Numeric.Signal:interpolate from hsignal-0.2.7.1

Percentage Accurate: 80.3% → 95.4%

Time: 25.2s

Alternatives: 23

Speedup: 0.3×

Specification

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = x + ((y - z) * ((t - x) / (a - z)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 80.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = x + ((y - z) * ((t - x) / (a - z)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 95.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \left(y - z\right) \cdot \frac{x - t}{a - z}\\ \mathbf{if}\;t_1 \leq -1 \cdot 10^{-305} \lor \neg \left(t_1 \leq 0\right):\\ \;\;\;\;x + \left(t - x\right) \cdot \frac{y - z}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* (- y z) (/ (- x t) (- a z))))))
   (if (or (<= t_1 -1e-305) (not (<= t_1 0.0)))
     (+ x (* (- t x) (/ (- y z) (- a z))))
     (+ t (/ (- x t) (/ z (- y a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y - z) * ((x - t) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-305) || !(t_1 <= 0.0)) {
		tmp = x + ((t - x) * ((y - z) / (a - z)));
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x - ((y - z) * ((x - t) / (a - z)))
    if ((t_1 <= (-1d-305)) .or. (.not. (t_1 <= 0.0d0))) then
        tmp = x + ((t - x) * ((y - z) / (a - z)))
    else
        tmp = t + ((x - t) / (z / (y - a)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y - z) * ((x - t) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-305) || !(t_1 <= 0.0)) {
		tmp = x + ((t - x) * ((y - z) / (a - z)));
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - ((y - z) * ((x - t) / (a - z)))
	tmp = 0
	if (t_1 <= -1e-305) or not (t_1 <= 0.0):
		tmp = x + ((t - x) * ((y - z) / (a - z)))
	else:
		tmp = t + ((x - t) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(Float64(y - z) * Float64(Float64(x - t) / Float64(a - z))))
	tmp = 0.0
	if ((t_1 <= -1e-305) || !(t_1 <= 0.0))
		tmp = Float64(x + Float64(Float64(t - x) * Float64(Float64(y - z) / Float64(a - z))));
	else
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / Float64(y - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - ((y - z) * ((x - t) / (a - z)));
	tmp = 0.0;
	if ((t_1 <= -1e-305) || ~((t_1 <= 0.0)))
		tmp = x + ((t - x) * ((y - z) / (a - z)));
	else
		tmp = t + ((x - t) / (z / (y - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(N[(y - z), $MachinePrecision] * N[(N[(x - t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e-305], N[Not[LessEqual[t$95$1, 0.0]], $MachinePrecision]], N[(x + N[(N[(t - x), $MachinePrecision] * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 91.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \left(y - z\right) \cdot \frac{x - t}{a - z}\\ \mathbf{if}\;t_1 \leq -1 \cdot 10^{-305} \lor \neg \left(t_1 \leq 0\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* (- y z) (/ (- x t) (- a z))))))
   (if (or (<= t_1 -1e-305) (not (<= t_1 0.0)))
     t_1
     (+ t (/ (- x t) (/ z (- y a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y - z) * ((x - t) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-305) || !(t_1 <= 0.0)) {
		tmp = t_1;
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x - ((y - z) * ((x - t) / (a - z)))
    if ((t_1 <= (-1d-305)) .or. (.not. (t_1 <= 0.0d0))) then
        tmp = t_1
    else
        tmp = t + ((x - t) / (z / (y - a)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y - z) * ((x - t) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-305) || !(t_1 <= 0.0)) {
		tmp = t_1;
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - ((y - z) * ((x - t) / (a - z)))
	tmp = 0
	if (t_1 <= -1e-305) or not (t_1 <= 0.0):
		tmp = t_1
	else:
		tmp = t + ((x - t) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(Float64(y - z) * Float64(Float64(x - t) / Float64(a - z))))
	tmp = 0.0
	if ((t_1 <= -1e-305) || !(t_1 <= 0.0))
		tmp = t_1;
	else
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / Float64(y - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - ((y - z) * ((x - t) / (a - z)));
	tmp = 0.0;
	if ((t_1 <= -1e-305) || ~((t_1 <= 0.0)))
		tmp = t_1;
	else
		tmp = t + ((x - t) / (z / (y - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(N[(y - z), $MachinePrecision] * N[(N[(x - t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e-305], N[Not[LessEqual[t$95$1, 0.0]], $MachinePrecision]], t$95$1, N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 3: 37.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{t}{a}\\ t_2 := x \cdot \frac{y}{z}\\ \mathbf{if}\;z \leq -2.1 \cdot 10^{+94}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.9 \cdot 10^{+30}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.46 \cdot 10^{-33}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -7.4 \cdot 10^{-62}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.28 \cdot 10^{-82}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-303}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.65 \cdot 10^{-219}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 8 \cdot 10^{-102}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-74}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ t a))) (t_2 (* x (/ y z))))
   (if (<= z -2.1e+94)
     t
     (if (<= z -1.9e+30)
       t_2
       (if (<= z -1.46e-33)
         t
         (if (<= z -7.4e-62)
           t_1
           (if (<= z -1.28e-82)
             t
             (if (<= z -4.2e-303)
               x
               (if (<= z 2.65e-219)
                 t_1
                 (if (<= z 8e-102)
                   x
                   (if (<= z 2e-74) t_1 (if (<= z 1.45e+60) t_2 t))))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (t / a);
	double t_2 = x * (y / z);
	double tmp;
	if (z <= -2.1e+94) {
		tmp = t;
	} else if (z <= -1.9e+30) {
		tmp = t_2;
	} else if (z <= -1.46e-33) {
		tmp = t;
	} else if (z <= -7.4e-62) {
		tmp = t_1;
	} else if (z <= -1.28e-82) {
		tmp = t;
	} else if (z <= -4.2e-303) {
		tmp = x;
	} else if (z <= 2.65e-219) {
		tmp = t_1;
	} else if (z <= 8e-102) {
		tmp = x;
	} else if (z <= 2e-74) {
		tmp = t_1;
	} else if (z <= 1.45e+60) {
		tmp = t_2;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = y * (t / a)
    t_2 = x * (y / z)
    if (z <= (-2.1d+94)) then
        tmp = t
    else if (z <= (-1.9d+30)) then
        tmp = t_2
    else if (z <= (-1.46d-33)) then
        tmp = t
    else if (z <= (-7.4d-62)) then
        tmp = t_1
    else if (z <= (-1.28d-82)) then
        tmp = t
    else if (z <= (-4.2d-303)) then
        tmp = x
    else if (z <= 2.65d-219) then
        tmp = t_1
    else if (z <= 8d-102) then
        tmp = x
    else if (z <= 2d-74) then
        tmp = t_1
    else if (z <= 1.45d+60) then
        tmp = t_2
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (t / a);
	double t_2 = x * (y / z);
	double tmp;
	if (z <= -2.1e+94) {
		tmp = t;
	} else if (z <= -1.9e+30) {
		tmp = t_2;
	} else if (z <= -1.46e-33) {
		tmp = t;
	} else if (z <= -7.4e-62) {
		tmp = t_1;
	} else if (z <= -1.28e-82) {
		tmp = t;
	} else if (z <= -4.2e-303) {
		tmp = x;
	} else if (z <= 2.65e-219) {
		tmp = t_1;
	} else if (z <= 8e-102) {
		tmp = x;
	} else if (z <= 2e-74) {
		tmp = t_1;
	} else if (z <= 1.45e+60) {
		tmp = t_2;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (t / a)
	t_2 = x * (y / z)
	tmp = 0
	if z <= -2.1e+94:
		tmp = t
	elif z <= -1.9e+30:
		tmp = t_2
	elif z <= -1.46e-33:
		tmp = t
	elif z <= -7.4e-62:
		tmp = t_1
	elif z <= -1.28e-82:
		tmp = t
	elif z <= -4.2e-303:
		tmp = x
	elif z <= 2.65e-219:
		tmp = t_1
	elif z <= 8e-102:
		tmp = x
	elif z <= 2e-74:
		tmp = t_1
	elif z <= 1.45e+60:
		tmp = t_2
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(t / a))
	t_2 = Float64(x * Float64(y / z))
	tmp = 0.0
	if (z <= -2.1e+94)
		tmp = t;
	elseif (z <= -1.9e+30)
		tmp = t_2;
	elseif (z <= -1.46e-33)
		tmp = t;
	elseif (z <= -7.4e-62)
		tmp = t_1;
	elseif (z <= -1.28e-82)
		tmp = t;
	elseif (z <= -4.2e-303)
		tmp = x;
	elseif (z <= 2.65e-219)
		tmp = t_1;
	elseif (z <= 8e-102)
		tmp = x;
	elseif (z <= 2e-74)
		tmp = t_1;
	elseif (z <= 1.45e+60)
		tmp = t_2;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (t / a);
	t_2 = x * (y / z);
	tmp = 0.0;
	if (z <= -2.1e+94)
		tmp = t;
	elseif (z <= -1.9e+30)
		tmp = t_2;
	elseif (z <= -1.46e-33)
		tmp = t;
	elseif (z <= -7.4e-62)
		tmp = t_1;
	elseif (z <= -1.28e-82)
		tmp = t;
	elseif (z <= -4.2e-303)
		tmp = x;
	elseif (z <= 2.65e-219)
		tmp = t_1;
	elseif (z <= 8e-102)
		tmp = x;
	elseif (z <= 2e-74)
		tmp = t_1;
	elseif (z <= 1.45e+60)
		tmp = t_2;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.1e+94], t, If[LessEqual[z, -1.9e+30], t$95$2, If[LessEqual[z, -1.46e-33], t, If[LessEqual[z, -7.4e-62], t$95$1, If[LessEqual[z, -1.28e-82], t, If[LessEqual[z, -4.2e-303], x, If[LessEqual[z, 2.65e-219], t$95$1, If[LessEqual[z, 8e-102], x, If[LessEqual[z, 2e-74], t$95$1, If[LessEqual[z, 1.45e+60], t$95$2, t]]]]]]]]]]]]

Derivation

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

Alternative 4: 37.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{\frac{a}{y}}\\ t_2 := x \cdot \frac{y}{z}\\ \mathbf{if}\;z \leq -2.6 \cdot 10^{+92}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -2.25 \cdot 10^{+32}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{-34}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{-105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-302}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.5 \cdot 10^{-217}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{-96}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3.9 \cdot 10^{-75}:\\ \;\;\;\;y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ t (/ a y))) (t_2 (* x (/ y z))))
   (if (<= z -2.6e+92)
     t
     (if (<= z -2.25e+32)
       t_2
       (if (<= z -4.8e-34)
         t
         (if (<= z -1.65e-105)
           t_1
           (if (<= z -3.8e-302)
             x
             (if (<= z 1.5e-217)
               t_1
               (if (<= z 1.9e-96)
                 x
                 (if (<= z 3.9e-75)
                   (* y (/ t a))
                   (if (<= z 1.95e+60) t_2 t)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (a / y);
	double t_2 = x * (y / z);
	double tmp;
	if (z <= -2.6e+92) {
		tmp = t;
	} else if (z <= -2.25e+32) {
		tmp = t_2;
	} else if (z <= -4.8e-34) {
		tmp = t;
	} else if (z <= -1.65e-105) {
		tmp = t_1;
	} else if (z <= -3.8e-302) {
		tmp = x;
	} else if (z <= 1.5e-217) {
		tmp = t_1;
	} else if (z <= 1.9e-96) {
		tmp = x;
	} else if (z <= 3.9e-75) {
		tmp = y * (t / a);
	} else if (z <= 1.95e+60) {
		tmp = t_2;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t / (a / y)
    t_2 = x * (y / z)
    if (z <= (-2.6d+92)) then
        tmp = t
    else if (z <= (-2.25d+32)) then
        tmp = t_2
    else if (z <= (-4.8d-34)) then
        tmp = t
    else if (z <= (-1.65d-105)) then
        tmp = t_1
    else if (z <= (-3.8d-302)) then
        tmp = x
    else if (z <= 1.5d-217) then
        tmp = t_1
    else if (z <= 1.9d-96) then
        tmp = x
    else if (z <= 3.9d-75) then
        tmp = y * (t / a)
    else if (z <= 1.95d+60) then
        tmp = t_2
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (a / y);
	double t_2 = x * (y / z);
	double tmp;
	if (z <= -2.6e+92) {
		tmp = t;
	} else if (z <= -2.25e+32) {
		tmp = t_2;
	} else if (z <= -4.8e-34) {
		tmp = t;
	} else if (z <= -1.65e-105) {
		tmp = t_1;
	} else if (z <= -3.8e-302) {
		tmp = x;
	} else if (z <= 1.5e-217) {
		tmp = t_1;
	} else if (z <= 1.9e-96) {
		tmp = x;
	} else if (z <= 3.9e-75) {
		tmp = y * (t / a);
	} else if (z <= 1.95e+60) {
		tmp = t_2;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t / (a / y)
	t_2 = x * (y / z)
	tmp = 0
	if z <= -2.6e+92:
		tmp = t
	elif z <= -2.25e+32:
		tmp = t_2
	elif z <= -4.8e-34:
		tmp = t
	elif z <= -1.65e-105:
		tmp = t_1
	elif z <= -3.8e-302:
		tmp = x
	elif z <= 1.5e-217:
		tmp = t_1
	elif z <= 1.9e-96:
		tmp = x
	elif z <= 3.9e-75:
		tmp = y * (t / a)
	elif z <= 1.95e+60:
		tmp = t_2
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t / Float64(a / y))
	t_2 = Float64(x * Float64(y / z))
	tmp = 0.0
	if (z <= -2.6e+92)
		tmp = t;
	elseif (z <= -2.25e+32)
		tmp = t_2;
	elseif (z <= -4.8e-34)
		tmp = t;
	elseif (z <= -1.65e-105)
		tmp = t_1;
	elseif (z <= -3.8e-302)
		tmp = x;
	elseif (z <= 1.5e-217)
		tmp = t_1;
	elseif (z <= 1.9e-96)
		tmp = x;
	elseif (z <= 3.9e-75)
		tmp = Float64(y * Float64(t / a));
	elseif (z <= 1.95e+60)
		tmp = t_2;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t / (a / y);
	t_2 = x * (y / z);
	tmp = 0.0;
	if (z <= -2.6e+92)
		tmp = t;
	elseif (z <= -2.25e+32)
		tmp = t_2;
	elseif (z <= -4.8e-34)
		tmp = t;
	elseif (z <= -1.65e-105)
		tmp = t_1;
	elseif (z <= -3.8e-302)
		tmp = x;
	elseif (z <= 1.5e-217)
		tmp = t_1;
	elseif (z <= 1.9e-96)
		tmp = x;
	elseif (z <= 3.9e-75)
		tmp = y * (t / a);
	elseif (z <= 1.95e+60)
		tmp = t_2;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.6e+92], t, If[LessEqual[z, -2.25e+32], t$95$2, If[LessEqual[z, -4.8e-34], t, If[LessEqual[z, -1.65e-105], t$95$1, If[LessEqual[z, -3.8e-302], x, If[LessEqual[z, 1.5e-217], t$95$1, If[LessEqual[z, 1.9e-96], x, If[LessEqual[z, 3.9e-75], N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.95e+60], t$95$2, t]]]]]]]]]]]

Derivation

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

Alternative 5: 37.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{\frac{a}{y}}\\ t_2 := x \cdot \frac{y}{z}\\ \mathbf{if}\;z \leq -2.6 \cdot 10^{+94}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.6 \cdot 10^{+32}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -7 \cdot 10^{-33}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -2 \cdot 10^{-105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-303}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{-218}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-97}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.15 \cdot 10^{-74}:\\ \;\;\;\;\frac{y \cdot t}{a}\\ \mathbf{elif}\;z \leq 5.8 \cdot 10^{+59}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ t (/ a y))) (t_2 (* x (/ y z))))
   (if (<= z -2.6e+94)
     t
     (if (<= z -1.6e+32)
       t_2
       (if (<= z -7e-33)
         t
         (if (<= z -2e-105)
           t_1
           (if (<= z -2.1e-303)
             x
             (if (<= z 5.2e-218)
               t_1
               (if (<= z 2.7e-97)
                 x
                 (if (<= z 1.15e-74)
                   (/ (* y t) a)
                   (if (<= z 5.8e+59) t_2 t)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (a / y);
	double t_2 = x * (y / z);
	double tmp;
	if (z <= -2.6e+94) {
		tmp = t;
	} else if (z <= -1.6e+32) {
		tmp = t_2;
	} else if (z <= -7e-33) {
		tmp = t;
	} else if (z <= -2e-105) {
		tmp = t_1;
	} else if (z <= -2.1e-303) {
		tmp = x;
	} else if (z <= 5.2e-218) {
		tmp = t_1;
	} else if (z <= 2.7e-97) {
		tmp = x;
	} else if (z <= 1.15e-74) {
		tmp = (y * t) / a;
	} else if (z <= 5.8e+59) {
		tmp = t_2;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t / (a / y)
    t_2 = x * (y / z)
    if (z <= (-2.6d+94)) then
        tmp = t
    else if (z <= (-1.6d+32)) then
        tmp = t_2
    else if (z <= (-7d-33)) then
        tmp = t
    else if (z <= (-2d-105)) then
        tmp = t_1
    else if (z <= (-2.1d-303)) then
        tmp = x
    else if (z <= 5.2d-218) then
        tmp = t_1
    else if (z <= 2.7d-97) then
        tmp = x
    else if (z <= 1.15d-74) then
        tmp = (y * t) / a
    else if (z <= 5.8d+59) then
        tmp = t_2
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (a / y);
	double t_2 = x * (y / z);
	double tmp;
	if (z <= -2.6e+94) {
		tmp = t;
	} else if (z <= -1.6e+32) {
		tmp = t_2;
	} else if (z <= -7e-33) {
		tmp = t;
	} else if (z <= -2e-105) {
		tmp = t_1;
	} else if (z <= -2.1e-303) {
		tmp = x;
	} else if (z <= 5.2e-218) {
		tmp = t_1;
	} else if (z <= 2.7e-97) {
		tmp = x;
	} else if (z <= 1.15e-74) {
		tmp = (y * t) / a;
	} else if (z <= 5.8e+59) {
		tmp = t_2;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t / (a / y)
	t_2 = x * (y / z)
	tmp = 0
	if z <= -2.6e+94:
		tmp = t
	elif z <= -1.6e+32:
		tmp = t_2
	elif z <= -7e-33:
		tmp = t
	elif z <= -2e-105:
		tmp = t_1
	elif z <= -2.1e-303:
		tmp = x
	elif z <= 5.2e-218:
		tmp = t_1
	elif z <= 2.7e-97:
		tmp = x
	elif z <= 1.15e-74:
		tmp = (y * t) / a
	elif z <= 5.8e+59:
		tmp = t_2
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t / Float64(a / y))
	t_2 = Float64(x * Float64(y / z))
	tmp = 0.0
	if (z <= -2.6e+94)
		tmp = t;
	elseif (z <= -1.6e+32)
		tmp = t_2;
	elseif (z <= -7e-33)
		tmp = t;
	elseif (z <= -2e-105)
		tmp = t_1;
	elseif (z <= -2.1e-303)
		tmp = x;
	elseif (z <= 5.2e-218)
		tmp = t_1;
	elseif (z <= 2.7e-97)
		tmp = x;
	elseif (z <= 1.15e-74)
		tmp = Float64(Float64(y * t) / a);
	elseif (z <= 5.8e+59)
		tmp = t_2;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t / (a / y);
	t_2 = x * (y / z);
	tmp = 0.0;
	if (z <= -2.6e+94)
		tmp = t;
	elseif (z <= -1.6e+32)
		tmp = t_2;
	elseif (z <= -7e-33)
		tmp = t;
	elseif (z <= -2e-105)
		tmp = t_1;
	elseif (z <= -2.1e-303)
		tmp = x;
	elseif (z <= 5.2e-218)
		tmp = t_1;
	elseif (z <= 2.7e-97)
		tmp = x;
	elseif (z <= 1.15e-74)
		tmp = (y * t) / a;
	elseif (z <= 5.8e+59)
		tmp = t_2;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.6e+94], t, If[LessEqual[z, -1.6e+32], t$95$2, If[LessEqual[z, -7e-33], t, If[LessEqual[z, -2e-105], t$95$1, If[LessEqual[z, -2.1e-303], x, If[LessEqual[z, 5.2e-218], t$95$1, If[LessEqual[z, 2.7e-97], x, If[LessEqual[z, 1.15e-74], N[(N[(y * t), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[z, 5.8e+59], t$95$2, t]]]]]]]]]]]

Derivation

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

Alternative 6: 37.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{y}{z}\\ \mathbf{if}\;z \leq -4.2 \cdot 10^{+92}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{+29}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-34}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-105}:\\ \;\;\;\;t \cdot \frac{y + z}{a}\\ \mathbf{elif}\;z \leq -1.7 \cdot 10^{-297}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{-217}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 1.62 \cdot 10^{-96}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{-74}:\\ \;\;\;\;\frac{y \cdot t}{a}\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (/ y z))))
   (if (<= z -4.2e+92)
     t
     (if (<= z -3.8e+29)
       t_1
       (if (<= z -3.3e-34)
         t
         (if (<= z -1.3e-105)
           (* t (/ (+ y z) a))
           (if (<= z -1.7e-297)
             x
             (if (<= z 1.45e-217)
               (/ t (/ a y))
               (if (<= z 1.62e-96)
                 x
                 (if (<= z 2.2e-74)
                   (/ (* y t) a)
                   (if (<= z 2.1e+59) t_1 t)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (y / z);
	double tmp;
	if (z <= -4.2e+92) {
		tmp = t;
	} else if (z <= -3.8e+29) {
		tmp = t_1;
	} else if (z <= -3.3e-34) {
		tmp = t;
	} else if (z <= -1.3e-105) {
		tmp = t * ((y + z) / a);
	} else if (z <= -1.7e-297) {
		tmp = x;
	} else if (z <= 1.45e-217) {
		tmp = t / (a / y);
	} else if (z <= 1.62e-96) {
		tmp = x;
	} else if (z <= 2.2e-74) {
		tmp = (y * t) / a;
	} else if (z <= 2.1e+59) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x * (y / z)
    if (z <= (-4.2d+92)) then
        tmp = t
    else if (z <= (-3.8d+29)) then
        tmp = t_1
    else if (z <= (-3.3d-34)) then
        tmp = t
    else if (z <= (-1.3d-105)) then
        tmp = t * ((y + z) / a)
    else if (z <= (-1.7d-297)) then
        tmp = x
    else if (z <= 1.45d-217) then
        tmp = t / (a / y)
    else if (z <= 1.62d-96) then
        tmp = x
    else if (z <= 2.2d-74) then
        tmp = (y * t) / a
    else if (z <= 2.1d+59) 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 a) {
	double t_1 = x * (y / z);
	double tmp;
	if (z <= -4.2e+92) {
		tmp = t;
	} else if (z <= -3.8e+29) {
		tmp = t_1;
	} else if (z <= -3.3e-34) {
		tmp = t;
	} else if (z <= -1.3e-105) {
		tmp = t * ((y + z) / a);
	} else if (z <= -1.7e-297) {
		tmp = x;
	} else if (z <= 1.45e-217) {
		tmp = t / (a / y);
	} else if (z <= 1.62e-96) {
		tmp = x;
	} else if (z <= 2.2e-74) {
		tmp = (y * t) / a;
	} else if (z <= 2.1e+59) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (y / z)
	tmp = 0
	if z <= -4.2e+92:
		tmp = t
	elif z <= -3.8e+29:
		tmp = t_1
	elif z <= -3.3e-34:
		tmp = t
	elif z <= -1.3e-105:
		tmp = t * ((y + z) / a)
	elif z <= -1.7e-297:
		tmp = x
	elif z <= 1.45e-217:
		tmp = t / (a / y)
	elif z <= 1.62e-96:
		tmp = x
	elif z <= 2.2e-74:
		tmp = (y * t) / a
	elif z <= 2.1e+59:
		tmp = t_1
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(y / z))
	tmp = 0.0
	if (z <= -4.2e+92)
		tmp = t;
	elseif (z <= -3.8e+29)
		tmp = t_1;
	elseif (z <= -3.3e-34)
		tmp = t;
	elseif (z <= -1.3e-105)
		tmp = Float64(t * Float64(Float64(y + z) / a));
	elseif (z <= -1.7e-297)
		tmp = x;
	elseif (z <= 1.45e-217)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 1.62e-96)
		tmp = x;
	elseif (z <= 2.2e-74)
		tmp = Float64(Float64(y * t) / a);
	elseif (z <= 2.1e+59)
		tmp = t_1;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (y / z);
	tmp = 0.0;
	if (z <= -4.2e+92)
		tmp = t;
	elseif (z <= -3.8e+29)
		tmp = t_1;
	elseif (z <= -3.3e-34)
		tmp = t;
	elseif (z <= -1.3e-105)
		tmp = t * ((y + z) / a);
	elseif (z <= -1.7e-297)
		tmp = x;
	elseif (z <= 1.45e-217)
		tmp = t / (a / y);
	elseif (z <= 1.62e-96)
		tmp = x;
	elseif (z <= 2.2e-74)
		tmp = (y * t) / a;
	elseif (z <= 2.1e+59)
		tmp = t_1;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.2e+92], t, If[LessEqual[z, -3.8e+29], t$95$1, If[LessEqual[z, -3.3e-34], t, If[LessEqual[z, -1.3e-105], N[(t * N[(N[(y + z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.7e-297], x, If[LessEqual[z, 1.45e-217], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.62e-96], x, If[LessEqual[z, 2.2e-74], N[(N[(y * t), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[z, 2.1e+59], t$95$1, t]]]]]]]]]]

Derivation

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

Alternative 7: 38.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{y - z}{a}\\ \mathbf{if}\;z \leq -2.6 \cdot 10^{+92}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.85 \cdot 10^{+32}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-14}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -7.2 \cdot 10^{-106}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3.2 \cdot 10^{-297}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{-219}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 7.2 \cdot 10^{-111}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{-28}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+30}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (/ (- y z) a))))
   (if (<= z -2.6e+92)
     t
     (if (<= z -1.85e+32)
       (* x (/ y z))
       (if (<= z -1.15e-14)
         t
         (if (<= z -7.2e-106)
           t_1
           (if (<= z -3.2e-297)
             x
             (if (<= z 2.8e-219)
               (/ t (/ a y))
               (if (<= z 7.2e-111)
                 x
                 (if (<= z 1.9e-28) t_1 (if (<= z 1.05e+30) x t)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / a);
	double tmp;
	if (z <= -2.6e+92) {
		tmp = t;
	} else if (z <= -1.85e+32) {
		tmp = x * (y / z);
	} else if (z <= -1.15e-14) {
		tmp = t;
	} else if (z <= -7.2e-106) {
		tmp = t_1;
	} else if (z <= -3.2e-297) {
		tmp = x;
	} else if (z <= 2.8e-219) {
		tmp = t / (a / y);
	} else if (z <= 7.2e-111) {
		tmp = x;
	} else if (z <= 1.9e-28) {
		tmp = t_1;
	} else if (z <= 1.05e+30) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = t * ((y - z) / a)
    if (z <= (-2.6d+92)) then
        tmp = t
    else if (z <= (-1.85d+32)) then
        tmp = x * (y / z)
    else if (z <= (-1.15d-14)) then
        tmp = t
    else if (z <= (-7.2d-106)) then
        tmp = t_1
    else if (z <= (-3.2d-297)) then
        tmp = x
    else if (z <= 2.8d-219) then
        tmp = t / (a / y)
    else if (z <= 7.2d-111) then
        tmp = x
    else if (z <= 1.9d-28) then
        tmp = t_1
    else if (z <= 1.05d+30) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / a);
	double tmp;
	if (z <= -2.6e+92) {
		tmp = t;
	} else if (z <= -1.85e+32) {
		tmp = x * (y / z);
	} else if (z <= -1.15e-14) {
		tmp = t;
	} else if (z <= -7.2e-106) {
		tmp = t_1;
	} else if (z <= -3.2e-297) {
		tmp = x;
	} else if (z <= 2.8e-219) {
		tmp = t / (a / y);
	} else if (z <= 7.2e-111) {
		tmp = x;
	} else if (z <= 1.9e-28) {
		tmp = t_1;
	} else if (z <= 1.05e+30) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * ((y - z) / a)
	tmp = 0
	if z <= -2.6e+92:
		tmp = t
	elif z <= -1.85e+32:
		tmp = x * (y / z)
	elif z <= -1.15e-14:
		tmp = t
	elif z <= -7.2e-106:
		tmp = t_1
	elif z <= -3.2e-297:
		tmp = x
	elif z <= 2.8e-219:
		tmp = t / (a / y)
	elif z <= 7.2e-111:
		tmp = x
	elif z <= 1.9e-28:
		tmp = t_1
	elif z <= 1.05e+30:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(Float64(y - z) / a))
	tmp = 0.0
	if (z <= -2.6e+92)
		tmp = t;
	elseif (z <= -1.85e+32)
		tmp = Float64(x * Float64(y / z));
	elseif (z <= -1.15e-14)
		tmp = t;
	elseif (z <= -7.2e-106)
		tmp = t_1;
	elseif (z <= -3.2e-297)
		tmp = x;
	elseif (z <= 2.8e-219)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 7.2e-111)
		tmp = x;
	elseif (z <= 1.9e-28)
		tmp = t_1;
	elseif (z <= 1.05e+30)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * ((y - z) / a);
	tmp = 0.0;
	if (z <= -2.6e+92)
		tmp = t;
	elseif (z <= -1.85e+32)
		tmp = x * (y / z);
	elseif (z <= -1.15e-14)
		tmp = t;
	elseif (z <= -7.2e-106)
		tmp = t_1;
	elseif (z <= -3.2e-297)
		tmp = x;
	elseif (z <= 2.8e-219)
		tmp = t / (a / y);
	elseif (z <= 7.2e-111)
		tmp = x;
	elseif (z <= 1.9e-28)
		tmp = t_1;
	elseif (z <= 1.05e+30)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.6e+92], t, If[LessEqual[z, -1.85e+32], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.15e-14], t, If[LessEqual[z, -7.2e-106], t$95$1, If[LessEqual[z, -3.2e-297], x, If[LessEqual[z, 2.8e-219], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.2e-111], x, If[LessEqual[z, 1.9e-28], t$95$1, If[LessEqual[z, 1.05e+30], x, t]]]]]]]]]]

Derivation

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

Alternative 8: 64.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \left(x - t\right) \cdot \frac{y}{a}\\ \mathbf{if}\;a \leq -6.5 \cdot 10^{-55}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -1.02 \cdot 10^{-80}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{t}{a - z}\\ \mathbf{elif}\;a \leq -3.4 \cdot 10^{-96}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;a \leq -6.6 \cdot 10^{-274}:\\ \;\;\;\;t - \frac{x}{\frac{a - z}{y}}\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{+42}:\\ \;\;\;\;\frac{t}{\frac{a - z}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* (- x t) (/ y a)))))
   (if (<= a -6.5e-55)
     t_1
     (if (<= a -1.02e-80)
       (* (- y z) (/ t (- a z)))
       (if (<= a -3.4e-96)
         (/ t (/ a y))
         (if (<= a -6.6e-274)
           (- t (/ x (/ (- a z) y)))
           (if (<= a 2.1e+42) (/ t (/ (- a z) (- y z))) t_1)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((x - t) * (y / a));
	double tmp;
	if (a <= -6.5e-55) {
		tmp = t_1;
	} else if (a <= -1.02e-80) {
		tmp = (y - z) * (t / (a - z));
	} else if (a <= -3.4e-96) {
		tmp = t / (a / y);
	} else if (a <= -6.6e-274) {
		tmp = t - (x / ((a - z) / y));
	} else if (a <= 2.1e+42) {
		tmp = t / ((a - z) / (y - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x - ((x - t) * (y / a))
    if (a <= (-6.5d-55)) then
        tmp = t_1
    else if (a <= (-1.02d-80)) then
        tmp = (y - z) * (t / (a - z))
    else if (a <= (-3.4d-96)) then
        tmp = t / (a / y)
    else if (a <= (-6.6d-274)) then
        tmp = t - (x / ((a - z) / y))
    else if (a <= 2.1d+42) then
        tmp = t / ((a - z) / (y - z))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((x - t) * (y / a));
	double tmp;
	if (a <= -6.5e-55) {
		tmp = t_1;
	} else if (a <= -1.02e-80) {
		tmp = (y - z) * (t / (a - z));
	} else if (a <= -3.4e-96) {
		tmp = t / (a / y);
	} else if (a <= -6.6e-274) {
		tmp = t - (x / ((a - z) / y));
	} else if (a <= 2.1e+42) {
		tmp = t / ((a - z) / (y - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - ((x - t) * (y / a))
	tmp = 0
	if a <= -6.5e-55:
		tmp = t_1
	elif a <= -1.02e-80:
		tmp = (y - z) * (t / (a - z))
	elif a <= -3.4e-96:
		tmp = t / (a / y)
	elif a <= -6.6e-274:
		tmp = t - (x / ((a - z) / y))
	elif a <= 2.1e+42:
		tmp = t / ((a - z) / (y - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(Float64(x - t) * Float64(y / a)))
	tmp = 0.0
	if (a <= -6.5e-55)
		tmp = t_1;
	elseif (a <= -1.02e-80)
		tmp = Float64(Float64(y - z) * Float64(t / Float64(a - z)));
	elseif (a <= -3.4e-96)
		tmp = Float64(t / Float64(a / y));
	elseif (a <= -6.6e-274)
		tmp = Float64(t - Float64(x / Float64(Float64(a - z) / y)));
	elseif (a <= 2.1e+42)
		tmp = Float64(t / Float64(Float64(a - z) / Float64(y - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - ((x - t) * (y / a));
	tmp = 0.0;
	if (a <= -6.5e-55)
		tmp = t_1;
	elseif (a <= -1.02e-80)
		tmp = (y - z) * (t / (a - z));
	elseif (a <= -3.4e-96)
		tmp = t / (a / y);
	elseif (a <= -6.6e-274)
		tmp = t - (x / ((a - z) / y));
	elseif (a <= 2.1e+42)
		tmp = t / ((a - z) / (y - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(N[(x - t), $MachinePrecision] * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -6.5e-55], t$95$1, If[LessEqual[a, -1.02e-80], N[(N[(y - z), $MachinePrecision] * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -3.4e-96], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -6.6e-274], N[(t - N[(x / N[(N[(a - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.1e+42], N[(t / N[(N[(a - z), $MachinePrecision] / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 9: 49.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -2.65 \cdot 10^{-55}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -8.2 \cdot 10^{-100}:\\ \;\;\;\;\frac{y \cdot t}{a - z}\\ \mathbf{elif}\;a \leq -1.35 \cdot 10^{-272}:\\ \;\;\;\;x \cdot \frac{-y}{a - z}\\ \mathbf{elif}\;a \leq 2.75 \cdot 10^{+35}:\\ \;\;\;\;\frac{t}{\frac{z}{z - y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -2.65e-55)
     t_1
     (if (<= a -8.2e-100)
       (/ (* y t) (- a z))
       (if (<= a -1.35e-272)
         (* x (/ (- y) (- a z)))
         (if (<= a 2.75e+35) (/ t (/ z (- z y))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (a <= -2.65e-55) {
		tmp = t_1;
	} else if (a <= -8.2e-100) {
		tmp = (y * t) / (a - z);
	} else if (a <= -1.35e-272) {
		tmp = x * (-y / (a - z));
	} else if (a <= 2.75e+35) {
		tmp = t / (z / (z - y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (a <= (-2.65d-55)) then
        tmp = t_1
    else if (a <= (-8.2d-100)) then
        tmp = (y * t) / (a - z)
    else if (a <= (-1.35d-272)) then
        tmp = x * (-y / (a - z))
    else if (a <= 2.75d+35) then
        tmp = t / (z / (z - y))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (a <= -2.65e-55) {
		tmp = t_1;
	} else if (a <= -8.2e-100) {
		tmp = (y * t) / (a - z);
	} else if (a <= -1.35e-272) {
		tmp = x * (-y / (a - z));
	} else if (a <= 2.75e+35) {
		tmp = t / (z / (z - y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -2.65e-55:
		tmp = t_1
	elif a <= -8.2e-100:
		tmp = (y * t) / (a - z)
	elif a <= -1.35e-272:
		tmp = x * (-y / (a - z))
	elif a <= 2.75e+35:
		tmp = t / (z / (z - y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (a <= -2.65e-55)
		tmp = t_1;
	elseif (a <= -8.2e-100)
		tmp = Float64(Float64(y * t) / Float64(a - z));
	elseif (a <= -1.35e-272)
		tmp = Float64(x * Float64(Float64(-y) / Float64(a - z)));
	elseif (a <= 2.75e+35)
		tmp = Float64(t / Float64(z / Float64(z - y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (a <= -2.65e-55)
		tmp = t_1;
	elseif (a <= -8.2e-100)
		tmp = (y * t) / (a - z);
	elseif (a <= -1.35e-272)
		tmp = x * (-y / (a - z));
	elseif (a <= 2.75e+35)
		tmp = t / (z / (z - y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.65e-55], t$95$1, If[LessEqual[a, -8.2e-100], N[(N[(y * t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1.35e-272], N[(x * N[((-y) / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.75e+35], N[(t / N[(z / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 10: 52.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -6.3 \cdot 10^{-55}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -3.3 \cdot 10^{-96}:\\ \;\;\;\;\frac{y \cdot t}{a - z}\\ \mathbf{elif}\;a \leq -1.95 \cdot 10^{-136}:\\ \;\;\;\;\frac{t}{\frac{z - a}{z}}\\ \mathbf{elif}\;a \leq 4.2 \cdot 10^{+33}:\\ \;\;\;\;\frac{t}{\frac{z}{z - y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -6.3e-55)
     t_1
     (if (<= a -3.3e-96)
       (/ (* y t) (- a z))
       (if (<= a -1.95e-136)
         (/ t (/ (- z a) z))
         (if (<= a 4.2e+33) (/ t (/ z (- z y))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (a <= -6.3e-55) {
		tmp = t_1;
	} else if (a <= -3.3e-96) {
		tmp = (y * t) / (a - z);
	} else if (a <= -1.95e-136) {
		tmp = t / ((z - a) / z);
	} else if (a <= 4.2e+33) {
		tmp = t / (z / (z - y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (a <= (-6.3d-55)) then
        tmp = t_1
    else if (a <= (-3.3d-96)) then
        tmp = (y * t) / (a - z)
    else if (a <= (-1.95d-136)) then
        tmp = t / ((z - a) / z)
    else if (a <= 4.2d+33) then
        tmp = t / (z / (z - y))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (a <= -6.3e-55) {
		tmp = t_1;
	} else if (a <= -3.3e-96) {
		tmp = (y * t) / (a - z);
	} else if (a <= -1.95e-136) {
		tmp = t / ((z - a) / z);
	} else if (a <= 4.2e+33) {
		tmp = t / (z / (z - y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -6.3e-55:
		tmp = t_1
	elif a <= -3.3e-96:
		tmp = (y * t) / (a - z)
	elif a <= -1.95e-136:
		tmp = t / ((z - a) / z)
	elif a <= 4.2e+33:
		tmp = t / (z / (z - y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (a <= -6.3e-55)
		tmp = t_1;
	elseif (a <= -3.3e-96)
		tmp = Float64(Float64(y * t) / Float64(a - z));
	elseif (a <= -1.95e-136)
		tmp = Float64(t / Float64(Float64(z - a) / z));
	elseif (a <= 4.2e+33)
		tmp = Float64(t / Float64(z / Float64(z - y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (a <= -6.3e-55)
		tmp = t_1;
	elseif (a <= -3.3e-96)
		tmp = (y * t) / (a - z);
	elseif (a <= -1.95e-136)
		tmp = t / ((z - a) / z);
	elseif (a <= 4.2e+33)
		tmp = t / (z / (z - y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -6.3e-55], t$95$1, If[LessEqual[a, -3.3e-96], N[(N[(y * t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1.95e-136], N[(t / N[(N[(z - a), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.2e+33], N[(t / N[(z / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 11: 74.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6 \cdot 10^{-33} \lor \neg \left(z \leq 1.95 \cdot 10^{-26}\right):\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y - a}}\\ \mathbf{else}:\\ \;\;\;\;x - \left(x - t\right) \cdot \frac{y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -6e-33) (not (<= z 1.95e-26)))
   (+ t (/ (- x t) (/ z (- y a))))
   (- x (* (- x t) (/ y a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -6e-33) || !(z <= 1.95e-26)) {
		tmp = t + ((x - t) / (z / (y - a)));
	} else {
		tmp = x - ((x - t) * (y / a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((z <= (-6d-33)) .or. (.not. (z <= 1.95d-26))) then
        tmp = t + ((x - t) / (z / (y - a)))
    else
        tmp = x - ((x - t) * (y / a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -6e-33) || !(z <= 1.95e-26)) {
		tmp = t + ((x - t) / (z / (y - a)));
	} else {
		tmp = x - ((x - t) * (y / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -6e-33) or not (z <= 1.95e-26):
		tmp = t + ((x - t) / (z / (y - a)))
	else:
		tmp = x - ((x - t) * (y / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -6e-33) || !(z <= 1.95e-26))
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / Float64(y - a))));
	else
		tmp = Float64(x - Float64(Float64(x - t) * Float64(y / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -6e-33) || ~((z <= 1.95e-26)))
		tmp = t + ((x - t) / (z / (y - a)));
	else
		tmp = x - ((x - t) * (y / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -6e-33], N[Not[LessEqual[z, 1.95e-26]], $MachinePrecision]], N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(x - t), $MachinePrecision] * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 41.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;x \leq -6.2 \cdot 10^{-39}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 3.2 \cdot 10^{-284}:\\ \;\;\;\;t \cdot \frac{y - z}{a}\\ \mathbf{elif}\;x \leq 1.6 \cdot 10^{-105}:\\ \;\;\;\;y \cdot \frac{t}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= x -6.2e-39)
     t_1
     (if (<= x 3.2e-284)
       (* t (/ (- y z) a))
       (if (<= x 1.6e-105) (* y (/ t (- a z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (x <= -6.2e-39) {
		tmp = t_1;
	} else if (x <= 3.2e-284) {
		tmp = t * ((y - z) / a);
	} else if (x <= 1.6e-105) {
		tmp = y * (t / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (x <= (-6.2d-39)) then
        tmp = t_1
    else if (x <= 3.2d-284) then
        tmp = t * ((y - z) / a)
    else if (x <= 1.6d-105) then
        tmp = y * (t / (a - z))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (x <= -6.2e-39) {
		tmp = t_1;
	} else if (x <= 3.2e-284) {
		tmp = t * ((y - z) / a);
	} else if (x <= 1.6e-105) {
		tmp = y * (t / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if x <= -6.2e-39:
		tmp = t_1
	elif x <= 3.2e-284:
		tmp = t * ((y - z) / a)
	elif x <= 1.6e-105:
		tmp = y * (t / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (x <= -6.2e-39)
		tmp = t_1;
	elseif (x <= 3.2e-284)
		tmp = Float64(t * Float64(Float64(y - z) / a));
	elseif (x <= 1.6e-105)
		tmp = Float64(y * Float64(t / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (x <= -6.2e-39)
		tmp = t_1;
	elseif (x <= 3.2e-284)
		tmp = t * ((y - z) / a);
	elseif (x <= 1.6e-105)
		tmp = y * (t / (a - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -6.2e-39], t$95$1, If[LessEqual[x, 3.2e-284], N[(t * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.6e-105], N[(y * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 13: 41.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{-38}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;x \leq 2.8 \cdot 10^{-286}:\\ \;\;\;\;t \cdot \frac{y - z}{a}\\ \mathbf{elif}\;x \leq 1.45 \cdot 10^{-98}:\\ \;\;\;\;y \cdot \frac{t}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -3.6e-38)
   (* x (- 1.0 (/ y a)))
   (if (<= x 2.8e-286)
     (* t (/ (- y z) a))
     (if (<= x 1.45e-98) (* y (/ t (- a z))) (- x (/ x (/ a y)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -3.6e-38) {
		tmp = x * (1.0 - (y / a));
	} else if (x <= 2.8e-286) {
		tmp = t * ((y - z) / a);
	} else if (x <= 1.45e-98) {
		tmp = y * (t / (a - z));
	} else {
		tmp = x - (x / (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (x <= (-3.6d-38)) then
        tmp = x * (1.0d0 - (y / a))
    else if (x <= 2.8d-286) then
        tmp = t * ((y - z) / a)
    else if (x <= 1.45d-98) then
        tmp = y * (t / (a - z))
    else
        tmp = x - (x / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -3.6e-38) {
		tmp = x * (1.0 - (y / a));
	} else if (x <= 2.8e-286) {
		tmp = t * ((y - z) / a);
	} else if (x <= 1.45e-98) {
		tmp = y * (t / (a - z));
	} else {
		tmp = x - (x / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -3.6e-38:
		tmp = x * (1.0 - (y / a))
	elif x <= 2.8e-286:
		tmp = t * ((y - z) / a)
	elif x <= 1.45e-98:
		tmp = y * (t / (a - z))
	else:
		tmp = x - (x / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -3.6e-38)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (x <= 2.8e-286)
		tmp = Float64(t * Float64(Float64(y - z) / a));
	elseif (x <= 1.45e-98)
		tmp = Float64(y * Float64(t / Float64(a - z)));
	else
		tmp = Float64(x - Float64(x / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -3.6e-38)
		tmp = x * (1.0 - (y / a));
	elseif (x <= 2.8e-286)
		tmp = t * ((y - z) / a);
	elseif (x <= 1.45e-98)
		tmp = y * (t / (a - z));
	else
		tmp = x - (x / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -3.6e-38], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.8e-286], N[(t * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.45e-98], N[(y * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 14: 41.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.15 \cdot 10^{-39}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{-274}:\\ \;\;\;\;\frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{-101}:\\ \;\;\;\;y \cdot \frac{t}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -2.15e-39)
   (* x (- 1.0 (/ y a)))
   (if (<= x 3.3e-274)
     (/ t (/ a (- y z)))
     (if (<= x 3.9e-101) (* y (/ t (- a z))) (- x (/ x (/ a y)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -2.15e-39) {
		tmp = x * (1.0 - (y / a));
	} else if (x <= 3.3e-274) {
		tmp = t / (a / (y - z));
	} else if (x <= 3.9e-101) {
		tmp = y * (t / (a - z));
	} else {
		tmp = x - (x / (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (x <= (-2.15d-39)) then
        tmp = x * (1.0d0 - (y / a))
    else if (x <= 3.3d-274) then
        tmp = t / (a / (y - z))
    else if (x <= 3.9d-101) then
        tmp = y * (t / (a - z))
    else
        tmp = x - (x / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -2.15e-39) {
		tmp = x * (1.0 - (y / a));
	} else if (x <= 3.3e-274) {
		tmp = t / (a / (y - z));
	} else if (x <= 3.9e-101) {
		tmp = y * (t / (a - z));
	} else {
		tmp = x - (x / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -2.15e-39:
		tmp = x * (1.0 - (y / a))
	elif x <= 3.3e-274:
		tmp = t / (a / (y - z))
	elif x <= 3.9e-101:
		tmp = y * (t / (a - z))
	else:
		tmp = x - (x / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -2.15e-39)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (x <= 3.3e-274)
		tmp = Float64(t / Float64(a / Float64(y - z)));
	elseif (x <= 3.9e-101)
		tmp = Float64(y * Float64(t / Float64(a - z)));
	else
		tmp = Float64(x - Float64(x / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -2.15e-39)
		tmp = x * (1.0 - (y / a));
	elseif (x <= 3.3e-274)
		tmp = t / (a / (y - z));
	elseif (x <= 3.9e-101)
		tmp = y * (t / (a - z));
	else
		tmp = x - (x / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -2.15e-39], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.3e-274], N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.9e-101], N[(y * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 15: 59.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.7 \cdot 10^{-37} \lor \neg \left(x \leq 7.8 \cdot 10^{+54}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -3.7e-37) (not (<= x 7.8e+54)))
   (* x (- 1.0 (/ y a)))
   (* t (/ (- y z) (- a z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -3.7e-37) || !(x <= 7.8e+54)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t * ((y - z) / (a - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((x <= (-3.7d-37)) .or. (.not. (x <= 7.8d+54))) then
        tmp = x * (1.0d0 - (y / a))
    else
        tmp = t * ((y - z) / (a - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -3.7e-37) || !(x <= 7.8e+54)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t * ((y - z) / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -3.7e-37) or not (x <= 7.8e+54):
		tmp = x * (1.0 - (y / a))
	else:
		tmp = t * ((y - z) / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -3.7e-37) || !(x <= 7.8e+54))
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	else
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -3.7e-37) || ~((x <= 7.8e+54)))
		tmp = x * (1.0 - (y / a));
	else
		tmp = t * ((y - z) / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -3.7e-37], N[Not[LessEqual[x, 7.8e+54]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 63.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.5 \cdot 10^{-39} \lor \neg \left(x \leq 6.5 \cdot 10^{+54}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a - z}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -8.5e-39) (not (<= x 6.5e+54)))
   (* x (- 1.0 (/ y (- a z))))
   (* t (/ (- y z) (- a z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -8.5e-39) || !(x <= 6.5e+54)) {
		tmp = x * (1.0 - (y / (a - z)));
	} else {
		tmp = t * ((y - z) / (a - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((x <= (-8.5d-39)) .or. (.not. (x <= 6.5d+54))) then
        tmp = x * (1.0d0 - (y / (a - z)))
    else
        tmp = t * ((y - z) / (a - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -8.5e-39) || !(x <= 6.5e+54)) {
		tmp = x * (1.0 - (y / (a - z)));
	} else {
		tmp = t * ((y - z) / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -8.5e-39) or not (x <= 6.5e+54):
		tmp = x * (1.0 - (y / (a - z)))
	else:
		tmp = t * ((y - z) / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -8.5e-39) || !(x <= 6.5e+54))
		tmp = Float64(x * Float64(1.0 - Float64(y / Float64(a - z))));
	else
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -8.5e-39) || ~((x <= 6.5e+54)))
		tmp = x * (1.0 - (y / (a - z)));
	else
		tmp = t * ((y - z) / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -8.5e-39], N[Not[LessEqual[x, 6.5e+54]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 64.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -6.1 \cdot 10^{-55} \lor \neg \left(a \leq 1.95 \cdot 10^{+36}\right):\\ \;\;\;\;x - \left(x - t\right) \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -6.1e-55) (not (<= a 1.95e+36)))
   (- x (* (- x t) (/ y a)))
   (* t (/ (- y z) (- a z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -6.1e-55) || !(a <= 1.95e+36)) {
		tmp = x - ((x - t) * (y / a));
	} else {
		tmp = t * ((y - z) / (a - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a <= (-6.1d-55)) .or. (.not. (a <= 1.95d+36))) then
        tmp = x - ((x - t) * (y / a))
    else
        tmp = t * ((y - z) / (a - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -6.1e-55) || !(a <= 1.95e+36)) {
		tmp = x - ((x - t) * (y / a));
	} else {
		tmp = t * ((y - z) / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -6.1e-55) or not (a <= 1.95e+36):
		tmp = x - ((x - t) * (y / a))
	else:
		tmp = t * ((y - z) / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -6.1e-55) || !(a <= 1.95e+36))
		tmp = Float64(x - Float64(Float64(x - t) * Float64(y / a)));
	else
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -6.1e-55) || ~((a <= 1.95e+36)))
		tmp = x - ((x - t) * (y / a));
	else
		tmp = t * ((y - z) / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -6.1e-55], N[Not[LessEqual[a, 1.95e+36]], $MachinePrecision]], N[(x - N[(N[(x - t), $MachinePrecision] * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 64.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.2 \cdot 10^{-55} \lor \neg \left(a \leq 1.75 \cdot 10^{+38}\right):\\ \;\;\;\;x - \left(x - t\right) \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{\frac{a - z}{y - z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -9.2e-55) (not (<= a 1.75e+38)))
   (- x (* (- x t) (/ y a)))
   (/ t (/ (- a z) (- y z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -9.2e-55) || !(a <= 1.75e+38)) {
		tmp = x - ((x - t) * (y / a));
	} else {
		tmp = t / ((a - z) / (y - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a <= (-9.2d-55)) .or. (.not. (a <= 1.75d+38))) then
        tmp = x - ((x - t) * (y / a))
    else
        tmp = t / ((a - z) / (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -9.2e-55) || !(a <= 1.75e+38)) {
		tmp = x - ((x - t) * (y / a));
	} else {
		tmp = t / ((a - z) / (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -9.2e-55) or not (a <= 1.75e+38):
		tmp = x - ((x - t) * (y / a))
	else:
		tmp = t / ((a - z) / (y - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -9.2e-55) || !(a <= 1.75e+38))
		tmp = Float64(x - Float64(Float64(x - t) * Float64(y / a)));
	else
		tmp = Float64(t / Float64(Float64(a - z) / Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -9.2e-55) || ~((a <= 1.75e+38)))
		tmp = x - ((x - t) * (y / a));
	else
		tmp = t / ((a - z) / (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -9.2e-55], N[Not[LessEqual[a, 1.75e+38]], $MachinePrecision]], N[(x - N[(N[(x - t), $MachinePrecision] * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t / N[(N[(a - z), $MachinePrecision] / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 19: 41.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.1 \cdot 10^{-39} \lor \neg \left(x \leq 1.5 \cdot 10^{-141}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{y - z}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -1.1e-39) (not (<= x 1.5e-141)))
   (* x (- 1.0 (/ y a)))
   (* t (/ (- y z) a))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -1.1e-39) || !(x <= 1.5e-141)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t * ((y - z) / a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((x <= (-1.1d-39)) .or. (.not. (x <= 1.5d-141))) then
        tmp = x * (1.0d0 - (y / a))
    else
        tmp = t * ((y - z) / a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -1.1e-39) || !(x <= 1.5e-141)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t * ((y - z) / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -1.1e-39) or not (x <= 1.5e-141):
		tmp = x * (1.0 - (y / a))
	else:
		tmp = t * ((y - z) / a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -1.1e-39) || !(x <= 1.5e-141))
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	else
		tmp = Float64(t * Float64(Float64(y - z) / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -1.1e-39) || ~((x <= 1.5e-141)))
		tmp = x * (1.0 - (y / a));
	else
		tmp = t * ((y - z) / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -1.1e-39], N[Not[LessEqual[x, 1.5e-141]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 20: 52.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.5 \cdot 10^{-55} \lor \neg \left(a \leq 1.25 \cdot 10^{+35}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{\frac{z}{z - y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -9.5e-55) (not (<= a 1.25e+35)))
   (* x (- 1.0 (/ y a)))
   (/ t (/ z (- z y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -9.5e-55) || !(a <= 1.25e+35)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t / (z / (z - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a <= (-9.5d-55)) .or. (.not. (a <= 1.25d+35))) then
        tmp = x * (1.0d0 - (y / a))
    else
        tmp = t / (z / (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -9.5e-55) || !(a <= 1.25e+35)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t / (z / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -9.5e-55) or not (a <= 1.25e+35):
		tmp = x * (1.0 - (y / a))
	else:
		tmp = t / (z / (z - y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -9.5e-55) || !(a <= 1.25e+35))
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	else
		tmp = Float64(t / Float64(z / Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -9.5e-55) || ~((a <= 1.25e+35)))
		tmp = x * (1.0 - (y / a));
	else
		tmp = t / (z / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -9.5e-55], N[Not[LessEqual[a, 1.25e+35]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t / N[(z / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 21: 38.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+93}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{+29}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-83}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{+29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.1e+93)
   t
   (if (<= z -1.3e+29)
     (* x (/ y z))
     (if (<= z -6e-83) t (if (<= z 2.6e+29) x t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.1e+93) {
		tmp = t;
	} else if (z <= -1.3e+29) {
		tmp = x * (y / z);
	} else if (z <= -6e-83) {
		tmp = t;
	} else if (z <= 2.6e+29) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (z <= (-2.1d+93)) then
        tmp = t
    else if (z <= (-1.3d+29)) then
        tmp = x * (y / z)
    else if (z <= (-6d-83)) then
        tmp = t
    else if (z <= 2.6d+29) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.1e+93) {
		tmp = t;
	} else if (z <= -1.3e+29) {
		tmp = x * (y / z);
	} else if (z <= -6e-83) {
		tmp = t;
	} else if (z <= 2.6e+29) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.1e+93:
		tmp = t
	elif z <= -1.3e+29:
		tmp = x * (y / z)
	elif z <= -6e-83:
		tmp = t
	elif z <= 2.6e+29:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.1e+93)
		tmp = t;
	elseif (z <= -1.3e+29)
		tmp = Float64(x * Float64(y / z));
	elseif (z <= -6e-83)
		tmp = t;
	elseif (z <= 2.6e+29)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.1e+93)
		tmp = t;
	elseif (z <= -1.3e+29)
		tmp = x * (y / z);
	elseif (z <= -6e-83)
		tmp = t;
	elseif (z <= 2.6e+29)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.1e+93], t, If[LessEqual[z, -1.3e+29], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6e-83], t, If[LessEqual[z, 2.6e+29], x, t]]]]

Derivation

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

Alternative 22: 38.5% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{-83}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 5.8 \cdot 10^{+31}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.3e-83) t (if (<= z 5.8e+31) x t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.3e-83) {
		tmp = t;
	} else if (z <= 5.8e+31) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (z <= (-2.3d-83)) then
        tmp = t
    else if (z <= 5.8d+31) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.3e-83) {
		tmp = t;
	} else if (z <= 5.8e+31) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.3e-83:
		tmp = t
	elif z <= 5.8e+31:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.3e-83)
		tmp = t;
	elseif (z <= 5.8e+31)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.3e-83)
		tmp = t;
	elseif (z <= 5.8e+31)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.3e-83], t, If[LessEqual[z, 5.8e+31], x, t]]

Derivation

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

Alternative 23: 25.3% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a)
  :name "Numeric.Signal:interpolate   from hsignal-0.2.7.1"
  :precision binary64
  (+ x (* (- y z) (/ (- t x) (- a z)))))