Development.Shake.Progress:decay from shake-0.15.5

Percentage Accurate: 67.1% → 94.8%

Time: 23.9s

Alternatives: 21

Speedup: 1.1×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 67.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 94.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + z \cdot \left(b - y\right)\\ t_2 := \frac{t - a}{b - y}\\ t_3 := z \cdot \left(t - a\right)\\ t_4 := \frac{x \cdot y + t_3}{t_1}\\ t_5 := \frac{x}{1 - z} + t_2\\ \mathbf{if}\;t_4 \leq -\infty:\\ \;\;\;\;t_5\\ \mathbf{elif}\;t_4 \leq -1 \cdot 10^{-220}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t_4 \leq 10^{-300}:\\ \;\;\;\;\left(t_2 + \frac{x}{\frac{b}{\frac{y}{z}} - z}\right) + \frac{y}{{\left(b - y\right)}^{2}} \cdot \frac{a - t}{z}\\ \mathbf{elif}\;t_4 \leq 2 \cdot 10^{+229}:\\ \;\;\;\;\frac{x \cdot y}{t_1} + \frac{t_3}{t_1}\\ \mathbf{else}:\\ \;\;\;\;t_5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (* z (- b y))))
        (t_2 (/ (- t a) (- b y)))
        (t_3 (* z (- t a)))
        (t_4 (/ (+ (* x y) t_3) t_1))
        (t_5 (+ (/ x (- 1.0 z)) t_2)))
   (if (<= t_4 (- INFINITY))
     t_5
     (if (<= t_4 -1e-220)
       t_4
       (if (<= t_4 1e-300)
         (+
          (+ t_2 (/ x (- (/ b (/ y z)) z)))
          (* (/ y (pow (- b y) 2.0)) (/ (- a t) z)))
         (if (<= t_4 2e+229) (+ (/ (* x y) t_1) (/ t_3 t_1)) t_5))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (z * (b - y));
	double t_2 = (t - a) / (b - y);
	double t_3 = z * (t - a);
	double t_4 = ((x * y) + t_3) / t_1;
	double t_5 = (x / (1.0 - z)) + t_2;
	double tmp;
	if (t_4 <= -((double) INFINITY)) {
		tmp = t_5;
	} else if (t_4 <= -1e-220) {
		tmp = t_4;
	} else if (t_4 <= 1e-300) {
		tmp = (t_2 + (x / ((b / (y / z)) - z))) + ((y / pow((b - y), 2.0)) * ((a - t) / z));
	} else if (t_4 <= 2e+229) {
		tmp = ((x * y) / t_1) + (t_3 / t_1);
	} else {
		tmp = t_5;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (z * (b - y));
	double t_2 = (t - a) / (b - y);
	double t_3 = z * (t - a);
	double t_4 = ((x * y) + t_3) / t_1;
	double t_5 = (x / (1.0 - z)) + t_2;
	double tmp;
	if (t_4 <= -Double.POSITIVE_INFINITY) {
		tmp = t_5;
	} else if (t_4 <= -1e-220) {
		tmp = t_4;
	} else if (t_4 <= 1e-300) {
		tmp = (t_2 + (x / ((b / (y / z)) - z))) + ((y / Math.pow((b - y), 2.0)) * ((a - t) / z));
	} else if (t_4 <= 2e+229) {
		tmp = ((x * y) / t_1) + (t_3 / t_1);
	} else {
		tmp = t_5;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (z * (b - y))
	t_2 = (t - a) / (b - y)
	t_3 = z * (t - a)
	t_4 = ((x * y) + t_3) / t_1
	t_5 = (x / (1.0 - z)) + t_2
	tmp = 0
	if t_4 <= -math.inf:
		tmp = t_5
	elif t_4 <= -1e-220:
		tmp = t_4
	elif t_4 <= 1e-300:
		tmp = (t_2 + (x / ((b / (y / z)) - z))) + ((y / math.pow((b - y), 2.0)) * ((a - t) / z))
	elif t_4 <= 2e+229:
		tmp = ((x * y) / t_1) + (t_3 / t_1)
	else:
		tmp = t_5
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(z * Float64(b - y)))
	t_2 = Float64(Float64(t - a) / Float64(b - y))
	t_3 = Float64(z * Float64(t - a))
	t_4 = Float64(Float64(Float64(x * y) + t_3) / t_1)
	t_5 = Float64(Float64(x / Float64(1.0 - z)) + t_2)
	tmp = 0.0
	if (t_4 <= Float64(-Inf))
		tmp = t_5;
	elseif (t_4 <= -1e-220)
		tmp = t_4;
	elseif (t_4 <= 1e-300)
		tmp = Float64(Float64(t_2 + Float64(x / Float64(Float64(b / Float64(y / z)) - z))) + Float64(Float64(y / (Float64(b - y) ^ 2.0)) * Float64(Float64(a - t) / z)));
	elseif (t_4 <= 2e+229)
		tmp = Float64(Float64(Float64(x * y) / t_1) + Float64(t_3 / t_1));
	else
		tmp = t_5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (z * (b - y));
	t_2 = (t - a) / (b - y);
	t_3 = z * (t - a);
	t_4 = ((x * y) + t_3) / t_1;
	t_5 = (x / (1.0 - z)) + t_2;
	tmp = 0.0;
	if (t_4 <= -Inf)
		tmp = t_5;
	elseif (t_4 <= -1e-220)
		tmp = t_4;
	elseif (t_4 <= 1e-300)
		tmp = (t_2 + (x / ((b / (y / z)) - z))) + ((y / ((b - y) ^ 2.0)) * ((a - t) / z));
	elseif (t_4 <= 2e+229)
		tmp = ((x * y) / t_1) + (t_3 / t_1);
	else
		tmp = t_5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(x * y), $MachinePrecision] + t$95$3), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]}, If[LessEqual[t$95$4, (-Infinity)], t$95$5, If[LessEqual[t$95$4, -1e-220], t$95$4, If[LessEqual[t$95$4, 1e-300], N[(N[(t$95$2 + N[(x / N[(N[(b / N[(y / z), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(y / N[Power[N[(b - y), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] * N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, 2e+229], N[(N[(N[(x * y), $MachinePrecision] / t$95$1), $MachinePrecision] + N[(t$95$3 / t$95$1), $MachinePrecision]), $MachinePrecision], t$95$5]]]]]]]]]

Derivation

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

Alternative 2: 93.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + z \cdot \left(b - y\right)\\ t_2 := \frac{t - a}{b - y}\\ t_3 := z \cdot \left(t - a\right)\\ t_4 := \frac{x \cdot y + t_3}{t_1}\\ t_5 := \frac{x}{1 - z} + t_2\\ \mathbf{if}\;t_4 \leq -\infty:\\ \;\;\;\;t_5\\ \mathbf{elif}\;t_4 \leq -1 \cdot 10^{-220}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t_4 \leq 0:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_4 \leq 2 \cdot 10^{+229}:\\ \;\;\;\;\frac{x \cdot y}{t_1} + \frac{t_3}{t_1}\\ \mathbf{else}:\\ \;\;\;\;t_5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (* z (- b y))))
        (t_2 (/ (- t a) (- b y)))
        (t_3 (* z (- t a)))
        (t_4 (/ (+ (* x y) t_3) t_1))
        (t_5 (+ (/ x (- 1.0 z)) t_2)))
   (if (<= t_4 (- INFINITY))
     t_5
     (if (<= t_4 -1e-220)
       t_4
       (if (<= t_4 0.0)
         t_2
         (if (<= t_4 2e+229) (+ (/ (* x y) t_1) (/ t_3 t_1)) t_5))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (z * (b - y));
	double t_2 = (t - a) / (b - y);
	double t_3 = z * (t - a);
	double t_4 = ((x * y) + t_3) / t_1;
	double t_5 = (x / (1.0 - z)) + t_2;
	double tmp;
	if (t_4 <= -((double) INFINITY)) {
		tmp = t_5;
	} else if (t_4 <= -1e-220) {
		tmp = t_4;
	} else if (t_4 <= 0.0) {
		tmp = t_2;
	} else if (t_4 <= 2e+229) {
		tmp = ((x * y) / t_1) + (t_3 / t_1);
	} else {
		tmp = t_5;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (z * (b - y));
	double t_2 = (t - a) / (b - y);
	double t_3 = z * (t - a);
	double t_4 = ((x * y) + t_3) / t_1;
	double t_5 = (x / (1.0 - z)) + t_2;
	double tmp;
	if (t_4 <= -Double.POSITIVE_INFINITY) {
		tmp = t_5;
	} else if (t_4 <= -1e-220) {
		tmp = t_4;
	} else if (t_4 <= 0.0) {
		tmp = t_2;
	} else if (t_4 <= 2e+229) {
		tmp = ((x * y) / t_1) + (t_3 / t_1);
	} else {
		tmp = t_5;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (z * (b - y))
	t_2 = (t - a) / (b - y)
	t_3 = z * (t - a)
	t_4 = ((x * y) + t_3) / t_1
	t_5 = (x / (1.0 - z)) + t_2
	tmp = 0
	if t_4 <= -math.inf:
		tmp = t_5
	elif t_4 <= -1e-220:
		tmp = t_4
	elif t_4 <= 0.0:
		tmp = t_2
	elif t_4 <= 2e+229:
		tmp = ((x * y) / t_1) + (t_3 / t_1)
	else:
		tmp = t_5
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(z * Float64(b - y)))
	t_2 = Float64(Float64(t - a) / Float64(b - y))
	t_3 = Float64(z * Float64(t - a))
	t_4 = Float64(Float64(Float64(x * y) + t_3) / t_1)
	t_5 = Float64(Float64(x / Float64(1.0 - z)) + t_2)
	tmp = 0.0
	if (t_4 <= Float64(-Inf))
		tmp = t_5;
	elseif (t_4 <= -1e-220)
		tmp = t_4;
	elseif (t_4 <= 0.0)
		tmp = t_2;
	elseif (t_4 <= 2e+229)
		tmp = Float64(Float64(Float64(x * y) / t_1) + Float64(t_3 / t_1));
	else
		tmp = t_5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (z * (b - y));
	t_2 = (t - a) / (b - y);
	t_3 = z * (t - a);
	t_4 = ((x * y) + t_3) / t_1;
	t_5 = (x / (1.0 - z)) + t_2;
	tmp = 0.0;
	if (t_4 <= -Inf)
		tmp = t_5;
	elseif (t_4 <= -1e-220)
		tmp = t_4;
	elseif (t_4 <= 0.0)
		tmp = t_2;
	elseif (t_4 <= 2e+229)
		tmp = ((x * y) / t_1) + (t_3 / t_1);
	else
		tmp = t_5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(x * y), $MachinePrecision] + t$95$3), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]}, If[LessEqual[t$95$4, (-Infinity)], t$95$5, If[LessEqual[t$95$4, -1e-220], t$95$4, If[LessEqual[t$95$4, 0.0], t$95$2, If[LessEqual[t$95$4, 2e+229], N[(N[(N[(x * y), $MachinePrecision] / t$95$1), $MachinePrecision] + N[(t$95$3 / t$95$1), $MachinePrecision]), $MachinePrecision], t$95$5]]]]]]]]]

Derivation

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

Alternative 3: 93.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - a}{b - y}\\ t_2 := \frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)}\\ t_3 := \frac{x}{1 - z} + t_1\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t_2 \leq -1 \cdot 10^{-220}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_2 \leq 2 \cdot 10^{+229}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- t a) (- b y)))
        (t_2 (/ (+ (* x y) (* z (- t a))) (+ y (* z (- b y)))))
        (t_3 (+ (/ x (- 1.0 z)) t_1)))
   (if (<= t_2 (- INFINITY))
     t_3
     (if (<= t_2 -1e-220)
       t_2
       (if (<= t_2 0.0) t_1 (if (<= t_2 2e+229) t_2 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - a) / (b - y);
	double t_2 = ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
	double t_3 = (x / (1.0 - z)) + t_1;
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_3;
	} else if (t_2 <= -1e-220) {
		tmp = t_2;
	} else if (t_2 <= 0.0) {
		tmp = t_1;
	} else if (t_2 <= 2e+229) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - a) / (b - y);
	double t_2 = ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
	double t_3 = (x / (1.0 - z)) + t_1;
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else if (t_2 <= -1e-220) {
		tmp = t_2;
	} else if (t_2 <= 0.0) {
		tmp = t_1;
	} else if (t_2 <= 2e+229) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (t - a) / (b - y)
	t_2 = ((x * y) + (z * (t - a))) / (y + (z * (b - y)))
	t_3 = (x / (1.0 - z)) + t_1
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_3
	elif t_2 <= -1e-220:
		tmp = t_2
	elif t_2 <= 0.0:
		tmp = t_1
	elif t_2 <= 2e+229:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(t - a) / Float64(b - y))
	t_2 = Float64(Float64(Float64(x * y) + Float64(z * Float64(t - a))) / Float64(y + Float64(z * Float64(b - y))))
	t_3 = Float64(Float64(x / Float64(1.0 - z)) + t_1)
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_3;
	elseif (t_2 <= -1e-220)
		tmp = t_2;
	elseif (t_2 <= 0.0)
		tmp = t_1;
	elseif (t_2 <= 2e+229)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (t - a) / (b - y);
	t_2 = ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
	t_3 = (x / (1.0 - z)) + t_1;
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_3;
	elseif (t_2 <= -1e-220)
		tmp = t_2;
	elseif (t_2 <= 0.0)
		tmp = t_1;
	elseif (t_2 <= 2e+229)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(x * y), $MachinePrecision] + N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$3, If[LessEqual[t$95$2, -1e-220], t$95$2, If[LessEqual[t$95$2, 0.0], t$95$1, If[LessEqual[t$95$2, 2e+229], t$95$2, t$95$3]]]]]]]

Derivation

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

Alternative 4: 67.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ t_2 := t_1 - a \cdot \frac{z}{y}\\ t_3 := \frac{t - a}{b - y}\\ t_4 := \frac{\left(t + \frac{x \cdot y}{z}\right) - a}{b}\\ \mathbf{if}\;z \leq -2.3 \cdot 10^{-11}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq -5.8 \cdot 10^{-80}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -3.6 \cdot 10^{-102}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;z \leq 3.3 \cdot 10^{-218}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-125}:\\ \;\;\;\;t_1 + \frac{z \cdot t}{y}\\ \mathbf{elif}\;z \leq 4 \cdot 10^{+18}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{+57}:\\ \;\;\;\;t_1 + \frac{a - t}{y}\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z)))
        (t_2 (- t_1 (* a (/ z y))))
        (t_3 (/ (- t a) (- b y)))
        (t_4 (/ (- (+ t (/ (* x y) z)) a) b)))
   (if (<= z -2.3e-11)
     t_3
     (if (<= z -5.8e-80)
       t_2
       (if (<= z -3.6e-102)
         t_4
         (if (<= z 3.3e-218)
           t_2
           (if (<= z 4.2e-125)
             (+ t_1 (/ (* z t) y))
             (if (<= z 4e+18)
               t_4
               (if (<= z 3.2e+57) (+ t_1 (/ (- a t) y)) t_3)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = t_1 - (a * (z / y));
	double t_3 = (t - a) / (b - y);
	double t_4 = ((t + ((x * y) / z)) - a) / b;
	double tmp;
	if (z <= -2.3e-11) {
		tmp = t_3;
	} else if (z <= -5.8e-80) {
		tmp = t_2;
	} else if (z <= -3.6e-102) {
		tmp = t_4;
	} else if (z <= 3.3e-218) {
		tmp = t_2;
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else if (z <= 4e+18) {
		tmp = t_4;
	} else if (z <= 3.2e+57) {
		tmp = t_1 + ((a - t) / y);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    t_2 = t_1 - (a * (z / y))
    t_3 = (t - a) / (b - y)
    t_4 = ((t + ((x * y) / z)) - a) / b
    if (z <= (-2.3d-11)) then
        tmp = t_3
    else if (z <= (-5.8d-80)) then
        tmp = t_2
    else if (z <= (-3.6d-102)) then
        tmp = t_4
    else if (z <= 3.3d-218) then
        tmp = t_2
    else if (z <= 4.2d-125) then
        tmp = t_1 + ((z * t) / y)
    else if (z <= 4d+18) then
        tmp = t_4
    else if (z <= 3.2d+57) then
        tmp = t_1 + ((a - t) / y)
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = t_1 - (a * (z / y));
	double t_3 = (t - a) / (b - y);
	double t_4 = ((t + ((x * y) / z)) - a) / b;
	double tmp;
	if (z <= -2.3e-11) {
		tmp = t_3;
	} else if (z <= -5.8e-80) {
		tmp = t_2;
	} else if (z <= -3.6e-102) {
		tmp = t_4;
	} else if (z <= 3.3e-218) {
		tmp = t_2;
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else if (z <= 4e+18) {
		tmp = t_4;
	} else if (z <= 3.2e+57) {
		tmp = t_1 + ((a - t) / y);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	t_2 = t_1 - (a * (z / y))
	t_3 = (t - a) / (b - y)
	t_4 = ((t + ((x * y) / z)) - a) / b
	tmp = 0
	if z <= -2.3e-11:
		tmp = t_3
	elif z <= -5.8e-80:
		tmp = t_2
	elif z <= -3.6e-102:
		tmp = t_4
	elif z <= 3.3e-218:
		tmp = t_2
	elif z <= 4.2e-125:
		tmp = t_1 + ((z * t) / y)
	elif z <= 4e+18:
		tmp = t_4
	elif z <= 3.2e+57:
		tmp = t_1 + ((a - t) / y)
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	t_2 = Float64(t_1 - Float64(a * Float64(z / y)))
	t_3 = Float64(Float64(t - a) / Float64(b - y))
	t_4 = Float64(Float64(Float64(t + Float64(Float64(x * y) / z)) - a) / b)
	tmp = 0.0
	if (z <= -2.3e-11)
		tmp = t_3;
	elseif (z <= -5.8e-80)
		tmp = t_2;
	elseif (z <= -3.6e-102)
		tmp = t_4;
	elseif (z <= 3.3e-218)
		tmp = t_2;
	elseif (z <= 4.2e-125)
		tmp = Float64(t_1 + Float64(Float64(z * t) / y));
	elseif (z <= 4e+18)
		tmp = t_4;
	elseif (z <= 3.2e+57)
		tmp = Float64(t_1 + Float64(Float64(a - t) / y));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	t_2 = t_1 - (a * (z / y));
	t_3 = (t - a) / (b - y);
	t_4 = ((t + ((x * y) / z)) - a) / b;
	tmp = 0.0;
	if (z <= -2.3e-11)
		tmp = t_3;
	elseif (z <= -5.8e-80)
		tmp = t_2;
	elseif (z <= -3.6e-102)
		tmp = t_4;
	elseif (z <= 3.3e-218)
		tmp = t_2;
	elseif (z <= 4.2e-125)
		tmp = t_1 + ((z * t) / y);
	elseif (z <= 4e+18)
		tmp = t_4;
	elseif (z <= 3.2e+57)
		tmp = t_1 + ((a - t) / y);
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 - N[(a * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t + N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision] / b), $MachinePrecision]}, If[LessEqual[z, -2.3e-11], t$95$3, If[LessEqual[z, -5.8e-80], t$95$2, If[LessEqual[z, -3.6e-102], t$95$4, If[LessEqual[z, 3.3e-218], t$95$2, If[LessEqual[z, 4.2e-125], N[(t$95$1 + N[(N[(z * t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4e+18], t$95$4, If[LessEqual[z, 3.2e+57], N[(t$95$1 + N[(N[(a - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]]]]]

Derivation

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

Alternative 5: 68.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ t_2 := t_1 - a \cdot \frac{z}{y}\\ t_3 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -6.2 \cdot 10^{-9}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq -5.9 \cdot 10^{-80}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-103}:\\ \;\;\;\;\frac{\left(t + \frac{x \cdot y}{z}\right) - a}{b}\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{-218}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-125}:\\ \;\;\;\;t_1 + \frac{z \cdot t}{y}\\ \mathbf{elif}\;z \leq 5.3 \cdot 10^{+14}:\\ \;\;\;\;\frac{y}{z} \cdot \frac{x}{b} + \frac{t - a}{b}\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z)))
        (t_2 (- t_1 (* a (/ z y))))
        (t_3 (/ (- t a) (- b y))))
   (if (<= z -6.2e-9)
     t_3
     (if (<= z -5.9e-80)
       t_2
       (if (<= z -4.2e-103)
         (/ (- (+ t (/ (* x y) z)) a) b)
         (if (<= z 2.2e-218)
           t_2
           (if (<= z 4.2e-125)
             (+ t_1 (/ (* z t) y))
             (if (<= z 5.3e+14)
               (+ (* (/ y z) (/ x b)) (/ (- t a) b))
               t_3))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = t_1 - (a * (z / y));
	double t_3 = (t - a) / (b - y);
	double tmp;
	if (z <= -6.2e-9) {
		tmp = t_3;
	} else if (z <= -5.9e-80) {
		tmp = t_2;
	} else if (z <= -4.2e-103) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 2.2e-218) {
		tmp = t_2;
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else if (z <= 5.3e+14) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    t_2 = t_1 - (a * (z / y))
    t_3 = (t - a) / (b - y)
    if (z <= (-6.2d-9)) then
        tmp = t_3
    else if (z <= (-5.9d-80)) then
        tmp = t_2
    else if (z <= (-4.2d-103)) then
        tmp = ((t + ((x * y) / z)) - a) / b
    else if (z <= 2.2d-218) then
        tmp = t_2
    else if (z <= 4.2d-125) then
        tmp = t_1 + ((z * t) / y)
    else if (z <= 5.3d+14) then
        tmp = ((y / z) * (x / b)) + ((t - a) / b)
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = t_1 - (a * (z / y));
	double t_3 = (t - a) / (b - y);
	double tmp;
	if (z <= -6.2e-9) {
		tmp = t_3;
	} else if (z <= -5.9e-80) {
		tmp = t_2;
	} else if (z <= -4.2e-103) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 2.2e-218) {
		tmp = t_2;
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else if (z <= 5.3e+14) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	t_2 = t_1 - (a * (z / y))
	t_3 = (t - a) / (b - y)
	tmp = 0
	if z <= -6.2e-9:
		tmp = t_3
	elif z <= -5.9e-80:
		tmp = t_2
	elif z <= -4.2e-103:
		tmp = ((t + ((x * y) / z)) - a) / b
	elif z <= 2.2e-218:
		tmp = t_2
	elif z <= 4.2e-125:
		tmp = t_1 + ((z * t) / y)
	elif z <= 5.3e+14:
		tmp = ((y / z) * (x / b)) + ((t - a) / b)
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	t_2 = Float64(t_1 - Float64(a * Float64(z / y)))
	t_3 = Float64(Float64(t - a) / Float64(b - y))
	tmp = 0.0
	if (z <= -6.2e-9)
		tmp = t_3;
	elseif (z <= -5.9e-80)
		tmp = t_2;
	elseif (z <= -4.2e-103)
		tmp = Float64(Float64(Float64(t + Float64(Float64(x * y) / z)) - a) / b);
	elseif (z <= 2.2e-218)
		tmp = t_2;
	elseif (z <= 4.2e-125)
		tmp = Float64(t_1 + Float64(Float64(z * t) / y));
	elseif (z <= 5.3e+14)
		tmp = Float64(Float64(Float64(y / z) * Float64(x / b)) + Float64(Float64(t - a) / b));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	t_2 = t_1 - (a * (z / y));
	t_3 = (t - a) / (b - y);
	tmp = 0.0;
	if (z <= -6.2e-9)
		tmp = t_3;
	elseif (z <= -5.9e-80)
		tmp = t_2;
	elseif (z <= -4.2e-103)
		tmp = ((t + ((x * y) / z)) - a) / b;
	elseif (z <= 2.2e-218)
		tmp = t_2;
	elseif (z <= 4.2e-125)
		tmp = t_1 + ((z * t) / y);
	elseif (z <= 5.3e+14)
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 - N[(a * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6.2e-9], t$95$3, If[LessEqual[z, -5.9e-80], t$95$2, If[LessEqual[z, -4.2e-103], N[(N[(N[(t + N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision] / b), $MachinePrecision], If[LessEqual[z, 2.2e-218], t$95$2, If[LessEqual[z, 4.2e-125], N[(t$95$1 + N[(N[(z * t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.3e+14], N[(N[(N[(y / z), $MachinePrecision] * N[(x / b), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]]]

Derivation

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

Alternative 6: 69.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ t_2 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -1.7 \cdot 10^{-6}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-80}:\\ \;\;\;\;t_1 + \frac{z}{\frac{y}{t - a}}\\ \mathbf{elif}\;z \leq -1.35 \cdot 10^{-103}:\\ \;\;\;\;\frac{\left(t + \frac{x \cdot y}{z}\right) - a}{b}\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{-218}:\\ \;\;\;\;t_1 - a \cdot \frac{z}{y}\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-125}:\\ \;\;\;\;t_1 + \frac{z \cdot t}{y}\\ \mathbf{elif}\;z \leq 10^{+16}:\\ \;\;\;\;\frac{y}{z} \cdot \frac{x}{b} + \frac{t - a}{b}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z))) (t_2 (/ (- t a) (- b y))))
   (if (<= z -1.7e-6)
     t_2
     (if (<= z -6e-80)
       (+ t_1 (/ z (/ y (- t a))))
       (if (<= z -1.35e-103)
         (/ (- (+ t (/ (* x y) z)) a) b)
         (if (<= z 2.1e-218)
           (- t_1 (* a (/ z y)))
           (if (<= z 4.2e-125)
             (+ t_1 (/ (* z t) y))
             (if (<= z 1e+16) (+ (* (/ y z) (/ x b)) (/ (- t a) b)) t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = (t - a) / (b - y);
	double tmp;
	if (z <= -1.7e-6) {
		tmp = t_2;
	} else if (z <= -6e-80) {
		tmp = t_1 + (z / (y / (t - a)));
	} else if (z <= -1.35e-103) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 2.1e-218) {
		tmp = t_1 - (a * (z / y));
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else if (z <= 1e+16) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    t_2 = (t - a) / (b - y)
    if (z <= (-1.7d-6)) then
        tmp = t_2
    else if (z <= (-6d-80)) then
        tmp = t_1 + (z / (y / (t - a)))
    else if (z <= (-1.35d-103)) then
        tmp = ((t + ((x * y) / z)) - a) / b
    else if (z <= 2.1d-218) then
        tmp = t_1 - (a * (z / y))
    else if (z <= 4.2d-125) then
        tmp = t_1 + ((z * t) / y)
    else if (z <= 1d+16) then
        tmp = ((y / z) * (x / b)) + ((t - a) / b)
    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 b) {
	double t_1 = x / (1.0 - z);
	double t_2 = (t - a) / (b - y);
	double tmp;
	if (z <= -1.7e-6) {
		tmp = t_2;
	} else if (z <= -6e-80) {
		tmp = t_1 + (z / (y / (t - a)));
	} else if (z <= -1.35e-103) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 2.1e-218) {
		tmp = t_1 - (a * (z / y));
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else if (z <= 1e+16) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	t_2 = (t - a) / (b - y)
	tmp = 0
	if z <= -1.7e-6:
		tmp = t_2
	elif z <= -6e-80:
		tmp = t_1 + (z / (y / (t - a)))
	elif z <= -1.35e-103:
		tmp = ((t + ((x * y) / z)) - a) / b
	elif z <= 2.1e-218:
		tmp = t_1 - (a * (z / y))
	elif z <= 4.2e-125:
		tmp = t_1 + ((z * t) / y)
	elif z <= 1e+16:
		tmp = ((y / z) * (x / b)) + ((t - a) / b)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	t_2 = Float64(Float64(t - a) / Float64(b - y))
	tmp = 0.0
	if (z <= -1.7e-6)
		tmp = t_2;
	elseif (z <= -6e-80)
		tmp = Float64(t_1 + Float64(z / Float64(y / Float64(t - a))));
	elseif (z <= -1.35e-103)
		tmp = Float64(Float64(Float64(t + Float64(Float64(x * y) / z)) - a) / b);
	elseif (z <= 2.1e-218)
		tmp = Float64(t_1 - Float64(a * Float64(z / y)));
	elseif (z <= 4.2e-125)
		tmp = Float64(t_1 + Float64(Float64(z * t) / y));
	elseif (z <= 1e+16)
		tmp = Float64(Float64(Float64(y / z) * Float64(x / b)) + Float64(Float64(t - a) / b));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	t_2 = (t - a) / (b - y);
	tmp = 0.0;
	if (z <= -1.7e-6)
		tmp = t_2;
	elseif (z <= -6e-80)
		tmp = t_1 + (z / (y / (t - a)));
	elseif (z <= -1.35e-103)
		tmp = ((t + ((x * y) / z)) - a) / b;
	elseif (z <= 2.1e-218)
		tmp = t_1 - (a * (z / y));
	elseif (z <= 4.2e-125)
		tmp = t_1 + ((z * t) / y);
	elseif (z <= 1e+16)
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.7e-6], t$95$2, If[LessEqual[z, -6e-80], N[(t$95$1 + N[(z / N[(y / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.35e-103], N[(N[(N[(t + N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision] / b), $MachinePrecision], If[LessEqual[z, 2.1e-218], N[(t$95$1 - N[(a * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.2e-125], N[(t$95$1 + N[(N[(z * t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1e+16], N[(N[(N[(y / z), $MachinePrecision] * N[(x / b), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]

Derivation

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

Alternative 7: 73.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z} + \frac{z \cdot \left(t - a\right)}{y}\\ t_2 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -6.6 \cdot 10^{-9}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -5.4 \cdot 10^{-80}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3.6 \cdot 10^{-102}:\\ \;\;\;\;\frac{\left(t + \frac{x \cdot y}{z}\right) - a}{b}\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-125}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 34000000000000:\\ \;\;\;\;\frac{y}{z} \cdot \frac{x}{b} + \frac{t - a}{b}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (/ x (- 1.0 z)) (/ (* z (- t a)) y)))
        (t_2 (/ (- t a) (- b y))))
   (if (<= z -6.6e-9)
     t_2
     (if (<= z -5.4e-80)
       t_1
       (if (<= z -3.6e-102)
         (/ (- (+ t (/ (* x y) z)) a) b)
         (if (<= z 4.2e-125)
           t_1
           (if (<= z 34000000000000.0)
             (+ (* (/ y z) (/ x b)) (/ (- t a) b))
             t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x / (1.0 - z)) + ((z * (t - a)) / y);
	double t_2 = (t - a) / (b - y);
	double tmp;
	if (z <= -6.6e-9) {
		tmp = t_2;
	} else if (z <= -5.4e-80) {
		tmp = t_1;
	} else if (z <= -3.6e-102) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 4.2e-125) {
		tmp = t_1;
	} else if (z <= 34000000000000.0) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x / (1.0d0 - z)) + ((z * (t - a)) / y)
    t_2 = (t - a) / (b - y)
    if (z <= (-6.6d-9)) then
        tmp = t_2
    else if (z <= (-5.4d-80)) then
        tmp = t_1
    else if (z <= (-3.6d-102)) then
        tmp = ((t + ((x * y) / z)) - a) / b
    else if (z <= 4.2d-125) then
        tmp = t_1
    else if (z <= 34000000000000.0d0) then
        tmp = ((y / z) * (x / b)) + ((t - a) / b)
    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 b) {
	double t_1 = (x / (1.0 - z)) + ((z * (t - a)) / y);
	double t_2 = (t - a) / (b - y);
	double tmp;
	if (z <= -6.6e-9) {
		tmp = t_2;
	} else if (z <= -5.4e-80) {
		tmp = t_1;
	} else if (z <= -3.6e-102) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 4.2e-125) {
		tmp = t_1;
	} else if (z <= 34000000000000.0) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x / (1.0 - z)) + ((z * (t - a)) / y)
	t_2 = (t - a) / (b - y)
	tmp = 0
	if z <= -6.6e-9:
		tmp = t_2
	elif z <= -5.4e-80:
		tmp = t_1
	elif z <= -3.6e-102:
		tmp = ((t + ((x * y) / z)) - a) / b
	elif z <= 4.2e-125:
		tmp = t_1
	elif z <= 34000000000000.0:
		tmp = ((y / z) * (x / b)) + ((t - a) / b)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x / Float64(1.0 - z)) + Float64(Float64(z * Float64(t - a)) / y))
	t_2 = Float64(Float64(t - a) / Float64(b - y))
	tmp = 0.0
	if (z <= -6.6e-9)
		tmp = t_2;
	elseif (z <= -5.4e-80)
		tmp = t_1;
	elseif (z <= -3.6e-102)
		tmp = Float64(Float64(Float64(t + Float64(Float64(x * y) / z)) - a) / b);
	elseif (z <= 4.2e-125)
		tmp = t_1;
	elseif (z <= 34000000000000.0)
		tmp = Float64(Float64(Float64(y / z) * Float64(x / b)) + Float64(Float64(t - a) / b));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x / (1.0 - z)) + ((z * (t - a)) / y);
	t_2 = (t - a) / (b - y);
	tmp = 0.0;
	if (z <= -6.6e-9)
		tmp = t_2;
	elseif (z <= -5.4e-80)
		tmp = t_1;
	elseif (z <= -3.6e-102)
		tmp = ((t + ((x * y) / z)) - a) / b;
	elseif (z <= 4.2e-125)
		tmp = t_1;
	elseif (z <= 34000000000000.0)
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision] + N[(N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6.6e-9], t$95$2, If[LessEqual[z, -5.4e-80], t$95$1, If[LessEqual[z, -3.6e-102], N[(N[(N[(t + N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision] / b), $MachinePrecision], If[LessEqual[z, 4.2e-125], t$95$1, If[LessEqual[z, 34000000000000.0], N[(N[(N[(y / z), $MachinePrecision] * N[(x / b), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 8: 74.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ t_2 := t_1 + \frac{z \cdot \left(t - a\right)}{y}\\ t_3 := t_1 + \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -28000:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq -5.4 \cdot 10^{-80}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.32 \cdot 10^{-103}:\\ \;\;\;\;\frac{\left(t + \frac{x \cdot y}{z}\right) - a}{b}\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{-125}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 4 \cdot 10^{+18}:\\ \;\;\;\;\frac{y}{z} \cdot \frac{x}{b} + \frac{t - a}{b}\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z)))
        (t_2 (+ t_1 (/ (* z (- t a)) y)))
        (t_3 (+ t_1 (/ (- t a) (- b y)))))
   (if (<= z -28000.0)
     t_3
     (if (<= z -5.4e-80)
       t_2
       (if (<= z -1.32e-103)
         (/ (- (+ t (/ (* x y) z)) a) b)
         (if (<= z 3.8e-125)
           t_2
           (if (<= z 4e+18) (+ (* (/ y z) (/ x b)) (/ (- t a) b)) t_3)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = t_1 + ((z * (t - a)) / y);
	double t_3 = t_1 + ((t - a) / (b - y));
	double tmp;
	if (z <= -28000.0) {
		tmp = t_3;
	} else if (z <= -5.4e-80) {
		tmp = t_2;
	} else if (z <= -1.32e-103) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 3.8e-125) {
		tmp = t_2;
	} else if (z <= 4e+18) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    t_2 = t_1 + ((z * (t - a)) / y)
    t_3 = t_1 + ((t - a) / (b - y))
    if (z <= (-28000.0d0)) then
        tmp = t_3
    else if (z <= (-5.4d-80)) then
        tmp = t_2
    else if (z <= (-1.32d-103)) then
        tmp = ((t + ((x * y) / z)) - a) / b
    else if (z <= 3.8d-125) then
        tmp = t_2
    else if (z <= 4d+18) then
        tmp = ((y / z) * (x / b)) + ((t - a) / b)
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = t_1 + ((z * (t - a)) / y);
	double t_3 = t_1 + ((t - a) / (b - y));
	double tmp;
	if (z <= -28000.0) {
		tmp = t_3;
	} else if (z <= -5.4e-80) {
		tmp = t_2;
	} else if (z <= -1.32e-103) {
		tmp = ((t + ((x * y) / z)) - a) / b;
	} else if (z <= 3.8e-125) {
		tmp = t_2;
	} else if (z <= 4e+18) {
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	t_2 = t_1 + ((z * (t - a)) / y)
	t_3 = t_1 + ((t - a) / (b - y))
	tmp = 0
	if z <= -28000.0:
		tmp = t_3
	elif z <= -5.4e-80:
		tmp = t_2
	elif z <= -1.32e-103:
		tmp = ((t + ((x * y) / z)) - a) / b
	elif z <= 3.8e-125:
		tmp = t_2
	elif z <= 4e+18:
		tmp = ((y / z) * (x / b)) + ((t - a) / b)
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	t_2 = Float64(t_1 + Float64(Float64(z * Float64(t - a)) / y))
	t_3 = Float64(t_1 + Float64(Float64(t - a) / Float64(b - y)))
	tmp = 0.0
	if (z <= -28000.0)
		tmp = t_3;
	elseif (z <= -5.4e-80)
		tmp = t_2;
	elseif (z <= -1.32e-103)
		tmp = Float64(Float64(Float64(t + Float64(Float64(x * y) / z)) - a) / b);
	elseif (z <= 3.8e-125)
		tmp = t_2;
	elseif (z <= 4e+18)
		tmp = Float64(Float64(Float64(y / z) * Float64(x / b)) + Float64(Float64(t - a) / b));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	t_2 = t_1 + ((z * (t - a)) / y);
	t_3 = t_1 + ((t - a) / (b - y));
	tmp = 0.0;
	if (z <= -28000.0)
		tmp = t_3;
	elseif (z <= -5.4e-80)
		tmp = t_2;
	elseif (z <= -1.32e-103)
		tmp = ((t + ((x * y) / z)) - a) / b;
	elseif (z <= 3.8e-125)
		tmp = t_2;
	elseif (z <= 4e+18)
		tmp = ((y / z) * (x / b)) + ((t - a) / b);
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 + N[(N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$1 + N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -28000.0], t$95$3, If[LessEqual[z, -5.4e-80], t$95$2, If[LessEqual[z, -1.32e-103], N[(N[(N[(t + N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision] / b), $MachinePrecision], If[LessEqual[z, 3.8e-125], t$95$2, If[LessEqual[z, 4e+18], N[(N[(N[(y / z), $MachinePrecision] * N[(x / b), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]]

Derivation

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

Alternative 9: 83.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.6 \cdot 10^{-9} \lor \neg \left(z \leq 0.092\right):\\ \;\;\;\;\frac{x}{1 - z} + \frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -6.6e-9) (not (<= z 0.092)))
   (+ (/ x (- 1.0 z)) (/ (- t a) (- b y)))
   (+ x (/ (* z (- t a)) (+ y (* z (- b y)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6.6e-9) || !(z <= 0.092)) {
		tmp = (x / (1.0 - z)) + ((t - a) / (b - y));
	} else {
		tmp = x + ((z * (t - a)) / (y + (z * (b - y))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-6.6d-9)) .or. (.not. (z <= 0.092d0))) then
        tmp = (x / (1.0d0 - z)) + ((t - a) / (b - y))
    else
        tmp = x + ((z * (t - a)) / (y + (z * (b - y))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6.6e-9) || !(z <= 0.092)) {
		tmp = (x / (1.0 - z)) + ((t - a) / (b - y));
	} else {
		tmp = x + ((z * (t - a)) / (y + (z * (b - y))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -6.6e-9) or not (z <= 0.092):
		tmp = (x / (1.0 - z)) + ((t - a) / (b - y))
	else:
		tmp = x + ((z * (t - a)) / (y + (z * (b - y))))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -6.6e-9) || !(z <= 0.092))
		tmp = Float64(Float64(x / Float64(1.0 - z)) + Float64(Float64(t - a) / Float64(b - y)));
	else
		tmp = Float64(x + Float64(Float64(z * Float64(t - a)) / Float64(y + Float64(z * Float64(b - y)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -6.6e-9) || ~((z <= 0.092)))
		tmp = (x / (1.0 - z)) + ((t - a) / (b - y));
	else
		tmp = x + ((z * (t - a)) / (y + (z * (b - y))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -6.6e-9], N[Not[LessEqual[z, 0.092]], $MachinePrecision]], N[(N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 67.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ t_2 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -5.2 \cdot 10^{-13}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-218}:\\ \;\;\;\;t_1 - a \cdot \frac{z}{y}\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-125}:\\ \;\;\;\;t_1 + \frac{z \cdot t}{y}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z))) (t_2 (/ (- t a) (- b y))))
   (if (<= z -5.2e-13)
     t_2
     (if (<= z 1.6e-218)
       (- t_1 (* a (/ z y)))
       (if (<= z 4.2e-125) (+ t_1 (/ (* z t) y)) t_2)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double t_2 = (t - a) / (b - y);
	double tmp;
	if (z <= -5.2e-13) {
		tmp = t_2;
	} else if (z <= 1.6e-218) {
		tmp = t_1 - (a * (z / y));
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    t_2 = (t - a) / (b - y)
    if (z <= (-5.2d-13)) then
        tmp = t_2
    else if (z <= 1.6d-218) then
        tmp = t_1 - (a * (z / y))
    else if (z <= 4.2d-125) then
        tmp = t_1 + ((z * t) / y)
    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 b) {
	double t_1 = x / (1.0 - z);
	double t_2 = (t - a) / (b - y);
	double tmp;
	if (z <= -5.2e-13) {
		tmp = t_2;
	} else if (z <= 1.6e-218) {
		tmp = t_1 - (a * (z / y));
	} else if (z <= 4.2e-125) {
		tmp = t_1 + ((z * t) / y);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	t_2 = (t - a) / (b - y)
	tmp = 0
	if z <= -5.2e-13:
		tmp = t_2
	elif z <= 1.6e-218:
		tmp = t_1 - (a * (z / y))
	elif z <= 4.2e-125:
		tmp = t_1 + ((z * t) / y)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	t_2 = Float64(Float64(t - a) / Float64(b - y))
	tmp = 0.0
	if (z <= -5.2e-13)
		tmp = t_2;
	elseif (z <= 1.6e-218)
		tmp = Float64(t_1 - Float64(a * Float64(z / y)));
	elseif (z <= 4.2e-125)
		tmp = Float64(t_1 + Float64(Float64(z * t) / y));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	t_2 = (t - a) / (b - y);
	tmp = 0.0;
	if (z <= -5.2e-13)
		tmp = t_2;
	elseif (z <= 1.6e-218)
		tmp = t_1 - (a * (z / y));
	elseif (z <= 4.2e-125)
		tmp = t_1 + ((z * t) / y);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.2e-13], t$95$2, If[LessEqual[z, 1.6e-218], N[(t$95$1 - N[(a * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.2e-125], N[(t$95$1 + N[(N[(z * t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

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

Alternative 11: 39.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{b - y}\\ \mathbf{if}\;z \leq -6.5 \cdot 10^{+146}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-36}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{-115}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{+46} \lor \neg \left(z \leq 2.55 \cdot 10^{+141}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{-x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ t (- b y))))
   (if (<= z -6.5e+146)
     t_1
     (if (<= z -6e-36)
       (/ (- a) b)
       (if (<= z 4.7e-115)
         x
         (if (or (<= z 3.2e+46) (not (<= z 2.55e+141))) t_1 (/ (- x) z)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t / (b - y);
	double tmp;
	if (z <= -6.5e+146) {
		tmp = t_1;
	} else if (z <= -6e-36) {
		tmp = -a / b;
	} else if (z <= 4.7e-115) {
		tmp = x;
	} else if ((z <= 3.2e+46) || !(z <= 2.55e+141)) {
		tmp = t_1;
	} else {
		tmp = -x / z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t / (b - y)
    if (z <= (-6.5d+146)) then
        tmp = t_1
    else if (z <= (-6d-36)) then
        tmp = -a / b
    else if (z <= 4.7d-115) then
        tmp = x
    else if ((z <= 3.2d+46) .or. (.not. (z <= 2.55d+141))) then
        tmp = t_1
    else
        tmp = -x / z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t / (b - y);
	double tmp;
	if (z <= -6.5e+146) {
		tmp = t_1;
	} else if (z <= -6e-36) {
		tmp = -a / b;
	} else if (z <= 4.7e-115) {
		tmp = x;
	} else if ((z <= 3.2e+46) || !(z <= 2.55e+141)) {
		tmp = t_1;
	} else {
		tmp = -x / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t / (b - y)
	tmp = 0
	if z <= -6.5e+146:
		tmp = t_1
	elif z <= -6e-36:
		tmp = -a / b
	elif z <= 4.7e-115:
		tmp = x
	elif (z <= 3.2e+46) or not (z <= 2.55e+141):
		tmp = t_1
	else:
		tmp = -x / z
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t / Float64(b - y))
	tmp = 0.0
	if (z <= -6.5e+146)
		tmp = t_1;
	elseif (z <= -6e-36)
		tmp = Float64(Float64(-a) / b);
	elseif (z <= 4.7e-115)
		tmp = x;
	elseif ((z <= 3.2e+46) || !(z <= 2.55e+141))
		tmp = t_1;
	else
		tmp = Float64(Float64(-x) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t / (b - y);
	tmp = 0.0;
	if (z <= -6.5e+146)
		tmp = t_1;
	elseif (z <= -6e-36)
		tmp = -a / b;
	elseif (z <= 4.7e-115)
		tmp = x;
	elseif ((z <= 3.2e+46) || ~((z <= 2.55e+141)))
		tmp = t_1;
	else
		tmp = -x / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6.5e+146], t$95$1, If[LessEqual[z, -6e-36], N[((-a) / b), $MachinePrecision], If[LessEqual[z, 4.7e-115], x, If[Or[LessEqual[z, 3.2e+46], N[Not[LessEqual[z, 2.55e+141]], $MachinePrecision]], t$95$1, N[((-x) / z), $MachinePrecision]]]]]]

Derivation

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

Alternative 12: 68.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{-36} \lor \neg \left(z \leq 3.8 \cdot 10^{-125}\right):\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - z} + \frac{z \cdot t}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -5.8e-36) (not (<= z 3.8e-125)))
   (/ (- t a) (- b y))
   (+ (/ x (- 1.0 z)) (/ (* z t) y))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -5.8e-36) || !(z <= 3.8e-125)) {
		tmp = (t - a) / (b - y);
	} else {
		tmp = (x / (1.0 - z)) + ((z * t) / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-5.8d-36)) .or. (.not. (z <= 3.8d-125))) then
        tmp = (t - a) / (b - y)
    else
        tmp = (x / (1.0d0 - z)) + ((z * t) / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -5.8e-36) || !(z <= 3.8e-125)) {
		tmp = (t - a) / (b - y);
	} else {
		tmp = (x / (1.0 - z)) + ((z * t) / y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -5.8e-36) or not (z <= 3.8e-125):
		tmp = (t - a) / (b - y)
	else:
		tmp = (x / (1.0 - z)) + ((z * t) / y)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -5.8e-36) || !(z <= 3.8e-125))
		tmp = Float64(Float64(t - a) / Float64(b - y));
	else
		tmp = Float64(Float64(x / Float64(1.0 - z)) + Float64(Float64(z * t) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -5.8e-36) || ~((z <= 3.8e-125)))
		tmp = (t - a) / (b - y);
	else
		tmp = (x / (1.0 - z)) + ((z * t) / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -5.8e-36], N[Not[LessEqual[z, 3.8e-125]], $MachinePrecision]], N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision], N[(N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 34.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-a}{b}\\ \mathbf{if}\;z \leq -3.2 \cdot 10^{+168}:\\ \;\;\;\;\frac{-t}{y}\\ \mathbf{elif}\;z \leq -7.5 \cdot 10^{-37}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{-115}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 9.2 \cdot 10^{+44}:\\ \;\;\;\;\frac{t}{b}\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{+229}:\\ \;\;\;\;\frac{-x}{z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- a) b)))
   (if (<= z -3.2e+168)
     (/ (- t) y)
     (if (<= z -7.5e-37)
       t_1
       (if (<= z 4.7e-115)
         x
         (if (<= z 9.2e+44) (/ t b) (if (<= z 1.9e+229) (/ (- x) z) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -3.2e+168) {
		tmp = -t / y;
	} else if (z <= -7.5e-37) {
		tmp = t_1;
	} else if (z <= 4.7e-115) {
		tmp = x;
	} else if (z <= 9.2e+44) {
		tmp = t / b;
	} else if (z <= 1.9e+229) {
		tmp = -x / z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = -a / b
    if (z <= (-3.2d+168)) then
        tmp = -t / y
    else if (z <= (-7.5d-37)) then
        tmp = t_1
    else if (z <= 4.7d-115) then
        tmp = x
    else if (z <= 9.2d+44) then
        tmp = t / b
    else if (z <= 1.9d+229) then
        tmp = -x / 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 b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -3.2e+168) {
		tmp = -t / y;
	} else if (z <= -7.5e-37) {
		tmp = t_1;
	} else if (z <= 4.7e-115) {
		tmp = x;
	} else if (z <= 9.2e+44) {
		tmp = t / b;
	} else if (z <= 1.9e+229) {
		tmp = -x / z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = -a / b
	tmp = 0
	if z <= -3.2e+168:
		tmp = -t / y
	elif z <= -7.5e-37:
		tmp = t_1
	elif z <= 4.7e-115:
		tmp = x
	elif z <= 9.2e+44:
		tmp = t / b
	elif z <= 1.9e+229:
		tmp = -x / z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(-a) / b)
	tmp = 0.0
	if (z <= -3.2e+168)
		tmp = Float64(Float64(-t) / y);
	elseif (z <= -7.5e-37)
		tmp = t_1;
	elseif (z <= 4.7e-115)
		tmp = x;
	elseif (z <= 9.2e+44)
		tmp = Float64(t / b);
	elseif (z <= 1.9e+229)
		tmp = Float64(Float64(-x) / z);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = -a / b;
	tmp = 0.0;
	if (z <= -3.2e+168)
		tmp = -t / y;
	elseif (z <= -7.5e-37)
		tmp = t_1;
	elseif (z <= 4.7e-115)
		tmp = x;
	elseif (z <= 9.2e+44)
		tmp = t / b;
	elseif (z <= 1.9e+229)
		tmp = -x / z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[((-a) / b), $MachinePrecision]}, If[LessEqual[z, -3.2e+168], N[((-t) / y), $MachinePrecision], If[LessEqual[z, -7.5e-37], t$95$1, If[LessEqual[z, 4.7e-115], x, If[LessEqual[z, 9.2e+44], N[(t / b), $MachinePrecision], If[LessEqual[z, 1.9e+229], N[((-x) / z), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 14: 42.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ \mathbf{if}\;y \leq -3.7 \cdot 10^{+76}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -6.2 \cdot 10^{-201}:\\ \;\;\;\;\frac{t}{b - y}\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{-259}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-58}:\\ \;\;\;\;\frac{t}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z))))
   (if (<= y -3.7e+76)
     t_1
     (if (<= y -6.2e-201)
       (/ t (- b y))
       (if (<= y 3.1e-259) (/ (- a) b) (if (<= y 8e-58) (/ t b) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -3.7e+76) {
		tmp = t_1;
	} else if (y <= -6.2e-201) {
		tmp = t / (b - y);
	} else if (y <= 3.1e-259) {
		tmp = -a / b;
	} else if (y <= 8e-58) {
		tmp = t / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    if (y <= (-3.7d+76)) then
        tmp = t_1
    else if (y <= (-6.2d-201)) then
        tmp = t / (b - y)
    else if (y <= 3.1d-259) then
        tmp = -a / b
    else if (y <= 8d-58) then
        tmp = t / b
    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 b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -3.7e+76) {
		tmp = t_1;
	} else if (y <= -6.2e-201) {
		tmp = t / (b - y);
	} else if (y <= 3.1e-259) {
		tmp = -a / b;
	} else if (y <= 8e-58) {
		tmp = t / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	tmp = 0
	if y <= -3.7e+76:
		tmp = t_1
	elif y <= -6.2e-201:
		tmp = t / (b - y)
	elif y <= 3.1e-259:
		tmp = -a / b
	elif y <= 8e-58:
		tmp = t / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	tmp = 0.0
	if (y <= -3.7e+76)
		tmp = t_1;
	elseif (y <= -6.2e-201)
		tmp = Float64(t / Float64(b - y));
	elseif (y <= 3.1e-259)
		tmp = Float64(Float64(-a) / b);
	elseif (y <= 8e-58)
		tmp = Float64(t / b);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	tmp = 0.0;
	if (y <= -3.7e+76)
		tmp = t_1;
	elseif (y <= -6.2e-201)
		tmp = t / (b - y);
	elseif (y <= 3.1e-259)
		tmp = -a / b;
	elseif (y <= 8e-58)
		tmp = t / b;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.7e+76], t$95$1, If[LessEqual[y, -6.2e-201], N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.1e-259], N[((-a) / b), $MachinePrecision], If[LessEqual[y, 8e-58], N[(t / b), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 15: 34.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-a}{b}\\ \mathbf{if}\;z \leq -4.8 \cdot 10^{+165}:\\ \;\;\;\;\frac{a}{y}\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-36}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.4 \cdot 10^{-115}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 7.3 \cdot 10^{+269}:\\ \;\;\;\;\frac{t}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- a) b)))
   (if (<= z -4.8e+165)
     (/ a y)
     (if (<= z -2.1e-36)
       t_1
       (if (<= z 4.4e-115) x (if (<= z 7.3e+269) (/ t b) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -4.8e+165) {
		tmp = a / y;
	} else if (z <= -2.1e-36) {
		tmp = t_1;
	} else if (z <= 4.4e-115) {
		tmp = x;
	} else if (z <= 7.3e+269) {
		tmp = t / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = -a / b
    if (z <= (-4.8d+165)) then
        tmp = a / y
    else if (z <= (-2.1d-36)) then
        tmp = t_1
    else if (z <= 4.4d-115) then
        tmp = x
    else if (z <= 7.3d+269) then
        tmp = t / b
    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 b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -4.8e+165) {
		tmp = a / y;
	} else if (z <= -2.1e-36) {
		tmp = t_1;
	} else if (z <= 4.4e-115) {
		tmp = x;
	} else if (z <= 7.3e+269) {
		tmp = t / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = -a / b
	tmp = 0
	if z <= -4.8e+165:
		tmp = a / y
	elif z <= -2.1e-36:
		tmp = t_1
	elif z <= 4.4e-115:
		tmp = x
	elif z <= 7.3e+269:
		tmp = t / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(-a) / b)
	tmp = 0.0
	if (z <= -4.8e+165)
		tmp = Float64(a / y);
	elseif (z <= -2.1e-36)
		tmp = t_1;
	elseif (z <= 4.4e-115)
		tmp = x;
	elseif (z <= 7.3e+269)
		tmp = Float64(t / b);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = -a / b;
	tmp = 0.0;
	if (z <= -4.8e+165)
		tmp = a / y;
	elseif (z <= -2.1e-36)
		tmp = t_1;
	elseif (z <= 4.4e-115)
		tmp = x;
	elseif (z <= 7.3e+269)
		tmp = t / b;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[((-a) / b), $MachinePrecision]}, If[LessEqual[z, -4.8e+165], N[(a / y), $MachinePrecision], If[LessEqual[z, -2.1e-36], t$95$1, If[LessEqual[z, 4.4e-115], x, If[LessEqual[z, 7.3e+269], N[(t / b), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 16: 34.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-a}{b}\\ \mathbf{if}\;z \leq -7 \cdot 10^{+162}:\\ \;\;\;\;\frac{-t}{y}\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-36}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{-115}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{+272}:\\ \;\;\;\;\frac{t}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- a) b)))
   (if (<= z -7e+162)
     (/ (- t) y)
     (if (<= z -6e-36)
       t_1
       (if (<= z 4.7e-115) x (if (<= z 1.6e+272) (/ t b) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -7e+162) {
		tmp = -t / y;
	} else if (z <= -6e-36) {
		tmp = t_1;
	} else if (z <= 4.7e-115) {
		tmp = x;
	} else if (z <= 1.6e+272) {
		tmp = t / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = -a / b
    if (z <= (-7d+162)) then
        tmp = -t / y
    else if (z <= (-6d-36)) then
        tmp = t_1
    else if (z <= 4.7d-115) then
        tmp = x
    else if (z <= 1.6d+272) then
        tmp = t / b
    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 b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -7e+162) {
		tmp = -t / y;
	} else if (z <= -6e-36) {
		tmp = t_1;
	} else if (z <= 4.7e-115) {
		tmp = x;
	} else if (z <= 1.6e+272) {
		tmp = t / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = -a / b
	tmp = 0
	if z <= -7e+162:
		tmp = -t / y
	elif z <= -6e-36:
		tmp = t_1
	elif z <= 4.7e-115:
		tmp = x
	elif z <= 1.6e+272:
		tmp = t / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(-a) / b)
	tmp = 0.0
	if (z <= -7e+162)
		tmp = Float64(Float64(-t) / y);
	elseif (z <= -6e-36)
		tmp = t_1;
	elseif (z <= 4.7e-115)
		tmp = x;
	elseif (z <= 1.6e+272)
		tmp = Float64(t / b);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = -a / b;
	tmp = 0.0;
	if (z <= -7e+162)
		tmp = -t / y;
	elseif (z <= -6e-36)
		tmp = t_1;
	elseif (z <= 4.7e-115)
		tmp = x;
	elseif (z <= 1.6e+272)
		tmp = t / b;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[((-a) / b), $MachinePrecision]}, If[LessEqual[z, -7e+162], N[((-t) / y), $MachinePrecision], If[LessEqual[z, -6e-36], t$95$1, If[LessEqual[z, 4.7e-115], x, If[LessEqual[z, 1.6e+272], N[(t / b), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 17: 64.0% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6 \cdot 10^{-102} \lor \neg \left(z \leq 3.8 \cdot 10^{-125}\right):\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -6e-102) (not (<= z 3.8e-125)))
   (/ (- t a) (- b y))
   (/ x (- 1.0 z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6e-102) || !(z <= 3.8e-125)) {
		tmp = (t - a) / (b - y);
	} else {
		tmp = x / (1.0 - z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-6d-102)) .or. (.not. (z <= 3.8d-125))) then
        tmp = (t - a) / (b - y)
    else
        tmp = x / (1.0d0 - z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6e-102) || !(z <= 3.8e-125)) {
		tmp = (t - a) / (b - y);
	} else {
		tmp = x / (1.0 - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -6e-102) or not (z <= 3.8e-125):
		tmp = (t - a) / (b - y)
	else:
		tmp = x / (1.0 - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -6e-102) || !(z <= 3.8e-125))
		tmp = Float64(Float64(t - a) / Float64(b - y));
	else
		tmp = Float64(x / Float64(1.0 - z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -6e-102) || ~((z <= 3.8e-125)))
		tmp = (t - a) / (b - y);
	else
		tmp = x / (1.0 - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -6e-102], N[Not[LessEqual[z, 3.8e-125]], $MachinePrecision]], N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 53.5% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{+80} \lor \neg \left(y \leq 5.5 \cdot 10^{-62}\right):\\ \;\;\;\;\frac{x}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{t - a}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -4.8e+80) (not (<= y 5.5e-62))) (/ x (- 1.0 z)) (/ (- t a) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -4.8e+80) || !(y <= 5.5e-62)) {
		tmp = x / (1.0 - z);
	} else {
		tmp = (t - a) / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-4.8d+80)) .or. (.not. (y <= 5.5d-62))) then
        tmp = x / (1.0d0 - z)
    else
        tmp = (t - a) / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -4.8e+80) || !(y <= 5.5e-62)) {
		tmp = x / (1.0 - z);
	} else {
		tmp = (t - a) / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -4.8e+80) or not (y <= 5.5e-62):
		tmp = x / (1.0 - z)
	else:
		tmp = (t - a) / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -4.8e+80) || !(y <= 5.5e-62))
		tmp = Float64(x / Float64(1.0 - z));
	else
		tmp = Float64(Float64(t - a) / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -4.8e+80) || ~((y <= 5.5e-62)))
		tmp = x / (1.0 - z);
	else
		tmp = (t - a) / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -4.8e+80], N[Not[LessEqual[y, 5.5e-62]], $MachinePrecision]], N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision], N[(N[(t - a), $MachinePrecision] / b), $MachinePrecision]]

Derivation

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

Alternative 19: 33.3% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.55 \cdot 10^{-13} \lor \neg \left(z \leq 0.9\right):\\ \;\;\;\;\frac{a}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -1.55e-13) (not (<= z 0.9))) (/ a y) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -1.55e-13) || !(z <= 0.9)) {
		tmp = a / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-1.55d-13)) .or. (.not. (z <= 0.9d0))) then
        tmp = a / 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 b) {
	double tmp;
	if ((z <= -1.55e-13) || !(z <= 0.9)) {
		tmp = a / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -1.55e-13) or not (z <= 0.9):
		tmp = a / y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -1.55e-13) || !(z <= 0.9))
		tmp = Float64(a / y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -1.55e-13) || ~((z <= 0.9)))
		tmp = a / y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -1.55e-13], N[Not[LessEqual[z, 0.9]], $MachinePrecision]], N[(a / y), $MachinePrecision], x]

Derivation

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

Alternative 20: 33.1% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.55 \cdot 10^{-13}:\\ \;\;\;\;\frac{a}{y}\\ \mathbf{elif}\;z \leq 2.25 \cdot 10^{-115}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.55e-13) (/ a y) (if (<= z 2.25e-115) x (/ t b))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.55e-13) {
		tmp = a / y;
	} else if (z <= 2.25e-115) {
		tmp = x;
	} else {
		tmp = t / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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), intent (in) :: b
    real(8) :: tmp
    if (z <= (-1.55d-13)) then
        tmp = a / y
    else if (z <= 2.25d-115) then
        tmp = x
    else
        tmp = t / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.55e-13) {
		tmp = a / y;
	} else if (z <= 2.25e-115) {
		tmp = x;
	} else {
		tmp = t / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.55e-13:
		tmp = a / y
	elif z <= 2.25e-115:
		tmp = x
	else:
		tmp = t / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.55e-13)
		tmp = Float64(a / y);
	elseif (z <= 2.25e-115)
		tmp = x;
	else
		tmp = Float64(t / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.55e-13)
		tmp = a / y;
	elseif (z <= 2.25e-115)
		tmp = x;
	else
		tmp = t / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.55e-13], N[(a / y), $MachinePrecision], If[LessEqual[z, 2.25e-115], x, N[(t / b), $MachinePrecision]]]

Derivation

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

Alternative 21: 24.5% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 74.1% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023347 
(FPCore (x y z t a b)
  :name "Development.Shake.Progress:decay from shake-0.15.5"
  :precision binary64

  :herbie-target
  (- (/ (+ (* z t) (* y x)) (+ y (* z (- b y)))) (/ a (+ (- b y) (/ y z))))

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