = y * (z / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -6.6e-93:
		tmp = x
	elif x <= 1.4e-150:
		tmp = y * (z / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -6.6e-93)
		tmp = x;
	elseif (x <= 1.4e-150)
		tmp = Float64(y * Float64(z / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -6.6e-93)
		tmp = x;
	elseif (x <= 1.4e-150)
		tmp = y * (z / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -6.6e-93], x, If[LessEqual[x, 1.4e-150], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], x]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 98.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 39.4% 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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Developer target: 91.7% accurate, 0.6× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z t)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, D"
  :precision binary64

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

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