Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, B

Percentage Accurate: 99.9% → 99.9%

Time: 7.8s

Alternatives: 12

Speedup: 1.0×

• 25.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot \left(\left(\left(\left(y + z\right) + z\right) + y\right) + t\right) + y \cdot 5 \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (* x (+ (+ (+ (+ y z) z) y) t)) (* y 5.0)))

C

double code(double x, double y, double z, double t) {
	return (x * ((((y + z) + z) + y) + t)) + (y * 5.0);
}

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) + z) + y) + t)) + (y * 5.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x * ((((y + z) + z) + y) + t)) + (y * 5.0);
}

Python

def code(x, y, z, t):
	return (x * ((((y + z) + z) + y) + t)) + (y * 5.0)

Julia

function code(x, y, z, t)
	return Float64(Float64(x * Float64(Float64(Float64(Float64(y + z) + z) + y) + t)) + Float64(y * 5.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * ((((y + z) + z) + y) + t)) + (y * 5.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x * N[(N[(N[(N[(y + z), $MachinePrecision] + z), $MachinePrecision] + y), $MachinePrecision] + t), $MachinePrecision]), $MachinePrecision] + N[(y * 5.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(\left(\left(\left(y + z\right) + z\right) + y\right) + t\right) + y \cdot 5 \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (* x (+ (+ (+ (+ y z) z) y) t)) (* y 5.0)))

C

double code(double x, double y, double z, double t) {
	return (x * ((((y + z) + z) + y) + t)) + (y * 5.0);
}

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) + z) + y) + t)) + (y * 5.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x * ((((y + z) + z) + y) + t)) + (y * 5.0);
}

Python

def code(x, y, z, t):
	return (x * ((((y + z) + z) + y) + t)) + (y * 5.0)

Julia

function code(x, y, z, t)
	return Float64(Float64(x * Float64(Float64(Float64(Float64(y + z) + z) + y) + t)) + Float64(y * 5.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * ((((y + z) + z) + y) + t)) + (y * 5.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x * N[(N[(N[(N[(y + z), $MachinePrecision] + z), $MachinePrecision] + y), $MachinePrecision] + t), $MachinePrecision]), $MachinePrecision] + N[(y * 5.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, 5, x \cdot \left(\left(y + z\right) \cdot 2 + t\right)\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (fma y 5.0 (* x (+ (* (+ y z) 2.0) t))))

C

double code(double x, double y, double z, double t) {
	return fma(y, 5.0, (x * (((y + z) * 2.0) + t)));
}

Julia

function code(x, y, z, t)
	return fma(y, 5.0, Float64(x * Float64(Float64(Float64(y + z) * 2.0) + t)))
end

Wolfram

code[x_, y_, z_, t_] := N[(y * 5.0 + N[(x * N[(N[(N[(y + z), $MachinePrecision] * 2.0), $MachinePrecision] + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 48.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{-7}:\\ \;\;\;\;x \cdot t\\ \mathbf{elif}\;x \leq 9.6 \cdot 10^{-31}:\\ \;\;\;\;y \cdot 5\\ \mathbf{elif}\;x \leq 1.65 \cdot 10^{+111} \lor \neg \left(x \leq 9.2 \cdot 10^{+170}\right) \land x \leq 1.62 \cdot 10^{+209}:\\ \;\;\;\;2 \cdot \left(x \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -4e-7)
   (* x t)
   (if (<= x 9.6e-31)
     (* y 5.0)
     (if (or (<= x 1.65e+111) (and (not (<= x 9.2e+170)) (<= x 1.62e+209)))
       (* 2.0 (* x z))
       (* x t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -4e-7) {
		tmp = x * t;
	} else if (x <= 9.6e-31) {
		tmp = y * 5.0;
	} else if ((x <= 1.65e+111) || (!(x <= 9.2e+170) && (x <= 1.62e+209))) {
		tmp = 2.0 * (x * z);
	} else {
		tmp = 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 <= (-4d-7)) then
        tmp = x * t
    else if (x <= 9.6d-31) then
        tmp = y * 5.0d0
    else if ((x <= 1.65d+111) .or. (.not. (x <= 9.2d+170)) .and. (x <= 1.62d+209)) then
        tmp = 2.0d0 * (x * z)
    else
        tmp = 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 <= -4e-7) {
		tmp = x * t;
	} else if (x <= 9.6e-31) {
		tmp = y * 5.0;
	} else if ((x <= 1.65e+111) || (!(x <= 9.2e+170) && (x <= 1.62e+209))) {
		tmp = 2.0 * (x * z);
	} else {
		tmp = x * t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -4e-7:
		tmp = x * t
	elif x <= 9.6e-31:
		tmp = y * 5.0
	elif (x <= 1.65e+111) or (not (x <= 9.2e+170) and (x <= 1.62e+209)):
		tmp = 2.0 * (x * z)
	else:
		tmp = x * t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -4e-7)
		tmp = Float64(x * t);
	elseif (x <= 9.6e-31)
		tmp = Float64(y * 5.0);
	elseif ((x <= 1.65e+111) || (!(x <= 9.2e+170) && (x <= 1.62e+209)))
		tmp = Float64(2.0 * Float64(x * z));
	else
		tmp = Float64(x * t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -4e-7)
		tmp = x * t;
	elseif (x <= 9.6e-31)
		tmp = y * 5.0;
	elseif ((x <= 1.65e+111) || (~((x <= 9.2e+170)) && (x <= 1.62e+209)))
		tmp = 2.0 * (x * z);
	else
		tmp = x * t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -4e-7], N[(x * t), $MachinePrecision], If[LessEqual[x, 9.6e-31], N[(y * 5.0), $MachinePrecision], If[Or[LessEqual[x, 1.65e+111], And[N[Not[LessEqual[x, 9.2e+170]], $MachinePrecision], LessEqual[x, 1.62e+209]]], N[(2.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], N[(x * t), $MachinePrecision]]]]

Derivation

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

Alternative 3: 64.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(t + z \cdot 2\right)\\ \mathbf{if}\;x \leq -6 \cdot 10^{-126}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 6.2 \cdot 10^{-31}:\\ \;\;\;\;y \cdot 5\\ \mathbf{elif}\;x \leq 3.85 \cdot 10^{+84} \lor \neg \left(x \leq 1.6 \cdot 10^{+154}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot 2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (+ t (* z 2.0)))))
   (if (<= x -6e-126)
     t_1
     (if (<= x 6.2e-31)
       (* y 5.0)
       (if (or (<= x 3.85e+84) (not (<= x 1.6e+154))) t_1 (* x (* y 2.0)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * (t + (z * 2.0));
	double tmp;
	if (x <= -6e-126) {
		tmp = t_1;
	} else if (x <= 6.2e-31) {
		tmp = y * 5.0;
	} else if ((x <= 3.85e+84) || !(x <= 1.6e+154)) {
		tmp = t_1;
	} else {
		tmp = x * (y * 2.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (t + (z * 2.0d0))
    if (x <= (-6d-126)) then
        tmp = t_1
    else if (x <= 6.2d-31) then
        tmp = y * 5.0d0
    else if ((x <= 3.85d+84) .or. (.not. (x <= 1.6d+154))) then
        tmp = t_1
    else
        tmp = x * (y * 2.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x * (t + (z * 2.0));
	double tmp;
	if (x <= -6e-126) {
		tmp = t_1;
	} else if (x <= 6.2e-31) {
		tmp = y * 5.0;
	} else if ((x <= 3.85e+84) || !(x <= 1.6e+154)) {
		tmp = t_1;
	} else {
		tmp = x * (y * 2.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * (t + (z * 2.0))
	tmp = 0
	if x <= -6e-126:
		tmp = t_1
	elif x <= 6.2e-31:
		tmp = y * 5.0
	elif (x <= 3.85e+84) or not (x <= 1.6e+154):
		tmp = t_1
	else:
		tmp = x * (y * 2.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(t + Float64(z * 2.0)))
	tmp = 0.0
	if (x <= -6e-126)
		tmp = t_1;
	elseif (x <= 6.2e-31)
		tmp = Float64(y * 5.0);
	elseif ((x <= 3.85e+84) || !(x <= 1.6e+154))
		tmp = t_1;
	else
		tmp = Float64(x * Float64(y * 2.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * (t + (z * 2.0));
	tmp = 0.0;
	if (x <= -6e-126)
		tmp = t_1;
	elseif (x <= 6.2e-31)
		tmp = y * 5.0;
	elseif ((x <= 3.85e+84) || ~((x <= 1.6e+154)))
		tmp = t_1;
	else
		tmp = x * (y * 2.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(t + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -6e-126], t$95$1, If[LessEqual[x, 6.2e-31], N[(y * 5.0), $MachinePrecision], If[Or[LessEqual[x, 3.85e+84], N[Not[LessEqual[x, 1.6e+154]], $MachinePrecision]], t$95$1, N[(x * N[(y * 2.0), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 4: 77.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(5 + x \cdot 2\right)\\ \mathbf{if}\;y \leq -2.55 \cdot 10^{-30}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.55 \cdot 10^{-54}:\\ \;\;\;\;x \cdot \left(t + z \cdot 2\right)\\ \mathbf{elif}\;y \leq 8.8 \cdot 10^{+89}:\\ \;\;\;\;y \cdot 5 + x \cdot t\\ \mathbf{elif}\;y \leq 1.75 \cdot 10^{+127}:\\ \;\;\;\;x \cdot \left(t + y \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (+ 5.0 (* x 2.0)))))
   (if (<= y -2.55e-30)
     t_1
     (if (<= y 2.55e-54)
       (* x (+ t (* z 2.0)))
       (if (<= y 8.8e+89)
         (+ (* y 5.0) (* x t))
         (if (<= y 1.75e+127) (* x (+ t (* y 2.0))) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (5.0 + (x * 2.0));
	double tmp;
	if (y <= -2.55e-30) {
		tmp = t_1;
	} else if (y <= 2.55e-54) {
		tmp = x * (t + (z * 2.0));
	} else if (y <= 8.8e+89) {
		tmp = (y * 5.0) + (x * t);
	} else if (y <= 1.75e+127) {
		tmp = x * (t + (y * 2.0));
	} 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 = y * (5.0d0 + (x * 2.0d0))
    if (y <= (-2.55d-30)) then
        tmp = t_1
    else if (y <= 2.55d-54) then
        tmp = x * (t + (z * 2.0d0))
    else if (y <= 8.8d+89) then
        tmp = (y * 5.0d0) + (x * t)
    else if (y <= 1.75d+127) then
        tmp = x * (t + (y * 2.0d0))
    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 = y * (5.0 + (x * 2.0));
	double tmp;
	if (y <= -2.55e-30) {
		tmp = t_1;
	} else if (y <= 2.55e-54) {
		tmp = x * (t + (z * 2.0));
	} else if (y <= 8.8e+89) {
		tmp = (y * 5.0) + (x * t);
	} else if (y <= 1.75e+127) {
		tmp = x * (t + (y * 2.0));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (5.0 + (x * 2.0))
	tmp = 0
	if y <= -2.55e-30:
		tmp = t_1
	elif y <= 2.55e-54:
		tmp = x * (t + (z * 2.0))
	elif y <= 8.8e+89:
		tmp = (y * 5.0) + (x * t)
	elif y <= 1.75e+127:
		tmp = x * (t + (y * 2.0))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(5.0 + Float64(x * 2.0)))
	tmp = 0.0
	if (y <= -2.55e-30)
		tmp = t_1;
	elseif (y <= 2.55e-54)
		tmp = Float64(x * Float64(t + Float64(z * 2.0)));
	elseif (y <= 8.8e+89)
		tmp = Float64(Float64(y * 5.0) + Float64(x * t));
	elseif (y <= 1.75e+127)
		tmp = Float64(x * Float64(t + Float64(y * 2.0)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (5.0 + (x * 2.0));
	tmp = 0.0;
	if (y <= -2.55e-30)
		tmp = t_1;
	elseif (y <= 2.55e-54)
		tmp = x * (t + (z * 2.0));
	elseif (y <= 8.8e+89)
		tmp = (y * 5.0) + (x * t);
	elseif (y <= 1.75e+127)
		tmp = x * (t + (y * 2.0));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(5.0 + N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.55e-30], t$95$1, If[LessEqual[y, 2.55e-54], N[(x * N[(t + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 8.8e+89], N[(N[(y * 5.0), $MachinePrecision] + N[(x * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.75e+127], N[(x * N[(t + N[(y * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 5: 48.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot 2\right)\\ \mathbf{if}\;x \leq -7 \cdot 10^{+146}:\\ \;\;\;\;x \cdot t\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{-5}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 7.8 \cdot 10^{-31}:\\ \;\;\;\;y \cdot 5\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{+80}:\\ \;\;\;\;2 \cdot \left(x \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (* y 2.0))))
   (if (<= x -7e+146)
     (* x t)
     (if (<= x -4.8e-5)
       t_1
       (if (<= x 7.8e-31)
         (* y 5.0)
         (if (<= x 3.7e+80) (* 2.0 (* x z)) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * (y * 2.0);
	double tmp;
	if (x <= -7e+146) {
		tmp = x * t;
	} else if (x <= -4.8e-5) {
		tmp = t_1;
	} else if (x <= 7.8e-31) {
		tmp = y * 5.0;
	} else if (x <= 3.7e+80) {
		tmp = 2.0 * (x * z);
	} 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 = x * (y * 2.0d0)
    if (x <= (-7d+146)) then
        tmp = x * t
    else if (x <= (-4.8d-5)) then
        tmp = t_1
    else if (x <= 7.8d-31) then
        tmp = y * 5.0d0
    else if (x <= 3.7d+80) then
        tmp = 2.0d0 * (x * 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 t_1 = x * (y * 2.0);
	double tmp;
	if (x <= -7e+146) {
		tmp = x * t;
	} else if (x <= -4.8e-5) {
		tmp = t_1;
	} else if (x <= 7.8e-31) {
		tmp = y * 5.0;
	} else if (x <= 3.7e+80) {
		tmp = 2.0 * (x * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * (y * 2.0)
	tmp = 0
	if x <= -7e+146:
		tmp = x * t
	elif x <= -4.8e-5:
		tmp = t_1
	elif x <= 7.8e-31:
		tmp = y * 5.0
	elif x <= 3.7e+80:
		tmp = 2.0 * (x * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(y * 2.0))
	tmp = 0.0
	if (x <= -7e+146)
		tmp = Float64(x * t);
	elseif (x <= -4.8e-5)
		tmp = t_1;
	elseif (x <= 7.8e-31)
		tmp = Float64(y * 5.0);
	elseif (x <= 3.7e+80)
		tmp = Float64(2.0 * Float64(x * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * (y * 2.0);
	tmp = 0.0;
	if (x <= -7e+146)
		tmp = x * t;
	elseif (x <= -4.8e-5)
		tmp = t_1;
	elseif (x <= 7.8e-31)
		tmp = y * 5.0;
	elseif (x <= 3.7e+80)
		tmp = 2.0 * (x * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -7e+146], N[(x * t), $MachinePrecision], If[LessEqual[x, -4.8e-5], t$95$1, If[LessEqual[x, 7.8e-31], N[(y * 5.0), $MachinePrecision], If[LessEqual[x, 3.7e+80], N[(2.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 6: 66.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(t + z \cdot 2\right)\\ \mathbf{if}\;x \leq -2.5 \cdot 10^{-124}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 9.5 \cdot 10^{-31}:\\ \;\;\;\;y \cdot 5\\ \mathbf{elif}\;x \leq 6.2 \cdot 10^{+81}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(t + y \cdot 2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (+ t (* z 2.0)))))
   (if (<= x -2.5e-124)
     t_1
     (if (<= x 9.5e-31)
       (* y 5.0)
       (if (<= x 6.2e+81) t_1 (* x (+ t (* y 2.0))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * (t + (z * 2.0));
	double tmp;
	if (x <= -2.5e-124) {
		tmp = t_1;
	} else if (x <= 9.5e-31) {
		tmp = y * 5.0;
	} else if (x <= 6.2e+81) {
		tmp = t_1;
	} else {
		tmp = x * (t + (y * 2.0));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (t + (z * 2.0d0))
    if (x <= (-2.5d-124)) then
        tmp = t_1
    else if (x <= 9.5d-31) then
        tmp = y * 5.0d0
    else if (x <= 6.2d+81) then
        tmp = t_1
    else
        tmp = x * (t + (y * 2.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x * (t + (z * 2.0));
	double tmp;
	if (x <= -2.5e-124) {
		tmp = t_1;
	} else if (x <= 9.5e-31) {
		tmp = y * 5.0;
	} else if (x <= 6.2e+81) {
		tmp = t_1;
	} else {
		tmp = x * (t + (y * 2.0));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * (t + (z * 2.0))
	tmp = 0
	if x <= -2.5e-124:
		tmp = t_1
	elif x <= 9.5e-31:
		tmp = y * 5.0
	elif x <= 6.2e+81:
		tmp = t_1
	else:
		tmp = x * (t + (y * 2.0))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(t + Float64(z * 2.0)))
	tmp = 0.0
	if (x <= -2.5e-124)
		tmp = t_1;
	elseif (x <= 9.5e-31)
		tmp = Float64(y * 5.0);
	elseif (x <= 6.2e+81)
		tmp = t_1;
	else
		tmp = Float64(x * Float64(t + Float64(y * 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * (t + (z * 2.0));
	tmp = 0.0;
	if (x <= -2.5e-124)
		tmp = t_1;
	elseif (x <= 9.5e-31)
		tmp = y * 5.0;
	elseif (x <= 6.2e+81)
		tmp = t_1;
	else
		tmp = x * (t + (y * 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(t + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.5e-124], t$95$1, If[LessEqual[x, 9.5e-31], N[(y * 5.0), $MachinePrecision], If[LessEqual[x, 6.2e+81], t$95$1, N[(x * N[(t + N[(y * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 7: 89.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.5 \cdot 10^{-8} \lor \neg \left(x \leq 2.15 \cdot 10^{-13}\right):\\ \;\;\;\;x \cdot \left(t + \left(y \cdot 2 + z \cdot 2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(x \cdot z\right) + y \cdot 5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -8.5e-8) (not (<= x 2.15e-13)))
   (* x (+ t (+ (* y 2.0) (* z 2.0))))
   (+ (* 2.0 (* x z)) (* y 5.0))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -8.5e-8) || !(x <= 2.15e-13)) {
		tmp = x * (t + ((y * 2.0) + (z * 2.0)));
	} else {
		tmp = (2.0 * (x * z)) + (y * 5.0);
	}
	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 <= (-8.5d-8)) .or. (.not. (x <= 2.15d-13))) then
        tmp = x * (t + ((y * 2.0d0) + (z * 2.0d0)))
    else
        tmp = (2.0d0 * (x * z)) + (y * 5.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -8.5e-8) || !(x <= 2.15e-13)) {
		tmp = x * (t + ((y * 2.0) + (z * 2.0)));
	} else {
		tmp = (2.0 * (x * z)) + (y * 5.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -8.5e-8) or not (x <= 2.15e-13):
		tmp = x * (t + ((y * 2.0) + (z * 2.0)))
	else:
		tmp = (2.0 * (x * z)) + (y * 5.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -8.5e-8) || !(x <= 2.15e-13))
		tmp = Float64(x * Float64(t + Float64(Float64(y * 2.0) + Float64(z * 2.0))));
	else
		tmp = Float64(Float64(2.0 * Float64(x * z)) + Float64(y * 5.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -8.5e-8) || ~((x <= 2.15e-13)))
		tmp = x * (t + ((y * 2.0) + (z * 2.0)));
	else
		tmp = (2.0 * (x * z)) + (y * 5.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -8.5e-8], N[Not[LessEqual[x, 2.15e-13]], $MachinePrecision]], N[(x * N[(t + N[(N[(y * 2.0), $MachinePrecision] + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(2.0 * N[(x * z), $MachinePrecision]), $MachinePrecision] + N[(y * 5.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 99.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.5 \lor \neg \left(x \leq 1.8 \cdot 10^{-11}\right):\\ \;\;\;\;x \cdot \left(t + \left(y \cdot 2 + z \cdot 2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot 5 + x \cdot \left(t + z \cdot 2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -2.5) (not (<= x 1.8e-11)))
   (* x (+ t (+ (* y 2.0) (* z 2.0))))
   (+ (* y 5.0) (* x (+ t (* z 2.0))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -2.5) || !(x <= 1.8e-11)) {
		tmp = x * (t + ((y * 2.0) + (z * 2.0)));
	} else {
		tmp = (y * 5.0) + (x * (t + (z * 2.0)));
	}
	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 <= (-2.5d0)) .or. (.not. (x <= 1.8d-11))) then
        tmp = x * (t + ((y * 2.0d0) + (z * 2.0d0)))
    else
        tmp = (y * 5.0d0) + (x * (t + (z * 2.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -2.5) || !(x <= 1.8e-11)) {
		tmp = x * (t + ((y * 2.0) + (z * 2.0)));
	} else {
		tmp = (y * 5.0) + (x * (t + (z * 2.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -2.5) or not (x <= 1.8e-11):
		tmp = x * (t + ((y * 2.0) + (z * 2.0)))
	else:
		tmp = (y * 5.0) + (x * (t + (z * 2.0)))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -2.5) || !(x <= 1.8e-11))
		tmp = Float64(x * Float64(t + Float64(Float64(y * 2.0) + Float64(z * 2.0))));
	else
		tmp = Float64(Float64(y * 5.0) + Float64(x * Float64(t + Float64(z * 2.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -2.5) || ~((x <= 1.8e-11)))
		tmp = x * (t + ((y * 2.0) + (z * 2.0)));
	else
		tmp = (y * 5.0) + (x * (t + (z * 2.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -2.5], N[Not[LessEqual[x, 1.8e-11]], $MachinePrecision]], N[(x * N[(t + N[(N[(y * 2.0), $MachinePrecision] + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y * 5.0), $MachinePrecision] + N[(x * N[(t + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 77.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-28} \lor \neg \left(y \leq 1.15 \cdot 10^{-23}\right):\\ \;\;\;\;y \cdot \left(5 + x \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(t + z \cdot 2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -9.5e-28) (not (<= y 1.15e-23)))
   (* y (+ 5.0 (* x 2.0)))
   (* x (+ t (* z 2.0)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.5e-28) || !(y <= 1.15e-23)) {
		tmp = y * (5.0 + (x * 2.0));
	} else {
		tmp = x * (t + (z * 2.0));
	}
	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-28)) .or. (.not. (y <= 1.15d-23))) then
        tmp = y * (5.0d0 + (x * 2.0d0))
    else
        tmp = x * (t + (z * 2.0d0))
    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-28) || !(y <= 1.15e-23)) {
		tmp = y * (5.0 + (x * 2.0));
	} else {
		tmp = x * (t + (z * 2.0));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -9.5e-28) or not (y <= 1.15e-23):
		tmp = y * (5.0 + (x * 2.0))
	else:
		tmp = x * (t + (z * 2.0))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -9.5e-28) || !(y <= 1.15e-23))
		tmp = Float64(y * Float64(5.0 + Float64(x * 2.0)));
	else
		tmp = Float64(x * Float64(t + Float64(z * 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -9.5e-28) || ~((y <= 1.15e-23)))
		tmp = y * (5.0 + (x * 2.0));
	else
		tmp = x * (t + (z * 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -9.5e-28], N[Not[LessEqual[y, 1.15e-23]], $MachinePrecision]], N[(y * N[(5.0 + N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x * N[(t + N[(z * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(t + \left(y + \left(z + \left(y + z\right)\right)\right)\right) + y \cdot 5 \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (* x (+ t (+ y (+ z (+ y z))))) (* y 5.0)))

C

double code(double x, double y, double z, double t) {
	return (x * (t + (y + (z + (y + z))))) + (y * 5.0);
}

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 * (t + (y + (z + (y + z))))) + (y * 5.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x * (t + (y + (z + (y + z))))) + (y * 5.0);
}

Python

def code(x, y, z, t):
	return (x * (t + (y + (z + (y + z))))) + (y * 5.0)

Julia

function code(x, y, z, t)
	return Float64(Float64(x * Float64(t + Float64(y + Float64(z + Float64(y + z))))) + Float64(y * 5.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * (t + (y + (z + (y + z))))) + (y * 5.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x * N[(t + N[(y + N[(z + N[(y + z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y * 5.0), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 11: 48.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.2 \cdot 10^{-7} \lor \neg \left(x \leq 2.35 \cdot 10^{-12}\right):\\ \;\;\;\;x \cdot t\\ \mathbf{else}:\\ \;\;\;\;y \cdot 5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -8.2e-7) (not (<= x 2.35e-12))) (* x t) (* y 5.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -8.2e-7) || !(x <= 2.35e-12)) {
		tmp = x * t;
	} else {
		tmp = y * 5.0;
	}
	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 <= (-8.2d-7)) .or. (.not. (x <= 2.35d-12))) then
        tmp = x * t
    else
        tmp = y * 5.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -8.2e-7) || !(x <= 2.35e-12)) {
		tmp = x * t;
	} else {
		tmp = y * 5.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -8.2e-7) or not (x <= 2.35e-12):
		tmp = x * t
	else:
		tmp = y * 5.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -8.2e-7) || !(x <= 2.35e-12))
		tmp = Float64(x * t);
	else
		tmp = Float64(y * 5.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -8.2e-7) || ~((x <= 2.35e-12)))
		tmp = x * t;
	else
		tmp = y * 5.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -8.2e-7], N[Not[LessEqual[x, 2.35e-12]], $MachinePrecision]], N[(x * t), $MachinePrecision], N[(y * 5.0), $MachinePrecision]]

Derivation

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

Alternative 12: 30.8% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ y \cdot 5 \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (* y 5.0))

C

double code(double x, double y, double z, double t) {
	return y * 5.0;
}

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 = y * 5.0d0
end function

Java

public static double code(double x, double y, double z, double t) {
	return y * 5.0;
}

Python

def code(x, y, z, t):
	return y * 5.0

Julia

function code(x, y, z, t)
	return Float64(y * 5.0)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = y * 5.0;
end

Wolfram

code[x_, y_, z_, t_] := N[(y * 5.0), $MachinePrecision]

Derivation

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

Reproduce

herbie shell --seed 2024006 
(FPCore (x y z t)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, B"
  :precision binary64
  (+ (* x (+ (+ (+ (+ y z) z) y) t)) (* y 5.0)))