Development.Shake.Progress:message from shake-0.15.5

Percentage Accurate: 99.4% → 99.7%

Time: 4.2s

Alternatives: 5

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: 99.4% 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

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

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(x * Float64(100.0 / Float64(x + y)))
end

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 71.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.5 \cdot 10^{+47} \lor \neg \left(y \leq -4.8 \cdot 10^{-56}\right) \land \left(y \leq -3.2 \cdot 10^{-106} \lor \neg \left(y \leq 2.3 \cdot 10^{+121}\right)\right):\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -4.5e+47)
         (and (not (<= y -4.8e-56))
              (or (<= y -3.2e-106) (not (<= y 2.3e+121)))))
   (* 100.0 (/ x y))
   100.0))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -4.5e+47) || (!(y <= -4.8e-56) && ((y <= -3.2e-106) || !(y <= 2.3e+121)))) {
		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 <= (-4.5d+47)) .or. (.not. (y <= (-4.8d-56))) .and. (y <= (-3.2d-106)) .or. (.not. (y <= 2.3d+121))) 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 <= -4.5e+47) || (!(y <= -4.8e-56) && ((y <= -3.2e-106) || !(y <= 2.3e+121)))) {
		tmp = 100.0 * (x / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -4.5e+47) or (not (y <= -4.8e-56) and ((y <= -3.2e-106) or not (y <= 2.3e+121))):
		tmp = 100.0 * (x / y)
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -4.5e+47) || (!(y <= -4.8e-56) && ((y <= -3.2e-106) || !(y <= 2.3e+121))))
		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 <= -4.5e+47) || (~((y <= -4.8e-56)) && ((y <= -3.2e-106) || ~((y <= 2.3e+121)))))
		tmp = 100.0 * (x / y);
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -4.5e+47], And[N[Not[LessEqual[y, -4.8e-56]], $MachinePrecision], Or[LessEqual[y, -3.2e-106], N[Not[LessEqual[y, 2.3e+121]], $MachinePrecision]]]], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], 100.0]

Derivation

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

Alternative 3: 71.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \frac{100}{y}\\ \mathbf{if}\;y \leq -1.85 \cdot 10^{+46}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -4.8 \cdot 10^{-56}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq -3.2 \cdot 10^{-106}:\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+121}:\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (/ 100.0 y))))
   (if (<= y -1.85e+46)
     t_0
     (if (<= y -4.8e-56)
       100.0
       (if (<= y -3.2e-106)
         (* 100.0 (/ x y))
         (if (<= y 1.85e+121) 100.0 t_0))))))

C

double code(double x, double y) {
	double t_0 = x * (100.0 / y);
	double tmp;
	if (y <= -1.85e+46) {
		tmp = t_0;
	} else if (y <= -4.8e-56) {
		tmp = 100.0;
	} else if (y <= -3.2e-106) {
		tmp = 100.0 * (x / y);
	} else if (y <= 1.85e+121) {
		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 * (100.0d0 / y)
    if (y <= (-1.85d+46)) then
        tmp = t_0
    else if (y <= (-4.8d-56)) then
        tmp = 100.0d0
    else if (y <= (-3.2d-106)) then
        tmp = 100.0d0 * (x / y)
    else if (y <= 1.85d+121) 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 * (100.0 / y);
	double tmp;
	if (y <= -1.85e+46) {
		tmp = t_0;
	} else if (y <= -4.8e-56) {
		tmp = 100.0;
	} else if (y <= -3.2e-106) {
		tmp = 100.0 * (x / y);
	} else if (y <= 1.85e+121) {
		tmp = 100.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x * (100.0 / y)
	tmp = 0
	if y <= -1.85e+46:
		tmp = t_0
	elif y <= -4.8e-56:
		tmp = 100.0
	elif y <= -3.2e-106:
		tmp = 100.0 * (x / y)
	elif y <= 1.85e+121:
		tmp = 100.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x * Float64(100.0 / y))
	tmp = 0.0
	if (y <= -1.85e+46)
		tmp = t_0;
	elseif (y <= -4.8e-56)
		tmp = 100.0;
	elseif (y <= -3.2e-106)
		tmp = Float64(100.0 * Float64(x / y));
	elseif (y <= 1.85e+121)
		tmp = 100.0;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x * (100.0 / y);
	tmp = 0.0;
	if (y <= -1.85e+46)
		tmp = t_0;
	elseif (y <= -4.8e-56)
		tmp = 100.0;
	elseif (y <= -3.2e-106)
		tmp = 100.0 * (x / y);
	elseif (y <= 1.85e+121)
		tmp = 100.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.85e+46], t$95$0, If[LessEqual[y, -4.8e-56], 100.0, If[LessEqual[y, -3.2e-106], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.85e+121], 100.0, t$95$0]]]]]

Derivation

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

Alternative 4: 71.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{\frac{y}{100}}\\ \mathbf{if}\;y \leq -7.2 \cdot 10^{+45}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -5.2 \cdot 10^{-56}:\\ \;\;\;\;100\\ \mathbf{elif}\;y \leq -1.9 \cdot 10^{-106}:\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{elif}\;y \leq 9.6 \cdot 10^{+122}:\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (/ y 100.0))))
   (if (<= y -7.2e+45)
     t_0
     (if (<= y -5.2e-56)
       100.0
       (if (<= y -1.9e-106)
         (* 100.0 (/ x y))
         (if (<= y 9.6e+122) 100.0 t_0))))))

C

double code(double x, double y) {
	double t_0 = x / (y / 100.0);
	double tmp;
	if (y <= -7.2e+45) {
		tmp = t_0;
	} else if (y <= -5.2e-56) {
		tmp = 100.0;
	} else if (y <= -1.9e-106) {
		tmp = 100.0 * (x / y);
	} else if (y <= 9.6e+122) {
		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 <= (-7.2d+45)) then
        tmp = t_0
    else if (y <= (-5.2d-56)) then
        tmp = 100.0d0
    else if (y <= (-1.9d-106)) then
        tmp = 100.0d0 * (x / y)
    else if (y <= 9.6d+122) 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 <= -7.2e+45) {
		tmp = t_0;
	} else if (y <= -5.2e-56) {
		tmp = 100.0;
	} else if (y <= -1.9e-106) {
		tmp = 100.0 * (x / y);
	} else if (y <= 9.6e+122) {
		tmp = 100.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y / 100.0)
	tmp = 0
	if y <= -7.2e+45:
		tmp = t_0
	elif y <= -5.2e-56:
		tmp = 100.0
	elif y <= -1.9e-106:
		tmp = 100.0 * (x / y)
	elif y <= 9.6e+122:
		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 <= -7.2e+45)
		tmp = t_0;
	elseif (y <= -5.2e-56)
		tmp = 100.0;
	elseif (y <= -1.9e-106)
		tmp = Float64(100.0 * Float64(x / y));
	elseif (y <= 9.6e+122)
		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 <= -7.2e+45)
		tmp = t_0;
	elseif (y <= -5.2e-56)
		tmp = 100.0;
	elseif (y <= -1.9e-106)
		tmp = 100.0 * (x / y);
	elseif (y <= 9.6e+122)
		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, -7.2e+45], t$95$0, If[LessEqual[y, -5.2e-56], 100.0, If[LessEqual[y, -1.9e-106], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 9.6e+122], 100.0, t$95$0]]]]]

Derivation

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

Alternative 5: 51.8% 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 2023343 
(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)))