Numeric.SpecFunctions:logBeta from math-functions-0.1.5.2, B

Percentage Accurate: 99.8% → 99.9%

Time: 19.5s

Alternatives: 21

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(x \cdot \log y + z\right) + t\right) + a\right) + \left(b - 0.5\right) \cdot \log c\right) + y \cdot i \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (+ (+ (+ (+ (+ (* x (log y)) z) t) a) (* (- b 0.5) (log c))) (* y i)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return (((((x * log(y)) + z) + t) + a) + ((b - 0.5) * log(c))) + (y * i);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    code = (((((x * log(y)) + z) + t) + a) + ((b - 0.5d0) * log(c))) + (y * i)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return (((((x * Math.log(y)) + z) + t) + a) + ((b - 0.5) * Math.log(c))) + (y * i);
}

Python

def code(x, y, z, t, a, b, c, i):
	return (((((x * math.log(y)) + z) + t) + a) + ((b - 0.5) * math.log(c))) + (y * i)

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(Float64(Float64(Float64(Float64(Float64(x * log(y)) + z) + t) + a) + Float64(Float64(b - 0.5) * log(c))) + Float64(y * i))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = (((((x * log(y)) + z) + t) + a) + ((b - 0.5) * log(c))) + (y * i);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(N[(N[(N[(N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision] + t), $MachinePrecision] + a), $MachinePrecision] + N[(N[(b - 0.5), $MachinePrecision] * N[Log[c], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(x \cdot \log y + z\right) + t\right) + a\right) + \left(b - 0.5\right) \cdot \log c\right) + y \cdot i \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (+ (+ (+ (+ (+ (* x (log y)) z) t) a) (* (- b 0.5) (log c))) (* y i)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return (((((x * log(y)) + z) + t) + a) + ((b - 0.5) * log(c))) + (y * i);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    code = (((((x * log(y)) + z) + t) + a) + ((b - 0.5d0) * log(c))) + (y * i)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return (((((x * Math.log(y)) + z) + t) + a) + ((b - 0.5) * Math.log(c))) + (y * i);
}

Python

def code(x, y, z, t, a, b, c, i):
	return (((((x * math.log(y)) + z) + t) + a) + ((b - 0.5) * math.log(c))) + (y * i)

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(Float64(Float64(Float64(Float64(Float64(x * log(y)) + z) + t) + a) + Float64(Float64(b - 0.5) * log(c))) + Float64(y * i))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = (((((x * log(y)) + z) + t) + a) + ((b - 0.5) * log(c))) + (y * i);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(N[(N[(N[(N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision] + t), $MachinePrecision] + a), $MachinePrecision] + N[(N[(b - 0.5), $MachinePrecision] * N[Log[c], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \left(\left(z + \log c \cdot \left(-0.5 + b\right)\right) + \mathsf{fma}\left(x, \log y, y \cdot i\right)\right) + \left(a + t\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (+ (+ (+ z (* (log c) (+ -0.5 b))) (fma x (log y) (* y i))) (+ a t)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return ((z + (log(c) * (-0.5 + b))) + fma(x, log(y), (y * i))) + (a + t);
}

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(Float64(Float64(z + Float64(log(c) * Float64(-0.5 + b))) + fma(x, log(y), Float64(y * i))) + Float64(a + t))
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(N[(N[(z + N[(N[Log[c], $MachinePrecision] * N[(-0.5 + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x * N[Log[y], $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a + t), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 88.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log c \cdot \left(b - 0.5\right)\\ \mathbf{if}\;a \leq 1.2 \cdot 10^{+121}:\\ \;\;\;\;t + \left(z + \left(y \cdot i + \left(x \cdot \log y + t_1\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a + \left(z + \left(y \cdot i + t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* (log c) (- b 0.5))))
   (if (<= a 1.2e+121)
     (+ t (+ z (+ (* y i) (+ (* x (log y)) t_1))))
     (+ a (+ z (+ (* y i) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = log(c) * (b - 0.5);
	double tmp;
	if (a <= 1.2e+121) {
		tmp = t + (z + ((y * i) + ((x * log(y)) + t_1)));
	} else {
		tmp = a + (z + ((y * i) + t_1));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = log(c) * (b - 0.5d0)
    if (a <= 1.2d+121) then
        tmp = t + (z + ((y * i) + ((x * log(y)) + t_1)))
    else
        tmp = a + (z + ((y * i) + t_1))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = Math.log(c) * (b - 0.5);
	double tmp;
	if (a <= 1.2e+121) {
		tmp = t + (z + ((y * i) + ((x * Math.log(y)) + t_1)));
	} else {
		tmp = a + (z + ((y * i) + t_1));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = math.log(c) * (b - 0.5)
	tmp = 0
	if a <= 1.2e+121:
		tmp = t + (z + ((y * i) + ((x * math.log(y)) + t_1)))
	else:
		tmp = a + (z + ((y * i) + t_1))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(log(c) * Float64(b - 0.5))
	tmp = 0.0
	if (a <= 1.2e+121)
		tmp = Float64(t + Float64(z + Float64(Float64(y * i) + Float64(Float64(x * log(y)) + t_1))));
	else
		tmp = Float64(a + Float64(z + Float64(Float64(y * i) + t_1)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = log(c) * (b - 0.5);
	tmp = 0.0;
	if (a <= 1.2e+121)
		tmp = t + (z + ((y * i) + ((x * log(y)) + t_1)));
	else
		tmp = a + (z + ((y * i) + t_1));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, 1.2e+121], N[(t + N[(z + N[(N[(y * i), $MachinePrecision] + N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(z + N[(N[(y * i), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 3: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y \cdot i + \left(\left(a + \left(t + \left(z + x \cdot \log y\right)\right)\right) + \log c \cdot \left(b - 0.5\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (+ (* y i) (+ (+ a (+ t (+ z (* x (log y))))) (* (log c) (- b 0.5)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return (y * i) + ((a + (t + (z + (x * log(y))))) + (log(c) * (b - 0.5)));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    code = (y * i) + ((a + (t + (z + (x * log(y))))) + (log(c) * (b - 0.5d0)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return (y * i) + ((a + (t + (z + (x * Math.log(y))))) + (Math.log(c) * (b - 0.5)));
}

Python

def code(x, y, z, t, a, b, c, i):
	return (y * i) + ((a + (t + (z + (x * math.log(y))))) + (math.log(c) * (b - 0.5)))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(Float64(y * i) + Float64(Float64(a + Float64(t + Float64(z + Float64(x * log(y))))) + Float64(log(c) * Float64(b - 0.5))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = (y * i) + ((a + (t + (z + (x * log(y))))) + (log(c) * (b - 0.5)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(N[(y * i), $MachinePrecision] + N[(N[(a + N[(t + N[(z + N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 4: 87.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.9 \cdot 10^{+264} \lor \neg \left(x \leq 5 \cdot 10^{+271}\right):\\ \;\;\;\;x \cdot \log y\\ \mathbf{else}:\\ \;\;\;\;a + \left(\left(z + t\right) + \mathsf{fma}\left(\log c, -0.5 + b, y \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= x -3.9e+264) (not (<= x 5e+271)))
   (* x (log y))
   (+ a (+ (+ z t) (fma (log c) (+ -0.5 b) (* y i))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((x <= -3.9e+264) || !(x <= 5e+271)) {
		tmp = x * log(y);
	} else {
		tmp = a + ((z + t) + fma(log(c), (-0.5 + b), (y * i)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((x <= -3.9e+264) || !(x <= 5e+271))
		tmp = Float64(x * log(y));
	else
		tmp = Float64(a + Float64(Float64(z + t) + fma(log(c), Float64(-0.5 + b), Float64(y * i))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[x, -3.9e+264], N[Not[LessEqual[x, 5e+271]], $MachinePrecision]], N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(z + t), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(-0.5 + b), $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 89.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.2 \cdot 10^{+224} \lor \neg \left(x \leq 1.18 \cdot 10^{+260}\right):\\ \;\;\;\;t + \left(z + \left(x \cdot \log y + \log c \cdot \left(b - 0.5\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a + \left(\left(z + t\right) + \mathsf{fma}\left(\log c, -0.5 + b, y \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= x -8.2e+224) (not (<= x 1.18e+260)))
   (+ t (+ z (+ (* x (log y)) (* (log c) (- b 0.5)))))
   (+ a (+ (+ z t) (fma (log c) (+ -0.5 b) (* y i))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((x <= -8.2e+224) || !(x <= 1.18e+260)) {
		tmp = t + (z + ((x * log(y)) + (log(c) * (b - 0.5))));
	} else {
		tmp = a + ((z + t) + fma(log(c), (-0.5 + b), (y * i)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((x <= -8.2e+224) || !(x <= 1.18e+260))
		tmp = Float64(t + Float64(z + Float64(Float64(x * log(y)) + Float64(log(c) * Float64(b - 0.5)))));
	else
		tmp = Float64(a + Float64(Float64(z + t) + fma(log(c), Float64(-0.5 + b), Float64(y * i))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[x, -8.2e+224], N[Not[LessEqual[x, 1.18e+260]], $MachinePrecision]], N[(t + N[(z + N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(z + t), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(-0.5 + b), $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 91.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.2 \cdot 10^{+31}:\\ \;\;\;\;a + \left(t + \left(z + \left(x \cdot \log y + \log c \cdot \left(b - 0.5\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a + \left(\left(z + t\right) + \mathsf{fma}\left(\log c, -0.5 + b, y \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y 3.2e+31)
   (+ a (+ t (+ z (+ (* x (log y)) (* (log c) (- b 0.5))))))
   (+ a (+ (+ z t) (fma (log c) (+ -0.5 b) (* y i))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 3.2e+31) {
		tmp = a + (t + (z + ((x * log(y)) + (log(c) * (b - 0.5)))));
	} else {
		tmp = a + ((z + t) + fma(log(c), (-0.5 + b), (y * i)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= 3.2e+31)
		tmp = Float64(a + Float64(t + Float64(z + Float64(Float64(x * log(y)) + Float64(log(c) * Float64(b - 0.5))))));
	else
		tmp = Float64(a + Float64(Float64(z + t) + fma(log(c), Float64(-0.5 + b), Float64(y * i))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, 3.2e+31], N[(a + N[(t + N[(z + N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(z + t), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(-0.5 + b), $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 81.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.5 \cdot 10^{+31}:\\ \;\;\;\;\log c \cdot \left(b - 0.5\right) + \left(a + \left(z + x \cdot \log y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a + \left(\left(z + t\right) + \mathsf{fma}\left(\log c, -0.5 + b, y \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y 3.5e+31)
   (+ (* (log c) (- b 0.5)) (+ a (+ z (* x (log y)))))
   (+ a (+ (+ z t) (fma (log c) (+ -0.5 b) (* y i))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 3.5e+31) {
		tmp = (log(c) * (b - 0.5)) + (a + (z + (x * log(y))));
	} else {
		tmp = a + ((z + t) + fma(log(c), (-0.5 + b), (y * i)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= 3.5e+31)
		tmp = Float64(Float64(log(c) * Float64(b - 0.5)) + Float64(a + Float64(z + Float64(x * log(y)))));
	else
		tmp = Float64(a + Float64(Float64(z + t) + fma(log(c), Float64(-0.5 + b), Float64(y * i))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, 3.5e+31], N[(N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision] + N[(a + N[(z + N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(z + t), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(-0.5 + b), $MachinePrecision] + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 58.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t + \left(z + y \cdot i\right)\\ t_2 := t + \left(z + \log c \cdot b\right)\\ \mathbf{if}\;b \leq -2.7 \cdot 10^{+118}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -1 \cdot 10^{+24}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -3300000000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -1.25 \cdot 10^{-82}:\\ \;\;\;\;z + a\\ \mathbf{elif}\;b \leq -7.8 \cdot 10^{-255}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 9.5 \cdot 10^{-206}:\\ \;\;\;\;z + \left(a + t\right)\\ \mathbf{elif}\;b \leq 3.15 \cdot 10^{+134}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ t (+ z (* y i)))) (t_2 (+ t (+ z (* (log c) b)))))
   (if (<= b -2.7e+118)
     t_2
     (if (<= b -1e+24)
       t_1
       (if (<= b -3300000000.0)
         t_2
         (if (<= b -1.25e-82)
           (+ z a)
           (if (<= b -7.8e-255)
             t_1
             (if (<= b 9.5e-206)
               (+ z (+ a t))
               (if (<= b 3.15e+134) t_1 t_2)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = t + (z + (y * i));
	double t_2 = t + (z + (log(c) * b));
	double tmp;
	if (b <= -2.7e+118) {
		tmp = t_2;
	} else if (b <= -1e+24) {
		tmp = t_1;
	} else if (b <= -3300000000.0) {
		tmp = t_2;
	} else if (b <= -1.25e-82) {
		tmp = z + a;
	} else if (b <= -7.8e-255) {
		tmp = t_1;
	} else if (b <= 9.5e-206) {
		tmp = z + (a + t);
	} else if (b <= 3.15e+134) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t + (z + (y * i))
    t_2 = t + (z + (log(c) * b))
    if (b <= (-2.7d+118)) then
        tmp = t_2
    else if (b <= (-1d+24)) then
        tmp = t_1
    else if (b <= (-3300000000.0d0)) then
        tmp = t_2
    else if (b <= (-1.25d-82)) then
        tmp = z + a
    else if (b <= (-7.8d-255)) then
        tmp = t_1
    else if (b <= 9.5d-206) then
        tmp = z + (a + t)
    else if (b <= 3.15d+134) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = t + (z + (y * i));
	double t_2 = t + (z + (Math.log(c) * b));
	double tmp;
	if (b <= -2.7e+118) {
		tmp = t_2;
	} else if (b <= -1e+24) {
		tmp = t_1;
	} else if (b <= -3300000000.0) {
		tmp = t_2;
	} else if (b <= -1.25e-82) {
		tmp = z + a;
	} else if (b <= -7.8e-255) {
		tmp = t_1;
	} else if (b <= 9.5e-206) {
		tmp = z + (a + t);
	} else if (b <= 3.15e+134) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = t + (z + (y * i))
	t_2 = t + (z + (math.log(c) * b))
	tmp = 0
	if b <= -2.7e+118:
		tmp = t_2
	elif b <= -1e+24:
		tmp = t_1
	elif b <= -3300000000.0:
		tmp = t_2
	elif b <= -1.25e-82:
		tmp = z + a
	elif b <= -7.8e-255:
		tmp = t_1
	elif b <= 9.5e-206:
		tmp = z + (a + t)
	elif b <= 3.15e+134:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(t + Float64(z + Float64(y * i)))
	t_2 = Float64(t + Float64(z + Float64(log(c) * b)))
	tmp = 0.0
	if (b <= -2.7e+118)
		tmp = t_2;
	elseif (b <= -1e+24)
		tmp = t_1;
	elseif (b <= -3300000000.0)
		tmp = t_2;
	elseif (b <= -1.25e-82)
		tmp = Float64(z + a);
	elseif (b <= -7.8e-255)
		tmp = t_1;
	elseif (b <= 9.5e-206)
		tmp = Float64(z + Float64(a + t));
	elseif (b <= 3.15e+134)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = t + (z + (y * i));
	t_2 = t + (z + (log(c) * b));
	tmp = 0.0;
	if (b <= -2.7e+118)
		tmp = t_2;
	elseif (b <= -1e+24)
		tmp = t_1;
	elseif (b <= -3300000000.0)
		tmp = t_2;
	elseif (b <= -1.25e-82)
		tmp = z + a;
	elseif (b <= -7.8e-255)
		tmp = t_1;
	elseif (b <= 9.5e-206)
		tmp = z + (a + t);
	elseif (b <= 3.15e+134)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(t + N[(z + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t + N[(z + N[(N[Log[c], $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -2.7e+118], t$95$2, If[LessEqual[b, -1e+24], t$95$1, If[LessEqual[b, -3300000000.0], t$95$2, If[LessEqual[b, -1.25e-82], N[(z + a), $MachinePrecision], If[LessEqual[b, -7.8e-255], t$95$1, If[LessEqual[b, 9.5e-206], N[(z + N[(a + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 3.15e+134], t$95$1, t$95$2]]]]]]]]]

Derivation

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

Alternative 9: 58.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log c \cdot b\\ t_2 := t + \left(z + y \cdot i\right)\\ t_3 := t + \left(z + t_1\right)\\ \mathbf{if}\;b \leq -6.6 \cdot 10^{+118}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq -4.5 \cdot 10^{+30}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -3300000000:\\ \;\;\;\;\left(a + t\right) + t_1\\ \mathbf{elif}\;b \leq -7 \cdot 10^{-82}:\\ \;\;\;\;z + a\\ \mathbf{elif}\;b \leq -3.1 \cdot 10^{-252}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 1.45 \cdot 10^{-206}:\\ \;\;\;\;z + \left(a + t\right)\\ \mathbf{elif}\;b \leq 6.5 \cdot 10^{+134}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* (log c) b)) (t_2 (+ t (+ z (* y i)))) (t_3 (+ t (+ z t_1))))
   (if (<= b -6.6e+118)
     t_3
     (if (<= b -4.5e+30)
       t_2
       (if (<= b -3300000000.0)
         (+ (+ a t) t_1)
         (if (<= b -7e-82)
           (+ z a)
           (if (<= b -3.1e-252)
             t_2
             (if (<= b 1.45e-206)
               (+ z (+ a t))
               (if (<= b 6.5e+134) t_2 t_3)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = log(c) * b;
	double t_2 = t + (z + (y * i));
	double t_3 = t + (z + t_1);
	double tmp;
	if (b <= -6.6e+118) {
		tmp = t_3;
	} else if (b <= -4.5e+30) {
		tmp = t_2;
	} else if (b <= -3300000000.0) {
		tmp = (a + t) + t_1;
	} else if (b <= -7e-82) {
		tmp = z + a;
	} else if (b <= -3.1e-252) {
		tmp = t_2;
	} else if (b <= 1.45e-206) {
		tmp = z + (a + t);
	} else if (b <= 6.5e+134) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = log(c) * b
    t_2 = t + (z + (y * i))
    t_3 = t + (z + t_1)
    if (b <= (-6.6d+118)) then
        tmp = t_3
    else if (b <= (-4.5d+30)) then
        tmp = t_2
    else if (b <= (-3300000000.0d0)) then
        tmp = (a + t) + t_1
    else if (b <= (-7d-82)) then
        tmp = z + a
    else if (b <= (-3.1d-252)) then
        tmp = t_2
    else if (b <= 1.45d-206) then
        tmp = z + (a + t)
    else if (b <= 6.5d+134) then
        tmp = t_2
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = Math.log(c) * b;
	double t_2 = t + (z + (y * i));
	double t_3 = t + (z + t_1);
	double tmp;
	if (b <= -6.6e+118) {
		tmp = t_3;
	} else if (b <= -4.5e+30) {
		tmp = t_2;
	} else if (b <= -3300000000.0) {
		tmp = (a + t) + t_1;
	} else if (b <= -7e-82) {
		tmp = z + a;
	} else if (b <= -3.1e-252) {
		tmp = t_2;
	} else if (b <= 1.45e-206) {
		tmp = z + (a + t);
	} else if (b <= 6.5e+134) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = math.log(c) * b
	t_2 = t + (z + (y * i))
	t_3 = t + (z + t_1)
	tmp = 0
	if b <= -6.6e+118:
		tmp = t_3
	elif b <= -4.5e+30:
		tmp = t_2
	elif b <= -3300000000.0:
		tmp = (a + t) + t_1
	elif b <= -7e-82:
		tmp = z + a
	elif b <= -3.1e-252:
		tmp = t_2
	elif b <= 1.45e-206:
		tmp = z + (a + t)
	elif b <= 6.5e+134:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(log(c) * b)
	t_2 = Float64(t + Float64(z + Float64(y * i)))
	t_3 = Float64(t + Float64(z + t_1))
	tmp = 0.0
	if (b <= -6.6e+118)
		tmp = t_3;
	elseif (b <= -4.5e+30)
		tmp = t_2;
	elseif (b <= -3300000000.0)
		tmp = Float64(Float64(a + t) + t_1);
	elseif (b <= -7e-82)
		tmp = Float64(z + a);
	elseif (b <= -3.1e-252)
		tmp = t_2;
	elseif (b <= 1.45e-206)
		tmp = Float64(z + Float64(a + t));
	elseif (b <= 6.5e+134)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = log(c) * b;
	t_2 = t + (z + (y * i));
	t_3 = t + (z + t_1);
	tmp = 0.0;
	if (b <= -6.6e+118)
		tmp = t_3;
	elseif (b <= -4.5e+30)
		tmp = t_2;
	elseif (b <= -3300000000.0)
		tmp = (a + t) + t_1;
	elseif (b <= -7e-82)
		tmp = z + a;
	elseif (b <= -3.1e-252)
		tmp = t_2;
	elseif (b <= 1.45e-206)
		tmp = z + (a + t);
	elseif (b <= 6.5e+134)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(N[Log[c], $MachinePrecision] * b), $MachinePrecision]}, Block[{t$95$2 = N[(t + N[(z + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t + N[(z + t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -6.6e+118], t$95$3, If[LessEqual[b, -4.5e+30], t$95$2, If[LessEqual[b, -3300000000.0], N[(N[(a + t), $MachinePrecision] + t$95$1), $MachinePrecision], If[LessEqual[b, -7e-82], N[(z + a), $MachinePrecision], If[LessEqual[b, -3.1e-252], t$95$2, If[LessEqual[b, 1.45e-206], N[(z + N[(a + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 6.5e+134], t$95$2, t$95$3]]]]]]]]]]

Derivation

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

Alternative 10: 87.8% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.35 \cdot 10^{+265} \lor \neg \left(x \leq 2.2 \cdot 10^{+270}\right):\\ \;\;\;\;x \cdot \log y\\ \mathbf{else}:\\ \;\;\;\;y \cdot i + \left(\log c \cdot \left(b - 0.5\right) + \left(a + \left(z + t\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= x -2.35e+265) (not (<= x 2.2e+270)))
   (* x (log y))
   (+ (* y i) (+ (* (log c) (- b 0.5)) (+ a (+ z t))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((x <= -2.35e+265) || !(x <= 2.2e+270)) {
		tmp = x * log(y);
	} else {
		tmp = (y * i) + ((log(c) * (b - 0.5)) + (a + (z + t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((x <= (-2.35d+265)) .or. (.not. (x <= 2.2d+270))) then
        tmp = x * log(y)
    else
        tmp = (y * i) + ((log(c) * (b - 0.5d0)) + (a + (z + t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((x <= -2.35e+265) || !(x <= 2.2e+270)) {
		tmp = x * Math.log(y);
	} else {
		tmp = (y * i) + ((Math.log(c) * (b - 0.5)) + (a + (z + t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (x <= -2.35e+265) or not (x <= 2.2e+270):
		tmp = x * math.log(y)
	else:
		tmp = (y * i) + ((math.log(c) * (b - 0.5)) + (a + (z + t)))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((x <= -2.35e+265) || !(x <= 2.2e+270))
		tmp = Float64(x * log(y));
	else
		tmp = Float64(Float64(y * i) + Float64(Float64(log(c) * Float64(b - 0.5)) + Float64(a + Float64(z + t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((x <= -2.35e+265) || ~((x <= 2.2e+270)))
		tmp = x * log(y);
	else
		tmp = (y * i) + ((log(c) * (b - 0.5)) + (a + (z + t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[x, -2.35e+265], N[Not[LessEqual[x, 2.2e+270]], $MachinePrecision]], N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision], N[(N[(y * i), $MachinePrecision] + N[(N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision] + N[(a + N[(z + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 72.8% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.85 \cdot 10^{+264} \lor \neg \left(x \leq 1.02 \cdot 10^{+271}\right):\\ \;\;\;\;x \cdot \log y\\ \mathbf{else}:\\ \;\;\;\;a + \left(z + \left(y \cdot i + \log c \cdot \left(b - 0.5\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= x -3.85e+264) (not (<= x 1.02e+271)))
   (* x (log y))
   (+ a (+ z (+ (* y i) (* (log c) (- b 0.5)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((x <= -3.85e+264) || !(x <= 1.02e+271)) {
		tmp = x * log(y);
	} else {
		tmp = a + (z + ((y * i) + (log(c) * (b - 0.5))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((x <= (-3.85d+264)) .or. (.not. (x <= 1.02d+271))) then
        tmp = x * log(y)
    else
        tmp = a + (z + ((y * i) + (log(c) * (b - 0.5d0))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((x <= -3.85e+264) || !(x <= 1.02e+271)) {
		tmp = x * Math.log(y);
	} else {
		tmp = a + (z + ((y * i) + (Math.log(c) * (b - 0.5))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (x <= -3.85e+264) or not (x <= 1.02e+271):
		tmp = x * math.log(y)
	else:
		tmp = a + (z + ((y * i) + (math.log(c) * (b - 0.5))))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((x <= -3.85e+264) || !(x <= 1.02e+271))
		tmp = Float64(x * log(y));
	else
		tmp = Float64(a + Float64(z + Float64(Float64(y * i) + Float64(log(c) * Float64(b - 0.5)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((x <= -3.85e+264) || ~((x <= 1.02e+271)))
		tmp = x * log(y);
	else
		tmp = a + (z + ((y * i) + (log(c) * (b - 0.5))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[x, -3.85e+264], N[Not[LessEqual[x, 1.02e+271]], $MachinePrecision]], N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision], N[(a + N[(z + N[(N[(y * i), $MachinePrecision] + N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 71.4% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.4 \cdot 10^{+91}:\\ \;\;\;\;a + \left(\log c \cdot \left(b - 0.5\right) + \left(z + t\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t + \left(z + y \cdot i\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y 3.4e+91)
   (+ a (+ (* (log c) (- b 0.5)) (+ z t)))
   (+ t (+ z (* y i)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 3.4e+91) {
		tmp = a + ((log(c) * (b - 0.5)) + (z + t));
	} else {
		tmp = t + (z + (y * i));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= 3.4d+91) then
        tmp = a + ((log(c) * (b - 0.5d0)) + (z + t))
    else
        tmp = t + (z + (y * i))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 3.4e+91) {
		tmp = a + ((Math.log(c) * (b - 0.5)) + (z + t));
	} else {
		tmp = t + (z + (y * i));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= 3.4e+91:
		tmp = a + ((math.log(c) * (b - 0.5)) + (z + t))
	else:
		tmp = t + (z + (y * i))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= 3.4e+91)
		tmp = Float64(a + Float64(Float64(log(c) * Float64(b - 0.5)) + Float64(z + t)));
	else
		tmp = Float64(t + Float64(z + Float64(y * i)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= 3.4e+91)
		tmp = a + ((log(c) * (b - 0.5)) + (z + t));
	else
		tmp = t + (z + (y * i));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, 3.4e+91], N[(a + N[(N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision] + N[(z + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(z + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 70.9% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log c \cdot \left(b - 0.5\right)\\ \mathbf{if}\;y \leq 1.76 \cdot 10^{-12}:\\ \;\;\;\;a + \left(t_1 + \left(z + t\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z + \left(y \cdot i + t_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* (log c) (- b 0.5))))
   (if (<= y 1.76e-12) (+ a (+ t_1 (+ z t))) (+ z (+ (* y i) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = log(c) * (b - 0.5);
	double tmp;
	if (y <= 1.76e-12) {
		tmp = a + (t_1 + (z + t));
	} else {
		tmp = z + ((y * i) + t_1);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = log(c) * (b - 0.5d0)
    if (y <= 1.76d-12) then
        tmp = a + (t_1 + (z + t))
    else
        tmp = z + ((y * i) + t_1)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = Math.log(c) * (b - 0.5);
	double tmp;
	if (y <= 1.76e-12) {
		tmp = a + (t_1 + (z + t));
	} else {
		tmp = z + ((y * i) + t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = math.log(c) * (b - 0.5)
	tmp = 0
	if y <= 1.76e-12:
		tmp = a + (t_1 + (z + t))
	else:
		tmp = z + ((y * i) + t_1)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(log(c) * Float64(b - 0.5))
	tmp = 0.0
	if (y <= 1.76e-12)
		tmp = Float64(a + Float64(t_1 + Float64(z + t)));
	else
		tmp = Float64(z + Float64(Float64(y * i) + t_1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = log(c) * (b - 0.5);
	tmp = 0.0;
	if (y <= 1.76e-12)
		tmp = a + (t_1 + (z + t));
	else
		tmp = z + ((y * i) + t_1);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, 1.76e-12], N[(a + N[(t$95$1 + N[(z + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z + N[(N[(y * i), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 14: 56.1% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t + \left(z + y \cdot i\right)\\ t_2 := \log c \cdot b\\ \mathbf{if}\;b \leq -1.2 \cdot 10^{+119}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -5.2 \cdot 10^{-255}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 7 \cdot 10^{-207}:\\ \;\;\;\;z + \left(a + t\right)\\ \mathbf{elif}\;b \leq 1.9 \cdot 10^{+238}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ t (+ z (* y i)))) (t_2 (* (log c) b)))
   (if (<= b -1.2e+119)
     t_2
     (if (<= b -5.2e-255)
       t_1
       (if (<= b 7e-207) (+ z (+ a t)) (if (<= b 1.9e+238) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = t + (z + (y * i));
	double t_2 = log(c) * b;
	double tmp;
	if (b <= -1.2e+119) {
		tmp = t_2;
	} else if (b <= -5.2e-255) {
		tmp = t_1;
	} else if (b <= 7e-207) {
		tmp = z + (a + t);
	} else if (b <= 1.9e+238) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t + (z + (y * i))
    t_2 = log(c) * b
    if (b <= (-1.2d+119)) then
        tmp = t_2
    else if (b <= (-5.2d-255)) then
        tmp = t_1
    else if (b <= 7d-207) then
        tmp = z + (a + t)
    else if (b <= 1.9d+238) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = t + (z + (y * i));
	double t_2 = Math.log(c) * b;
	double tmp;
	if (b <= -1.2e+119) {
		tmp = t_2;
	} else if (b <= -5.2e-255) {
		tmp = t_1;
	} else if (b <= 7e-207) {
		tmp = z + (a + t);
	} else if (b <= 1.9e+238) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = t + (z + (y * i))
	t_2 = math.log(c) * b
	tmp = 0
	if b <= -1.2e+119:
		tmp = t_2
	elif b <= -5.2e-255:
		tmp = t_1
	elif b <= 7e-207:
		tmp = z + (a + t)
	elif b <= 1.9e+238:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(t + Float64(z + Float64(y * i)))
	t_2 = Float64(log(c) * b)
	tmp = 0.0
	if (b <= -1.2e+119)
		tmp = t_2;
	elseif (b <= -5.2e-255)
		tmp = t_1;
	elseif (b <= 7e-207)
		tmp = Float64(z + Float64(a + t));
	elseif (b <= 1.9e+238)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = t + (z + (y * i));
	t_2 = log(c) * b;
	tmp = 0.0;
	if (b <= -1.2e+119)
		tmp = t_2;
	elseif (b <= -5.2e-255)
		tmp = t_1;
	elseif (b <= 7e-207)
		tmp = z + (a + t);
	elseif (b <= 1.9e+238)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(t + N[(z + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Log[c], $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[b, -1.2e+119], t$95$2, If[LessEqual[b, -5.2e-255], t$95$1, If[LessEqual[b, 7e-207], N[(z + N[(a + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.9e+238], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 15: 59.6% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.7 \cdot 10^{+91}:\\ \;\;\;\;a + \left(z + \log c \cdot \left(b - 0.5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t + \left(z + y \cdot i\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y 2.7e+91) (+ a (+ z (* (log c) (- b 0.5)))) (+ t (+ z (* y i)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 2.7e+91) {
		tmp = a + (z + (log(c) * (b - 0.5)));
	} else {
		tmp = t + (z + (y * i));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= 2.7d+91) then
        tmp = a + (z + (log(c) * (b - 0.5d0)))
    else
        tmp = t + (z + (y * i))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 2.7e+91) {
		tmp = a + (z + (Math.log(c) * (b - 0.5)));
	} else {
		tmp = t + (z + (y * i));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= 2.7e+91:
		tmp = a + (z + (math.log(c) * (b - 0.5)))
	else:
		tmp = t + (z + (y * i))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= 2.7e+91)
		tmp = Float64(a + Float64(z + Float64(log(c) * Float64(b - 0.5))));
	else
		tmp = Float64(t + Float64(z + Float64(y * i)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= 2.7e+91)
		tmp = a + (z + (log(c) * (b - 0.5)));
	else
		tmp = t + (z + (y * i));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, 2.7e+91], N[(a + N[(z + N[(N[Log[c], $MachinePrecision] * N[(b - 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(z + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 59.4% accurate, 19.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;i \leq -2.1 \cdot 10^{+66} \lor \neg \left(i \leq 1.05 \cdot 10^{-99}\right):\\ \;\;\;\;t + \left(z + y \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;z + \left(a + t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= i -2.1e+66) (not (<= i 1.05e-99)))
   (+ t (+ z (* y i)))
   (+ z (+ a t))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((i <= -2.1e+66) || !(i <= 1.05e-99)) {
		tmp = t + (z + (y * i));
	} else {
		tmp = z + (a + t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((i <= (-2.1d+66)) .or. (.not. (i <= 1.05d-99))) then
        tmp = t + (z + (y * i))
    else
        tmp = z + (a + t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((i <= -2.1e+66) || !(i <= 1.05e-99)) {
		tmp = t + (z + (y * i));
	} else {
		tmp = z + (a + t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (i <= -2.1e+66) or not (i <= 1.05e-99):
		tmp = t + (z + (y * i))
	else:
		tmp = z + (a + t)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((i <= -2.1e+66) || !(i <= 1.05e-99))
		tmp = Float64(t + Float64(z + Float64(y * i)));
	else
		tmp = Float64(z + Float64(a + t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((i <= -2.1e+66) || ~((i <= 1.05e-99)))
		tmp = t + (z + (y * i));
	else
		tmp = z + (a + t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[i, -2.1e+66], N[Not[LessEqual[i, 1.05e-99]], $MachinePrecision]], N[(t + N[(z + N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z + N[(a + t), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 28.3% accurate, 23.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+125}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{-48} \lor \neg \left(z \leq -8.4 \cdot 10^{-116}\right):\\ \;\;\;\;y \cdot i\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= z -4.2e+125)
   z
   (if (or (<= z -8.5e-48) (not (<= z -8.4e-116))) (* y i) a)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (z <= -4.2e+125) {
		tmp = z;
	} else if ((z <= -8.5e-48) || !(z <= -8.4e-116)) {
		tmp = y * i;
	} else {
		tmp = a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (z <= (-4.2d+125)) then
        tmp = z
    else if ((z <= (-8.5d-48)) .or. (.not. (z <= (-8.4d-116)))) then
        tmp = y * i
    else
        tmp = a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (z <= -4.2e+125) {
		tmp = z;
	} else if ((z <= -8.5e-48) || !(z <= -8.4e-116)) {
		tmp = y * i;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if z <= -4.2e+125:
		tmp = z
	elif (z <= -8.5e-48) or not (z <= -8.4e-116):
		tmp = y * i
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (z <= -4.2e+125)
		tmp = z;
	elseif ((z <= -8.5e-48) || !(z <= -8.4e-116))
		tmp = Float64(y * i);
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (z <= -4.2e+125)
		tmp = z;
	elseif ((z <= -8.5e-48) || ~((z <= -8.4e-116)))
		tmp = y * i;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[z, -4.2e+125], z, If[Or[LessEqual[z, -8.5e-48], N[Not[LessEqual[z, -8.4e-116]], $MachinePrecision]], N[(y * i), $MachinePrecision], a]]

Derivation

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

Alternative 18: 40.0% accurate, 23.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 4.1 \cdot 10^{+91} \lor \neg \left(y \leq 7.8 \cdot 10^{+171}\right) \land y \leq 2.35 \cdot 10^{+191}:\\ \;\;\;\;z + a\\ \mathbf{else}:\\ \;\;\;\;y \cdot i\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= y 4.1e+91) (and (not (<= y 7.8e+171)) (<= y 2.35e+191)))
   (+ z a)
   (* y i)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((y <= 4.1e+91) || (!(y <= 7.8e+171) && (y <= 2.35e+191))) {
		tmp = z + a;
	} else {
		tmp = y * i;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((y <= 4.1d+91) .or. (.not. (y <= 7.8d+171)) .and. (y <= 2.35d+191)) then
        tmp = z + a
    else
        tmp = y * i
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((y <= 4.1e+91) || (!(y <= 7.8e+171) && (y <= 2.35e+191))) {
		tmp = z + a;
	} else {
		tmp = y * i;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (y <= 4.1e+91) or (not (y <= 7.8e+171) and (y <= 2.35e+191)):
		tmp = z + a
	else:
		tmp = y * i
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((y <= 4.1e+91) || (!(y <= 7.8e+171) && (y <= 2.35e+191)))
		tmp = Float64(z + a);
	else
		tmp = Float64(y * i);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((y <= 4.1e+91) || (~((y <= 7.8e+171)) && (y <= 2.35e+191)))
		tmp = z + a;
	else
		tmp = y * i;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[y, 4.1e+91], And[N[Not[LessEqual[y, 7.8e+171]], $MachinePrecision], LessEqual[y, 2.35e+191]]], N[(z + a), $MachinePrecision], N[(y * i), $MachinePrecision]]

Derivation

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

Alternative 19: 51.6% accurate, 23.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 4.8 \cdot 10^{+91}:\\ \;\;\;\;z + \left(a + t\right)\\ \mathbf{elif}\;y \leq 7.8 \cdot 10^{+171} \lor \neg \left(y \leq 3.7 \cdot 10^{+189}\right):\\ \;\;\;\;y \cdot i\\ \mathbf{else}:\\ \;\;\;\;z + a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y 4.8e+91)
   (+ z (+ a t))
   (if (or (<= y 7.8e+171) (not (<= y 3.7e+189))) (* y i) (+ z a))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 4.8e+91) {
		tmp = z + (a + t);
	} else if ((y <= 7.8e+171) || !(y <= 3.7e+189)) {
		tmp = y * i;
	} else {
		tmp = z + a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= 4.8d+91) then
        tmp = z + (a + t)
    else if ((y <= 7.8d+171) .or. (.not. (y <= 3.7d+189))) then
        tmp = y * i
    else
        tmp = z + a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= 4.8e+91) {
		tmp = z + (a + t);
	} else if ((y <= 7.8e+171) || !(y <= 3.7e+189)) {
		tmp = y * i;
	} else {
		tmp = z + a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= 4.8e+91:
		tmp = z + (a + t)
	elif (y <= 7.8e+171) or not (y <= 3.7e+189):
		tmp = y * i
	else:
		tmp = z + a
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= 4.8e+91)
		tmp = Float64(z + Float64(a + t));
	elseif ((y <= 7.8e+171) || !(y <= 3.7e+189))
		tmp = Float64(y * i);
	else
		tmp = Float64(z + a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= 4.8e+91)
		tmp = z + (a + t);
	elseif ((y <= 7.8e+171) || ~((y <= 3.7e+189)))
		tmp = y * i;
	else
		tmp = z + a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, 4.8e+91], N[(z + N[(a + t), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[y, 7.8e+171], N[Not[LessEqual[y, 3.7e+189]], $MachinePrecision]], N[(y * i), $MachinePrecision], N[(z + a), $MachinePrecision]]]

Derivation

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

Alternative 20: 20.7% accurate, 71.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.2 \cdot 10^{+26}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i) :precision binary64 (if (<= z -6.2e+26) z a))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (z <= -6.2e+26) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (z <= (-6.2d+26)) then
        tmp = z
    else
        tmp = a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (z <= -6.2e+26) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if z <= -6.2e+26:
		tmp = z
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (z <= -6.2e+26)
		tmp = z;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (z <= -6.2e+26)
		tmp = z;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[z, -6.2e+26], z, a]

Derivation

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

Alternative 21: 15.8% accurate, 219.0× speedup

Math

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

FPCore

(FPCore (x y z t a b c i) :precision binary64 a)

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := a

Derivation

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

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a b c i)
  :name "Numeric.SpecFunctions:logBeta from math-functions-0.1.5.2, B"
  :precision binary64
  (+ (+ (+ (+ (+ (* x (log y)) z) t) a) (* (- b 0.5) (log c))) (* y i)))