Numeric.Signal.Multichannel:$cput from hsignal-0.2.7.1

Percentage Accurate: 96.9% → 97.0%

Time: 14.7s

Alternatives: 17

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 96.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 97.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 58.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.4 \cdot 10^{+127}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -1.45 \cdot 10^{+55}:\\ \;\;\;\;\frac{t \cdot \left(-x\right)}{y}\\ \mathbf{elif}\;y \leq -6.5 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-130}:\\ \;\;\;\;\frac{t \cdot x}{z}\\ \mathbf{elif}\;y \leq 2.4 \cdot 10^{-52}:\\ \;\;\;\;\frac{t}{\frac{-z}{y}}\\ \mathbf{elif}\;y \leq 6.5 \cdot 10^{+17}:\\ \;\;\;\;\frac{t}{\frac{z}{x}}\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{+70}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+103}:\\ \;\;\;\;\frac{-t}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -3.4e+127)
   t
   (if (<= y -1.45e+55)
     (/ (* t (- x)) y)
     (if (<= y -6.5e-38)
       t
       (if (<= y 1.7e-130)
         (/ (* t x) z)
         (if (<= y 2.4e-52)
           (/ t (/ (- z) y))
           (if (<= y 6.5e+17)
             (/ t (/ z x))
             (if (<= y 1.7e+70)
               t
               (if (<= y 1.15e+103) (/ (- t) (/ y x)) t)))))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.4e+127) {
		tmp = t;
	} else if (y <= -1.45e+55) {
		tmp = (t * -x) / y;
	} else if (y <= -6.5e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 2.4e-52) {
		tmp = t / (-z / y);
	} else if (y <= 6.5e+17) {
		tmp = t / (z / x);
	} else if (y <= 1.7e+70) {
		tmp = t;
	} else if (y <= 1.15e+103) {
		tmp = -t / (y / x);
	} else {
		tmp = 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 (y <= (-3.4d+127)) then
        tmp = t
    else if (y <= (-1.45d+55)) then
        tmp = (t * -x) / y
    else if (y <= (-6.5d-38)) then
        tmp = t
    else if (y <= 1.7d-130) then
        tmp = (t * x) / z
    else if (y <= 2.4d-52) then
        tmp = t / (-z / y)
    else if (y <= 6.5d+17) then
        tmp = t / (z / x)
    else if (y <= 1.7d+70) then
        tmp = t
    else if (y <= 1.15d+103) then
        tmp = -t / (y / x)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.4e+127) {
		tmp = t;
	} else if (y <= -1.45e+55) {
		tmp = (t * -x) / y;
	} else if (y <= -6.5e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 2.4e-52) {
		tmp = t / (-z / y);
	} else if (y <= 6.5e+17) {
		tmp = t / (z / x);
	} else if (y <= 1.7e+70) {
		tmp = t;
	} else if (y <= 1.15e+103) {
		tmp = -t / (y / x);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -3.4e+127:
		tmp = t
	elif y <= -1.45e+55:
		tmp = (t * -x) / y
	elif y <= -6.5e-38:
		tmp = t
	elif y <= 1.7e-130:
		tmp = (t * x) / z
	elif y <= 2.4e-52:
		tmp = t / (-z / y)
	elif y <= 6.5e+17:
		tmp = t / (z / x)
	elif y <= 1.7e+70:
		tmp = t
	elif y <= 1.15e+103:
		tmp = -t / (y / x)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -3.4e+127)
		tmp = t;
	elseif (y <= -1.45e+55)
		tmp = Float64(Float64(t * Float64(-x)) / y);
	elseif (y <= -6.5e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = Float64(Float64(t * x) / z);
	elseif (y <= 2.4e-52)
		tmp = Float64(t / Float64(Float64(-z) / y));
	elseif (y <= 6.5e+17)
		tmp = Float64(t / Float64(z / x));
	elseif (y <= 1.7e+70)
		tmp = t;
	elseif (y <= 1.15e+103)
		tmp = Float64(Float64(-t) / Float64(y / x));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -3.4e+127)
		tmp = t;
	elseif (y <= -1.45e+55)
		tmp = (t * -x) / y;
	elseif (y <= -6.5e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = (t * x) / z;
	elseif (y <= 2.4e-52)
		tmp = t / (-z / y);
	elseif (y <= 6.5e+17)
		tmp = t / (z / x);
	elseif (y <= 1.7e+70)
		tmp = t;
	elseif (y <= 1.15e+103)
		tmp = -t / (y / x);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -3.4e+127], t, If[LessEqual[y, -1.45e+55], N[(N[(t * (-x)), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, -6.5e-38], t, If[LessEqual[y, 1.7e-130], N[(N[(t * x), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 2.4e-52], N[(t / N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.5e+17], N[(t / N[(z / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.7e+70], t, If[LessEqual[y, 1.15e+103], N[((-t) / N[(y / x), $MachinePrecision]), $MachinePrecision], t]]]]]]]]

Derivation

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

Alternative 3: 67.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.6 \cdot 10^{+127}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -2 \cdot 10^{+101}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z}\\ \mathbf{elif}\;y \leq -6.6 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{+39} \lor \neg \left(y \leq 4.9 \cdot 10^{+69}\right) \land y \leq 2 \cdot 10^{+108}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.6e+127)
   t
   (if (<= y -2e+101)
     (* (- x y) (/ t z))
     (if (<= y -6.6e-38)
       t
       (if (or (<= y 8.2e+39) (and (not (<= y 4.9e+69)) (<= y 2e+108)))
         (* t (/ x (- z y)))
         t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.6e+127) {
		tmp = t;
	} else if (y <= -2e+101) {
		tmp = (x - y) * (t / z);
	} else if (y <= -6.6e-38) {
		tmp = t;
	} else if ((y <= 8.2e+39) || (!(y <= 4.9e+69) && (y <= 2e+108))) {
		tmp = t * (x / (z - y));
	} else {
		tmp = 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 (y <= (-4.6d+127)) then
        tmp = t
    else if (y <= (-2d+101)) then
        tmp = (x - y) * (t / z)
    else if (y <= (-6.6d-38)) then
        tmp = t
    else if ((y <= 8.2d+39) .or. (.not. (y <= 4.9d+69)) .and. (y <= 2d+108)) then
        tmp = t * (x / (z - y))
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.6e+127) {
		tmp = t;
	} else if (y <= -2e+101) {
		tmp = (x - y) * (t / z);
	} else if (y <= -6.6e-38) {
		tmp = t;
	} else if ((y <= 8.2e+39) || (!(y <= 4.9e+69) && (y <= 2e+108))) {
		tmp = t * (x / (z - y));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.6e+127:
		tmp = t
	elif y <= -2e+101:
		tmp = (x - y) * (t / z)
	elif y <= -6.6e-38:
		tmp = t
	elif (y <= 8.2e+39) or (not (y <= 4.9e+69) and (y <= 2e+108)):
		tmp = t * (x / (z - y))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.6e+127)
		tmp = t;
	elseif (y <= -2e+101)
		tmp = Float64(Float64(x - y) * Float64(t / z));
	elseif (y <= -6.6e-38)
		tmp = t;
	elseif ((y <= 8.2e+39) || (!(y <= 4.9e+69) && (y <= 2e+108)))
		tmp = Float64(t * Float64(x / Float64(z - y)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.6e+127)
		tmp = t;
	elseif (y <= -2e+101)
		tmp = (x - y) * (t / z);
	elseif (y <= -6.6e-38)
		tmp = t;
	elseif ((y <= 8.2e+39) || (~((y <= 4.9e+69)) && (y <= 2e+108)))
		tmp = t * (x / (z - y));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.6e+127], t, If[LessEqual[y, -2e+101], N[(N[(x - y), $MachinePrecision] * N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -6.6e-38], t, If[Or[LessEqual[y, 8.2e+39], And[N[Not[LessEqual[y, 4.9e+69]], $MachinePrecision], LessEqual[y, 2e+108]]], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t]]]]

Derivation

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

Alternative 4: 59.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(-\frac{t}{z}\right)\\ \mathbf{if}\;y \leq -1.14 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-130}:\\ \;\;\;\;\frac{t \cdot x}{z}\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{-51}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.35 \cdot 10^{+20}:\\ \;\;\;\;\frac{t}{\frac{z}{x}}\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+87}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.22 \cdot 10^{+103}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- (/ t z)))))
   (if (<= y -1.14e-38)
     t
     (if (<= y 1.7e-130)
       (/ (* t x) z)
       (if (<= y 1.95e-51)
         t_1
         (if (<= y 2.35e+20)
           (/ t (/ z x))
           (if (<= y 7.5e+87) t (if (<= y 1.22e+103) t_1 t))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * -(t / z);
	double tmp;
	if (y <= -1.14e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 1.95e-51) {
		tmp = t_1;
	} else if (y <= 2.35e+20) {
		tmp = t / (z / x);
	} else if (y <= 7.5e+87) {
		tmp = t;
	} else if (y <= 1.22e+103) {
		tmp = t_1;
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = y * -(t / z)
    if (y <= (-1.14d-38)) then
        tmp = t
    else if (y <= 1.7d-130) then
        tmp = (t * x) / z
    else if (y <= 1.95d-51) then
        tmp = t_1
    else if (y <= 2.35d+20) then
        tmp = t / (z / x)
    else if (y <= 7.5d+87) then
        tmp = t
    else if (y <= 1.22d+103) then
        tmp = t_1
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * -(t / z);
	double tmp;
	if (y <= -1.14e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 1.95e-51) {
		tmp = t_1;
	} else if (y <= 2.35e+20) {
		tmp = t / (z / x);
	} else if (y <= 7.5e+87) {
		tmp = t;
	} else if (y <= 1.22e+103) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * -(t / z)
	tmp = 0
	if y <= -1.14e-38:
		tmp = t
	elif y <= 1.7e-130:
		tmp = (t * x) / z
	elif y <= 1.95e-51:
		tmp = t_1
	elif y <= 2.35e+20:
		tmp = t / (z / x)
	elif y <= 7.5e+87:
		tmp = t
	elif y <= 1.22e+103:
		tmp = t_1
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(-Float64(t / z)))
	tmp = 0.0
	if (y <= -1.14e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = Float64(Float64(t * x) / z);
	elseif (y <= 1.95e-51)
		tmp = t_1;
	elseif (y <= 2.35e+20)
		tmp = Float64(t / Float64(z / x));
	elseif (y <= 7.5e+87)
		tmp = t;
	elseif (y <= 1.22e+103)
		tmp = t_1;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * -(t / z);
	tmp = 0.0;
	if (y <= -1.14e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = (t * x) / z;
	elseif (y <= 1.95e-51)
		tmp = t_1;
	elseif (y <= 2.35e+20)
		tmp = t / (z / x);
	elseif (y <= 7.5e+87)
		tmp = t;
	elseif (y <= 1.22e+103)
		tmp = t_1;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * (-N[(t / z), $MachinePrecision])), $MachinePrecision]}, If[LessEqual[y, -1.14e-38], t, If[LessEqual[y, 1.7e-130], N[(N[(t * x), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 1.95e-51], t$95$1, If[LessEqual[y, 2.35e+20], N[(t / N[(z / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.5e+87], t, If[LessEqual[y, 1.22e+103], t$95$1, t]]]]]]]

Derivation

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

Alternative 5: 59.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-130}:\\ \;\;\;\;\frac{t \cdot x}{z}\\ \mathbf{elif}\;y \leq 1.26 \cdot 10^{-49}:\\ \;\;\;\;t \cdot \frac{-y}{z}\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+20}:\\ \;\;\;\;\frac{t}{\frac{z}{x}}\\ \mathbf{elif}\;y \leq 1.8 \cdot 10^{+89}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+103}:\\ \;\;\;\;y \cdot \left(-\frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.6e-38)
   t
   (if (<= y 1.7e-130)
     (/ (* t x) z)
     (if (<= y 1.26e-49)
       (* t (/ (- y) z))
       (if (<= y 1.02e+20)
         (/ t (/ z x))
         (if (<= y 1.8e+89) t (if (<= y 1.35e+103) (* y (- (/ t z))) t)))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 1.26e-49) {
		tmp = t * (-y / z);
	} else if (y <= 1.02e+20) {
		tmp = t / (z / x);
	} else if (y <= 1.8e+89) {
		tmp = t;
	} else if (y <= 1.35e+103) {
		tmp = y * -(t / z);
	} else {
		tmp = 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 (y <= (-6.6d-38)) then
        tmp = t
    else if (y <= 1.7d-130) then
        tmp = (t * x) / z
    else if (y <= 1.26d-49) then
        tmp = t * (-y / z)
    else if (y <= 1.02d+20) then
        tmp = t / (z / x)
    else if (y <= 1.8d+89) then
        tmp = t
    else if (y <= 1.35d+103) then
        tmp = y * -(t / z)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 1.26e-49) {
		tmp = t * (-y / z);
	} else if (y <= 1.02e+20) {
		tmp = t / (z / x);
	} else if (y <= 1.8e+89) {
		tmp = t;
	} else if (y <= 1.35e+103) {
		tmp = y * -(t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -6.6e-38:
		tmp = t
	elif y <= 1.7e-130:
		tmp = (t * x) / z
	elif y <= 1.26e-49:
		tmp = t * (-y / z)
	elif y <= 1.02e+20:
		tmp = t / (z / x)
	elif y <= 1.8e+89:
		tmp = t
	elif y <= 1.35e+103:
		tmp = y * -(t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = Float64(Float64(t * x) / z);
	elseif (y <= 1.26e-49)
		tmp = Float64(t * Float64(Float64(-y) / z));
	elseif (y <= 1.02e+20)
		tmp = Float64(t / Float64(z / x));
	elseif (y <= 1.8e+89)
		tmp = t;
	elseif (y <= 1.35e+103)
		tmp = Float64(y * Float64(-Float64(t / z)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = (t * x) / z;
	elseif (y <= 1.26e-49)
		tmp = t * (-y / z);
	elseif (y <= 1.02e+20)
		tmp = t / (z / x);
	elseif (y <= 1.8e+89)
		tmp = t;
	elseif (y <= 1.35e+103)
		tmp = y * -(t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.6e-38], t, If[LessEqual[y, 1.7e-130], N[(N[(t * x), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 1.26e-49], N[(t * N[((-y) / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.02e+20], N[(t / N[(z / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.8e+89], t, If[LessEqual[y, 1.35e+103], N[(y * (-N[(t / z), $MachinePrecision])), $MachinePrecision], t]]]]]]

Derivation

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

Alternative 6: 59.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.65 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-130}:\\ \;\;\;\;\frac{t \cdot x}{z}\\ \mathbf{elif}\;y \leq 2.45 \cdot 10^{-51}:\\ \;\;\;\;\frac{t}{\frac{-z}{y}}\\ \mathbf{elif}\;y \leq 32500000000000:\\ \;\;\;\;\frac{t}{\frac{z}{x}}\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{+90}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.22 \cdot 10^{+103}:\\ \;\;\;\;y \cdot \left(-\frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -3.65e-38)
   t
   (if (<= y 1.7e-130)
     (/ (* t x) z)
     (if (<= y 2.45e-51)
       (/ t (/ (- z) y))
       (if (<= y 32500000000000.0)
         (/ t (/ z x))
         (if (<= y 2.8e+90) t (if (<= y 1.22e+103) (* y (- (/ t z))) t)))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.65e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 2.45e-51) {
		tmp = t / (-z / y);
	} else if (y <= 32500000000000.0) {
		tmp = t / (z / x);
	} else if (y <= 2.8e+90) {
		tmp = t;
	} else if (y <= 1.22e+103) {
		tmp = y * -(t / z);
	} else {
		tmp = 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 (y <= (-3.65d-38)) then
        tmp = t
    else if (y <= 1.7d-130) then
        tmp = (t * x) / z
    else if (y <= 2.45d-51) then
        tmp = t / (-z / y)
    else if (y <= 32500000000000.0d0) then
        tmp = t / (z / x)
    else if (y <= 2.8d+90) then
        tmp = t
    else if (y <= 1.22d+103) then
        tmp = y * -(t / z)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.65e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 2.45e-51) {
		tmp = t / (-z / y);
	} else if (y <= 32500000000000.0) {
		tmp = t / (z / x);
	} else if (y <= 2.8e+90) {
		tmp = t;
	} else if (y <= 1.22e+103) {
		tmp = y * -(t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -3.65e-38:
		tmp = t
	elif y <= 1.7e-130:
		tmp = (t * x) / z
	elif y <= 2.45e-51:
		tmp = t / (-z / y)
	elif y <= 32500000000000.0:
		tmp = t / (z / x)
	elif y <= 2.8e+90:
		tmp = t
	elif y <= 1.22e+103:
		tmp = y * -(t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -3.65e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = Float64(Float64(t * x) / z);
	elseif (y <= 2.45e-51)
		tmp = Float64(t / Float64(Float64(-z) / y));
	elseif (y <= 32500000000000.0)
		tmp = Float64(t / Float64(z / x));
	elseif (y <= 2.8e+90)
		tmp = t;
	elseif (y <= 1.22e+103)
		tmp = Float64(y * Float64(-Float64(t / z)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -3.65e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = (t * x) / z;
	elseif (y <= 2.45e-51)
		tmp = t / (-z / y);
	elseif (y <= 32500000000000.0)
		tmp = t / (z / x);
	elseif (y <= 2.8e+90)
		tmp = t;
	elseif (y <= 1.22e+103)
		tmp = y * -(t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -3.65e-38], t, If[LessEqual[y, 1.7e-130], N[(N[(t * x), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 2.45e-51], N[(t / N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 32500000000000.0], N[(t / N[(z / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.8e+90], t, If[LessEqual[y, 1.22e+103], N[(y * (-N[(t / z), $MachinePrecision])), $MachinePrecision], t]]]]]]

Derivation

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

Alternative 7: 59.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-130}:\\ \;\;\;\;\frac{t \cdot x}{z}\\ \mathbf{elif}\;y \leq 2.75 \cdot 10^{-52}:\\ \;\;\;\;\frac{t}{\frac{-z}{y}}\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{+16}:\\ \;\;\;\;\frac{t}{\frac{z}{x}}\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{+69}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+103}:\\ \;\;\;\;\frac{-t}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.6e-38)
   t
   (if (<= y 1.7e-130)
     (/ (* t x) z)
     (if (<= y 2.75e-52)
       (/ t (/ (- z) y))
       (if (<= y 1.25e+16)
         (/ t (/ z x))
         (if (<= y 7.2e+69) t (if (<= y 1.15e+103) (/ (- t) (/ y x)) t)))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 2.75e-52) {
		tmp = t / (-z / y);
	} else if (y <= 1.25e+16) {
		tmp = t / (z / x);
	} else if (y <= 7.2e+69) {
		tmp = t;
	} else if (y <= 1.15e+103) {
		tmp = -t / (y / x);
	} else {
		tmp = 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 (y <= (-6.6d-38)) then
        tmp = t
    else if (y <= 1.7d-130) then
        tmp = (t * x) / z
    else if (y <= 2.75d-52) then
        tmp = t / (-z / y)
    else if (y <= 1.25d+16) then
        tmp = t / (z / x)
    else if (y <= 7.2d+69) then
        tmp = t
    else if (y <= 1.15d+103) then
        tmp = -t / (y / x)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 1.7e-130) {
		tmp = (t * x) / z;
	} else if (y <= 2.75e-52) {
		tmp = t / (-z / y);
	} else if (y <= 1.25e+16) {
		tmp = t / (z / x);
	} else if (y <= 7.2e+69) {
		tmp = t;
	} else if (y <= 1.15e+103) {
		tmp = -t / (y / x);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -6.6e-38:
		tmp = t
	elif y <= 1.7e-130:
		tmp = (t * x) / z
	elif y <= 2.75e-52:
		tmp = t / (-z / y)
	elif y <= 1.25e+16:
		tmp = t / (z / x)
	elif y <= 7.2e+69:
		tmp = t
	elif y <= 1.15e+103:
		tmp = -t / (y / x)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = Float64(Float64(t * x) / z);
	elseif (y <= 2.75e-52)
		tmp = Float64(t / Float64(Float64(-z) / y));
	elseif (y <= 1.25e+16)
		tmp = Float64(t / Float64(z / x));
	elseif (y <= 7.2e+69)
		tmp = t;
	elseif (y <= 1.15e+103)
		tmp = Float64(Float64(-t) / Float64(y / x));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 1.7e-130)
		tmp = (t * x) / z;
	elseif (y <= 2.75e-52)
		tmp = t / (-z / y);
	elseif (y <= 1.25e+16)
		tmp = t / (z / x);
	elseif (y <= 7.2e+69)
		tmp = t;
	elseif (y <= 1.15e+103)
		tmp = -t / (y / x);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.6e-38], t, If[LessEqual[y, 1.7e-130], N[(N[(t * x), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 2.75e-52], N[(t / N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.25e+16], N[(t / N[(z / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.2e+69], t, If[LessEqual[y, 1.15e+103], N[((-t) / N[(y / x), $MachinePrecision]), $MachinePrecision], t]]]]]]

Derivation

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

Alternative 8: 66.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.3 \cdot 10^{+122}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 8 \cdot 10^{+32}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z}\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+70}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+103}:\\ \;\;\;\;\frac{-t}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.3e+122)
   t
   (if (<= y 8e+32)
     (* (- x y) (/ t z))
     (if (<= y 1.55e+70) t (if (<= y 1.15e+103) (/ (- t) (/ y x)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.3e+122) {
		tmp = t;
	} else if (y <= 8e+32) {
		tmp = (x - y) * (t / z);
	} else if (y <= 1.55e+70) {
		tmp = t;
	} else if (y <= 1.15e+103) {
		tmp = -t / (y / x);
	} else {
		tmp = 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 (y <= (-4.3d+122)) then
        tmp = t
    else if (y <= 8d+32) then
        tmp = (x - y) * (t / z)
    else if (y <= 1.55d+70) then
        tmp = t
    else if (y <= 1.15d+103) then
        tmp = -t / (y / x)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.3e+122) {
		tmp = t;
	} else if (y <= 8e+32) {
		tmp = (x - y) * (t / z);
	} else if (y <= 1.55e+70) {
		tmp = t;
	} else if (y <= 1.15e+103) {
		tmp = -t / (y / x);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.3e+122:
		tmp = t
	elif y <= 8e+32:
		tmp = (x - y) * (t / z)
	elif y <= 1.55e+70:
		tmp = t
	elif y <= 1.15e+103:
		tmp = -t / (y / x)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.3e+122)
		tmp = t;
	elseif (y <= 8e+32)
		tmp = Float64(Float64(x - y) * Float64(t / z));
	elseif (y <= 1.55e+70)
		tmp = t;
	elseif (y <= 1.15e+103)
		tmp = Float64(Float64(-t) / Float64(y / x));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.3e+122)
		tmp = t;
	elseif (y <= 8e+32)
		tmp = (x - y) * (t / z);
	elseif (y <= 1.55e+70)
		tmp = t;
	elseif (y <= 1.15e+103)
		tmp = -t / (y / x);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.3e+122], t, If[LessEqual[y, 8e+32], N[(N[(x - y), $MachinePrecision] * N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.55e+70], t, If[LessEqual[y, 1.15e+103], N[((-t) / N[(y / x), $MachinePrecision]), $MachinePrecision], t]]]]

Derivation

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

Alternative 9: 89.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{+211}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+212}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.9e+211)
   (/ t (- 1.0 (/ z y)))
   (if (<= y 7.5e+212) (* (- x y) (/ t (- z y))) (- t (/ t (/ y x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.9e+211) {
		tmp = t / (1.0 - (z / y));
	} else if (y <= 7.5e+212) {
		tmp = (x - y) * (t / (z - y));
	} else {
		tmp = t - (t / (y / x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-4.9d+211)) then
        tmp = t / (1.0d0 - (z / y))
    else if (y <= 7.5d+212) then
        tmp = (x - y) * (t / (z - y))
    else
        tmp = t - (t / (y / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.9e+211) {
		tmp = t / (1.0 - (z / y));
	} else if (y <= 7.5e+212) {
		tmp = (x - y) * (t / (z - y));
	} else {
		tmp = t - (t / (y / x));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.9e+211:
		tmp = t / (1.0 - (z / y))
	elif y <= 7.5e+212:
		tmp = (x - y) * (t / (z - y))
	else:
		tmp = t - (t / (y / x))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.9e+211)
		tmp = Float64(t / Float64(1.0 - Float64(z / y)));
	elseif (y <= 7.5e+212)
		tmp = Float64(Float64(x - y) * Float64(t / Float64(z - y)));
	else
		tmp = Float64(t - Float64(t / Float64(y / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.9e+211)
		tmp = t / (1.0 - (z / y));
	elseif (y <= 7.5e+212)
		tmp = (x - y) * (t / (z - y));
	else
		tmp = t - (t / (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.9e+211], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.5e+212], N[(N[(x - y), $MachinePrecision] * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t - N[(t / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 10: 71.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{-32} \lor \neg \left(z \leq 10^{+55}\right):\\ \;\;\;\;\left(x - y\right) \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.9e-32) (not (<= z 1e+55)))
   (* (- x y) (/ t z))
   (- t (/ t (/ y x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.9e-32) || !(z <= 1e+55)) {
		tmp = (x - y) * (t / z);
	} else {
		tmp = t - (t / (y / x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-1.9d-32)) .or. (.not. (z <= 1d+55))) then
        tmp = (x - y) * (t / z)
    else
        tmp = t - (t / (y / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.9e-32) || !(z <= 1e+55)) {
		tmp = (x - y) * (t / z);
	} else {
		tmp = t - (t / (y / x));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.9e-32) or not (z <= 1e+55):
		tmp = (x - y) * (t / z)
	else:
		tmp = t - (t / (y / x))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.9e-32) || !(z <= 1e+55))
		tmp = Float64(Float64(x - y) * Float64(t / z));
	else
		tmp = Float64(t - Float64(t / Float64(y / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.9e-32) || ~((z <= 1e+55)))
		tmp = (x - y) * (t / z);
	else
		tmp = t - (t / (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.9e-32], N[Not[LessEqual[z, 1e+55]], $MachinePrecision]], N[(N[(x - y), $MachinePrecision] * N[(t / z), $MachinePrecision]), $MachinePrecision], N[(t - N[(t / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 74.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{+93} \lor \neg \left(x \leq 5.5 \cdot 10^{+60}\right):\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -4.2e+93) (not (<= x 5.5e+60)))
   (* t (/ x (- z y)))
   (/ t (- 1.0 (/ z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4.2e+93) || !(x <= 5.5e+60)) {
		tmp = t * (x / (z - y));
	} else {
		tmp = t / (1.0 - (z / y));
	}
	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 <= (-4.2d+93)) .or. (.not. (x <= 5.5d+60))) then
        tmp = t * (x / (z - y))
    else
        tmp = t / (1.0d0 - (z / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -4.2e+93) || !(x <= 5.5e+60)) {
		tmp = t * (x / (z - y));
	} else {
		tmp = t / (1.0 - (z / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -4.2e+93) or not (x <= 5.5e+60):
		tmp = t * (x / (z - y))
	else:
		tmp = t / (1.0 - (z / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -4.2e+93) || !(x <= 5.5e+60))
		tmp = Float64(t * Float64(x / Float64(z - y)));
	else
		tmp = Float64(t / Float64(1.0 - Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -4.2e+93) || ~((x <= 5.5e+60)))
		tmp = t * (x / (z - y));
	else
		tmp = t / (1.0 - (z / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -4.2e+93], N[Not[LessEqual[x, 5.5e+60]], $MachinePrecision]], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 74.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+93}:\\ \;\;\;\;\frac{t}{\frac{z - y}{x}}\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{+61}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -4e+93)
   (/ t (/ (- z y) x))
   (if (<= x 4.8e+61) (/ t (- 1.0 (/ z y))) (* t (/ x (- z y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -4e+93) {
		tmp = t / ((z - y) / x);
	} else if (x <= 4.8e+61) {
		tmp = t / (1.0 - (z / y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-4d+93)) then
        tmp = t / ((z - y) / x)
    else if (x <= 4.8d+61) then
        tmp = t / (1.0d0 - (z / y))
    else
        tmp = t * (x / (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -4e+93) {
		tmp = t / ((z - y) / x);
	} else if (x <= 4.8e+61) {
		tmp = t / (1.0 - (z / y));
	} else {
		tmp = t * (x / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -4e+93:
		tmp = t / ((z - y) / x)
	elif x <= 4.8e+61:
		tmp = t / (1.0 - (z / y))
	else:
		tmp = t * (x / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -4e+93)
		tmp = Float64(t / Float64(Float64(z - y) / x));
	elseif (x <= 4.8e+61)
		tmp = Float64(t / Float64(1.0 - Float64(z / y)));
	else
		tmp = Float64(t * Float64(x / Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -4e+93)
		tmp = t / ((z - y) / x);
	elseif (x <= 4.8e+61)
		tmp = t / (1.0 - (z / y));
	else
		tmp = t * (x / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -4e+93], N[(t / N[(N[(z - y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.8e+61], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 13: 37.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2 \cdot 10^{+268} \lor \neg \left(z \leq 5.1 \cdot 10^{+213}\right):\\ \;\;\;\;t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -2e+268) (not (<= z 5.1e+213))) (* t (/ y z)) t))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2e+268) || !(z <= 5.1e+213)) {
		tmp = t * (y / z);
	} else {
		tmp = 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 ((z <= (-2d+268)) .or. (.not. (z <= 5.1d+213))) then
        tmp = t * (y / z)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2e+268) || !(z <= 5.1e+213)) {
		tmp = t * (y / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -2e+268) or not (z <= 5.1e+213):
		tmp = t * (y / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -2e+268) || !(z <= 5.1e+213))
		tmp = Float64(t * Float64(y / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -2e+268) || ~((z <= 5.1e+213)))
		tmp = t * (y / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -2e+268], N[Not[LessEqual[z, 5.1e+213]], $MachinePrecision]], N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision], t]

Derivation

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

Alternative 14: 60.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 3600000000000:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.6e-38) t (if (<= y 3600000000000.0) (* x (/ t z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 3600000000000.0) {
		tmp = x * (t / z);
	} else {
		tmp = 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 (y <= (-6.6d-38)) then
        tmp = t
    else if (y <= 3600000000000.0d0) then
        tmp = x * (t / z)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 3600000000000.0) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -6.6e-38:
		tmp = t
	elif y <= 3600000000000.0:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 3600000000000.0)
		tmp = Float64(x * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 3600000000000.0)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.6e-38], t, If[LessEqual[y, 3600000000000.0], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

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

Alternative 15: 61.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{-38}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 10^{+22}:\\ \;\;\;\;\frac{t}{\frac{z}{x}}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.6e-38) t (if (<= y 1e+22) (/ t (/ z x)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 1e+22) {
		tmp = t / (z / x);
	} else {
		tmp = 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 (y <= (-6.6d-38)) then
        tmp = t
    else if (y <= 1d+22) then
        tmp = t / (z / x)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e-38) {
		tmp = t;
	} else if (y <= 1e+22) {
		tmp = t / (z / x);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -6.6e-38:
		tmp = t
	elif y <= 1e+22:
		tmp = t / (z / x)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 1e+22)
		tmp = Float64(t / Float64(z / x));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -6.6e-38)
		tmp = t;
	elseif (y <= 1e+22)
		tmp = t / (z / x);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.6e-38], t, If[LessEqual[y, 1e+22], N[(t / N[(z / x), $MachinePrecision]), $MachinePrecision], t]]

Derivation

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

Alternative 16: 96.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 17: 34.8% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z, t)
	return t
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := t

Derivation

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

Developer target: 97.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024006 
(FPCore (x y z t)
  :name "Numeric.Signal.Multichannel:$cput from hsignal-0.2.7.1"
  :precision binary64

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

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