Development.Shake.Progress:message from shake-0.15.5

Percentage Accurate: 99.3% → 99.7%

Time: 5.0s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot 100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* x 100.0) (+ x y)))

C

double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 100.0d0) / (x + y)
end function

Java

public static double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Python

def code(x, y):
	return (x * 100.0) / (x + y)

Julia

function code(x, y)
	return Float64(Float64(x * 100.0) / Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 100.0) / (x + y);
end

Wolfram

code[x_, y_] := N[(N[(x * 100.0), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot 100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* x 100.0) (+ x y)))

C

double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 100.0d0) / (x + y)
end function

Java

public static double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Python

def code(x, y):
	return (x * 100.0) / (x + y)

Julia

function code(x, y)
	return Float64(Float64(x * 100.0) / Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 100.0) / (x + y);
end

Wolfram

code[x_, y_] := N[(N[(x * 100.0), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{100}{y + x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ 100.0 (+ y x))))

C

double code(double x, double y) {
	return x * (100.0 / (y + x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (100.0d0 / (y + x))
end function

Java

public static double code(double x, double y) {
	return x * (100.0 / (y + x));
}

Python

def code(x, y):
	return x * (100.0 / (y + x))

Julia

function code(x, y)
	return Float64(x * Float64(100.0 / Float64(y + x)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (100.0 / (y + x));
end

Wolfram

code[x_, y_] := N[(x * N[(100.0 / N[(y + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{-40} \lor \neg \left(y \leq -4.4 \cdot 10^{-88} \lor \neg \left(y \leq -1 \cdot 10^{-107}\right) \land \left(y \leq 1.96 \cdot 10^{-75} \lor \neg \left(y \leq 6.2 \cdot 10^{-51}\right) \land y \leq 3.4 \cdot 10^{+25}\right)\right):\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -2e-40)
         (not
          (or (<= y -4.4e-88)
              (and (not (<= y -1e-107))
                   (or (<= y 1.96e-75)
                       (and (not (<= y 6.2e-51)) (<= y 3.4e+25)))))))
   (* 100.0 (/ x y))
   100.0))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -2e-40) || !((y <= -4.4e-88) || (!(y <= -1e-107) && ((y <= 1.96e-75) || (!(y <= 6.2e-51) && (y <= 3.4e+25)))))) {
		tmp = 100.0 * (x / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-2d-40)) .or. (.not. (y <= (-4.4d-88)) .or. (.not. (y <= (-1d-107))) .and. (y <= 1.96d-75) .or. (.not. (y <= 6.2d-51)) .and. (y <= 3.4d+25))) then
        tmp = 100.0d0 * (x / y)
    else
        tmp = 100.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -2e-40) || !((y <= -4.4e-88) || (!(y <= -1e-107) && ((y <= 1.96e-75) || (!(y <= 6.2e-51) && (y <= 3.4e+25)))))) {
		tmp = 100.0 * (x / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -2e-40) or not ((y <= -4.4e-88) or (not (y <= -1e-107) and ((y <= 1.96e-75) or (not (y <= 6.2e-51) and (y <= 3.4e+25))))):
		tmp = 100.0 * (x / y)
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -2e-40) || !((y <= -4.4e-88) || (!(y <= -1e-107) && ((y <= 1.96e-75) || (!(y <= 6.2e-51) && (y <= 3.4e+25))))))
		tmp = Float64(100.0 * Float64(x / y));
	else
		tmp = 100.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -2e-40) || ~(((y <= -4.4e-88) || (~((y <= -1e-107)) && ((y <= 1.96e-75) || (~((y <= 6.2e-51)) && (y <= 3.4e+25)))))))
		tmp = 100.0 * (x / y);
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -2e-40], N[Not[Or[LessEqual[y, -4.4e-88], And[N[Not[LessEqual[y, -1e-107]], $MachinePrecision], Or[LessEqual[y, 1.96e-75], And[N[Not[LessEqual[y, 6.2e-51]], $MachinePrecision], LessEqual[y, 3.4e+25]]]]]], $MachinePrecision]], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], 100.0]

Derivation

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

Alternative 3: 73.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 100 \cdot \frac{x}{y}\\ t_1 := x \cdot \frac{100}{y}\\ \mathbf{if}\;y \leq -1.45 \cdot 10^{-40}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.5 \cdot 10^{-88}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{-108}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-76}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq 1.38 \cdot 10^{-49}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 2.6 \cdot 10^{+25}:\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* 100.0 (/ x y))) (t_1 (* x (/ 100.0 y))))
   (if (<= y -1.45e-40)
     t_1
     (if (<= y -2.5e-88)
       100.0
       (if (<= y -3.5e-108)
         t_0
         (if (<= y 4e-76)
           100.0
           (if (<= y 1.38e-49) t_0 (if (<= y 2.6e+25) 100.0 t_1))))))))

C

double code(double x, double y) {
	double t_0 = 100.0 * (x / y);
	double t_1 = x * (100.0 / y);
	double tmp;
	if (y <= -1.45e-40) {
		tmp = t_1;
	} else if (y <= -2.5e-88) {
		tmp = 100.0;
	} else if (y <= -3.5e-108) {
		tmp = t_0;
	} else if (y <= 4e-76) {
		tmp = 100.0;
	} else if (y <= 1.38e-49) {
		tmp = t_0;
	} else if (y <= 2.6e+25) {
		tmp = 100.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 100.0d0 * (x / y)
    t_1 = x * (100.0d0 / y)
    if (y <= (-1.45d-40)) then
        tmp = t_1
    else if (y <= (-2.5d-88)) then
        tmp = 100.0d0
    else if (y <= (-3.5d-108)) then
        tmp = t_0
    else if (y <= 4d-76) then
        tmp = 100.0d0
    else if (y <= 1.38d-49) then
        tmp = t_0
    else if (y <= 2.6d+25) then
        tmp = 100.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 100.0 * (x / y);
	double t_1 = x * (100.0 / y);
	double tmp;
	if (y <= -1.45e-40) {
		tmp = t_1;
	} else if (y <= -2.5e-88) {
		tmp = 100.0;
	} else if (y <= -3.5e-108) {
		tmp = t_0;
	} else if (y <= 4e-76) {
		tmp = 100.0;
	} else if (y <= 1.38e-49) {
		tmp = t_0;
	} else if (y <= 2.6e+25) {
		tmp = 100.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 100.0 * (x / y)
	t_1 = x * (100.0 / y)
	tmp = 0
	if y <= -1.45e-40:
		tmp = t_1
	elif y <= -2.5e-88:
		tmp = 100.0
	elif y <= -3.5e-108:
		tmp = t_0
	elif y <= 4e-76:
		tmp = 100.0
	elif y <= 1.38e-49:
		tmp = t_0
	elif y <= 2.6e+25:
		tmp = 100.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y)
	t_0 = Float64(100.0 * Float64(x / y))
	t_1 = Float64(x * Float64(100.0 / y))
	tmp = 0.0
	if (y <= -1.45e-40)
		tmp = t_1;
	elseif (y <= -2.5e-88)
		tmp = 100.0;
	elseif (y <= -3.5e-108)
		tmp = t_0;
	elseif (y <= 4e-76)
		tmp = 100.0;
	elseif (y <= 1.38e-49)
		tmp = t_0;
	elseif (y <= 2.6e+25)
		tmp = 100.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 100.0 * (x / y);
	t_1 = x * (100.0 / y);
	tmp = 0.0;
	if (y <= -1.45e-40)
		tmp = t_1;
	elseif (y <= -2.5e-88)
		tmp = 100.0;
	elseif (y <= -3.5e-108)
		tmp = t_0;
	elseif (y <= 4e-76)
		tmp = 100.0;
	elseif (y <= 1.38e-49)
		tmp = t_0;
	elseif (y <= 2.6e+25)
		tmp = 100.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.45e-40], t$95$1, If[LessEqual[y, -2.5e-88], 100.0, If[LessEqual[y, -3.5e-108], t$95$0, If[LessEqual[y, 4e-76], 100.0, If[LessEqual[y, 1.38e-49], t$95$0, If[LessEqual[y, 2.6e+25], 100.0, t$95$1]]]]]]]]

Derivation

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

Alternative 4: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 100 \cdot \frac{x}{y}\\ t_1 := \frac{x}{\frac{y}{100}}\\ \mathbf{if}\;y \leq -1.35 \cdot 10^{-40}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{-88}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq -6.6 \cdot 10^{-108}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.8 \cdot 10^{-75}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{-50}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+25}:\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* 100.0 (/ x y))) (t_1 (/ x (/ y 100.0))))
   (if (<= y -1.35e-40)
     t_1
     (if (<= y -2.1e-88)
       100.0
       (if (<= y -6.6e-108)
         t_0
         (if (<= y 1.8e-75)
           100.0
           (if (<= y 1.5e-50) t_0 (if (<= y 2.2e+25) 100.0 t_1))))))))

C

double code(double x, double y) {
	double t_0 = 100.0 * (x / y);
	double t_1 = x / (y / 100.0);
	double tmp;
	if (y <= -1.35e-40) {
		tmp = t_1;
	} else if (y <= -2.1e-88) {
		tmp = 100.0;
	} else if (y <= -6.6e-108) {
		tmp = t_0;
	} else if (y <= 1.8e-75) {
		tmp = 100.0;
	} else if (y <= 1.5e-50) {
		tmp = t_0;
	} else if (y <= 2.2e+25) {
		tmp = 100.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 100.0d0 * (x / y)
    t_1 = x / (y / 100.0d0)
    if (y <= (-1.35d-40)) then
        tmp = t_1
    else if (y <= (-2.1d-88)) then
        tmp = 100.0d0
    else if (y <= (-6.6d-108)) then
        tmp = t_0
    else if (y <= 1.8d-75) then
        tmp = 100.0d0
    else if (y <= 1.5d-50) then
        tmp = t_0
    else if (y <= 2.2d+25) then
        tmp = 100.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 100.0 * (x / y);
	double t_1 = x / (y / 100.0);
	double tmp;
	if (y <= -1.35e-40) {
		tmp = t_1;
	} else if (y <= -2.1e-88) {
		tmp = 100.0;
	} else if (y <= -6.6e-108) {
		tmp = t_0;
	} else if (y <= 1.8e-75) {
		tmp = 100.0;
	} else if (y <= 1.5e-50) {
		tmp = t_0;
	} else if (y <= 2.2e+25) {
		tmp = 100.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 100.0 * (x / y)
	t_1 = x / (y / 100.0)
	tmp = 0
	if y <= -1.35e-40:
		tmp = t_1
	elif y <= -2.1e-88:
		tmp = 100.0
	elif y <= -6.6e-108:
		tmp = t_0
	elif y <= 1.8e-75:
		tmp = 100.0
	elif y <= 1.5e-50:
		tmp = t_0
	elif y <= 2.2e+25:
		tmp = 100.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y)
	t_0 = Float64(100.0 * Float64(x / y))
	t_1 = Float64(x / Float64(y / 100.0))
	tmp = 0.0
	if (y <= -1.35e-40)
		tmp = t_1;
	elseif (y <= -2.1e-88)
		tmp = 100.0;
	elseif (y <= -6.6e-108)
		tmp = t_0;
	elseif (y <= 1.8e-75)
		tmp = 100.0;
	elseif (y <= 1.5e-50)
		tmp = t_0;
	elseif (y <= 2.2e+25)
		tmp = 100.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 100.0 * (x / y);
	t_1 = x / (y / 100.0);
	tmp = 0.0;
	if (y <= -1.35e-40)
		tmp = t_1;
	elseif (y <= -2.1e-88)
		tmp = 100.0;
	elseif (y <= -6.6e-108)
		tmp = t_0;
	elseif (y <= 1.8e-75)
		tmp = 100.0;
	elseif (y <= 1.5e-50)
		tmp = t_0;
	elseif (y <= 2.2e+25)
		tmp = 100.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x / N[(y / 100.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.35e-40], t$95$1, If[LessEqual[y, -2.1e-88], 100.0, If[LessEqual[y, -6.6e-108], t$95$0, If[LessEqual[y, 1.8e-75], 100.0, If[LessEqual[y, 1.5e-50], t$95$0, If[LessEqual[y, 2.2e+25], 100.0, t$95$1]]]]]]]]

Derivation

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

Alternative 5: 73.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{\frac{y}{100}}\\ \mathbf{if}\;y \leq -1 \cdot 10^{-40}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.6 \cdot 10^{-88}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{-108}:\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{elif}\;y \leq 6.5 \cdot 10^{-76}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-51}:\\ \;\;\;\;\frac{100 \cdot x}{y}\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+25}:\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (/ y 100.0))))
   (if (<= y -1e-40)
     t_0
     (if (<= y -2.6e-88)
       100.0
       (if (<= y -8.5e-108)
         (* 100.0 (/ x y))
         (if (<= y 6.5e-76)
           100.0
           (if (<= y 4.8e-51)
             (/ (* 100.0 x) y)
             (if (<= y 3e+25) 100.0 t_0))))))))

C

double code(double x, double y) {
	double t_0 = x / (y / 100.0);
	double tmp;
	if (y <= -1e-40) {
		tmp = t_0;
	} else if (y <= -2.6e-88) {
		tmp = 100.0;
	} else if (y <= -8.5e-108) {
		tmp = 100.0 * (x / y);
	} else if (y <= 6.5e-76) {
		tmp = 100.0;
	} else if (y <= 4.8e-51) {
		tmp = (100.0 * x) / y;
	} else if (y <= 3e+25) {
		tmp = 100.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x / (y / 100.0d0)
    if (y <= (-1d-40)) then
        tmp = t_0
    else if (y <= (-2.6d-88)) then
        tmp = 100.0d0
    else if (y <= (-8.5d-108)) then
        tmp = 100.0d0 * (x / y)
    else if (y <= 6.5d-76) then
        tmp = 100.0d0
    else if (y <= 4.8d-51) then
        tmp = (100.0d0 * x) / y
    else if (y <= 3d+25) then
        tmp = 100.0d0
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y / 100.0);
	double tmp;
	if (y <= -1e-40) {
		tmp = t_0;
	} else if (y <= -2.6e-88) {
		tmp = 100.0;
	} else if (y <= -8.5e-108) {
		tmp = 100.0 * (x / y);
	} else if (y <= 6.5e-76) {
		tmp = 100.0;
	} else if (y <= 4.8e-51) {
		tmp = (100.0 * x) / y;
	} else if (y <= 3e+25) {
		tmp = 100.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y / 100.0)
	tmp = 0
	if y <= -1e-40:
		tmp = t_0
	elif y <= -2.6e-88:
		tmp = 100.0
	elif y <= -8.5e-108:
		tmp = 100.0 * (x / y)
	elif y <= 6.5e-76:
		tmp = 100.0
	elif y <= 4.8e-51:
		tmp = (100.0 * x) / y
	elif y <= 3e+25:
		tmp = 100.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y / 100.0))
	tmp = 0.0
	if (y <= -1e-40)
		tmp = t_0;
	elseif (y <= -2.6e-88)
		tmp = 100.0;
	elseif (y <= -8.5e-108)
		tmp = Float64(100.0 * Float64(x / y));
	elseif (y <= 6.5e-76)
		tmp = 100.0;
	elseif (y <= 4.8e-51)
		tmp = Float64(Float64(100.0 * x) / y);
	elseif (y <= 3e+25)
		tmp = 100.0;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x / (y / 100.0);
	tmp = 0.0;
	if (y <= -1e-40)
		tmp = t_0;
	elseif (y <= -2.6e-88)
		tmp = 100.0;
	elseif (y <= -8.5e-108)
		tmp = 100.0 * (x / y);
	elseif (y <= 6.5e-76)
		tmp = 100.0;
	elseif (y <= 4.8e-51)
		tmp = (100.0 * x) / y;
	elseif (y <= 3e+25)
		tmp = 100.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y / 100.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1e-40], t$95$0, If[LessEqual[y, -2.6e-88], 100.0, If[LessEqual[y, -8.5e-108], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.5e-76], 100.0, If[LessEqual[y, 4.8e-51], N[(N[(100.0 * x), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, 3e+25], 100.0, t$95$0]]]]]]]

Derivation

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

Alternative 6: 51.7% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ 100 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 100.0)

C

double code(double x, double y) {
	return 100.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 100.0d0
end function

Java

public static double code(double x, double y) {
	return 100.0;
}

Python

def code(x, y):
	return 100.0

Julia

function code(x, y)
	return 100.0
end

MATLAB

function tmp = code(x, y)
	tmp = 100.0;
end

Wolfram

code[x_, y_] := 100.0

Derivation

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

Developer target: 99.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{x}{1} \cdot \frac{100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (/ x 1.0) (/ 100.0 (+ x y))))

C

double code(double x, double y) {
	return (x / 1.0) * (100.0 / (x + y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x / 1.0d0) * (100.0d0 / (x + y))
end function

Java

public static double code(double x, double y) {
	return (x / 1.0) * (100.0 / (x + y));
}

Python

def code(x, y):
	return (x / 1.0) * (100.0 / (x + y))

Julia

function code(x, y)
	return Float64(Float64(x / 1.0) * Float64(100.0 / Float64(x + y)))
end

MATLAB

function tmp = code(x, y)
	tmp = (x / 1.0) * (100.0 / (x + y));
end

Wolfram

code[x_, y_] := N[(N[(x / 1.0), $MachinePrecision] * N[(100.0 / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023350 
(FPCore (x y)
  :name "Development.Shake.Progress:message from shake-0.15.5"
  :precision binary64

  :herbie-target
  (* (/ x 1.0) (/ 100.0 (+ x y)))

  (/ (* x 100.0) (+ x y)))