Numeric.Signal:interpolate from hsignal-0.2.7.1

Percentage Accurate: 80.6% → 90.9%

Time: 19.7s

Alternatives: 24

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: 80.6% 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: 90.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - x}{a - z}\\ t_2 := x + \left(y - z\right) \cdot t_1\\ \mathbf{if}\;t_2 \leq -1 \cdot 10^{-216}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_2 \leq 10^{-251}:\\ \;\;\;\;t + \left(t - x\right) \cdot \frac{a - y}{z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y - z, t_1, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- t x) (- a z))) (t_2 (+ x (* (- y z) t_1))))
   (if (<= t_2 -1e-216)
     t_2
     (if (<= t_2 1e-251)
       (+ t (* (- t x) (/ (- a y) z)))
       (fma (- y z) t_1 x)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (t - x) / (a - z);
	double t_2 = x + ((y - z) * t_1);
	double tmp;
	if (t_2 <= -1e-216) {
		tmp = t_2;
	} else if (t_2 <= 1e-251) {
		tmp = t + ((t - x) * ((a - y) / z));
	} else {
		tmp = fma((y - z), t_1, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(t - x) / Float64(a - z))
	t_2 = Float64(x + Float64(Float64(y - z) * t_1))
	tmp = 0.0
	if (t_2 <= -1e-216)
		tmp = t_2;
	elseif (t_2 <= 1e-251)
		tmp = Float64(t + Float64(Float64(t - x) * Float64(Float64(a - y) / z)));
	else
		tmp = fma(Float64(y - z), t_1, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(y - z), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -1e-216], t$95$2, If[LessEqual[t$95$2, 1e-251], N[(t + N[(N[(t - x), $MachinePrecision] * N[(N[(a - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * t$95$1 + x), $MachinePrecision]]]]]

Derivation

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

Alternative 2: 90.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t_1 \leq -1 \cdot 10^{-216} \lor \neg \left(t_1 \leq 10^{-251}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t + \left(t - x\right) \cdot \frac{a - y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (or (<= t_1 -1e-216) (not (<= t_1 1e-251)))
     t_1
     (+ t (* (- t x) (/ (- a y) z))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-216) || !(t_1 <= 1e-251)) {
		tmp = t_1;
	} else {
		tmp = t + ((t - x) * ((a - 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) :: t_1
    real(8) :: tmp
    t_1 = x + ((y - z) * ((t - x) / (a - z)))
    if ((t_1 <= (-1d-216)) .or. (.not. (t_1 <= 1d-251))) then
        tmp = t_1
    else
        tmp = t + ((t - x) * ((a - y) / z))
    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) * ((t - x) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-216) || !(t_1 <= 1e-251)) {
		tmp = t_1;
	} else {
		tmp = t + ((t - x) * ((a - y) / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if (t_1 <= -1e-216) or not (t_1 <= 1e-251):
		tmp = t_1
	else:
		tmp = t + ((t - x) * ((a - y) / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if ((t_1 <= -1e-216) || !(t_1 <= 1e-251))
		tmp = t_1;
	else
		tmp = Float64(t + Float64(Float64(t - x) * Float64(Float64(a - y) / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if ((t_1 <= -1e-216) || ~((t_1 <= 1e-251)))
		tmp = t_1;
	else
		tmp = t + ((t - x) * ((a - y) / z));
	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[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e-216], N[Not[LessEqual[t$95$1, 1e-251]], $MachinePrecision]], t$95$1, N[(t + N[(N[(t - x), $MachinePrecision] * N[(N[(a - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 3: 56.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - \frac{y}{-\frac{z}{x}}\\ \mathbf{if}\;a \leq -6.4 \cdot 10^{-9}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 2.3 \cdot 10^{-19}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 60000000000:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{elif}\;a \leq 1.35 \cdot 10^{+72}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (/ y (- (/ z x))))))
   (if (<= a -6.4e-9)
     (* x (- 1.0 (/ y a)))
     (if (<= a 2.3e-19)
       t_1
       (if (<= a 60000000000.0)
         (* t (/ y (- a z)))
         (if (<= a 1.35e+72) t_1 (+ x (* y (/ t a)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (y / -(z / x));
	double tmp;
	if (a <= -6.4e-9) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 2.3e-19) {
		tmp = t_1;
	} else if (a <= 60000000000.0) {
		tmp = t * (y / (a - z));
	} else if (a <= 1.35e+72) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / 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 = t - (y / -(z / x))
    if (a <= (-6.4d-9)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 2.3d-19) then
        tmp = t_1
    else if (a <= 60000000000.0d0) then
        tmp = t * (y / (a - z))
    else if (a <= 1.35d+72) then
        tmp = t_1
    else
        tmp = x + (y * (t / 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 = t - (y / -(z / x));
	double tmp;
	if (a <= -6.4e-9) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 2.3e-19) {
		tmp = t_1;
	} else if (a <= 60000000000.0) {
		tmp = t * (y / (a - z));
	} else if (a <= 1.35e+72) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - (y / -(z / x))
	tmp = 0
	if a <= -6.4e-9:
		tmp = x * (1.0 - (y / a))
	elif a <= 2.3e-19:
		tmp = t_1
	elif a <= 60000000000.0:
		tmp = t * (y / (a - z))
	elif a <= 1.35e+72:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(y / Float64(-Float64(z / x))))
	tmp = 0.0
	if (a <= -6.4e-9)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 2.3e-19)
		tmp = t_1;
	elseif (a <= 60000000000.0)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	elseif (a <= 1.35e+72)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - (y / -(z / x));
	tmp = 0.0;
	if (a <= -6.4e-9)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 2.3e-19)
		tmp = t_1;
	elseif (a <= 60000000000.0)
		tmp = t * (y / (a - z));
	elseif (a <= 1.35e+72)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(y / (-N[(z / x), $MachinePrecision])), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -6.4e-9], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.3e-19], t$95$1, If[LessEqual[a, 60000000000.0], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.35e+72], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 55.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.4 \cdot 10^{-8}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 8 \cdot 10^{-21}:\\ \;\;\;\;t - \frac{y}{-\frac{z}{x}}\\ \mathbf{elif}\;a \leq 65000000000:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{elif}\;a \leq 6.8 \cdot 10^{+59}:\\ \;\;\;\;\frac{-x}{\frac{a - z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -9.4e-8)
   (* x (- 1.0 (/ y a)))
   (if (<= a 8e-21)
     (- t (/ y (- (/ z x))))
     (if (<= a 65000000000.0)
       (* t (/ y (- a z)))
       (if (<= a 6.8e+59) (/ (- x) (/ (- a z) y)) (+ x (* y (/ t a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -9.4e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 8e-21) {
		tmp = t - (y / -(z / x));
	} else if (a <= 65000000000.0) {
		tmp = t * (y / (a - z));
	} else if (a <= 6.8e+59) {
		tmp = -x / ((a - z) / y);
	} else {
		tmp = x + (y * (t / 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 (a <= (-9.4d-8)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 8d-21) then
        tmp = t - (y / -(z / x))
    else if (a <= 65000000000.0d0) then
        tmp = t * (y / (a - z))
    else if (a <= 6.8d+59) then
        tmp = -x / ((a - z) / y)
    else
        tmp = x + (y * (t / 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 (a <= -9.4e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 8e-21) {
		tmp = t - (y / -(z / x));
	} else if (a <= 65000000000.0) {
		tmp = t * (y / (a - z));
	} else if (a <= 6.8e+59) {
		tmp = -x / ((a - z) / y);
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -9.4e-8:
		tmp = x * (1.0 - (y / a))
	elif a <= 8e-21:
		tmp = t - (y / -(z / x))
	elif a <= 65000000000.0:
		tmp = t * (y / (a - z))
	elif a <= 6.8e+59:
		tmp = -x / ((a - z) / y)
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -9.4e-8)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 8e-21)
		tmp = Float64(t - Float64(y / Float64(-Float64(z / x))));
	elseif (a <= 65000000000.0)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	elseif (a <= 6.8e+59)
		tmp = Float64(Float64(-x) / Float64(Float64(a - z) / y));
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -9.4e-8)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 8e-21)
		tmp = t - (y / -(z / x));
	elseif (a <= 65000000000.0)
		tmp = t * (y / (a - z));
	elseif (a <= 6.8e+59)
		tmp = -x / ((a - z) / y);
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -9.4e-8], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 8e-21], N[(t - N[(y / (-N[(z / x), $MachinePrecision])), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 65000000000.0], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 6.8e+59], N[((-x) / N[(N[(a - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 5: 44.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -5 \cdot 10^{-29}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 7.5 \cdot 10^{-112}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 8.8 \cdot 10^{+24}:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{elif}\;a \leq 9.6 \cdot 10^{+58}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -5e-29)
     t_1
     (if (<= a 7.5e-112)
       t
       (if (<= a 8.8e+24)
         (* t (/ y (- a z)))
         (if (<= a 9.6e+58) t_1 (+ x (* y (/ t a)))))))))

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 <= -5e-29) {
		tmp = t_1;
	} else if (a <= 7.5e-112) {
		tmp = t;
	} else if (a <= 8.8e+24) {
		tmp = t * (y / (a - z));
	} else if (a <= 9.6e+58) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / 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 * (1.0d0 - (y / a))
    if (a <= (-5d-29)) then
        tmp = t_1
    else if (a <= 7.5d-112) then
        tmp = t
    else if (a <= 8.8d+24) then
        tmp = t * (y / (a - z))
    else if (a <= 9.6d+58) then
        tmp = t_1
    else
        tmp = x + (y * (t / 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 * (1.0 - (y / a));
	double tmp;
	if (a <= -5e-29) {
		tmp = t_1;
	} else if (a <= 7.5e-112) {
		tmp = t;
	} else if (a <= 8.8e+24) {
		tmp = t * (y / (a - z));
	} else if (a <= 9.6e+58) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -5e-29:
		tmp = t_1
	elif a <= 7.5e-112:
		tmp = t
	elif a <= 8.8e+24:
		tmp = t * (y / (a - z))
	elif a <= 9.6e+58:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	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 <= -5e-29)
		tmp = t_1;
	elseif (a <= 7.5e-112)
		tmp = t;
	elseif (a <= 8.8e+24)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	elseif (a <= 9.6e+58)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	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 <= -5e-29)
		tmp = t_1;
	elseif (a <= 7.5e-112)
		tmp = t;
	elseif (a <= 8.8e+24)
		tmp = t * (y / (a - z));
	elseif (a <= 9.6e+58)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	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, -5e-29], t$95$1, If[LessEqual[a, 7.5e-112], t, If[LessEqual[a, 8.8e+24], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 9.6e+58], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 45.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -2.02 \cdot 10^{-7}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{-116}:\\ \;\;\;\;t - x \cdot \frac{a}{z}\\ \mathbf{elif}\;a \leq 1.1 \cdot 10^{+24}:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{elif}\;a \leq 1.25 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -2.02e-7)
     t_1
     (if (<= a 2.4e-116)
       (- t (* x (/ a z)))
       (if (<= a 1.1e+24)
         (* t (/ y (- a z)))
         (if (<= a 1.25e+59) t_1 (+ x (* y (/ t a)))))))))

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 <= -2.02e-7) {
		tmp = t_1;
	} else if (a <= 2.4e-116) {
		tmp = t - (x * (a / z));
	} else if (a <= 1.1e+24) {
		tmp = t * (y / (a - z));
	} else if (a <= 1.25e+59) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / 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 * (1.0d0 - (y / a))
    if (a <= (-2.02d-7)) then
        tmp = t_1
    else if (a <= 2.4d-116) then
        tmp = t - (x * (a / z))
    else if (a <= 1.1d+24) then
        tmp = t * (y / (a - z))
    else if (a <= 1.25d+59) then
        tmp = t_1
    else
        tmp = x + (y * (t / 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 * (1.0 - (y / a));
	double tmp;
	if (a <= -2.02e-7) {
		tmp = t_1;
	} else if (a <= 2.4e-116) {
		tmp = t - (x * (a / z));
	} else if (a <= 1.1e+24) {
		tmp = t * (y / (a - z));
	} else if (a <= 1.25e+59) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -2.02e-7:
		tmp = t_1
	elif a <= 2.4e-116:
		tmp = t - (x * (a / z))
	elif a <= 1.1e+24:
		tmp = t * (y / (a - z))
	elif a <= 1.25e+59:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	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 <= -2.02e-7)
		tmp = t_1;
	elseif (a <= 2.4e-116)
		tmp = Float64(t - Float64(x * Float64(a / z)));
	elseif (a <= 1.1e+24)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	elseif (a <= 1.25e+59)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	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 <= -2.02e-7)
		tmp = t_1;
	elseif (a <= 2.4e-116)
		tmp = t - (x * (a / z));
	elseif (a <= 1.1e+24)
		tmp = t * (y / (a - z));
	elseif (a <= 1.25e+59)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	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, -2.02e-7], t$95$1, If[LessEqual[a, 2.4e-116], N[(t - N[(x * N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.1e+24], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.25e+59], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 45.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -1.56 \cdot 10^{-8}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 8.5 \cdot 10^{-116}:\\ \;\;\;\;t - \frac{x}{\frac{z}{a}}\\ \mathbf{elif}\;a \leq 4.6 \cdot 10^{+24}:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{elif}\;a \leq 2.5 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -1.56e-8)
     t_1
     (if (<= a 8.5e-116)
       (- t (/ x (/ z a)))
       (if (<= a 4.6e+24)
         (* t (/ y (- a z)))
         (if (<= a 2.5e+59) t_1 (+ x (* y (/ t a)))))))))

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 <= -1.56e-8) {
		tmp = t_1;
	} else if (a <= 8.5e-116) {
		tmp = t - (x / (z / a));
	} else if (a <= 4.6e+24) {
		tmp = t * (y / (a - z));
	} else if (a <= 2.5e+59) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / 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 * (1.0d0 - (y / a))
    if (a <= (-1.56d-8)) then
        tmp = t_1
    else if (a <= 8.5d-116) then
        tmp = t - (x / (z / a))
    else if (a <= 4.6d+24) then
        tmp = t * (y / (a - z))
    else if (a <= 2.5d+59) then
        tmp = t_1
    else
        tmp = x + (y * (t / 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 * (1.0 - (y / a));
	double tmp;
	if (a <= -1.56e-8) {
		tmp = t_1;
	} else if (a <= 8.5e-116) {
		tmp = t - (x / (z / a));
	} else if (a <= 4.6e+24) {
		tmp = t * (y / (a - z));
	} else if (a <= 2.5e+59) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -1.56e-8:
		tmp = t_1
	elif a <= 8.5e-116:
		tmp = t - (x / (z / a))
	elif a <= 4.6e+24:
		tmp = t * (y / (a - z))
	elif a <= 2.5e+59:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	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 <= -1.56e-8)
		tmp = t_1;
	elseif (a <= 8.5e-116)
		tmp = Float64(t - Float64(x / Float64(z / a)));
	elseif (a <= 4.6e+24)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	elseif (a <= 2.5e+59)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	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 <= -1.56e-8)
		tmp = t_1;
	elseif (a <= 8.5e-116)
		tmp = t - (x / (z / a));
	elseif (a <= 4.6e+24)
		tmp = t * (y / (a - z));
	elseif (a <= 2.5e+59)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	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, -1.56e-8], t$95$1, If[LessEqual[a, 8.5e-116], N[(t - N[(x / N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.6e+24], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.5e+59], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 8: 45.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -3.8 \cdot 10^{-9}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 3 \cdot 10^{-115}:\\ \;\;\;\;t - \frac{x \cdot a}{z}\\ \mathbf{elif}\;a \leq 8.5 \cdot 10^{+25}:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{elif}\;a \leq 7.8 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -3.8e-9)
     t_1
     (if (<= a 3e-115)
       (- t (/ (* x a) z))
       (if (<= a 8.5e+25)
         (* t (/ y (- a z)))
         (if (<= a 7.8e+59) t_1 (+ x (* y (/ t a)))))))))

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 <= -3.8e-9) {
		tmp = t_1;
	} else if (a <= 3e-115) {
		tmp = t - ((x * a) / z);
	} else if (a <= 8.5e+25) {
		tmp = t * (y / (a - z));
	} else if (a <= 7.8e+59) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / 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 * (1.0d0 - (y / a))
    if (a <= (-3.8d-9)) then
        tmp = t_1
    else if (a <= 3d-115) then
        tmp = t - ((x * a) / z)
    else if (a <= 8.5d+25) then
        tmp = t * (y / (a - z))
    else if (a <= 7.8d+59) then
        tmp = t_1
    else
        tmp = x + (y * (t / 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 * (1.0 - (y / a));
	double tmp;
	if (a <= -3.8e-9) {
		tmp = t_1;
	} else if (a <= 3e-115) {
		tmp = t - ((x * a) / z);
	} else if (a <= 8.5e+25) {
		tmp = t * (y / (a - z));
	} else if (a <= 7.8e+59) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -3.8e-9:
		tmp = t_1
	elif a <= 3e-115:
		tmp = t - ((x * a) / z)
	elif a <= 8.5e+25:
		tmp = t * (y / (a - z))
	elif a <= 7.8e+59:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	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 <= -3.8e-9)
		tmp = t_1;
	elseif (a <= 3e-115)
		tmp = Float64(t - Float64(Float64(x * a) / z));
	elseif (a <= 8.5e+25)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	elseif (a <= 7.8e+59)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	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 <= -3.8e-9)
		tmp = t_1;
	elseif (a <= 3e-115)
		tmp = t - ((x * a) / z);
	elseif (a <= 8.5e+25)
		tmp = t * (y / (a - z));
	elseif (a <= 7.8e+59)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	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, -3.8e-9], t$95$1, If[LessEqual[a, 3e-115], N[(t - N[(N[(x * a), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 8.5e+25], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7.8e+59], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 9: 52.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - \frac{y \cdot t}{z}\\ t_2 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -2.1 \cdot 10^{-29}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 4.4 \cdot 10^{-37}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.4 \cdot 10^{+69}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (/ (* y t) z))) (t_2 (* x (- 1.0 (/ y a)))))
   (if (<= a -2.1e-29)
     t_2
     (if (<= a 4.4e-37)
       t_1
       (if (<= a 1e+60) t_2 (if (<= a 1.4e+69) t_1 (+ x (* y (/ t a)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - ((y * t) / z);
	double t_2 = x * (1.0 - (y / a));
	double tmp;
	if (a <= -2.1e-29) {
		tmp = t_2;
	} else if (a <= 4.4e-37) {
		tmp = t_1;
	} else if (a <= 1e+60) {
		tmp = t_2;
	} else if (a <= 1.4e+69) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / 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) :: t_2
    real(8) :: tmp
    t_1 = t - ((y * t) / z)
    t_2 = x * (1.0d0 - (y / a))
    if (a <= (-2.1d-29)) then
        tmp = t_2
    else if (a <= 4.4d-37) then
        tmp = t_1
    else if (a <= 1d+60) then
        tmp = t_2
    else if (a <= 1.4d+69) then
        tmp = t_1
    else
        tmp = x + (y * (t / 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 = t - ((y * t) / z);
	double t_2 = x * (1.0 - (y / a));
	double tmp;
	if (a <= -2.1e-29) {
		tmp = t_2;
	} else if (a <= 4.4e-37) {
		tmp = t_1;
	} else if (a <= 1e+60) {
		tmp = t_2;
	} else if (a <= 1.4e+69) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - ((y * t) / z)
	t_2 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -2.1e-29:
		tmp = t_2
	elif a <= 4.4e-37:
		tmp = t_1
	elif a <= 1e+60:
		tmp = t_2
	elif a <= 1.4e+69:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(Float64(y * t) / z))
	t_2 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (a <= -2.1e-29)
		tmp = t_2;
	elseif (a <= 4.4e-37)
		tmp = t_1;
	elseif (a <= 1e+60)
		tmp = t_2;
	elseif (a <= 1.4e+69)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - ((y * t) / z);
	t_2 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (a <= -2.1e-29)
		tmp = t_2;
	elseif (a <= 4.4e-37)
		tmp = t_1;
	elseif (a <= 1e+60)
		tmp = t_2;
	elseif (a <= 1.4e+69)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(N[(y * t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.1e-29], t$95$2, If[LessEqual[a, 4.4e-37], t$95$1, If[LessEqual[a, 1e+60], t$95$2, If[LessEqual[a, 1.4e+69], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 10: 51.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -1.5 \cdot 10^{-29}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.75 \cdot 10^{-38}:\\ \;\;\;\;t - \frac{y \cdot t}{z}\\ \mathbf{elif}\;a \leq 2.7 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.45 \cdot 10^{+73}:\\ \;\;\;\;\frac{t}{1 - \frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -1.5e-29)
     t_1
     (if (<= a 1.75e-38)
       (- t (/ (* y t) z))
       (if (<= a 2.7e+59)
         t_1
         (if (<= a 1.45e+73) (/ t (- 1.0 (/ a z))) (+ x (* y (/ t a)))))))))

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 <= -1.5e-29) {
		tmp = t_1;
	} else if (a <= 1.75e-38) {
		tmp = t - ((y * t) / z);
	} else if (a <= 2.7e+59) {
		tmp = t_1;
	} else if (a <= 1.45e+73) {
		tmp = t / (1.0 - (a / z));
	} else {
		tmp = x + (y * (t / 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 * (1.0d0 - (y / a))
    if (a <= (-1.5d-29)) then
        tmp = t_1
    else if (a <= 1.75d-38) then
        tmp = t - ((y * t) / z)
    else if (a <= 2.7d+59) then
        tmp = t_1
    else if (a <= 1.45d+73) then
        tmp = t / (1.0d0 - (a / z))
    else
        tmp = x + (y * (t / 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 * (1.0 - (y / a));
	double tmp;
	if (a <= -1.5e-29) {
		tmp = t_1;
	} else if (a <= 1.75e-38) {
		tmp = t - ((y * t) / z);
	} else if (a <= 2.7e+59) {
		tmp = t_1;
	} else if (a <= 1.45e+73) {
		tmp = t / (1.0 - (a / z));
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if a <= -1.5e-29:
		tmp = t_1
	elif a <= 1.75e-38:
		tmp = t - ((y * t) / z)
	elif a <= 2.7e+59:
		tmp = t_1
	elif a <= 1.45e+73:
		tmp = t / (1.0 - (a / z))
	else:
		tmp = x + (y * (t / a))
	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 <= -1.5e-29)
		tmp = t_1;
	elseif (a <= 1.75e-38)
		tmp = Float64(t - Float64(Float64(y * t) / z));
	elseif (a <= 2.7e+59)
		tmp = t_1;
	elseif (a <= 1.45e+73)
		tmp = Float64(t / Float64(1.0 - Float64(a / z)));
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	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 <= -1.5e-29)
		tmp = t_1;
	elseif (a <= 1.75e-38)
		tmp = t - ((y * t) / z);
	elseif (a <= 2.7e+59)
		tmp = t_1;
	elseif (a <= 1.45e+73)
		tmp = t / (1.0 - (a / z));
	else
		tmp = x + (y * (t / a));
	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, -1.5e-29], t$95$1, If[LessEqual[a, 1.75e-38], N[(t - N[(N[(y * t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.7e+59], t$95$1, If[LessEqual[a, 1.45e+73], N[(t / N[(1.0 - N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 11: 56.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.2 \cdot 10^{-8}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 1.6 \cdot 10^{-137}:\\ \;\;\;\;t - \frac{y}{-\frac{z}{x}}\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{+75}:\\ \;\;\;\;y \cdot \frac{t - x}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.2e-8)
   (* x (- 1.0 (/ y a)))
   (if (<= a 1.6e-137)
     (- t (/ y (- (/ z x))))
     (if (<= a 2.4e+75) (* y (/ (- t x) (- a z))) (+ x (* y (/ t a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.2e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.6e-137) {
		tmp = t - (y / -(z / x));
	} else if (a <= 2.4e+75) {
		tmp = y * ((t - x) / (a - z));
	} else {
		tmp = x + (y * (t / 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 (a <= (-3.2d-8)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 1.6d-137) then
        tmp = t - (y / -(z / x))
    else if (a <= 2.4d+75) then
        tmp = y * ((t - x) / (a - z))
    else
        tmp = x + (y * (t / 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 (a <= -3.2e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.6e-137) {
		tmp = t - (y / -(z / x));
	} else if (a <= 2.4e+75) {
		tmp = y * ((t - x) / (a - z));
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.2e-8:
		tmp = x * (1.0 - (y / a))
	elif a <= 1.6e-137:
		tmp = t - (y / -(z / x))
	elif a <= 2.4e+75:
		tmp = y * ((t - x) / (a - z))
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.2e-8)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 1.6e-137)
		tmp = Float64(t - Float64(y / Float64(-Float64(z / x))));
	elseif (a <= 2.4e+75)
		tmp = Float64(y * Float64(Float64(t - x) / Float64(a - z)));
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.2e-8)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 1.6e-137)
		tmp = t - (y / -(z / x));
	elseif (a <= 2.4e+75)
		tmp = y * ((t - x) / (a - z));
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.2e-8], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.6e-137], N[(t - N[(y / (-N[(z / x), $MachinePrecision])), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.4e+75], N[(y * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 12: 56.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -6.4 \cdot 10^{-8}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 4.1 \cdot 10^{-112}:\\ \;\;\;\;t - \frac{y}{-\frac{z}{x}}\\ \mathbf{elif}\;a \leq 4.5 \cdot 10^{+74}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -6.4e-8)
   (* x (- 1.0 (/ y a)))
   (if (<= a 4.1e-112)
     (- t (/ y (- (/ z x))))
     (if (<= a 4.5e+74) (* (- t x) (/ y (- a z))) (+ x (* y (/ t a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -6.4e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 4.1e-112) {
		tmp = t - (y / -(z / x));
	} else if (a <= 4.5e+74) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = x + (y * (t / 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 (a <= (-6.4d-8)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 4.1d-112) then
        tmp = t - (y / -(z / x))
    else if (a <= 4.5d+74) then
        tmp = (t - x) * (y / (a - z))
    else
        tmp = x + (y * (t / 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 (a <= -6.4e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 4.1e-112) {
		tmp = t - (y / -(z / x));
	} else if (a <= 4.5e+74) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -6.4e-8:
		tmp = x * (1.0 - (y / a))
	elif a <= 4.1e-112:
		tmp = t - (y / -(z / x))
	elif a <= 4.5e+74:
		tmp = (t - x) * (y / (a - z))
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -6.4e-8)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 4.1e-112)
		tmp = Float64(t - Float64(y / Float64(-Float64(z / x))));
	elseif (a <= 4.5e+74)
		tmp = Float64(Float64(t - x) * Float64(y / Float64(a - z)));
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -6.4e-8)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 4.1e-112)
		tmp = t - (y / -(z / x));
	elseif (a <= 4.5e+74)
		tmp = (t - x) * (y / (a - z));
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -6.4e-8], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.1e-112], N[(t - N[(y / (-N[(z / x), $MachinePrecision])), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.5e+74], N[(N[(t - x), $MachinePrecision] * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 13: 62.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{t - x}{a}\\ \mathbf{if}\;a \leq -2 \cdot 10^{-10}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 7.8 \cdot 10^{-113}:\\ \;\;\;\;t - \frac{y}{-\frac{z}{x}}\\ \mathbf{elif}\;a \leq 9.2 \cdot 10^{+33}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ (- t x) a)))))
   (if (<= a -2e-10)
     t_1
     (if (<= a 7.8e-113)
       (- t (/ y (- (/ z x))))
       (if (<= a 9.2e+33) (* (- t x) (/ y (- a z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * ((t - x) / a));
	double tmp;
	if (a <= -2e-10) {
		tmp = t_1;
	} else if (a <= 7.8e-113) {
		tmp = t - (y / -(z / x));
	} else if (a <= 9.2e+33) {
		tmp = (t - x) * (y / (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 + (y * ((t - x) / a))
    if (a <= (-2d-10)) then
        tmp = t_1
    else if (a <= 7.8d-113) then
        tmp = t - (y / -(z / x))
    else if (a <= 9.2d+33) then
        tmp = (t - x) * (y / (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 + (y * ((t - x) / a));
	double tmp;
	if (a <= -2e-10) {
		tmp = t_1;
	} else if (a <= 7.8e-113) {
		tmp = t - (y / -(z / x));
	} else if (a <= 9.2e+33) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * ((t - x) / a))
	tmp = 0
	if a <= -2e-10:
		tmp = t_1
	elif a <= 7.8e-113:
		tmp = t - (y / -(z / x))
	elif a <= 9.2e+33:
		tmp = (t - x) * (y / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(Float64(t - x) / a)))
	tmp = 0.0
	if (a <= -2e-10)
		tmp = t_1;
	elseif (a <= 7.8e-113)
		tmp = Float64(t - Float64(y / Float64(-Float64(z / x))));
	elseif (a <= 9.2e+33)
		tmp = Float64(Float64(t - x) * Float64(y / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * ((t - x) / a));
	tmp = 0.0;
	if (a <= -2e-10)
		tmp = t_1;
	elseif (a <= 7.8e-113)
		tmp = t - (y / -(z / x));
	elseif (a <= 9.2e+33)
		tmp = (t - x) * (y / (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[(y * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2e-10], t$95$1, If[LessEqual[a, 7.8e-113], N[(t - N[(y / (-N[(z / x), $MachinePrecision])), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 9.2e+33], N[(N[(t - x), $MachinePrecision] * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 14: 68.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{t - x}{a}\\ \mathbf{if}\;a \leq -2.95 \cdot 10^{-8}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 3.8 \cdot 10^{-110}:\\ \;\;\;\;t - \frac{y}{\frac{z}{t - x}}\\ \mathbf{elif}\;a \leq 1.66 \cdot 10^{+34}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ (- t x) a)))))
   (if (<= a -2.95e-8)
     t_1
     (if (<= a 3.8e-110)
       (- t (/ y (/ z (- t x))))
       (if (<= a 1.66e+34) (* (- t x) (/ y (- a z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * ((t - x) / a));
	double tmp;
	if (a <= -2.95e-8) {
		tmp = t_1;
	} else if (a <= 3.8e-110) {
		tmp = t - (y / (z / (t - x)));
	} else if (a <= 1.66e+34) {
		tmp = (t - x) * (y / (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 + (y * ((t - x) / a))
    if (a <= (-2.95d-8)) then
        tmp = t_1
    else if (a <= 3.8d-110) then
        tmp = t - (y / (z / (t - x)))
    else if (a <= 1.66d+34) then
        tmp = (t - x) * (y / (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 + (y * ((t - x) / a));
	double tmp;
	if (a <= -2.95e-8) {
		tmp = t_1;
	} else if (a <= 3.8e-110) {
		tmp = t - (y / (z / (t - x)));
	} else if (a <= 1.66e+34) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * ((t - x) / a))
	tmp = 0
	if a <= -2.95e-8:
		tmp = t_1
	elif a <= 3.8e-110:
		tmp = t - (y / (z / (t - x)))
	elif a <= 1.66e+34:
		tmp = (t - x) * (y / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(Float64(t - x) / a)))
	tmp = 0.0
	if (a <= -2.95e-8)
		tmp = t_1;
	elseif (a <= 3.8e-110)
		tmp = Float64(t - Float64(y / Float64(z / Float64(t - x))));
	elseif (a <= 1.66e+34)
		tmp = Float64(Float64(t - x) * Float64(y / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * ((t - x) / a));
	tmp = 0.0;
	if (a <= -2.95e-8)
		tmp = t_1;
	elseif (a <= 3.8e-110)
		tmp = t - (y / (z / (t - x)));
	elseif (a <= 1.66e+34)
		tmp = (t - x) * (y / (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[(y * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.95e-8], t$95$1, If[LessEqual[a, 3.8e-110], N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.66e+34], N[(N[(t - x), $MachinePrecision] * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 15: 74.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.8 \cdot 10^{-12} \lor \neg \left(a \leq 1.75 \cdot 10^{-38}\right):\\ \;\;\;\;x + \frac{t - x}{\frac{a}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{y}{\frac{z}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -9.8e-12) (not (<= a 1.75e-38)))
   (+ x (/ (- t x) (/ a (- y z))))
   (- t (/ y (/ z (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -9.8e-12) || !(a <= 1.75e-38)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t - (y / (z / (t - x)));
	}
	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 <= (-9.8d-12)) .or. (.not. (a <= 1.75d-38))) then
        tmp = x + ((t - x) / (a / (y - z)))
    else
        tmp = t - (y / (z / (t - x)))
    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 <= -9.8e-12) || !(a <= 1.75e-38)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t - (y / (z / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -9.8e-12) or not (a <= 1.75e-38):
		tmp = x + ((t - x) / (a / (y - z)))
	else:
		tmp = t - (y / (z / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -9.8e-12) || !(a <= 1.75e-38))
		tmp = Float64(x + Float64(Float64(t - x) / Float64(a / Float64(y - z))));
	else
		tmp = Float64(t - Float64(y / Float64(z / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -9.8e-12) || ~((a <= 1.75e-38)))
		tmp = x + ((t - x) / (a / (y - z)));
	else
		tmp = t - (y / (z / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -9.8e-12], N[Not[LessEqual[a, 1.75e-38]], $MachinePrecision]], N[(x + N[(N[(t - x), $MachinePrecision] / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 76.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1 \cdot 10^{+64} \lor \neg \left(a \leq 6.5 \cdot 10^{-21}\right):\\ \;\;\;\;x + \frac{t - x}{\frac{a}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t + \left(t - x\right) \cdot \frac{a - y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -1e+64) (not (<= a 6.5e-21)))
   (+ x (/ (- t x) (/ a (- y z))))
   (+ t (* (- t x) (/ (- a y) z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -1e+64) || !(a <= 6.5e-21)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t + ((t - x) * ((a - 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 <= (-1d+64)) .or. (.not. (a <= 6.5d-21))) then
        tmp = x + ((t - x) / (a / (y - z)))
    else
        tmp = t + ((t - x) * ((a - 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 <= -1e+64) || !(a <= 6.5e-21)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t + ((t - x) * ((a - y) / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -1e+64) or not (a <= 6.5e-21):
		tmp = x + ((t - x) / (a / (y - z)))
	else:
		tmp = t + ((t - x) * ((a - y) / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -1e+64) || !(a <= 6.5e-21))
		tmp = Float64(x + Float64(Float64(t - x) / Float64(a / Float64(y - z))));
	else
		tmp = Float64(t + Float64(Float64(t - x) * Float64(Float64(a - y) / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -1e+64) || ~((a <= 6.5e-21)))
		tmp = x + ((t - x) / (a / (y - z)));
	else
		tmp = t + ((t - x) * ((a - y) / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -1e+64], N[Not[LessEqual[a, 6.5e-21]], $MachinePrecision]], N[(x + N[(N[(t - x), $MachinePrecision] / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(N[(t - x), $MachinePrecision] * N[(N[(a - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 77.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -5.6 \cdot 10^{-11} \lor \neg \left(a \leq 4.1 \cdot 10^{-21}\right):\\ \;\;\;\;x + \frac{t - x}{\frac{a}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -5.6e-11) (not (<= a 4.1e-21)))
   (+ x (/ (- t x) (/ a (- y z))))
   (+ t (/ (- x t) (/ z (- y a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -5.6e-11) || !(a <= 4.1e-21)) {
		tmp = x + ((t - x) / (a / (y - 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) :: tmp
    if ((a <= (-5.6d-11)) .or. (.not. (a <= 4.1d-21))) then
        tmp = x + ((t - x) / (a / (y - 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 tmp;
	if ((a <= -5.6e-11) || !(a <= 4.1e-21)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -5.6e-11) or not (a <= 4.1e-21):
		tmp = x + ((t - x) / (a / (y - z)))
	else:
		tmp = t + ((x - t) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -5.6e-11) || !(a <= 4.1e-21))
		tmp = Float64(x + Float64(Float64(t - x) / Float64(a / Float64(y - 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)
	tmp = 0.0;
	if ((a <= -5.6e-11) || ~((a <= 4.1e-21)))
		tmp = x + ((t - x) / (a / (y - z)));
	else
		tmp = t + ((x - t) / (z / (y - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -5.6e-11], N[Not[LessEqual[a, 4.1e-21]], $MachinePrecision]], N[(x + N[(N[(t - x), $MachinePrecision] / N[(a / N[(y - 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 18: 38.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.1 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.95 \cdot 10^{-114}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 2.05 \cdot 10^{+80}:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.1e-12)
   x
   (if (<= a 1.95e-114) t (if (<= a 2.05e+80) (* t (/ y (- a z))) x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.1e-12) {
		tmp = x;
	} else if (a <= 1.95e-114) {
		tmp = t;
	} else if (a <= 2.05e+80) {
		tmp = t * (y / (a - z));
	} else {
		tmp = x;
	}
	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 <= (-2.1d-12)) then
        tmp = x
    else if (a <= 1.95d-114) then
        tmp = t
    else if (a <= 2.05d+80) then
        tmp = t * (y / (a - z))
    else
        tmp = x
    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 <= -2.1e-12) {
		tmp = x;
	} else if (a <= 1.95e-114) {
		tmp = t;
	} else if (a <= 2.05e+80) {
		tmp = t * (y / (a - z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.1e-12:
		tmp = x
	elif a <= 1.95e-114:
		tmp = t
	elif a <= 2.05e+80:
		tmp = t * (y / (a - z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.1e-12)
		tmp = x;
	elseif (a <= 1.95e-114)
		tmp = t;
	elseif (a <= 2.05e+80)
		tmp = Float64(t * Float64(y / Float64(a - z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.1e-12)
		tmp = x;
	elseif (a <= 1.95e-114)
		tmp = t;
	elseif (a <= 2.05e+80)
		tmp = t * (y / (a - z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.1e-12], x, If[LessEqual[a, 1.95e-114], t, If[LessEqual[a, 2.05e+80], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

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

Alternative 19: 42.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;a \leq -1.7 \cdot 10^{-24}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 3.9 \cdot 10^{-111}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 10^{+25}:\\ \;\;\;\;t \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= a -1.7e-24)
     t_1
     (if (<= a 3.9e-111) t (if (<= a 1e+25) (* t (/ y (- 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 (a <= -1.7e-24) {
		tmp = t_1;
	} else if (a <= 3.9e-111) {
		tmp = t;
	} else if (a <= 1e+25) {
		tmp = t * (y / (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 (a <= (-1.7d-24)) then
        tmp = t_1
    else if (a <= 3.9d-111) then
        tmp = t
    else if (a <= 1d+25) then
        tmp = t * (y / (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 (a <= -1.7e-24) {
		tmp = t_1;
	} else if (a <= 3.9e-111) {
		tmp = t;
	} else if (a <= 1e+25) {
		tmp = t * (y / (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 a <= -1.7e-24:
		tmp = t_1
	elif a <= 3.9e-111:
		tmp = t
	elif a <= 1e+25:
		tmp = t * (y / (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 (a <= -1.7e-24)
		tmp = t_1;
	elseif (a <= 3.9e-111)
		tmp = t;
	elseif (a <= 1e+25)
		tmp = Float64(t * Float64(y / 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 (a <= -1.7e-24)
		tmp = t_1;
	elseif (a <= 3.9e-111)
		tmp = t;
	elseif (a <= 1e+25)
		tmp = t * (y / (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[a, -1.7e-24], t$95$1, If[LessEqual[a, 3.9e-111], t, If[LessEqual[a, 1e+25], N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 20: 51.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.4 \cdot 10^{-29}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{-38}:\\ \;\;\;\;t - \frac{y \cdot t}{z}\\ \mathbf{elif}\;a \leq 5.8 \cdot 10^{+74}:\\ \;\;\;\;\frac{y}{\frac{a}{t - x}}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.4e-29)
   (* x (- 1.0 (/ y a)))
   (if (<= a 2.4e-38)
     (- t (/ (* y t) z))
     (if (<= a 5.8e+74) (/ y (/ a (- t x))) (+ x (* y (/ t a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.4e-29) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 2.4e-38) {
		tmp = t - ((y * t) / z);
	} else if (a <= 5.8e+74) {
		tmp = y / (a / (t - x));
	} else {
		tmp = x + (y * (t / 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 (a <= (-1.4d-29)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 2.4d-38) then
        tmp = t - ((y * t) / z)
    else if (a <= 5.8d+74) then
        tmp = y / (a / (t - x))
    else
        tmp = x + (y * (t / 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 (a <= -1.4e-29) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 2.4e-38) {
		tmp = t - ((y * t) / z);
	} else if (a <= 5.8e+74) {
		tmp = y / (a / (t - x));
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.4e-29:
		tmp = x * (1.0 - (y / a))
	elif a <= 2.4e-38:
		tmp = t - ((y * t) / z)
	elif a <= 5.8e+74:
		tmp = y / (a / (t - x))
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.4e-29)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 2.4e-38)
		tmp = Float64(t - Float64(Float64(y * t) / z));
	elseif (a <= 5.8e+74)
		tmp = Float64(y / Float64(a / Float64(t - x)));
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.4e-29)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 2.4e-38)
		tmp = t - ((y * t) / z);
	elseif (a <= 5.8e+74)
		tmp = y / (a / (t - x));
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.4e-29], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.4e-38], N[(t - N[(N[(y * t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.8e+74], N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 21: 37.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{-115}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 6.2 \cdot 10^{+14}:\\ \;\;\;\;t \cdot \frac{y}{a}\\ \mathbf{elif}\;a \leq 7 \cdot 10^{+70}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.7e-12)
   x
   (if (<= a 3.1e-115)
     t
     (if (<= a 6.2e+14) (* t (/ y a)) (if (<= a 7e+70) t x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 3.1e-115) {
		tmp = t;
	} else if (a <= 6.2e+14) {
		tmp = t * (y / a);
	} else if (a <= 7e+70) {
		tmp = t;
	} else {
		tmp = x;
	}
	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 <= (-3.7d-12)) then
        tmp = x
    else if (a <= 3.1d-115) then
        tmp = t
    else if (a <= 6.2d+14) then
        tmp = t * (y / a)
    else if (a <= 7d+70) then
        tmp = t
    else
        tmp = x
    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 <= -3.7e-12) {
		tmp = x;
	} else if (a <= 3.1e-115) {
		tmp = t;
	} else if (a <= 6.2e+14) {
		tmp = t * (y / a);
	} else if (a <= 7e+70) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.7e-12:
		tmp = x
	elif a <= 3.1e-115:
		tmp = t
	elif a <= 6.2e+14:
		tmp = t * (y / a)
	elif a <= 7e+70:
		tmp = t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 3.1e-115)
		tmp = t;
	elseif (a <= 6.2e+14)
		tmp = Float64(t * Float64(y / a));
	elseif (a <= 7e+70)
		tmp = t;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 3.1e-115)
		tmp = t;
	elseif (a <= 6.2e+14)
		tmp = t * (y / a);
	elseif (a <= 7e+70)
		tmp = t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 3.1e-115], t, If[LessEqual[a, 6.2e+14], N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7e+70], t, x]]]]

Derivation

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

Alternative 22: 37.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.6 \cdot 10^{-116}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 10^{+59}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.7e-12)
   x
   (if (<= a 2.6e-116) t (if (<= a 1e+59) (* x (/ y z)) x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 2.6e-116) {
		tmp = t;
	} else if (a <= 1e+59) {
		tmp = x * (y / z);
	} else {
		tmp = x;
	}
	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 <= (-3.7d-12)) then
        tmp = x
    else if (a <= 2.6d-116) then
        tmp = t
    else if (a <= 1d+59) then
        tmp = x * (y / z)
    else
        tmp = x
    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 <= -3.7e-12) {
		tmp = x;
	} else if (a <= 2.6e-116) {
		tmp = t;
	} else if (a <= 1e+59) {
		tmp = x * (y / z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.7e-12:
		tmp = x
	elif a <= 2.6e-116:
		tmp = t
	elif a <= 1e+59:
		tmp = x * (y / z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 2.6e-116)
		tmp = t;
	elseif (a <= 1e+59)
		tmp = Float64(x * Float64(y / z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 2.6e-116)
		tmp = t;
	elseif (a <= 1e+59)
		tmp = x * (y / z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 2.6e-116], t, If[LessEqual[a, 1e+59], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

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

Alternative 23: 39.0% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 7.5 \cdot 10^{+73}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.7e-12) x (if (<= a 7.5e+73) t x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 7.5e+73) {
		tmp = t;
	} else {
		tmp = x;
	}
	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 <= (-3.7d-12)) then
        tmp = x
    else if (a <= 7.5d+73) then
        tmp = t
    else
        tmp = x
    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 <= -3.7e-12) {
		tmp = x;
	} else if (a <= 7.5e+73) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.7e-12:
		tmp = x
	elif a <= 7.5e+73:
		tmp = t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 7.5e+73)
		tmp = t;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 7.5e+73)
		tmp = t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 7.5e+73], t, x]]

Derivation

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

Alternative 24: 25.4% 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 2023343 
(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)))))