Graphics.Rendering.Chart.Axis.Types:linMap from Chart-1.5.3

Percentage Accurate: 68.0% → 90.4%

Time: 23.7s

Alternatives: 21

Speedup: 0.9×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 68.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 90.4% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{a - t}\\ t_2 := x + \frac{\left(y - x\right) \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t_2 \leq -5 \cdot 10^{-281}:\\ \;\;\;\;y \cdot t_1 - \left(\frac{x}{\frac{a - t}{z - t}} - x\right)\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;y - \frac{\left(x - y\right) \cdot \left(a - z\right)}{t}\\ \mathbf{elif}\;t_2 \leq 10^{+260}:\\ \;\;\;\;\frac{y \cdot \left(z - t\right)}{a - t} - x \cdot \left(t_1 + -1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y - x}{a - t}, z - t, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- a t))) (t_2 (+ x (/ (* (- y x) (- z t)) (- a t)))))
   (if (<= t_2 -5e-281)
     (- (* y t_1) (- (/ x (/ (- a t) (- z t))) x))
     (if (<= t_2 0.0)
       (- y (/ (* (- x y) (- a z)) t))
       (if (<= t_2 1e+260)
         (- (/ (* y (- z t)) (- a t)) (* x (+ t_1 -1.0)))
         (fma (/ (- y x) (- a t)) (- z t) x))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (a - t);
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -5e-281) {
		tmp = (y * t_1) - ((x / ((a - t) / (z - t))) - x);
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = ((y * (z - t)) / (a - t)) - (x * (t_1 + -1.0));
	} else {
		tmp = fma(((y - x) / (a - t)), (z - t), x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(a - t))
	t_2 = Float64(x + Float64(Float64(Float64(y - x) * Float64(z - t)) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= -5e-281)
		tmp = Float64(Float64(y * t_1) - Float64(Float64(x / Float64(Float64(a - t) / Float64(z - t))) - x));
	elseif (t_2 <= 0.0)
		tmp = Float64(y - Float64(Float64(Float64(x - y) * Float64(a - z)) / t));
	elseif (t_2 <= 1e+260)
		tmp = Float64(Float64(Float64(y * Float64(z - t)) / Float64(a - t)) - Float64(x * Float64(t_1 + -1.0)));
	else
		tmp = fma(Float64(Float64(y - x) / Float64(a - t)), Float64(z - t), x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(y - x), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -5e-281], N[(N[(y * t$95$1), $MachinePrecision] - N[(N[(x / N[(N[(a - t), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0], N[(y - N[(N[(N[(x - y), $MachinePrecision] * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+260], N[(N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision] - N[(x * N[(t$95$1 + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y - x), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision] * N[(z - t), $MachinePrecision] + x), $MachinePrecision]]]]]]

Derivation

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

Alternative 2: 90.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{\left(y - x\right) \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t_1 \leq -\infty:\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{x - y}{a - t}\\ \mathbf{elif}\;t_1 \leq -1 \cdot 10^{-272}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_1 \leq 0:\\ \;\;\;\;y - \frac{\left(x - y\right) \cdot \left(a - z\right)}{t}\\ \mathbf{elif}\;t_1 \leq 10^{+260}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z - t}{\left(a - t\right) \cdot \frac{1}{y - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* (- y x) (- z t)) (- a t)))))
   (if (<= t_1 (- INFINITY))
     (+ x (* (- t z) (/ (- x y) (- a t))))
     (if (<= t_1 -1e-272)
       t_1
       (if (<= t_1 0.0)
         (- y (/ (* (- x y) (- a z)) t))
         (if (<= t_1 1e+260)
           t_1
           (+ x (/ (- z t) (* (- a t) (/ 1.0 (- y x)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	} else if (t_1 <= -1e-272) {
		tmp = t_1;
	} else if (t_1 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_1 <= 1e+260) {
		tmp = t_1;
	} else {
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	} else if (t_1 <= -1e-272) {
		tmp = t_1;
	} else if (t_1 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_1 <= 1e+260) {
		tmp = t_1;
	} else {
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (((y - x) * (z - t)) / (a - t))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = x + ((t - z) * ((x - y) / (a - t)))
	elif t_1 <= -1e-272:
		tmp = t_1
	elif t_1 <= 0.0:
		tmp = y - (((x - y) * (a - z)) / t)
	elif t_1 <= 1e+260:
		tmp = t_1
	else:
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(y - x) * Float64(z - t)) / Float64(a - t)))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / Float64(a - t))));
	elseif (t_1 <= -1e-272)
		tmp = t_1;
	elseif (t_1 <= 0.0)
		tmp = Float64(y - Float64(Float64(Float64(x - y) * Float64(a - z)) / t));
	elseif (t_1 <= 1e+260)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(Float64(z - t) / Float64(Float64(a - t) * Float64(1.0 / Float64(y - x)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (((y - x) * (z - t)) / (a - t));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	elseif (t_1 <= -1e-272)
		tmp = t_1;
	elseif (t_1 <= 0.0)
		tmp = y - (((x - y) * (a - z)) / t);
	elseif (t_1 <= 1e+260)
		tmp = t_1;
	else
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(y - x), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -1e-272], t$95$1, If[LessEqual[t$95$1, 0.0], N[(y - N[(N[(N[(x - y), $MachinePrecision] * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 1e+260], t$95$1, N[(x + N[(N[(z - t), $MachinePrecision] / N[(N[(a - t), $MachinePrecision] * N[(1.0 / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 3: 90.5% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(t - z\right) \cdot \frac{x - y}{a - t}\\ t_2 := x + \frac{\left(y - x\right) \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_2 \leq -1 \cdot 10^{-272}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;y - \frac{\left(x - y\right) \cdot \left(a - z\right)}{t}\\ \mathbf{elif}\;t_2 \leq 10^{+260}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- t z) (/ (- x y) (- a t)))))
        (t_2 (+ x (/ (* (- y x) (- z t)) (- a t)))))
   (if (<= t_2 (- INFINITY))
     t_1
     (if (<= t_2 -1e-272)
       t_2
       (if (<= t_2 0.0)
         (- y (/ (* (- x y) (- a z)) t))
         (if (<= t_2 1e+260) t_2 t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * ((x - y) / (a - t)));
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_1;
	} else if (t_2 <= -1e-272) {
		tmp = t_2;
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * ((x - y) / (a - t)));
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else if (t_2 <= -1e-272) {
		tmp = t_2;
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((t - z) * ((x - y) / (a - t)))
	t_2 = x + (((y - x) * (z - t)) / (a - t))
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_1
	elif t_2 <= -1e-272:
		tmp = t_2
	elif t_2 <= 0.0:
		tmp = y - (((x - y) * (a - z)) / t)
	elif t_2 <= 1e+260:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / Float64(a - t))))
	t_2 = Float64(x + Float64(Float64(Float64(y - x) * Float64(z - t)) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_1;
	elseif (t_2 <= -1e-272)
		tmp = t_2;
	elseif (t_2 <= 0.0)
		tmp = Float64(y - Float64(Float64(Float64(x - y) * Float64(a - z)) / t));
	elseif (t_2 <= 1e+260)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((t - z) * ((x - y) / (a - t)));
	t_2 = x + (((y - x) * (z - t)) / (a - t));
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_1;
	elseif (t_2 <= -1e-272)
		tmp = t_2;
	elseif (t_2 <= 0.0)
		tmp = y - (((x - y) * (a - z)) / t);
	elseif (t_2 <= 1e+260)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(y - x), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$1, If[LessEqual[t$95$2, -1e-272], t$95$2, If[LessEqual[t$95$2, 0.0], N[(y - N[(N[(N[(x - y), $MachinePrecision] * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+260], t$95$2, t$95$1]]]]]]

Derivation

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

Alternative 4: 90.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - t}{a - t} - \left(\frac{x}{\frac{a - t}{z - t}} - x\right)\\ t_2 := x + \frac{\left(y - x\right) \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t_2 \leq -5 \cdot 10^{-281}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;y - \frac{\left(x - y\right) \cdot \left(a - z\right)}{t}\\ \mathbf{elif}\;t_2 \leq 10^{+260}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z - t}{\left(a - t\right) \cdot \frac{1}{y - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- (* y (/ (- z t) (- a t))) (- (/ x (/ (- a t) (- z t))) x)))
        (t_2 (+ x (/ (* (- y x) (- z t)) (- a t)))))
   (if (<= t_2 -5e-281)
     t_1
     (if (<= t_2 0.0)
       (- y (/ (* (- x y) (- a z)) t))
       (if (<= t_2 1e+260)
         t_1
         (+ x (/ (- z t) (* (- a t) (/ 1.0 (- y x))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y * ((z - t) / (a - t))) - ((x / ((a - t) / (z - t))) - x);
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -5e-281) {
		tmp = t_1;
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = t_1;
	} else {
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - 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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (y * ((z - t) / (a - t))) - ((x / ((a - t) / (z - t))) - x)
    t_2 = x + (((y - x) * (z - t)) / (a - t))
    if (t_2 <= (-5d-281)) then
        tmp = t_1
    else if (t_2 <= 0.0d0) then
        tmp = y - (((x - y) * (a - z)) / t)
    else if (t_2 <= 1d+260) then
        tmp = t_1
    else
        tmp = x + ((z - t) / ((a - t) * (1.0d0 / (y - x))))
    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 * ((z - t) / (a - t))) - ((x / ((a - t) / (z - t))) - x);
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -5e-281) {
		tmp = t_1;
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = t_1;
	} else {
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y * ((z - t) / (a - t))) - ((x / ((a - t) / (z - t))) - x)
	t_2 = x + (((y - x) * (z - t)) / (a - t))
	tmp = 0
	if t_2 <= -5e-281:
		tmp = t_1
	elif t_2 <= 0.0:
		tmp = y - (((x - y) * (a - z)) / t)
	elif t_2 <= 1e+260:
		tmp = t_1
	else:
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y * Float64(Float64(z - t) / Float64(a - t))) - Float64(Float64(x / Float64(Float64(a - t) / Float64(z - t))) - x))
	t_2 = Float64(x + Float64(Float64(Float64(y - x) * Float64(z - t)) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= -5e-281)
		tmp = t_1;
	elseif (t_2 <= 0.0)
		tmp = Float64(y - Float64(Float64(Float64(x - y) * Float64(a - z)) / t));
	elseif (t_2 <= 1e+260)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(Float64(z - t) / Float64(Float64(a - t) * Float64(1.0 / Float64(y - x)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y * ((z - t) / (a - t))) - ((x / ((a - t) / (z - t))) - x);
	t_2 = x + (((y - x) * (z - t)) / (a - t));
	tmp = 0.0;
	if (t_2 <= -5e-281)
		tmp = t_1;
	elseif (t_2 <= 0.0)
		tmp = y - (((x - y) * (a - z)) / t);
	elseif (t_2 <= 1e+260)
		tmp = t_1;
	else
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(x / N[(N[(a - t), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(y - x), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -5e-281], t$95$1, If[LessEqual[t$95$2, 0.0], N[(y - N[(N[(N[(x - y), $MachinePrecision] * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+260], t$95$1, N[(x + N[(N[(z - t), $MachinePrecision] / N[(N[(a - t), $MachinePrecision] * N[(1.0 / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 5: 90.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{a - t}\\ t_2 := x + \frac{\left(y - x\right) \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t_2 \leq -5 \cdot 10^{-281}:\\ \;\;\;\;y \cdot t_1 - \left(\frac{x}{\frac{a - t}{z - t}} - x\right)\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;y - \frac{\left(x - y\right) \cdot \left(a - z\right)}{t}\\ \mathbf{elif}\;t_2 \leq 10^{+260}:\\ \;\;\;\;\frac{y \cdot \left(z - t\right)}{a - t} - x \cdot \left(t_1 + -1\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z - t}{\left(a - t\right) \cdot \frac{1}{y - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- a t))) (t_2 (+ x (/ (* (- y x) (- z t)) (- a t)))))
   (if (<= t_2 -5e-281)
     (- (* y t_1) (- (/ x (/ (- a t) (- z t))) x))
     (if (<= t_2 0.0)
       (- y (/ (* (- x y) (- a z)) t))
       (if (<= t_2 1e+260)
         (- (/ (* y (- z t)) (- a t)) (* x (+ t_1 -1.0)))
         (+ x (/ (- z t) (* (- a t) (/ 1.0 (- y x))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (a - t);
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -5e-281) {
		tmp = (y * t_1) - ((x / ((a - t) / (z - t))) - x);
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = ((y * (z - t)) / (a - t)) - (x * (t_1 + -1.0));
	} else {
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - 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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (z - t) / (a - t)
    t_2 = x + (((y - x) * (z - t)) / (a - t))
    if (t_2 <= (-5d-281)) then
        tmp = (y * t_1) - ((x / ((a - t) / (z - t))) - x)
    else if (t_2 <= 0.0d0) then
        tmp = y - (((x - y) * (a - z)) / t)
    else if (t_2 <= 1d+260) then
        tmp = ((y * (z - t)) / (a - t)) - (x * (t_1 + (-1.0d0)))
    else
        tmp = x + ((z - t) / ((a - t) * (1.0d0 / (y - x))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (a - t);
	double t_2 = x + (((y - x) * (z - t)) / (a - t));
	double tmp;
	if (t_2 <= -5e-281) {
		tmp = (y * t_1) - ((x / ((a - t) / (z - t))) - x);
	} else if (t_2 <= 0.0) {
		tmp = y - (((x - y) * (a - z)) / t);
	} else if (t_2 <= 1e+260) {
		tmp = ((y * (z - t)) / (a - t)) - (x * (t_1 + -1.0));
	} else {
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (a - t)
	t_2 = x + (((y - x) * (z - t)) / (a - t))
	tmp = 0
	if t_2 <= -5e-281:
		tmp = (y * t_1) - ((x / ((a - t) / (z - t))) - x)
	elif t_2 <= 0.0:
		tmp = y - (((x - y) * (a - z)) / t)
	elif t_2 <= 1e+260:
		tmp = ((y * (z - t)) / (a - t)) - (x * (t_1 + -1.0))
	else:
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(a - t))
	t_2 = Float64(x + Float64(Float64(Float64(y - x) * Float64(z - t)) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= -5e-281)
		tmp = Float64(Float64(y * t_1) - Float64(Float64(x / Float64(Float64(a - t) / Float64(z - t))) - x));
	elseif (t_2 <= 0.0)
		tmp = Float64(y - Float64(Float64(Float64(x - y) * Float64(a - z)) / t));
	elseif (t_2 <= 1e+260)
		tmp = Float64(Float64(Float64(y * Float64(z - t)) / Float64(a - t)) - Float64(x * Float64(t_1 + -1.0)));
	else
		tmp = Float64(x + Float64(Float64(z - t) / Float64(Float64(a - t) * Float64(1.0 / Float64(y - x)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (z - t) / (a - t);
	t_2 = x + (((y - x) * (z - t)) / (a - t));
	tmp = 0.0;
	if (t_2 <= -5e-281)
		tmp = (y * t_1) - ((x / ((a - t) / (z - t))) - x);
	elseif (t_2 <= 0.0)
		tmp = y - (((x - y) * (a - z)) / t);
	elseif (t_2 <= 1e+260)
		tmp = ((y * (z - t)) / (a - t)) - (x * (t_1 + -1.0));
	else
		tmp = x + ((z - t) / ((a - t) * (1.0 / (y - x))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(y - x), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -5e-281], N[(N[(y * t$95$1), $MachinePrecision] - N[(N[(x / N[(N[(a - t), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0], N[(y - N[(N[(N[(x - y), $MachinePrecision] * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+260], N[(N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision] - N[(x * N[(t$95$1 + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(z - t), $MachinePrecision] / N[(N[(a - t), $MachinePrecision] * N[(1.0 / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 45.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{z}{a}\right)\\ t_2 := y \cdot \left(\frac{a}{t} + 1\right)\\ \mathbf{if}\;t \leq -5 \cdot 10^{+23}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -2.35 \cdot 10^{-190}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -4.4 \cdot 10^{-249}:\\ \;\;\;\;z \cdot \frac{y}{a}\\ \mathbf{elif}\;t \leq 0.00088:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{+50}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{+68}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 9.8 \cdot 10^{+137}:\\ \;\;\;\;\frac{z \cdot \left(-y\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ z a)))) (t_2 (* y (+ (/ a t) 1.0))))
   (if (<= t -5e+23)
     t_2
     (if (<= t -2.35e-190)
       t_1
       (if (<= t -4.4e-249)
         (* z (/ y a))
         (if (<= t 0.00088)
           t_1
           (if (<= t 1.45e+50)
             y
             (if (<= t 7.2e+68)
               x
               (if (<= t 9.8e+137) (/ (* z (- y)) t) t_2)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (z / a));
	double t_2 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -5e+23) {
		tmp = t_2;
	} else if (t <= -2.35e-190) {
		tmp = t_1;
	} else if (t <= -4.4e-249) {
		tmp = z * (y / a);
	} else if (t <= 0.00088) {
		tmp = t_1;
	} else if (t <= 1.45e+50) {
		tmp = y;
	} else if (t <= 7.2e+68) {
		tmp = x;
	} else if (t <= 9.8e+137) {
		tmp = (z * -y) / t;
	} else {
		tmp = t_2;
	}
	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 = x * (1.0d0 - (z / a))
    t_2 = y * ((a / t) + 1.0d0)
    if (t <= (-5d+23)) then
        tmp = t_2
    else if (t <= (-2.35d-190)) then
        tmp = t_1
    else if (t <= (-4.4d-249)) then
        tmp = z * (y / a)
    else if (t <= 0.00088d0) then
        tmp = t_1
    else if (t <= 1.45d+50) then
        tmp = y
    else if (t <= 7.2d+68) then
        tmp = x
    else if (t <= 9.8d+137) then
        tmp = (z * -y) / t
    else
        tmp = t_2
    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 - (z / a));
	double t_2 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -5e+23) {
		tmp = t_2;
	} else if (t <= -2.35e-190) {
		tmp = t_1;
	} else if (t <= -4.4e-249) {
		tmp = z * (y / a);
	} else if (t <= 0.00088) {
		tmp = t_1;
	} else if (t <= 1.45e+50) {
		tmp = y;
	} else if (t <= 7.2e+68) {
		tmp = x;
	} else if (t <= 9.8e+137) {
		tmp = (z * -y) / t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (z / a))
	t_2 = y * ((a / t) + 1.0)
	tmp = 0
	if t <= -5e+23:
		tmp = t_2
	elif t <= -2.35e-190:
		tmp = t_1
	elif t <= -4.4e-249:
		tmp = z * (y / a)
	elif t <= 0.00088:
		tmp = t_1
	elif t <= 1.45e+50:
		tmp = y
	elif t <= 7.2e+68:
		tmp = x
	elif t <= 9.8e+137:
		tmp = (z * -y) / t
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(z / a)))
	t_2 = Float64(y * Float64(Float64(a / t) + 1.0))
	tmp = 0.0
	if (t <= -5e+23)
		tmp = t_2;
	elseif (t <= -2.35e-190)
		tmp = t_1;
	elseif (t <= -4.4e-249)
		tmp = Float64(z * Float64(y / a));
	elseif (t <= 0.00088)
		tmp = t_1;
	elseif (t <= 1.45e+50)
		tmp = y;
	elseif (t <= 7.2e+68)
		tmp = x;
	elseif (t <= 9.8e+137)
		tmp = Float64(Float64(z * Float64(-y)) / t);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (z / a));
	t_2 = y * ((a / t) + 1.0);
	tmp = 0.0;
	if (t <= -5e+23)
		tmp = t_2;
	elseif (t <= -2.35e-190)
		tmp = t_1;
	elseif (t <= -4.4e-249)
		tmp = z * (y / a);
	elseif (t <= 0.00088)
		tmp = t_1;
	elseif (t <= 1.45e+50)
		tmp = y;
	elseif (t <= 7.2e+68)
		tmp = x;
	elseif (t <= 9.8e+137)
		tmp = (z * -y) / t;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(N[(a / t), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5e+23], t$95$2, If[LessEqual[t, -2.35e-190], t$95$1, If[LessEqual[t, -4.4e-249], N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.00088], t$95$1, If[LessEqual[t, 1.45e+50], y, If[LessEqual[t, 7.2e+68], x, If[LessEqual[t, 9.8e+137], N[(N[(z * (-y)), $MachinePrecision] / t), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

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

Alternative 7: 46.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{z}{a}\right)\\ t_2 := y \cdot \left(\frac{a}{t} + 1\right)\\ \mathbf{if}\;t \leq -5.8 \cdot 10^{+23}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -1.72 \cdot 10^{-190}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.4 \cdot 10^{-248}:\\ \;\;\;\;z \cdot \frac{y}{a}\\ \mathbf{elif}\;t \leq 0.058:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{+49}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 3.5 \cdot 10^{+68}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 9.8 \cdot 10^{+137}:\\ \;\;\;\;y \cdot \frac{z}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ z a)))) (t_2 (* y (+ (/ a t) 1.0))))
   (if (<= t -5.8e+23)
     t_2
     (if (<= t -1.72e-190)
       t_1
       (if (<= t -3.4e-248)
         (* z (/ y a))
         (if (<= t 0.058)
           t_1
           (if (<= t 7.2e+49)
             y
             (if (<= t 3.5e+68)
               x
               (if (<= t 9.8e+137) (* y (/ z (- a t))) t_2)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (z / a));
	double t_2 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -5.8e+23) {
		tmp = t_2;
	} else if (t <= -1.72e-190) {
		tmp = t_1;
	} else if (t <= -3.4e-248) {
		tmp = z * (y / a);
	} else if (t <= 0.058) {
		tmp = t_1;
	} else if (t <= 7.2e+49) {
		tmp = y;
	} else if (t <= 3.5e+68) {
		tmp = x;
	} else if (t <= 9.8e+137) {
		tmp = y * (z / (a - t));
	} else {
		tmp = t_2;
	}
	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 = x * (1.0d0 - (z / a))
    t_2 = y * ((a / t) + 1.0d0)
    if (t <= (-5.8d+23)) then
        tmp = t_2
    else if (t <= (-1.72d-190)) then
        tmp = t_1
    else if (t <= (-3.4d-248)) then
        tmp = z * (y / a)
    else if (t <= 0.058d0) then
        tmp = t_1
    else if (t <= 7.2d+49) then
        tmp = y
    else if (t <= 3.5d+68) then
        tmp = x
    else if (t <= 9.8d+137) then
        tmp = y * (z / (a - t))
    else
        tmp = t_2
    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 - (z / a));
	double t_2 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -5.8e+23) {
		tmp = t_2;
	} else if (t <= -1.72e-190) {
		tmp = t_1;
	} else if (t <= -3.4e-248) {
		tmp = z * (y / a);
	} else if (t <= 0.058) {
		tmp = t_1;
	} else if (t <= 7.2e+49) {
		tmp = y;
	} else if (t <= 3.5e+68) {
		tmp = x;
	} else if (t <= 9.8e+137) {
		tmp = y * (z / (a - t));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (z / a))
	t_2 = y * ((a / t) + 1.0)
	tmp = 0
	if t <= -5.8e+23:
		tmp = t_2
	elif t <= -1.72e-190:
		tmp = t_1
	elif t <= -3.4e-248:
		tmp = z * (y / a)
	elif t <= 0.058:
		tmp = t_1
	elif t <= 7.2e+49:
		tmp = y
	elif t <= 3.5e+68:
		tmp = x
	elif t <= 9.8e+137:
		tmp = y * (z / (a - t))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(z / a)))
	t_2 = Float64(y * Float64(Float64(a / t) + 1.0))
	tmp = 0.0
	if (t <= -5.8e+23)
		tmp = t_2;
	elseif (t <= -1.72e-190)
		tmp = t_1;
	elseif (t <= -3.4e-248)
		tmp = Float64(z * Float64(y / a));
	elseif (t <= 0.058)
		tmp = t_1;
	elseif (t <= 7.2e+49)
		tmp = y;
	elseif (t <= 3.5e+68)
		tmp = x;
	elseif (t <= 9.8e+137)
		tmp = Float64(y * Float64(z / Float64(a - t)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (z / a));
	t_2 = y * ((a / t) + 1.0);
	tmp = 0.0;
	if (t <= -5.8e+23)
		tmp = t_2;
	elseif (t <= -1.72e-190)
		tmp = t_1;
	elseif (t <= -3.4e-248)
		tmp = z * (y / a);
	elseif (t <= 0.058)
		tmp = t_1;
	elseif (t <= 7.2e+49)
		tmp = y;
	elseif (t <= 3.5e+68)
		tmp = x;
	elseif (t <= 9.8e+137)
		tmp = y * (z / (a - t));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(N[(a / t), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.8e+23], t$95$2, If[LessEqual[t, -1.72e-190], t$95$1, If[LessEqual[t, -3.4e-248], N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.058], t$95$1, If[LessEqual[t, 7.2e+49], y, If[LessEqual[t, 3.5e+68], x, If[LessEqual[t, 9.8e+137], N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

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

Alternative 8: 46.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{z}{a}\right)\\ \mathbf{if}\;t \leq -7 \cdot 10^{+23}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq -1.8 \cdot 10^{-190}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.4 \cdot 10^{-248}:\\ \;\;\;\;z \cdot \frac{y}{a}\\ \mathbf{elif}\;t \leq 0.06:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.2 \cdot 10^{+46}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 9.5 \cdot 10^{+66}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 4.4 \cdot 10^{+108}:\\ \;\;\;\;\frac{z \cdot \left(-y\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ z a)))))
   (if (<= t -7e+23)
     y
     (if (<= t -1.8e-190)
       t_1
       (if (<= t -3.4e-248)
         (* z (/ y a))
         (if (<= t 0.06)
           t_1
           (if (<= t 4.2e+46)
             y
             (if (<= t 9.5e+66)
               x
               (if (<= t 4.4e+108) (/ (* z (- y)) t) y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (z / a));
	double tmp;
	if (t <= -7e+23) {
		tmp = y;
	} else if (t <= -1.8e-190) {
		tmp = t_1;
	} else if (t <= -3.4e-248) {
		tmp = z * (y / a);
	} else if (t <= 0.06) {
		tmp = t_1;
	} else if (t <= 4.2e+46) {
		tmp = y;
	} else if (t <= 9.5e+66) {
		tmp = x;
	} else if (t <= 4.4e+108) {
		tmp = (z * -y) / t;
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (z / a))
    if (t <= (-7d+23)) then
        tmp = y
    else if (t <= (-1.8d-190)) then
        tmp = t_1
    else if (t <= (-3.4d-248)) then
        tmp = z * (y / a)
    else if (t <= 0.06d0) then
        tmp = t_1
    else if (t <= 4.2d+46) then
        tmp = y
    else if (t <= 9.5d+66) then
        tmp = x
    else if (t <= 4.4d+108) then
        tmp = (z * -y) / t
    else
        tmp = y
    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 - (z / a));
	double tmp;
	if (t <= -7e+23) {
		tmp = y;
	} else if (t <= -1.8e-190) {
		tmp = t_1;
	} else if (t <= -3.4e-248) {
		tmp = z * (y / a);
	} else if (t <= 0.06) {
		tmp = t_1;
	} else if (t <= 4.2e+46) {
		tmp = y;
	} else if (t <= 9.5e+66) {
		tmp = x;
	} else if (t <= 4.4e+108) {
		tmp = (z * -y) / t;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (z / a))
	tmp = 0
	if t <= -7e+23:
		tmp = y
	elif t <= -1.8e-190:
		tmp = t_1
	elif t <= -3.4e-248:
		tmp = z * (y / a)
	elif t <= 0.06:
		tmp = t_1
	elif t <= 4.2e+46:
		tmp = y
	elif t <= 9.5e+66:
		tmp = x
	elif t <= 4.4e+108:
		tmp = (z * -y) / t
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(z / a)))
	tmp = 0.0
	if (t <= -7e+23)
		tmp = y;
	elseif (t <= -1.8e-190)
		tmp = t_1;
	elseif (t <= -3.4e-248)
		tmp = Float64(z * Float64(y / a));
	elseif (t <= 0.06)
		tmp = t_1;
	elseif (t <= 4.2e+46)
		tmp = y;
	elseif (t <= 9.5e+66)
		tmp = x;
	elseif (t <= 4.4e+108)
		tmp = Float64(Float64(z * Float64(-y)) / t);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (z / a));
	tmp = 0.0;
	if (t <= -7e+23)
		tmp = y;
	elseif (t <= -1.8e-190)
		tmp = t_1;
	elseif (t <= -3.4e-248)
		tmp = z * (y / a);
	elseif (t <= 0.06)
		tmp = t_1;
	elseif (t <= 4.2e+46)
		tmp = y;
	elseif (t <= 9.5e+66)
		tmp = x;
	elseif (t <= 4.4e+108)
		tmp = (z * -y) / t;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7e+23], y, If[LessEqual[t, -1.8e-190], t$95$1, If[LessEqual[t, -3.4e-248], N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.06], t$95$1, If[LessEqual[t, 4.2e+46], y, If[LessEqual[t, 9.5e+66], x, If[LessEqual[t, 4.4e+108], N[(N[(z * (-y)), $MachinePrecision] / t), $MachinePrecision], y]]]]]]]]

Derivation

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

Alternative 9: 46.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{z}{a}\right)\\ t_2 := y \cdot \left(\frac{a}{t} + 1\right)\\ \mathbf{if}\;t \leq -7.2 \cdot 10^{+23}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -1.72 \cdot 10^{-190}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -2.65 \cdot 10^{-248}:\\ \;\;\;\;z \cdot \frac{y}{a}\\ \mathbf{elif}\;t \leq 0.06:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 3.6 \cdot 10^{+45}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 1.2 \cdot 10^{+138}:\\ \;\;\;\;x + x \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ z a)))) (t_2 (* y (+ (/ a t) 1.0))))
   (if (<= t -7.2e+23)
     t_2
     (if (<= t -1.72e-190)
       t_1
       (if (<= t -2.65e-248)
         (* z (/ y a))
         (if (<= t 0.06)
           t_1
           (if (<= t 3.6e+45)
             y
             (if (<= t 1.2e+138) (+ x (* x (/ z a))) t_2))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (z / a));
	double t_2 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -7.2e+23) {
		tmp = t_2;
	} else if (t <= -1.72e-190) {
		tmp = t_1;
	} else if (t <= -2.65e-248) {
		tmp = z * (y / a);
	} else if (t <= 0.06) {
		tmp = t_1;
	} else if (t <= 3.6e+45) {
		tmp = y;
	} else if (t <= 1.2e+138) {
		tmp = x + (x * (z / a));
	} else {
		tmp = t_2;
	}
	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 = x * (1.0d0 - (z / a))
    t_2 = y * ((a / t) + 1.0d0)
    if (t <= (-7.2d+23)) then
        tmp = t_2
    else if (t <= (-1.72d-190)) then
        tmp = t_1
    else if (t <= (-2.65d-248)) then
        tmp = z * (y / a)
    else if (t <= 0.06d0) then
        tmp = t_1
    else if (t <= 3.6d+45) then
        tmp = y
    else if (t <= 1.2d+138) then
        tmp = x + (x * (z / a))
    else
        tmp = t_2
    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 - (z / a));
	double t_2 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -7.2e+23) {
		tmp = t_2;
	} else if (t <= -1.72e-190) {
		tmp = t_1;
	} else if (t <= -2.65e-248) {
		tmp = z * (y / a);
	} else if (t <= 0.06) {
		tmp = t_1;
	} else if (t <= 3.6e+45) {
		tmp = y;
	} else if (t <= 1.2e+138) {
		tmp = x + (x * (z / a));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (z / a))
	t_2 = y * ((a / t) + 1.0)
	tmp = 0
	if t <= -7.2e+23:
		tmp = t_2
	elif t <= -1.72e-190:
		tmp = t_1
	elif t <= -2.65e-248:
		tmp = z * (y / a)
	elif t <= 0.06:
		tmp = t_1
	elif t <= 3.6e+45:
		tmp = y
	elif t <= 1.2e+138:
		tmp = x + (x * (z / a))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(z / a)))
	t_2 = Float64(y * Float64(Float64(a / t) + 1.0))
	tmp = 0.0
	if (t <= -7.2e+23)
		tmp = t_2;
	elseif (t <= -1.72e-190)
		tmp = t_1;
	elseif (t <= -2.65e-248)
		tmp = Float64(z * Float64(y / a));
	elseif (t <= 0.06)
		tmp = t_1;
	elseif (t <= 3.6e+45)
		tmp = y;
	elseif (t <= 1.2e+138)
		tmp = Float64(x + Float64(x * Float64(z / a)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (z / a));
	t_2 = y * ((a / t) + 1.0);
	tmp = 0.0;
	if (t <= -7.2e+23)
		tmp = t_2;
	elseif (t <= -1.72e-190)
		tmp = t_1;
	elseif (t <= -2.65e-248)
		tmp = z * (y / a);
	elseif (t <= 0.06)
		tmp = t_1;
	elseif (t <= 3.6e+45)
		tmp = y;
	elseif (t <= 1.2e+138)
		tmp = x + (x * (z / a));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(N[(a / t), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7.2e+23], t$95$2, If[LessEqual[t, -1.72e-190], t$95$1, If[LessEqual[t, -2.65e-248], N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.06], t$95$1, If[LessEqual[t, 3.6e+45], y, If[LessEqual[t, 1.2e+138], N[(x + N[(x * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]

Derivation

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

Alternative 10: 49.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(\frac{a}{t} + 1\right)\\ \mathbf{if}\;t \leq -7.2 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -6 \cdot 10^{-143}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{a}\right)\\ \mathbf{elif}\;t \leq 0.06:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq 2.5 \cdot 10^{+48}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 1.1 \cdot 10^{+138}:\\ \;\;\;\;x + x \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (+ (/ a t) 1.0))))
   (if (<= t -7.2e+23)
     t_1
     (if (<= t -6e-143)
       (* x (- 1.0 (/ z a)))
       (if (<= t 0.06)
         (+ x (/ (* y z) a))
         (if (<= t 2.5e+48)
           y
           (if (<= t 1.1e+138) (+ x (* x (/ z a))) t_1)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -7.2e+23) {
		tmp = t_1;
	} else if (t <= -6e-143) {
		tmp = x * (1.0 - (z / a));
	} else if (t <= 0.06) {
		tmp = x + ((y * z) / a);
	} else if (t <= 2.5e+48) {
		tmp = y;
	} else if (t <= 1.1e+138) {
		tmp = x + (x * (z / a));
	} 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 = y * ((a / t) + 1.0d0)
    if (t <= (-7.2d+23)) then
        tmp = t_1
    else if (t <= (-6d-143)) then
        tmp = x * (1.0d0 - (z / a))
    else if (t <= 0.06d0) then
        tmp = x + ((y * z) / a)
    else if (t <= 2.5d+48) then
        tmp = y
    else if (t <= 1.1d+138) then
        tmp = x + (x * (z / a))
    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 = y * ((a / t) + 1.0);
	double tmp;
	if (t <= -7.2e+23) {
		tmp = t_1;
	} else if (t <= -6e-143) {
		tmp = x * (1.0 - (z / a));
	} else if (t <= 0.06) {
		tmp = x + ((y * z) / a);
	} else if (t <= 2.5e+48) {
		tmp = y;
	} else if (t <= 1.1e+138) {
		tmp = x + (x * (z / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * ((a / t) + 1.0)
	tmp = 0
	if t <= -7.2e+23:
		tmp = t_1
	elif t <= -6e-143:
		tmp = x * (1.0 - (z / a))
	elif t <= 0.06:
		tmp = x + ((y * z) / a)
	elif t <= 2.5e+48:
		tmp = y
	elif t <= 1.1e+138:
		tmp = x + (x * (z / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(Float64(a / t) + 1.0))
	tmp = 0.0
	if (t <= -7.2e+23)
		tmp = t_1;
	elseif (t <= -6e-143)
		tmp = Float64(x * Float64(1.0 - Float64(z / a)));
	elseif (t <= 0.06)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif (t <= 2.5e+48)
		tmp = y;
	elseif (t <= 1.1e+138)
		tmp = Float64(x + Float64(x * Float64(z / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * ((a / t) + 1.0);
	tmp = 0.0;
	if (t <= -7.2e+23)
		tmp = t_1;
	elseif (t <= -6e-143)
		tmp = x * (1.0 - (z / a));
	elseif (t <= 0.06)
		tmp = x + ((y * z) / a);
	elseif (t <= 2.5e+48)
		tmp = y;
	elseif (t <= 1.1e+138)
		tmp = x + (x * (z / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(N[(a / t), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7.2e+23], t$95$1, If[LessEqual[t, -6e-143], N[(x * N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.06], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.5e+48], y, If[LessEqual[t, 1.1e+138], N[(x + N[(x * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 11: 48.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \frac{a}{\frac{t}{y}}\\ \mathbf{if}\;t \leq -3.4 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.15 \cdot 10^{-142}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{a}\right)\\ \mathbf{elif}\;t \leq 0.06:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq 3.55 \cdot 10^{+46}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 1.05 \cdot 10^{+138}:\\ \;\;\;\;x + x \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ y (/ a (/ t y)))))
   (if (<= t -3.4e+23)
     t_1
     (if (<= t -1.15e-142)
       (* x (- 1.0 (/ z a)))
       (if (<= t 0.06)
         (+ x (/ (* y z) a))
         (if (<= t 3.55e+46)
           y
           (if (<= t 1.05e+138) (+ x (* x (/ z a))) t_1)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y + (a / (t / y));
	double tmp;
	if (t <= -3.4e+23) {
		tmp = t_1;
	} else if (t <= -1.15e-142) {
		tmp = x * (1.0 - (z / a));
	} else if (t <= 0.06) {
		tmp = x + ((y * z) / a);
	} else if (t <= 3.55e+46) {
		tmp = y;
	} else if (t <= 1.05e+138) {
		tmp = x + (x * (z / a));
	} 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 = y + (a / (t / y))
    if (t <= (-3.4d+23)) then
        tmp = t_1
    else if (t <= (-1.15d-142)) then
        tmp = x * (1.0d0 - (z / a))
    else if (t <= 0.06d0) then
        tmp = x + ((y * z) / a)
    else if (t <= 3.55d+46) then
        tmp = y
    else if (t <= 1.05d+138) then
        tmp = x + (x * (z / a))
    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 = y + (a / (t / y));
	double tmp;
	if (t <= -3.4e+23) {
		tmp = t_1;
	} else if (t <= -1.15e-142) {
		tmp = x * (1.0 - (z / a));
	} else if (t <= 0.06) {
		tmp = x + ((y * z) / a);
	} else if (t <= 3.55e+46) {
		tmp = y;
	} else if (t <= 1.05e+138) {
		tmp = x + (x * (z / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y + (a / (t / y))
	tmp = 0
	if t <= -3.4e+23:
		tmp = t_1
	elif t <= -1.15e-142:
		tmp = x * (1.0 - (z / a))
	elif t <= 0.06:
		tmp = x + ((y * z) / a)
	elif t <= 3.55e+46:
		tmp = y
	elif t <= 1.05e+138:
		tmp = x + (x * (z / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y + Float64(a / Float64(t / y)))
	tmp = 0.0
	if (t <= -3.4e+23)
		tmp = t_1;
	elseif (t <= -1.15e-142)
		tmp = Float64(x * Float64(1.0 - Float64(z / a)));
	elseif (t <= 0.06)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif (t <= 3.55e+46)
		tmp = y;
	elseif (t <= 1.05e+138)
		tmp = Float64(x + Float64(x * Float64(z / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y + (a / (t / y));
	tmp = 0.0;
	if (t <= -3.4e+23)
		tmp = t_1;
	elseif (t <= -1.15e-142)
		tmp = x * (1.0 - (z / a));
	elseif (t <= 0.06)
		tmp = x + ((y * z) / a);
	elseif (t <= 3.55e+46)
		tmp = y;
	elseif (t <= 1.05e+138)
		tmp = x + (x * (z / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y + N[(a / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.4e+23], t$95$1, If[LessEqual[t, -1.15e-142], N[(x * N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.06], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.55e+46], y, If[LessEqual[t, 1.05e+138], N[(x + N[(x * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 12: 48.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \frac{a}{\frac{t}{y}}\\ \mathbf{if}\;t \leq -5.6 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.3 \cdot 10^{-143}:\\ \;\;\;\;x - \frac{x}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 0.044:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq 1.75 \cdot 10^{+43}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 1.1 \cdot 10^{+138}:\\ \;\;\;\;x + x \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ y (/ a (/ t y)))))
   (if (<= t -5.6e+23)
     t_1
     (if (<= t -3.3e-143)
       (- x (/ x (/ a z)))
       (if (<= t 0.044)
         (+ x (/ (* y z) a))
         (if (<= t 1.75e+43)
           y
           (if (<= t 1.1e+138) (+ x (* x (/ z a))) t_1)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y + (a / (t / y));
	double tmp;
	if (t <= -5.6e+23) {
		tmp = t_1;
	} else if (t <= -3.3e-143) {
		tmp = x - (x / (a / z));
	} else if (t <= 0.044) {
		tmp = x + ((y * z) / a);
	} else if (t <= 1.75e+43) {
		tmp = y;
	} else if (t <= 1.1e+138) {
		tmp = x + (x * (z / a));
	} 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 = y + (a / (t / y))
    if (t <= (-5.6d+23)) then
        tmp = t_1
    else if (t <= (-3.3d-143)) then
        tmp = x - (x / (a / z))
    else if (t <= 0.044d0) then
        tmp = x + ((y * z) / a)
    else if (t <= 1.75d+43) then
        tmp = y
    else if (t <= 1.1d+138) then
        tmp = x + (x * (z / a))
    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 = y + (a / (t / y));
	double tmp;
	if (t <= -5.6e+23) {
		tmp = t_1;
	} else if (t <= -3.3e-143) {
		tmp = x - (x / (a / z));
	} else if (t <= 0.044) {
		tmp = x + ((y * z) / a);
	} else if (t <= 1.75e+43) {
		tmp = y;
	} else if (t <= 1.1e+138) {
		tmp = x + (x * (z / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y + (a / (t / y))
	tmp = 0
	if t <= -5.6e+23:
		tmp = t_1
	elif t <= -3.3e-143:
		tmp = x - (x / (a / z))
	elif t <= 0.044:
		tmp = x + ((y * z) / a)
	elif t <= 1.75e+43:
		tmp = y
	elif t <= 1.1e+138:
		tmp = x + (x * (z / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y + Float64(a / Float64(t / y)))
	tmp = 0.0
	if (t <= -5.6e+23)
		tmp = t_1;
	elseif (t <= -3.3e-143)
		tmp = Float64(x - Float64(x / Float64(a / z)));
	elseif (t <= 0.044)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif (t <= 1.75e+43)
		tmp = y;
	elseif (t <= 1.1e+138)
		tmp = Float64(x + Float64(x * Float64(z / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y + (a / (t / y));
	tmp = 0.0;
	if (t <= -5.6e+23)
		tmp = t_1;
	elseif (t <= -3.3e-143)
		tmp = x - (x / (a / z));
	elseif (t <= 0.044)
		tmp = x + ((y * z) / a);
	elseif (t <= 1.75e+43)
		tmp = y;
	elseif (t <= 1.1e+138)
		tmp = x + (x * (z / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y + N[(a / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.6e+23], t$95$1, If[LessEqual[t, -3.3e-143], N[(x - N[(x / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.044], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.75e+43], y, If[LessEqual[t, 1.1e+138], N[(x + N[(x * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 13: 52.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y - \frac{a}{\frac{t}{x}}\\ \mathbf{if}\;t \leq -1.6 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -7.5 \cdot 10^{-143}:\\ \;\;\;\;x - \frac{x}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 0.06:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq 7.6 \cdot 10^{+43}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 1.05 \cdot 10^{+138}:\\ \;\;\;\;x + x \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- y (/ a (/ t x)))))
   (if (<= t -1.6e+23)
     t_1
     (if (<= t -7.5e-143)
       (- x (/ x (/ a z)))
       (if (<= t 0.06)
         (+ x (/ (* y z) a))
         (if (<= t 7.6e+43)
           y
           (if (<= t 1.05e+138) (+ x (* x (/ z a))) t_1)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y - (a / (t / x));
	double tmp;
	if (t <= -1.6e+23) {
		tmp = t_1;
	} else if (t <= -7.5e-143) {
		tmp = x - (x / (a / z));
	} else if (t <= 0.06) {
		tmp = x + ((y * z) / a);
	} else if (t <= 7.6e+43) {
		tmp = y;
	} else if (t <= 1.05e+138) {
		tmp = x + (x * (z / a));
	} 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 = y - (a / (t / x))
    if (t <= (-1.6d+23)) then
        tmp = t_1
    else if (t <= (-7.5d-143)) then
        tmp = x - (x / (a / z))
    else if (t <= 0.06d0) then
        tmp = x + ((y * z) / a)
    else if (t <= 7.6d+43) then
        tmp = y
    else if (t <= 1.05d+138) then
        tmp = x + (x * (z / a))
    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 = y - (a / (t / x));
	double tmp;
	if (t <= -1.6e+23) {
		tmp = t_1;
	} else if (t <= -7.5e-143) {
		tmp = x - (x / (a / z));
	} else if (t <= 0.06) {
		tmp = x + ((y * z) / a);
	} else if (t <= 7.6e+43) {
		tmp = y;
	} else if (t <= 1.05e+138) {
		tmp = x + (x * (z / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y - (a / (t / x))
	tmp = 0
	if t <= -1.6e+23:
		tmp = t_1
	elif t <= -7.5e-143:
		tmp = x - (x / (a / z))
	elif t <= 0.06:
		tmp = x + ((y * z) / a)
	elif t <= 7.6e+43:
		tmp = y
	elif t <= 1.05e+138:
		tmp = x + (x * (z / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y - Float64(a / Float64(t / x)))
	tmp = 0.0
	if (t <= -1.6e+23)
		tmp = t_1;
	elseif (t <= -7.5e-143)
		tmp = Float64(x - Float64(x / Float64(a / z)));
	elseif (t <= 0.06)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif (t <= 7.6e+43)
		tmp = y;
	elseif (t <= 1.05e+138)
		tmp = Float64(x + Float64(x * Float64(z / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y - (a / (t / x));
	tmp = 0.0;
	if (t <= -1.6e+23)
		tmp = t_1;
	elseif (t <= -7.5e-143)
		tmp = x - (x / (a / z));
	elseif (t <= 0.06)
		tmp = x + ((y * z) / a);
	elseif (t <= 7.6e+43)
		tmp = y;
	elseif (t <= 1.05e+138)
		tmp = x + (x * (z / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y - N[(a / N[(t / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.6e+23], t$95$1, If[LessEqual[t, -7.5e-143], N[(x - N[(x / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.06], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 7.6e+43], y, If[LessEqual[t, 1.05e+138], N[(x + N[(x * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 14: 84.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.05 \cdot 10^{-193} \lor \neg \left(a \leq 7.5 \cdot 10^{-166}\right):\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{x - y}{a - t}\\ \mathbf{else}:\\ \;\;\;\;y + \left(y - x\right) \cdot \frac{a - z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -2.05e-193) (not (<= a 7.5e-166)))
   (+ x (* (- t z) (/ (- x y) (- a t))))
   (+ y (* (- y x) (/ (- a z) t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -2.05e-193) || !(a <= 7.5e-166)) {
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	} else {
		tmp = y + ((y - x) * ((a - z) / 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 ((a <= (-2.05d-193)) .or. (.not. (a <= 7.5d-166))) then
        tmp = x + ((t - z) * ((x - y) / (a - t)))
    else
        tmp = y + ((y - x) * ((a - z) / 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 ((a <= -2.05e-193) || !(a <= 7.5e-166)) {
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	} else {
		tmp = y + ((y - x) * ((a - z) / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -2.05e-193) or not (a <= 7.5e-166):
		tmp = x + ((t - z) * ((x - y) / (a - t)))
	else:
		tmp = y + ((y - x) * ((a - z) / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -2.05e-193) || !(a <= 7.5e-166))
		tmp = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / Float64(a - t))));
	else
		tmp = Float64(y + Float64(Float64(y - x) * Float64(Float64(a - z) / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -2.05e-193) || ~((a <= 7.5e-166)))
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	else
		tmp = y + ((y - x) * ((a - z) / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -2.05e-193], N[Not[LessEqual[a, 7.5e-166]], $MachinePrecision]], N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + N[(N[(y - x), $MachinePrecision] * N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 58.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;t \leq -1.48 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -6.2 \cdot 10^{-143}:\\ \;\;\;\;x - \frac{x}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 0.00056:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ (- z t) (- a t)))))
   (if (<= t -1.48e+23)
     t_1
     (if (<= t -6.2e-143)
       (- x (/ x (/ a z)))
       (if (<= t 0.00056) (+ x (/ (* y z) a)) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * ((z - t) / (a - t));
	double tmp;
	if (t <= -1.48e+23) {
		tmp = t_1;
	} else if (t <= -6.2e-143) {
		tmp = x - (x / (a / z));
	} else if (t <= 0.00056) {
		tmp = x + ((y * z) / a);
	} 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 = y * ((z - t) / (a - t))
    if (t <= (-1.48d+23)) then
        tmp = t_1
    else if (t <= (-6.2d-143)) then
        tmp = x - (x / (a / z))
    else if (t <= 0.00056d0) then
        tmp = x + ((y * z) / a)
    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 = y * ((z - t) / (a - t));
	double tmp;
	if (t <= -1.48e+23) {
		tmp = t_1;
	} else if (t <= -6.2e-143) {
		tmp = x - (x / (a / z));
	} else if (t <= 0.00056) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * ((z - t) / (a - t))
	tmp = 0
	if t <= -1.48e+23:
		tmp = t_1
	elif t <= -6.2e-143:
		tmp = x - (x / (a / z))
	elif t <= 0.00056:
		tmp = x + ((y * z) / a)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(Float64(z - t) / Float64(a - t)))
	tmp = 0.0
	if (t <= -1.48e+23)
		tmp = t_1;
	elseif (t <= -6.2e-143)
		tmp = Float64(x - Float64(x / Float64(a / z)));
	elseif (t <= 0.00056)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * ((z - t) / (a - t));
	tmp = 0.0;
	if (t <= -1.48e+23)
		tmp = t_1;
	elseif (t <= -6.2e-143)
		tmp = x - (x / (a / z));
	elseif (t <= 0.00056)
		tmp = x + ((y * z) / a);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.48e+23], t$95$1, If[LessEqual[t, -6.2e-143], N[(x - N[(x / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.00056], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 16: 62.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \frac{y}{a} \cdot \left(t - z\right)\\ \mathbf{if}\;a \leq -3.55 \cdot 10^{+44}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -1.15 \cdot 10^{-251}:\\ \;\;\;\;z \cdot \frac{y - x}{a - t}\\ \mathbf{elif}\;a \leq 4.1 \cdot 10^{+73}:\\ \;\;\;\;y \cdot \frac{z - t}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* (/ y a) (- t z)))))
   (if (<= a -3.55e+44)
     t_1
     (if (<= a -1.15e-251)
       (* z (/ (- y x) (- a t)))
       (if (<= a 4.1e+73) (* y (/ (- z t) (- a t))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - ((y / a) * (t - z));
	double tmp;
	if (a <= -3.55e+44) {
		tmp = t_1;
	} else if (a <= -1.15e-251) {
		tmp = z * ((y - x) / (a - t));
	} else if (a <= 4.1e+73) {
		tmp = y * ((z - t) / (a - t));
	} 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 / a) * (t - z))
    if (a <= (-3.55d+44)) then
        tmp = t_1
    else if (a <= (-1.15d-251)) then
        tmp = z * ((y - x) / (a - t))
    else if (a <= 4.1d+73) then
        tmp = y * ((z - t) / (a - t))
    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 / a) * (t - z));
	double tmp;
	if (a <= -3.55e+44) {
		tmp = t_1;
	} else if (a <= -1.15e-251) {
		tmp = z * ((y - x) / (a - t));
	} else if (a <= 4.1e+73) {
		tmp = y * ((z - t) / (a - t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - ((y / a) * (t - z))
	tmp = 0
	if a <= -3.55e+44:
		tmp = t_1
	elif a <= -1.15e-251:
		tmp = z * ((y - x) / (a - t))
	elif a <= 4.1e+73:
		tmp = y * ((z - t) / (a - t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(Float64(y / a) * Float64(t - z)))
	tmp = 0.0
	if (a <= -3.55e+44)
		tmp = t_1;
	elseif (a <= -1.15e-251)
		tmp = Float64(z * Float64(Float64(y - x) / Float64(a - t)));
	elseif (a <= 4.1e+73)
		tmp = Float64(y * Float64(Float64(z - t) / Float64(a - t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - ((y / a) * (t - z));
	tmp = 0.0;
	if (a <= -3.55e+44)
		tmp = t_1;
	elseif (a <= -1.15e-251)
		tmp = z * ((y - x) / (a - t));
	elseif (a <= 4.1e+73)
		tmp = y * ((z - t) / (a - t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(N[(y / a), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.55e+44], t$95$1, If[LessEqual[a, -1.15e-251], N[(z * N[(N[(y - x), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.1e+73], N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 17: 68.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -7.4 \cdot 10^{+15} \lor \neg \left(t \leq 0.042\right):\\ \;\;\;\;y \cdot \frac{z - t}{a - t}\\ \mathbf{else}:\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{x - y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -7.4e+15) (not (<= t 0.042)))
   (* y (/ (- z t) (- a t)))
   (+ x (* (- t z) (/ (- x y) a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -7.4e+15) || !(t <= 0.042)) {
		tmp = y * ((z - t) / (a - t));
	} else {
		tmp = x + ((t - z) * ((x - 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 ((t <= (-7.4d+15)) .or. (.not. (t <= 0.042d0))) then
        tmp = y * ((z - t) / (a - t))
    else
        tmp = x + ((t - z) * ((x - 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 ((t <= -7.4e+15) || !(t <= 0.042)) {
		tmp = y * ((z - t) / (a - t));
	} else {
		tmp = x + ((t - z) * ((x - y) / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -7.4e+15) or not (t <= 0.042):
		tmp = y * ((z - t) / (a - t))
	else:
		tmp = x + ((t - z) * ((x - y) / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -7.4e+15) || !(t <= 0.042))
		tmp = Float64(y * Float64(Float64(z - t) / Float64(a - t)));
	else
		tmp = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -7.4e+15) || ~((t <= 0.042)))
		tmp = y * ((z - t) / (a - t));
	else
		tmp = x + ((t - z) * ((x - y) / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -7.4e+15], N[Not[LessEqual[t, 0.042]], $MachinePrecision]], N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 75.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8.5 \cdot 10^{+16} \lor \neg \left(t \leq 4.1 \cdot 10^{+21}\right):\\ \;\;\;\;y + \left(y - x\right) \cdot \frac{a - z}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{x - y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -8.5e+16) (not (<= t 4.1e+21)))
   (+ y (* (- y x) (/ (- a z) t)))
   (+ x (* (- t z) (/ (- x y) a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -8.5e+16) || !(t <= 4.1e+21)) {
		tmp = y + ((y - x) * ((a - z) / t));
	} else {
		tmp = x + ((t - z) * ((x - 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 ((t <= (-8.5d+16)) .or. (.not. (t <= 4.1d+21))) then
        tmp = y + ((y - x) * ((a - z) / t))
    else
        tmp = x + ((t - z) * ((x - 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 ((t <= -8.5e+16) || !(t <= 4.1e+21)) {
		tmp = y + ((y - x) * ((a - z) / t));
	} else {
		tmp = x + ((t - z) * ((x - y) / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -8.5e+16) or not (t <= 4.1e+21):
		tmp = y + ((y - x) * ((a - z) / t))
	else:
		tmp = x + ((t - z) * ((x - y) / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -8.5e+16) || !(t <= 4.1e+21))
		tmp = Float64(y + Float64(Float64(y - x) * Float64(Float64(a - z) / t)));
	else
		tmp = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -8.5e+16) || ~((t <= 4.1e+21)))
		tmp = y + ((y - x) * ((a - z) / t));
	else
		tmp = x + ((t - z) * ((x - y) / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -8.5e+16], N[Not[LessEqual[t, 4.1e+21]], $MachinePrecision]], N[(y + N[(N[(y - x), $MachinePrecision] * N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 19: 66.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.05 \cdot 10^{+18} \lor \neg \left(t \leq 0.03\right):\\ \;\;\;\;y \cdot \frac{z - t}{a - t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z}{\frac{a}{y - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -1.05e+18) (not (<= t 0.03)))
   (* y (/ (- z t) (- a t)))
   (+ x (/ z (/ a (- y x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -1.05e+18) || !(t <= 0.03)) {
		tmp = y * ((z - t) / (a - t));
	} else {
		tmp = x + (z / (a / (y - 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 ((t <= (-1.05d+18)) .or. (.not. (t <= 0.03d0))) then
        tmp = y * ((z - t) / (a - t))
    else
        tmp = x + (z / (a / (y - 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 ((t <= -1.05e+18) || !(t <= 0.03)) {
		tmp = y * ((z - t) / (a - t));
	} else {
		tmp = x + (z / (a / (y - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -1.05e+18) or not (t <= 0.03):
		tmp = y * ((z - t) / (a - t))
	else:
		tmp = x + (z / (a / (y - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -1.05e+18) || !(t <= 0.03))
		tmp = Float64(y * Float64(Float64(z - t) / Float64(a - t)));
	else
		tmp = Float64(x + Float64(z / Float64(a / Float64(y - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -1.05e+18) || ~((t <= 0.03)))
		tmp = y * ((z - t) / (a - t));
	else
		tmp = x + (z / (a / (y - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -1.05e+18], N[Not[LessEqual[t, 0.03]], $MachinePrecision]], N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z / N[(a / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 20: 38.3% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.6 \cdot 10^{+72}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.9 \cdot 10^{+73}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -4.6e+72) x (if (<= a 1.9e+73) y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.6e+72) {
		tmp = x;
	} else if (a <= 1.9e+73) {
		tmp = y;
	} 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 <= (-4.6d+72)) then
        tmp = x
    else if (a <= 1.9d+73) then
        tmp = y
    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 <= -4.6e+72) {
		tmp = x;
	} else if (a <= 1.9e+73) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -4.6e+72:
		tmp = x
	elif a <= 1.9e+73:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -4.6e+72)
		tmp = x;
	elseif (a <= 1.9e+73)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -4.6e+72)
		tmp = x;
	elseif (a <= 1.9e+73)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -4.6e+72], x, If[LessEqual[a, 1.9e+73], y, x]]

Derivation

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

Alternative 21: 24.7% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 86.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y - x}{1} \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;a < -1.6153062845442575 \cdot 10^{-142}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a < 3.774403170083174 \cdot 10^{-182}:\\ \;\;\;\;y - \frac{z}{t} \cdot \left(y - x\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (/ (- y x) 1.0) (/ (- z t) (- a t))))))
   (if (< a -1.6153062845442575e-142)
     t_1
     (if (< a 3.774403170083174e-182) (- y (* (/ z t) (- y x))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)));
	double tmp;
	if (a < -1.6153062845442575e-142) {
		tmp = t_1;
	} else if (a < 3.774403170083174e-182) {
		tmp = y - ((z / t) * (y - x));
	} 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 - x) / 1.0d0) * ((z - t) / (a - t)))
    if (a < (-1.6153062845442575d-142)) then
        tmp = t_1
    else if (a < 3.774403170083174d-182) then
        tmp = y - ((z / t) * (y - x))
    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 - x) / 1.0) * ((z - t) / (a - t)));
	double tmp;
	if (a < -1.6153062845442575e-142) {
		tmp = t_1;
	} else if (a < 3.774403170083174e-182) {
		tmp = y - ((z / t) * (y - x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)))
	tmp = 0
	if a < -1.6153062845442575e-142:
		tmp = t_1
	elif a < 3.774403170083174e-182:
		tmp = y - ((z / t) * (y - x))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(y - x) / 1.0) * Float64(Float64(z - t) / Float64(a - t))))
	tmp = 0.0
	if (a < -1.6153062845442575e-142)
		tmp = t_1;
	elseif (a < 3.774403170083174e-182)
		tmp = Float64(y - Float64(Float64(z / t) * Float64(y - x)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)));
	tmp = 0.0;
	if (a < -1.6153062845442575e-142)
		tmp = t_1;
	elseif (a < 3.774403170083174e-182)
		tmp = y - ((z / t) * (y - x));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(y - x), $MachinePrecision] / 1.0), $MachinePrecision] * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[a, -1.6153062845442575e-142], t$95$1, If[Less[a, 3.774403170083174e-182], N[(y - N[(N[(z / t), $MachinePrecision] * N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Chart.Axis.Types:linMap from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (if (< a -1.6153062845442575e-142) (+ x (* (/ (- y x) 1.0) (/ (- z t) (- a t)))) (if (< a 3.774403170083174e-182) (- y (* (/ z t) (- y x))) (+ x (* (/ (- y x) 1.0) (/ (- z t) (- a t))))))

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