Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2

Percentage Accurate: 95.5% → 97.9%

Time: 20.3s

Alternatives: 28

Speedup: 0.5×

• 36.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

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 - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (((y + t) - 2.0d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Initial Program: 95.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

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 - 1.0d0) * z)) - ((t - 1.0d0) * a)) + (((y + t) - 2.0d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := y + \left(t + -2\right)\\ \mathbf{if}\;\left(\left(x + t_1\right) + a \cdot \left(1 - t\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b \leq \infty:\\ \;\;\;\;\mathsf{fma}\left(t_2, b, x - \mathsf{fma}\left(y + -1, z, \left(t + -1\right) \cdot a\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t_2, b, x + \left(a + t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y))) (t_2 (+ y (+ t -2.0))))
   (if (<= (+ (+ (+ x t_1) (* a (- 1.0 t))) (* (- (+ y t) 2.0) b)) INFINITY)
     (fma t_2 b (- x (fma (+ y -1.0) z (* (+ t -1.0) a))))
     (fma t_2 b (+ x (+ a t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = y + (t + -2.0);
	double tmp;
	if ((((x + t_1) + (a * (1.0 - t))) + (((y + t) - 2.0) * b)) <= ((double) INFINITY)) {
		tmp = fma(t_2, b, (x - fma((y + -1.0), z, ((t + -1.0) * a))));
	} else {
		tmp = fma(t_2, b, (x + (a + t_1)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(y + Float64(t + -2.0))
	tmp = 0.0
	if (Float64(Float64(Float64(x + t_1) + Float64(a * Float64(1.0 - t))) + Float64(Float64(Float64(y + t) - 2.0) * b)) <= Inf)
		tmp = fma(t_2, b, Float64(x - fma(Float64(y + -1.0), z, Float64(Float64(t + -1.0) * a))));
	else
		tmp = fma(t_2, b, Float64(x + Float64(a + t_1)));
	end
	return tmp
end

Wolfram

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

Derivation

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

Alternative 2: 97.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := \left(\left(x + t_1\right) + a \cdot \left(1 - t\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;t_2 \leq \infty:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y + \left(t + -2\right), b, x + \left(a + t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y)))
        (t_2 (+ (+ (+ x t_1) (* a (- 1.0 t))) (* (- (+ y t) 2.0) b))))
   (if (<= t_2 INFINITY) t_2 (fma (+ y (+ t -2.0)) b (+ x (+ a t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = ((x + t_1) + (a * (1.0 - t))) + (((y + t) - 2.0) * b);
	double tmp;
	if (t_2 <= ((double) INFINITY)) {
		tmp = t_2;
	} else {
		tmp = fma((y + (t + -2.0)), b, (x + (a + t_1)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(Float64(Float64(x + t_1) + Float64(a * Float64(1.0 - t))) + Float64(Float64(Float64(y + t) - 2.0) * b))
	tmp = 0.0
	if (t_2 <= Inf)
		tmp = t_2;
	else
		tmp = fma(Float64(y + Float64(t + -2.0)), b, Float64(x + Float64(a + t_1)));
	end
	return tmp
end

Wolfram

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

Derivation

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

Alternative 3: 98.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;t_1 \leq \infty:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1
         (+ (+ (+ x (* z (- 1.0 y))) (* a (- 1.0 t))) (* (- (+ y t) 2.0) b))))
   (if (<= t_1 INFINITY) t_1 (* y (- b z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (((y + t) - 2.0) * b);
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (((y + t) - 2.0) * b);
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (((y + t) - 2.0) * b)
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = y * (b - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(x + Float64(z * Float64(1.0 - y))) + Float64(a * Float64(1.0 - t))) + Float64(Float64(Float64(y + t) - 2.0) * b))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(y * Float64(b - z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (((y + t) - 2.0) * b);
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = y * (b - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 4: 53.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := t \cdot \left(b - a\right)\\ t_3 := b \cdot \left(y - 2\right)\\ t_4 := x + t_3\\ \mathbf{if}\;t \leq -2 \cdot 10^{+79}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -9.2 \cdot 10^{-67}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.35 \cdot 10^{-133}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t \leq -2.4 \cdot 10^{-145}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-286}:\\ \;\;\;\;a + t_3\\ \mathbf{elif}\;t \leq 1.55 \cdot 10^{-242}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 10^{-195}:\\ \;\;\;\;a + b \cdot -2\\ \mathbf{elif}\;t \leq 1.02 \cdot 10^{+33}:\\ \;\;\;\;t_4\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y)))
        (t_2 (* t (- b a)))
        (t_3 (* b (- y 2.0)))
        (t_4 (+ x t_3)))
   (if (<= t -2e+79)
     t_2
     (if (<= t -9.2e-67)
       t_1
       (if (<= t -3.35e-133)
         t_4
         (if (<= t -2.4e-145)
           t_1
           (if (<= t 1.25e-286)
             (+ a t_3)
             (if (<= t 1.55e-242)
               t_1
               (if (<= t 1e-195)
                 (+ a (* b -2.0))
                 (if (<= t 1.02e+33) t_4 t_2))))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = t * (b - a);
	double t_3 = b * (y - 2.0);
	double t_4 = x + t_3;
	double tmp;
	if (t <= -2e+79) {
		tmp = t_2;
	} else if (t <= -9.2e-67) {
		tmp = t_1;
	} else if (t <= -3.35e-133) {
		tmp = t_4;
	} else if (t <= -2.4e-145) {
		tmp = t_1;
	} else if (t <= 1.25e-286) {
		tmp = a + t_3;
	} else if (t <= 1.55e-242) {
		tmp = t_1;
	} else if (t <= 1e-195) {
		tmp = a + (b * -2.0);
	} else if (t <= 1.02e+33) {
		tmp = t_4;
	} 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) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = z * (1.0d0 - y)
    t_2 = t * (b - a)
    t_3 = b * (y - 2.0d0)
    t_4 = x + t_3
    if (t <= (-2d+79)) then
        tmp = t_2
    else if (t <= (-9.2d-67)) then
        tmp = t_1
    else if (t <= (-3.35d-133)) then
        tmp = t_4
    else if (t <= (-2.4d-145)) then
        tmp = t_1
    else if (t <= 1.25d-286) then
        tmp = a + t_3
    else if (t <= 1.55d-242) then
        tmp = t_1
    else if (t <= 1d-195) then
        tmp = a + (b * (-2.0d0))
    else if (t <= 1.02d+33) then
        tmp = t_4
    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 = z * (1.0 - y);
	double t_2 = t * (b - a);
	double t_3 = b * (y - 2.0);
	double t_4 = x + t_3;
	double tmp;
	if (t <= -2e+79) {
		tmp = t_2;
	} else if (t <= -9.2e-67) {
		tmp = t_1;
	} else if (t <= -3.35e-133) {
		tmp = t_4;
	} else if (t <= -2.4e-145) {
		tmp = t_1;
	} else if (t <= 1.25e-286) {
		tmp = a + t_3;
	} else if (t <= 1.55e-242) {
		tmp = t_1;
	} else if (t <= 1e-195) {
		tmp = a + (b * -2.0);
	} else if (t <= 1.02e+33) {
		tmp = t_4;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = t * (b - a)
	t_3 = b * (y - 2.0)
	t_4 = x + t_3
	tmp = 0
	if t <= -2e+79:
		tmp = t_2
	elif t <= -9.2e-67:
		tmp = t_1
	elif t <= -3.35e-133:
		tmp = t_4
	elif t <= -2.4e-145:
		tmp = t_1
	elif t <= 1.25e-286:
		tmp = a + t_3
	elif t <= 1.55e-242:
		tmp = t_1
	elif t <= 1e-195:
		tmp = a + (b * -2.0)
	elif t <= 1.02e+33:
		tmp = t_4
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(t * Float64(b - a))
	t_3 = Float64(b * Float64(y - 2.0))
	t_4 = Float64(x + t_3)
	tmp = 0.0
	if (t <= -2e+79)
		tmp = t_2;
	elseif (t <= -9.2e-67)
		tmp = t_1;
	elseif (t <= -3.35e-133)
		tmp = t_4;
	elseif (t <= -2.4e-145)
		tmp = t_1;
	elseif (t <= 1.25e-286)
		tmp = Float64(a + t_3);
	elseif (t <= 1.55e-242)
		tmp = t_1;
	elseif (t <= 1e-195)
		tmp = Float64(a + Float64(b * -2.0));
	elseif (t <= 1.02e+33)
		tmp = t_4;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (1.0 - y);
	t_2 = t * (b - a);
	t_3 = b * (y - 2.0);
	t_4 = x + t_3;
	tmp = 0.0;
	if (t <= -2e+79)
		tmp = t_2;
	elseif (t <= -9.2e-67)
		tmp = t_1;
	elseif (t <= -3.35e-133)
		tmp = t_4;
	elseif (t <= -2.4e-145)
		tmp = t_1;
	elseif (t <= 1.25e-286)
		tmp = a + t_3;
	elseif (t <= 1.55e-242)
		tmp = t_1;
	elseif (t <= 1e-195)
		tmp = a + (b * -2.0);
	elseif (t <= 1.02e+33)
		tmp = t_4;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x + t$95$3), $MachinePrecision]}, If[LessEqual[t, -2e+79], t$95$2, If[LessEqual[t, -9.2e-67], t$95$1, If[LessEqual[t, -3.35e-133], t$95$4, If[LessEqual[t, -2.4e-145], t$95$1, If[LessEqual[t, 1.25e-286], N[(a + t$95$3), $MachinePrecision], If[LessEqual[t, 1.55e-242], t$95$1, If[LessEqual[t, 1e-195], N[(a + N[(b * -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.02e+33], t$95$4, t$95$2]]]]]]]]]]]]

Derivation

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

Alternative 5: 63.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + z \cdot \left(1 - y\right)\\ t_2 := t \cdot \left(b - a\right)\\ t_3 := a + \left(x + b \cdot \left(y + -2\right)\right)\\ \mathbf{if}\;t \leq -3 \cdot 10^{+76}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -5.7 \cdot 10^{-67}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.14 \cdot 10^{-138}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -6 \cdot 10^{-185}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.8 \cdot 10^{-300}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq 2.9 \cdot 10^{-246}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 7.5 \cdot 10^{+36}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ a (* z (- 1.0 y))))
        (t_2 (* t (- b a)))
        (t_3 (+ a (+ x (* b (+ y -2.0))))))
   (if (<= t -3e+76)
     t_2
     (if (<= t -5.7e-67)
       t_1
       (if (<= t -1.14e-138)
         t_3
         (if (<= t -6e-185)
           t_1
           (if (<= t 4.8e-300)
             t_3
             (if (<= t 2.9e-246) t_1 (if (<= t 7.5e+36) t_3 t_2)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double t_3 = a + (x + (b * (y + -2.0)));
	double tmp;
	if (t <= -3e+76) {
		tmp = t_2;
	} else if (t <= -5.7e-67) {
		tmp = t_1;
	} else if (t <= -1.14e-138) {
		tmp = t_3;
	} else if (t <= -6e-185) {
		tmp = t_1;
	} else if (t <= 4.8e-300) {
		tmp = t_3;
	} else if (t <= 2.9e-246) {
		tmp = t_1;
	} else if (t <= 7.5e+36) {
		tmp = t_3;
	} 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) :: t_3
    real(8) :: tmp
    t_1 = a + (z * (1.0d0 - y))
    t_2 = t * (b - a)
    t_3 = a + (x + (b * (y + (-2.0d0))))
    if (t <= (-3d+76)) then
        tmp = t_2
    else if (t <= (-5.7d-67)) then
        tmp = t_1
    else if (t <= (-1.14d-138)) then
        tmp = t_3
    else if (t <= (-6d-185)) then
        tmp = t_1
    else if (t <= 4.8d-300) then
        tmp = t_3
    else if (t <= 2.9d-246) then
        tmp = t_1
    else if (t <= 7.5d+36) then
        tmp = t_3
    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 = a + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double t_3 = a + (x + (b * (y + -2.0)));
	double tmp;
	if (t <= -3e+76) {
		tmp = t_2;
	} else if (t <= -5.7e-67) {
		tmp = t_1;
	} else if (t <= -1.14e-138) {
		tmp = t_3;
	} else if (t <= -6e-185) {
		tmp = t_1;
	} else if (t <= 4.8e-300) {
		tmp = t_3;
	} else if (t <= 2.9e-246) {
		tmp = t_1;
	} else if (t <= 7.5e+36) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a + (z * (1.0 - y))
	t_2 = t * (b - a)
	t_3 = a + (x + (b * (y + -2.0)))
	tmp = 0
	if t <= -3e+76:
		tmp = t_2
	elif t <= -5.7e-67:
		tmp = t_1
	elif t <= -1.14e-138:
		tmp = t_3
	elif t <= -6e-185:
		tmp = t_1
	elif t <= 4.8e-300:
		tmp = t_3
	elif t <= 2.9e-246:
		tmp = t_1
	elif t <= 7.5e+36:
		tmp = t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a + Float64(z * Float64(1.0 - y)))
	t_2 = Float64(t * Float64(b - a))
	t_3 = Float64(a + Float64(x + Float64(b * Float64(y + -2.0))))
	tmp = 0.0
	if (t <= -3e+76)
		tmp = t_2;
	elseif (t <= -5.7e-67)
		tmp = t_1;
	elseif (t <= -1.14e-138)
		tmp = t_3;
	elseif (t <= -6e-185)
		tmp = t_1;
	elseif (t <= 4.8e-300)
		tmp = t_3;
	elseif (t <= 2.9e-246)
		tmp = t_1;
	elseif (t <= 7.5e+36)
		tmp = t_3;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a + (z * (1.0 - y));
	t_2 = t * (b - a);
	t_3 = a + (x + (b * (y + -2.0)));
	tmp = 0.0;
	if (t <= -3e+76)
		tmp = t_2;
	elseif (t <= -5.7e-67)
		tmp = t_1;
	elseif (t <= -1.14e-138)
		tmp = t_3;
	elseif (t <= -6e-185)
		tmp = t_1;
	elseif (t <= 4.8e-300)
		tmp = t_3;
	elseif (t <= 2.9e-246)
		tmp = t_1;
	elseif (t <= 7.5e+36)
		tmp = t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(a + N[(x + N[(b * N[(y + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3e+76], t$95$2, If[LessEqual[t, -5.7e-67], t$95$1, If[LessEqual[t, -1.14e-138], t$95$3, If[LessEqual[t, -6e-185], t$95$1, If[LessEqual[t, 4.8e-300], t$95$3, If[LessEqual[t, 2.9e-246], t$95$1, If[LessEqual[t, 7.5e+36], t$95$3, t$95$2]]]]]]]]]]

Derivation

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

Alternative 6: 63.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := a + t_1\\ t_3 := t \cdot \left(b - a\right)\\ t_4 := a + \left(x + b \cdot \left(y + -2\right)\right)\\ \mathbf{if}\;t \leq -4.5 \cdot 10^{+96}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -3.6 \cdot 10^{-67}:\\ \;\;\;\;t_1 - t \cdot a\\ \mathbf{elif}\;t \leq -1.15 \cdot 10^{-137}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t \leq -4.3 \cdot 10^{-184}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 3 \cdot 10^{-301}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t \leq 3.7 \cdot 10^{-229}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 2.5 \cdot 10^{+33}:\\ \;\;\;\;t_4\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y)))
        (t_2 (+ a t_1))
        (t_3 (* t (- b a)))
        (t_4 (+ a (+ x (* b (+ y -2.0))))))
   (if (<= t -4.5e+96)
     t_3
     (if (<= t -3.6e-67)
       (- t_1 (* t a))
       (if (<= t -1.15e-137)
         t_4
         (if (<= t -4.3e-184)
           t_2
           (if (<= t 3e-301)
             t_4
             (if (<= t 3.7e-229) t_2 (if (<= t 2.5e+33) t_4 t_3)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = a + t_1;
	double t_3 = t * (b - a);
	double t_4 = a + (x + (b * (y + -2.0)));
	double tmp;
	if (t <= -4.5e+96) {
		tmp = t_3;
	} else if (t <= -3.6e-67) {
		tmp = t_1 - (t * a);
	} else if (t <= -1.15e-137) {
		tmp = t_4;
	} else if (t <= -4.3e-184) {
		tmp = t_2;
	} else if (t <= 3e-301) {
		tmp = t_4;
	} else if (t <= 3.7e-229) {
		tmp = t_2;
	} else if (t <= 2.5e+33) {
		tmp = t_4;
	} 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 = z * (1.0d0 - y)
    t_2 = a + t_1
    t_3 = t * (b - a)
    t_4 = a + (x + (b * (y + (-2.0d0))))
    if (t <= (-4.5d+96)) then
        tmp = t_3
    else if (t <= (-3.6d-67)) then
        tmp = t_1 - (t * a)
    else if (t <= (-1.15d-137)) then
        tmp = t_4
    else if (t <= (-4.3d-184)) then
        tmp = t_2
    else if (t <= 3d-301) then
        tmp = t_4
    else if (t <= 3.7d-229) then
        tmp = t_2
    else if (t <= 2.5d+33) then
        tmp = t_4
    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 = z * (1.0 - y);
	double t_2 = a + t_1;
	double t_3 = t * (b - a);
	double t_4 = a + (x + (b * (y + -2.0)));
	double tmp;
	if (t <= -4.5e+96) {
		tmp = t_3;
	} else if (t <= -3.6e-67) {
		tmp = t_1 - (t * a);
	} else if (t <= -1.15e-137) {
		tmp = t_4;
	} else if (t <= -4.3e-184) {
		tmp = t_2;
	} else if (t <= 3e-301) {
		tmp = t_4;
	} else if (t <= 3.7e-229) {
		tmp = t_2;
	} else if (t <= 2.5e+33) {
		tmp = t_4;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = a + t_1
	t_3 = t * (b - a)
	t_4 = a + (x + (b * (y + -2.0)))
	tmp = 0
	if t <= -4.5e+96:
		tmp = t_3
	elif t <= -3.6e-67:
		tmp = t_1 - (t * a)
	elif t <= -1.15e-137:
		tmp = t_4
	elif t <= -4.3e-184:
		tmp = t_2
	elif t <= 3e-301:
		tmp = t_4
	elif t <= 3.7e-229:
		tmp = t_2
	elif t <= 2.5e+33:
		tmp = t_4
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(a + t_1)
	t_3 = Float64(t * Float64(b - a))
	t_4 = Float64(a + Float64(x + Float64(b * Float64(y + -2.0))))
	tmp = 0.0
	if (t <= -4.5e+96)
		tmp = t_3;
	elseif (t <= -3.6e-67)
		tmp = Float64(t_1 - Float64(t * a));
	elseif (t <= -1.15e-137)
		tmp = t_4;
	elseif (t <= -4.3e-184)
		tmp = t_2;
	elseif (t <= 3e-301)
		tmp = t_4;
	elseif (t <= 3.7e-229)
		tmp = t_2;
	elseif (t <= 2.5e+33)
		tmp = t_4;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (1.0 - y);
	t_2 = a + t_1;
	t_3 = t * (b - a);
	t_4 = a + (x + (b * (y + -2.0)));
	tmp = 0.0;
	if (t <= -4.5e+96)
		tmp = t_3;
	elseif (t <= -3.6e-67)
		tmp = t_1 - (t * a);
	elseif (t <= -1.15e-137)
		tmp = t_4;
	elseif (t <= -4.3e-184)
		tmp = t_2;
	elseif (t <= 3e-301)
		tmp = t_4;
	elseif (t <= 3.7e-229)
		tmp = t_2;
	elseif (t <= 2.5e+33)
		tmp = t_4;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a + t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(a + N[(x + N[(b * N[(y + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -4.5e+96], t$95$3, If[LessEqual[t, -3.6e-67], N[(t$95$1 - N[(t * a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -1.15e-137], t$95$4, If[LessEqual[t, -4.3e-184], t$95$2, If[LessEqual[t, 3e-301], t$95$4, If[LessEqual[t, 3.7e-229], t$95$2, If[LessEqual[t, 2.5e+33], t$95$4, t$95$3]]]]]]]]]]]

Derivation

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

Alternative 7: 64.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(z + b \cdot \left(t + -2\right)\right)\\ t_2 := z \cdot \left(1 - y\right) + a \cdot \left(1 - t\right)\\ t_3 := \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;b \leq -2.5 \cdot 10^{+251}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq -3.5 \cdot 10^{+143}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -6.2 \cdot 10^{-17}:\\ \;\;\;\;a + \left(x + b \cdot \left(y + -2\right)\right)\\ \mathbf{elif}\;b \leq 5.5 \cdot 10^{-147}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 1.05 \cdot 10^{-65}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 6.2 \cdot 10^{+137}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (+ z (* b (+ t -2.0)))))
        (t_2 (+ (* z (- 1.0 y)) (* a (- 1.0 t))))
        (t_3 (* (- (+ y t) 2.0) b)))
   (if (<= b -2.5e+251)
     t_3
     (if (<= b -3.5e+143)
       t_1
       (if (<= b -6.2e-17)
         (+ a (+ x (* b (+ y -2.0))))
         (if (<= b 5.5e-147)
           t_2
           (if (<= b 1.05e-65) t_1 (if (<= b 6.2e+137) t_2 t_3))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (z + (b * (t + -2.0)));
	double t_2 = (z * (1.0 - y)) + (a * (1.0 - t));
	double t_3 = ((y + t) - 2.0) * b;
	double tmp;
	if (b <= -2.5e+251) {
		tmp = t_3;
	} else if (b <= -3.5e+143) {
		tmp = t_1;
	} else if (b <= -6.2e-17) {
		tmp = a + (x + (b * (y + -2.0)));
	} else if (b <= 5.5e-147) {
		tmp = t_2;
	} else if (b <= 1.05e-65) {
		tmp = t_1;
	} else if (b <= 6.2e+137) {
		tmp = t_2;
	} 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 + (z + (b * (t + (-2.0d0))))
    t_2 = (z * (1.0d0 - y)) + (a * (1.0d0 - t))
    t_3 = ((y + t) - 2.0d0) * b
    if (b <= (-2.5d+251)) then
        tmp = t_3
    else if (b <= (-3.5d+143)) then
        tmp = t_1
    else if (b <= (-6.2d-17)) then
        tmp = a + (x + (b * (y + (-2.0d0))))
    else if (b <= 5.5d-147) then
        tmp = t_2
    else if (b <= 1.05d-65) then
        tmp = t_1
    else if (b <= 6.2d+137) then
        tmp = t_2
    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 + (z + (b * (t + -2.0)));
	double t_2 = (z * (1.0 - y)) + (a * (1.0 - t));
	double t_3 = ((y + t) - 2.0) * b;
	double tmp;
	if (b <= -2.5e+251) {
		tmp = t_3;
	} else if (b <= -3.5e+143) {
		tmp = t_1;
	} else if (b <= -6.2e-17) {
		tmp = a + (x + (b * (y + -2.0)));
	} else if (b <= 5.5e-147) {
		tmp = t_2;
	} else if (b <= 1.05e-65) {
		tmp = t_1;
	} else if (b <= 6.2e+137) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (z + (b * (t + -2.0)))
	t_2 = (z * (1.0 - y)) + (a * (1.0 - t))
	t_3 = ((y + t) - 2.0) * b
	tmp = 0
	if b <= -2.5e+251:
		tmp = t_3
	elif b <= -3.5e+143:
		tmp = t_1
	elif b <= -6.2e-17:
		tmp = a + (x + (b * (y + -2.0)))
	elif b <= 5.5e-147:
		tmp = t_2
	elif b <= 1.05e-65:
		tmp = t_1
	elif b <= 6.2e+137:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(z + Float64(b * Float64(t + -2.0))))
	t_2 = Float64(Float64(z * Float64(1.0 - y)) + Float64(a * Float64(1.0 - t)))
	t_3 = Float64(Float64(Float64(y + t) - 2.0) * b)
	tmp = 0.0
	if (b <= -2.5e+251)
		tmp = t_3;
	elseif (b <= -3.5e+143)
		tmp = t_1;
	elseif (b <= -6.2e-17)
		tmp = Float64(a + Float64(x + Float64(b * Float64(y + -2.0))));
	elseif (b <= 5.5e-147)
		tmp = t_2;
	elseif (b <= 1.05e-65)
		tmp = t_1;
	elseif (b <= 6.2e+137)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (z + (b * (t + -2.0)));
	t_2 = (z * (1.0 - y)) + (a * (1.0 - t));
	t_3 = ((y + t) - 2.0) * b;
	tmp = 0.0;
	if (b <= -2.5e+251)
		tmp = t_3;
	elseif (b <= -3.5e+143)
		tmp = t_1;
	elseif (b <= -6.2e-17)
		tmp = a + (x + (b * (y + -2.0)));
	elseif (b <= 5.5e-147)
		tmp = t_2;
	elseif (b <= 1.05e-65)
		tmp = t_1;
	elseif (b <= 6.2e+137)
		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[(x + N[(z + N[(b * N[(t + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[b, -2.5e+251], t$95$3, If[LessEqual[b, -3.5e+143], t$95$1, If[LessEqual[b, -6.2e-17], N[(a + N[(x + N[(b * N[(y + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 5.5e-147], t$95$2, If[LessEqual[b, 1.05e-65], t$95$1, If[LessEqual[b, 6.2e+137], t$95$2, t$95$3]]]]]]]]]

Derivation

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

Alternative 8: 79.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(y + t\right) - 2\right) \cdot b\\ \mathbf{if}\;b \leq -1.35 \cdot 10^{+251}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -9.6 \cdot 10^{+117}:\\ \;\;\;\;x + \left(z + b \cdot \left(t + -2\right)\right)\\ \mathbf{elif}\;b \leq -1.16 \cdot 10^{-14}:\\ \;\;\;\;a + \left(x + y \cdot \left(b - z\right)\right)\\ \mathbf{elif}\;b \leq 1.1 \cdot 10^{+138}:\\ \;\;\;\;x + \left(z \cdot \left(1 - y\right) + a \cdot \left(1 - t\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- (+ y t) 2.0) b)))
   (if (<= b -1.35e+251)
     t_1
     (if (<= b -9.6e+117)
       (+ x (+ z (* b (+ t -2.0))))
       (if (<= b -1.16e-14)
         (+ a (+ x (* y (- b z))))
         (if (<= b 1.1e+138)
           (+ x (+ (* z (- 1.0 y)) (* a (- 1.0 t))))
           t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((y + t) - 2.0) * b;
	double tmp;
	if (b <= -1.35e+251) {
		tmp = t_1;
	} else if (b <= -9.6e+117) {
		tmp = x + (z + (b * (t + -2.0)));
	} else if (b <= -1.16e-14) {
		tmp = a + (x + (y * (b - z)));
	} else if (b <= 1.1e+138) {
		tmp = x + ((z * (1.0 - y)) + (a * (1.0 - t)));
	} 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 = ((y + t) - 2.0d0) * b
    if (b <= (-1.35d+251)) then
        tmp = t_1
    else if (b <= (-9.6d+117)) then
        tmp = x + (z + (b * (t + (-2.0d0))))
    else if (b <= (-1.16d-14)) then
        tmp = a + (x + (y * (b - z)))
    else if (b <= 1.1d+138) then
        tmp = x + ((z * (1.0d0 - y)) + (a * (1.0d0 - 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 b) {
	double t_1 = ((y + t) - 2.0) * b;
	double tmp;
	if (b <= -1.35e+251) {
		tmp = t_1;
	} else if (b <= -9.6e+117) {
		tmp = x + (z + (b * (t + -2.0)));
	} else if (b <= -1.16e-14) {
		tmp = a + (x + (y * (b - z)));
	} else if (b <= 1.1e+138) {
		tmp = x + ((z * (1.0 - y)) + (a * (1.0 - t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((y + t) - 2.0) * b
	tmp = 0
	if b <= -1.35e+251:
		tmp = t_1
	elif b <= -9.6e+117:
		tmp = x + (z + (b * (t + -2.0)))
	elif b <= -1.16e-14:
		tmp = a + (x + (y * (b - z)))
	elif b <= 1.1e+138:
		tmp = x + ((z * (1.0 - y)) + (a * (1.0 - t)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(y + t) - 2.0) * b)
	tmp = 0.0
	if (b <= -1.35e+251)
		tmp = t_1;
	elseif (b <= -9.6e+117)
		tmp = Float64(x + Float64(z + Float64(b * Float64(t + -2.0))));
	elseif (b <= -1.16e-14)
		tmp = Float64(a + Float64(x + Float64(y * Float64(b - z))));
	elseif (b <= 1.1e+138)
		tmp = Float64(x + Float64(Float64(z * Float64(1.0 - y)) + Float64(a * Float64(1.0 - t))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((y + t) - 2.0) * b;
	tmp = 0.0;
	if (b <= -1.35e+251)
		tmp = t_1;
	elseif (b <= -9.6e+117)
		tmp = x + (z + (b * (t + -2.0)));
	elseif (b <= -1.16e-14)
		tmp = a + (x + (y * (b - z)));
	elseif (b <= 1.1e+138)
		tmp = x + ((z * (1.0 - y)) + (a * (1.0 - t)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[b, -1.35e+251], t$95$1, If[LessEqual[b, -9.6e+117], N[(x + N[(z + N[(b * N[(t + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -1.16e-14], N[(a + N[(x + N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.1e+138], N[(x + N[(N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 9: 79.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(z \cdot \left(1 - y\right) + a \cdot \left(1 - t\right)\right)\\ t_2 := \left(\left(y + t\right) - 2\right) \cdot b\\ t_3 := \left(x + t_2\right) - y \cdot z\\ \mathbf{if}\;b \leq -2.95 \cdot 10^{-18}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 6 \cdot 10^{-119}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 2.75 \cdot 10^{+48}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 1.22 \cdot 10^{+140}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (+ (* z (- 1.0 y)) (* a (- 1.0 t)))))
        (t_2 (* (- (+ y t) 2.0) b))
        (t_3 (- (+ x t_2) (* y z))))
   (if (<= b -2.95e-18)
     t_3
     (if (<= b 6e-119)
       t_1
       (if (<= b 2.75e+48) t_3 (if (<= b 1.22e+140) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + ((z * (1.0 - y)) + (a * (1.0 - t)));
	double t_2 = ((y + t) - 2.0) * b;
	double t_3 = (x + t_2) - (y * z);
	double tmp;
	if (b <= -2.95e-18) {
		tmp = t_3;
	} else if (b <= 6e-119) {
		tmp = t_1;
	} else if (b <= 2.75e+48) {
		tmp = t_3;
	} else if (b <= 1.22e+140) {
		tmp = t_1;
	} 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) :: t_3
    real(8) :: tmp
    t_1 = x + ((z * (1.0d0 - y)) + (a * (1.0d0 - t)))
    t_2 = ((y + t) - 2.0d0) * b
    t_3 = (x + t_2) - (y * z)
    if (b <= (-2.95d-18)) then
        tmp = t_3
    else if (b <= 6d-119) then
        tmp = t_1
    else if (b <= 2.75d+48) then
        tmp = t_3
    else if (b <= 1.22d+140) then
        tmp = t_1
    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 + ((z * (1.0 - y)) + (a * (1.0 - t)));
	double t_2 = ((y + t) - 2.0) * b;
	double t_3 = (x + t_2) - (y * z);
	double tmp;
	if (b <= -2.95e-18) {
		tmp = t_3;
	} else if (b <= 6e-119) {
		tmp = t_1;
	} else if (b <= 2.75e+48) {
		tmp = t_3;
	} else if (b <= 1.22e+140) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + ((z * (1.0 - y)) + (a * (1.0 - t)))
	t_2 = ((y + t) - 2.0) * b
	t_3 = (x + t_2) - (y * z)
	tmp = 0
	if b <= -2.95e-18:
		tmp = t_3
	elif b <= 6e-119:
		tmp = t_1
	elif b <= 2.75e+48:
		tmp = t_3
	elif b <= 1.22e+140:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(Float64(z * Float64(1.0 - y)) + Float64(a * Float64(1.0 - t))))
	t_2 = Float64(Float64(Float64(y + t) - 2.0) * b)
	t_3 = Float64(Float64(x + t_2) - Float64(y * z))
	tmp = 0.0
	if (b <= -2.95e-18)
		tmp = t_3;
	elseif (b <= 6e-119)
		tmp = t_1;
	elseif (b <= 2.75e+48)
		tmp = t_3;
	elseif (b <= 1.22e+140)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + ((z * (1.0 - y)) + (a * (1.0 - t)));
	t_2 = ((y + t) - 2.0) * b;
	t_3 = (x + t_2) - (y * z);
	tmp = 0.0;
	if (b <= -2.95e-18)
		tmp = t_3;
	elseif (b <= 6e-119)
		tmp = t_1;
	elseif (b <= 2.75e+48)
		tmp = t_3;
	elseif (b <= 1.22e+140)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x + t$95$2), $MachinePrecision] - N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -2.95e-18], t$95$3, If[LessEqual[b, 6e-119], t$95$1, If[LessEqual[b, 2.75e+48], t$95$3, If[LessEqual[b, 1.22e+140], t$95$1, t$95$2]]]]]]]

Derivation

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

Alternative 10: 82.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(\left(y + t\right) - 2\right) \cdot b\\ t_2 := t_1 - y \cdot z\\ t_3 := a \cdot \left(1 - t\right)\\ \mathbf{if}\;b \leq -9.5 \cdot 10^{-17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 6 \cdot 10^{-119}:\\ \;\;\;\;x + \left(z \cdot \left(1 - y\right) + t_3\right)\\ \mathbf{elif}\;b \leq 7.5 \cdot 10^{+29}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1 + t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* (- (+ y t) 2.0) b)))
        (t_2 (- t_1 (* y z)))
        (t_3 (* a (- 1.0 t))))
   (if (<= b -9.5e-17)
     t_2
     (if (<= b 6e-119)
       (+ x (+ (* z (- 1.0 y)) t_3))
       (if (<= b 7.5e+29) t_2 (+ t_1 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (((y + t) - 2.0) * b);
	double t_2 = t_1 - (y * z);
	double t_3 = a * (1.0 - t);
	double tmp;
	if (b <= -9.5e-17) {
		tmp = t_2;
	} else if (b <= 6e-119) {
		tmp = x + ((z * (1.0 - y)) + t_3);
	} else if (b <= 7.5e+29) {
		tmp = t_2;
	} else {
		tmp = t_1 + 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 + (((y + t) - 2.0d0) * b)
    t_2 = t_1 - (y * z)
    t_3 = a * (1.0d0 - t)
    if (b <= (-9.5d-17)) then
        tmp = t_2
    else if (b <= 6d-119) then
        tmp = x + ((z * (1.0d0 - y)) + t_3)
    else if (b <= 7.5d+29) then
        tmp = t_2
    else
        tmp = t_1 + 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 + (((y + t) - 2.0) * b);
	double t_2 = t_1 - (y * z);
	double t_3 = a * (1.0 - t);
	double tmp;
	if (b <= -9.5e-17) {
		tmp = t_2;
	} else if (b <= 6e-119) {
		tmp = x + ((z * (1.0 - y)) + t_3);
	} else if (b <= 7.5e+29) {
		tmp = t_2;
	} else {
		tmp = t_1 + t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (((y + t) - 2.0) * b)
	t_2 = t_1 - (y * z)
	t_3 = a * (1.0 - t)
	tmp = 0
	if b <= -9.5e-17:
		tmp = t_2
	elif b <= 6e-119:
		tmp = x + ((z * (1.0 - y)) + t_3)
	elif b <= 7.5e+29:
		tmp = t_2
	else:
		tmp = t_1 + t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(Float64(Float64(y + t) - 2.0) * b))
	t_2 = Float64(t_1 - Float64(y * z))
	t_3 = Float64(a * Float64(1.0 - t))
	tmp = 0.0
	if (b <= -9.5e-17)
		tmp = t_2;
	elseif (b <= 6e-119)
		tmp = Float64(x + Float64(Float64(z * Float64(1.0 - y)) + t_3));
	elseif (b <= 7.5e+29)
		tmp = t_2;
	else
		tmp = Float64(t_1 + t_3);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (((y + t) - 2.0) * b);
	t_2 = t_1 - (y * z);
	t_3 = a * (1.0 - t);
	tmp = 0.0;
	if (b <= -9.5e-17)
		tmp = t_2;
	elseif (b <= 6e-119)
		tmp = x + ((z * (1.0 - y)) + t_3);
	elseif (b <= 7.5e+29)
		tmp = t_2;
	else
		tmp = t_1 + t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 - N[(y * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -9.5e-17], t$95$2, If[LessEqual[b, 6e-119], N[(x + N[(N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 7.5e+29], t$95$2, N[(t$95$1 + t$95$3), $MachinePrecision]]]]]]]

Derivation

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

Alternative 11: 84.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := x + \left(\left(y + t\right) - 2\right) \cdot b\\ t_3 := t_2 + t_1\\ t_4 := a \cdot \left(1 - t\right)\\ \mathbf{if}\;b \leq -4.3 \cdot 10^{-17}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 3.4 \cdot 10^{-148}:\\ \;\;\;\;x + \left(t_1 + t_4\right)\\ \mathbf{elif}\;b \leq 1.4 \cdot 10^{+82}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2 + t_4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y)))
        (t_2 (+ x (* (- (+ y t) 2.0) b)))
        (t_3 (+ t_2 t_1))
        (t_4 (* a (- 1.0 t))))
   (if (<= b -4.3e-17)
     t_3
     (if (<= b 3.4e-148)
       (+ x (+ t_1 t_4))
       (if (<= b 1.4e+82) t_3 (+ t_2 t_4))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = x + (((y + t) - 2.0) * b);
	double t_3 = t_2 + t_1;
	double t_4 = a * (1.0 - t);
	double tmp;
	if (b <= -4.3e-17) {
		tmp = t_3;
	} else if (b <= 3.4e-148) {
		tmp = x + (t_1 + t_4);
	} else if (b <= 1.4e+82) {
		tmp = t_3;
	} else {
		tmp = t_2 + t_4;
	}
	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 = z * (1.0d0 - y)
    t_2 = x + (((y + t) - 2.0d0) * b)
    t_3 = t_2 + t_1
    t_4 = a * (1.0d0 - t)
    if (b <= (-4.3d-17)) then
        tmp = t_3
    else if (b <= 3.4d-148) then
        tmp = x + (t_1 + t_4)
    else if (b <= 1.4d+82) then
        tmp = t_3
    else
        tmp = t_2 + t_4
    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 = z * (1.0 - y);
	double t_2 = x + (((y + t) - 2.0) * b);
	double t_3 = t_2 + t_1;
	double t_4 = a * (1.0 - t);
	double tmp;
	if (b <= -4.3e-17) {
		tmp = t_3;
	} else if (b <= 3.4e-148) {
		tmp = x + (t_1 + t_4);
	} else if (b <= 1.4e+82) {
		tmp = t_3;
	} else {
		tmp = t_2 + t_4;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = x + (((y + t) - 2.0) * b)
	t_3 = t_2 + t_1
	t_4 = a * (1.0 - t)
	tmp = 0
	if b <= -4.3e-17:
		tmp = t_3
	elif b <= 3.4e-148:
		tmp = x + (t_1 + t_4)
	elif b <= 1.4e+82:
		tmp = t_3
	else:
		tmp = t_2 + t_4
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(x + Float64(Float64(Float64(y + t) - 2.0) * b))
	t_3 = Float64(t_2 + t_1)
	t_4 = Float64(a * Float64(1.0 - t))
	tmp = 0.0
	if (b <= -4.3e-17)
		tmp = t_3;
	elseif (b <= 3.4e-148)
		tmp = Float64(x + Float64(t_1 + t_4));
	elseif (b <= 1.4e+82)
		tmp = t_3;
	else
		tmp = Float64(t_2 + t_4);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (1.0 - y);
	t_2 = x + (((y + t) - 2.0) * b);
	t_3 = t_2 + t_1;
	t_4 = a * (1.0 - t);
	tmp = 0.0;
	if (b <= -4.3e-17)
		tmp = t_3;
	elseif (b <= 3.4e-148)
		tmp = x + (t_1 + t_4);
	elseif (b <= 1.4e+82)
		tmp = t_3;
	else
		tmp = t_2 + t_4;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 + t$95$1), $MachinePrecision]}, Block[{t$95$4 = N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -4.3e-17], t$95$3, If[LessEqual[b, 3.4e-148], N[(x + N[(t$95$1 + t$95$4), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.4e+82], t$95$3, N[(t$95$2 + t$95$4), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 12: 46.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := a + b \cdot -2\\ t_3 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -3.1 \cdot 10^{+76}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -8.5 \cdot 10^{-186}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -2.75 \cdot 10^{-295}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 7.4 \cdot 10^{-233}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.2 \cdot 10^{-195}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 1.8 \cdot 10^{-158}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{+18}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y))) (t_2 (+ a (* b -2.0))) (t_3 (* t (- b a))))
   (if (<= t -3.1e+76)
     t_3
     (if (<= t -8.5e-186)
       t_1
       (if (<= t -2.75e-295)
         t_2
         (if (<= t 7.4e-233)
           t_1
           (if (<= t 1.2e-195)
             t_2
             (if (<= t 1.8e-158)
               (* y (- b z))
               (if (<= t 2.8e+18) (+ x a) t_3)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = a + (b * -2.0);
	double t_3 = t * (b - a);
	double tmp;
	if (t <= -3.1e+76) {
		tmp = t_3;
	} else if (t <= -8.5e-186) {
		tmp = t_1;
	} else if (t <= -2.75e-295) {
		tmp = t_2;
	} else if (t <= 7.4e-233) {
		tmp = t_1;
	} else if (t <= 1.2e-195) {
		tmp = t_2;
	} else if (t <= 1.8e-158) {
		tmp = y * (b - z);
	} else if (t <= 2.8e+18) {
		tmp = x + a;
	} 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 = z * (1.0d0 - y)
    t_2 = a + (b * (-2.0d0))
    t_3 = t * (b - a)
    if (t <= (-3.1d+76)) then
        tmp = t_3
    else if (t <= (-8.5d-186)) then
        tmp = t_1
    else if (t <= (-2.75d-295)) then
        tmp = t_2
    else if (t <= 7.4d-233) then
        tmp = t_1
    else if (t <= 1.2d-195) then
        tmp = t_2
    else if (t <= 1.8d-158) then
        tmp = y * (b - z)
    else if (t <= 2.8d+18) then
        tmp = x + a
    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 = z * (1.0 - y);
	double t_2 = a + (b * -2.0);
	double t_3 = t * (b - a);
	double tmp;
	if (t <= -3.1e+76) {
		tmp = t_3;
	} else if (t <= -8.5e-186) {
		tmp = t_1;
	} else if (t <= -2.75e-295) {
		tmp = t_2;
	} else if (t <= 7.4e-233) {
		tmp = t_1;
	} else if (t <= 1.2e-195) {
		tmp = t_2;
	} else if (t <= 1.8e-158) {
		tmp = y * (b - z);
	} else if (t <= 2.8e+18) {
		tmp = x + a;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = a + (b * -2.0)
	t_3 = t * (b - a)
	tmp = 0
	if t <= -3.1e+76:
		tmp = t_3
	elif t <= -8.5e-186:
		tmp = t_1
	elif t <= -2.75e-295:
		tmp = t_2
	elif t <= 7.4e-233:
		tmp = t_1
	elif t <= 1.2e-195:
		tmp = t_2
	elif t <= 1.8e-158:
		tmp = y * (b - z)
	elif t <= 2.8e+18:
		tmp = x + a
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(a + Float64(b * -2.0))
	t_3 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -3.1e+76)
		tmp = t_3;
	elseif (t <= -8.5e-186)
		tmp = t_1;
	elseif (t <= -2.75e-295)
		tmp = t_2;
	elseif (t <= 7.4e-233)
		tmp = t_1;
	elseif (t <= 1.2e-195)
		tmp = t_2;
	elseif (t <= 1.8e-158)
		tmp = Float64(y * Float64(b - z));
	elseif (t <= 2.8e+18)
		tmp = Float64(x + a);
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (1.0 - y);
	t_2 = a + (b * -2.0);
	t_3 = t * (b - a);
	tmp = 0.0;
	if (t <= -3.1e+76)
		tmp = t_3;
	elseif (t <= -8.5e-186)
		tmp = t_1;
	elseif (t <= -2.75e-295)
		tmp = t_2;
	elseif (t <= 7.4e-233)
		tmp = t_1;
	elseif (t <= 1.2e-195)
		tmp = t_2;
	elseif (t <= 1.8e-158)
		tmp = y * (b - z);
	elseif (t <= 2.8e+18)
		tmp = x + a;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a + N[(b * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.1e+76], t$95$3, If[LessEqual[t, -8.5e-186], t$95$1, If[LessEqual[t, -2.75e-295], t$95$2, If[LessEqual[t, 7.4e-233], t$95$1, If[LessEqual[t, 1.2e-195], t$95$2, If[LessEqual[t, 1.8e-158], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.8e+18], N[(x + a), $MachinePrecision], t$95$3]]]]]]]]]]

Derivation

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

Alternative 13: 48.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot -2\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -1.15 \cdot 10^{+51}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -4.2 \cdot 10^{-192}:\\ \;\;\;\;x - y \cdot z\\ \mathbf{elif}\;t \leq -4.1 \cdot 10^{-295}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.82 \cdot 10^{-227}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq 8.2 \cdot 10^{-196}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{-158}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{+18}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ a (* b -2.0))) (t_2 (* t (- b a))))
   (if (<= t -1.15e+51)
     t_2
     (if (<= t -4.2e-192)
       (- x (* y z))
       (if (<= t -4.1e-295)
         t_1
         (if (<= t 1.82e-227)
           (* z (- 1.0 y))
           (if (<= t 8.2e-196)
             t_1
             (if (<= t 1.65e-158)
               (* y (- b z))
               (if (<= t 2.8e+18) (+ x a) t_2)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (b * -2.0);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -1.15e+51) {
		tmp = t_2;
	} else if (t <= -4.2e-192) {
		tmp = x - (y * z);
	} else if (t <= -4.1e-295) {
		tmp = t_1;
	} else if (t <= 1.82e-227) {
		tmp = z * (1.0 - y);
	} else if (t <= 8.2e-196) {
		tmp = t_1;
	} else if (t <= 1.65e-158) {
		tmp = y * (b - z);
	} else if (t <= 2.8e+18) {
		tmp = x + a;
	} 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 = a + (b * (-2.0d0))
    t_2 = t * (b - a)
    if (t <= (-1.15d+51)) then
        tmp = t_2
    else if (t <= (-4.2d-192)) then
        tmp = x - (y * z)
    else if (t <= (-4.1d-295)) then
        tmp = t_1
    else if (t <= 1.82d-227) then
        tmp = z * (1.0d0 - y)
    else if (t <= 8.2d-196) then
        tmp = t_1
    else if (t <= 1.65d-158) then
        tmp = y * (b - z)
    else if (t <= 2.8d+18) then
        tmp = x + 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 b) {
	double t_1 = a + (b * -2.0);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -1.15e+51) {
		tmp = t_2;
	} else if (t <= -4.2e-192) {
		tmp = x - (y * z);
	} else if (t <= -4.1e-295) {
		tmp = t_1;
	} else if (t <= 1.82e-227) {
		tmp = z * (1.0 - y);
	} else if (t <= 8.2e-196) {
		tmp = t_1;
	} else if (t <= 1.65e-158) {
		tmp = y * (b - z);
	} else if (t <= 2.8e+18) {
		tmp = x + a;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a + (b * -2.0)
	t_2 = t * (b - a)
	tmp = 0
	if t <= -1.15e+51:
		tmp = t_2
	elif t <= -4.2e-192:
		tmp = x - (y * z)
	elif t <= -4.1e-295:
		tmp = t_1
	elif t <= 1.82e-227:
		tmp = z * (1.0 - y)
	elif t <= 8.2e-196:
		tmp = t_1
	elif t <= 1.65e-158:
		tmp = y * (b - z)
	elif t <= 2.8e+18:
		tmp = x + a
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a + Float64(b * -2.0))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -1.15e+51)
		tmp = t_2;
	elseif (t <= -4.2e-192)
		tmp = Float64(x - Float64(y * z));
	elseif (t <= -4.1e-295)
		tmp = t_1;
	elseif (t <= 1.82e-227)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (t <= 8.2e-196)
		tmp = t_1;
	elseif (t <= 1.65e-158)
		tmp = Float64(y * Float64(b - z));
	elseif (t <= 2.8e+18)
		tmp = Float64(x + a);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a + (b * -2.0);
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -1.15e+51)
		tmp = t_2;
	elseif (t <= -4.2e-192)
		tmp = x - (y * z);
	elseif (t <= -4.1e-295)
		tmp = t_1;
	elseif (t <= 1.82e-227)
		tmp = z * (1.0 - y);
	elseif (t <= 8.2e-196)
		tmp = t_1;
	elseif (t <= 1.65e-158)
		tmp = y * (b - z);
	elseif (t <= 2.8e+18)
		tmp = x + a;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a + N[(b * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.15e+51], t$95$2, If[LessEqual[t, -4.2e-192], N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -4.1e-295], t$95$1, If[LessEqual[t, 1.82e-227], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 8.2e-196], t$95$1, If[LessEqual[t, 1.65e-158], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.8e+18], N[(x + a), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

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

Alternative 14: 52.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := a + b \cdot \left(y - 2\right)\\ t_3 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -3 \cdot 10^{+76}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -5.2 \cdot 10^{-67}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.9 \cdot 10^{-287}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 8 \cdot 10^{-232}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.7 \cdot 10^{-143}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 1.1 \cdot 10^{+20}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y))) (t_2 (+ a (* b (- y 2.0)))) (t_3 (* t (- b a))))
   (if (<= t -3e+76)
     t_3
     (if (<= t -5.2e-67)
       t_1
       (if (<= t 2.9e-287)
         t_2
         (if (<= t 8e-232)
           t_1
           (if (<= t 2.7e-143) t_2 (if (<= t 1.1e+20) (+ x a) t_3))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double t_2 = a + (b * (y - 2.0));
	double t_3 = t * (b - a);
	double tmp;
	if (t <= -3e+76) {
		tmp = t_3;
	} else if (t <= -5.2e-67) {
		tmp = t_1;
	} else if (t <= 2.9e-287) {
		tmp = t_2;
	} else if (t <= 8e-232) {
		tmp = t_1;
	} else if (t <= 2.7e-143) {
		tmp = t_2;
	} else if (t <= 1.1e+20) {
		tmp = x + a;
	} 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 = z * (1.0d0 - y)
    t_2 = a + (b * (y - 2.0d0))
    t_3 = t * (b - a)
    if (t <= (-3d+76)) then
        tmp = t_3
    else if (t <= (-5.2d-67)) then
        tmp = t_1
    else if (t <= 2.9d-287) then
        tmp = t_2
    else if (t <= 8d-232) then
        tmp = t_1
    else if (t <= 2.7d-143) then
        tmp = t_2
    else if (t <= 1.1d+20) then
        tmp = x + a
    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 = z * (1.0 - y);
	double t_2 = a + (b * (y - 2.0));
	double t_3 = t * (b - a);
	double tmp;
	if (t <= -3e+76) {
		tmp = t_3;
	} else if (t <= -5.2e-67) {
		tmp = t_1;
	} else if (t <= 2.9e-287) {
		tmp = t_2;
	} else if (t <= 8e-232) {
		tmp = t_1;
	} else if (t <= 2.7e-143) {
		tmp = t_2;
	} else if (t <= 1.1e+20) {
		tmp = x + a;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = a + (b * (y - 2.0))
	t_3 = t * (b - a)
	tmp = 0
	if t <= -3e+76:
		tmp = t_3
	elif t <= -5.2e-67:
		tmp = t_1
	elif t <= 2.9e-287:
		tmp = t_2
	elif t <= 8e-232:
		tmp = t_1
	elif t <= 2.7e-143:
		tmp = t_2
	elif t <= 1.1e+20:
		tmp = x + a
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	t_2 = Float64(a + Float64(b * Float64(y - 2.0)))
	t_3 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -3e+76)
		tmp = t_3;
	elseif (t <= -5.2e-67)
		tmp = t_1;
	elseif (t <= 2.9e-287)
		tmp = t_2;
	elseif (t <= 8e-232)
		tmp = t_1;
	elseif (t <= 2.7e-143)
		tmp = t_2;
	elseif (t <= 1.1e+20)
		tmp = Float64(x + a);
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (1.0 - y);
	t_2 = a + (b * (y - 2.0));
	t_3 = t * (b - a);
	tmp = 0.0;
	if (t <= -3e+76)
		tmp = t_3;
	elseif (t <= -5.2e-67)
		tmp = t_1;
	elseif (t <= 2.9e-287)
		tmp = t_2;
	elseif (t <= 8e-232)
		tmp = t_1;
	elseif (t <= 2.7e-143)
		tmp = t_2;
	elseif (t <= 1.1e+20)
		tmp = x + a;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a + N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3e+76], t$95$3, If[LessEqual[t, -5.2e-67], t$95$1, If[LessEqual[t, 2.9e-287], t$95$2, If[LessEqual[t, 8e-232], t$95$1, If[LessEqual[t, 2.7e-143], t$95$2, If[LessEqual[t, 1.1e+20], N[(x + a), $MachinePrecision], t$95$3]]]]]]]]]

Derivation

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

Alternative 15: 57.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := t \cdot \left(b - a\right)\\ t_3 := b \cdot \left(y - 2\right)\\ \mathbf{if}\;t \leq -3.7 \cdot 10^{+76}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -6 \cdot 10^{-146}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.6 \cdot 10^{-300}:\\ \;\;\;\;a + t_3\\ \mathbf{elif}\;t \leq 7.6 \cdot 10^{-232}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.16 \cdot 10^{+33}:\\ \;\;\;\;x + t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* z (- 1.0 y)))) (t_2 (* t (- b a))) (t_3 (* b (- y 2.0))))
   (if (<= t -3.7e+76)
     t_2
     (if (<= t -6e-146)
       t_1
       (if (<= t 4.6e-300)
         (+ a t_3)
         (if (<= t 7.6e-232) t_1 (if (<= t 1.16e+33) (+ x t_3) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double t_3 = b * (y - 2.0);
	double tmp;
	if (t <= -3.7e+76) {
		tmp = t_2;
	} else if (t <= -6e-146) {
		tmp = t_1;
	} else if (t <= 4.6e-300) {
		tmp = a + t_3;
	} else if (t <= 7.6e-232) {
		tmp = t_1;
	} else if (t <= 1.16e+33) {
		tmp = x + t_3;
	} 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) :: t_3
    real(8) :: tmp
    t_1 = x + (z * (1.0d0 - y))
    t_2 = t * (b - a)
    t_3 = b * (y - 2.0d0)
    if (t <= (-3.7d+76)) then
        tmp = t_2
    else if (t <= (-6d-146)) then
        tmp = t_1
    else if (t <= 4.6d-300) then
        tmp = a + t_3
    else if (t <= 7.6d-232) then
        tmp = t_1
    else if (t <= 1.16d+33) then
        tmp = x + t_3
    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 + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double t_3 = b * (y - 2.0);
	double tmp;
	if (t <= -3.7e+76) {
		tmp = t_2;
	} else if (t <= -6e-146) {
		tmp = t_1;
	} else if (t <= 4.6e-300) {
		tmp = a + t_3;
	} else if (t <= 7.6e-232) {
		tmp = t_1;
	} else if (t <= 1.16e+33) {
		tmp = x + t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (z * (1.0 - y))
	t_2 = t * (b - a)
	t_3 = b * (y - 2.0)
	tmp = 0
	if t <= -3.7e+76:
		tmp = t_2
	elif t <= -6e-146:
		tmp = t_1
	elif t <= 4.6e-300:
		tmp = a + t_3
	elif t <= 7.6e-232:
		tmp = t_1
	elif t <= 1.16e+33:
		tmp = x + t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(z * Float64(1.0 - y)))
	t_2 = Float64(t * Float64(b - a))
	t_3 = Float64(b * Float64(y - 2.0))
	tmp = 0.0
	if (t <= -3.7e+76)
		tmp = t_2;
	elseif (t <= -6e-146)
		tmp = t_1;
	elseif (t <= 4.6e-300)
		tmp = Float64(a + t_3);
	elseif (t <= 7.6e-232)
		tmp = t_1;
	elseif (t <= 1.16e+33)
		tmp = Float64(x + t_3);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (z * (1.0 - y));
	t_2 = t * (b - a);
	t_3 = b * (y - 2.0);
	tmp = 0.0;
	if (t <= -3.7e+76)
		tmp = t_2;
	elseif (t <= -6e-146)
		tmp = t_1;
	elseif (t <= 4.6e-300)
		tmp = a + t_3;
	elseif (t <= 7.6e-232)
		tmp = t_1;
	elseif (t <= 1.16e+33)
		tmp = x + t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.7e+76], t$95$2, If[LessEqual[t, -6e-146], t$95$1, If[LessEqual[t, 4.6e-300], N[(a + t$95$3), $MachinePrecision], If[LessEqual[t, 7.6e-232], t$95$1, If[LessEqual[t, 1.16e+33], N[(x + t$95$3), $MachinePrecision], t$95$2]]]]]]]]

Derivation

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

Alternative 16: 56.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + z \cdot \left(1 - y\right)\\ t_2 := t \cdot \left(b - a\right)\\ t_3 := b \cdot \left(y - 2\right)\\ \mathbf{if}\;t \leq -3.8 \cdot 10^{+76}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -1.45 \cdot 10^{-227}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{-301}:\\ \;\;\;\;a + t_3\\ \mathbf{elif}\;t \leq 1.15 \cdot 10^{-220}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.2 \cdot 10^{+37}:\\ \;\;\;\;x + t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ a (* z (- 1.0 y)))) (t_2 (* t (- b a))) (t_3 (* b (- y 2.0))))
   (if (<= t -3.8e+76)
     t_2
     (if (<= t -1.45e-227)
       t_1
       (if (<= t 1.45e-301)
         (+ a t_3)
         (if (<= t 1.15e-220) t_1 (if (<= t 1.2e+37) (+ x t_3) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double t_3 = b * (y - 2.0);
	double tmp;
	if (t <= -3.8e+76) {
		tmp = t_2;
	} else if (t <= -1.45e-227) {
		tmp = t_1;
	} else if (t <= 1.45e-301) {
		tmp = a + t_3;
	} else if (t <= 1.15e-220) {
		tmp = t_1;
	} else if (t <= 1.2e+37) {
		tmp = x + t_3;
	} 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) :: t_3
    real(8) :: tmp
    t_1 = a + (z * (1.0d0 - y))
    t_2 = t * (b - a)
    t_3 = b * (y - 2.0d0)
    if (t <= (-3.8d+76)) then
        tmp = t_2
    else if (t <= (-1.45d-227)) then
        tmp = t_1
    else if (t <= 1.45d-301) then
        tmp = a + t_3
    else if (t <= 1.15d-220) then
        tmp = t_1
    else if (t <= 1.2d+37) then
        tmp = x + t_3
    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 = a + (z * (1.0 - y));
	double t_2 = t * (b - a);
	double t_3 = b * (y - 2.0);
	double tmp;
	if (t <= -3.8e+76) {
		tmp = t_2;
	} else if (t <= -1.45e-227) {
		tmp = t_1;
	} else if (t <= 1.45e-301) {
		tmp = a + t_3;
	} else if (t <= 1.15e-220) {
		tmp = t_1;
	} else if (t <= 1.2e+37) {
		tmp = x + t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a + (z * (1.0 - y))
	t_2 = t * (b - a)
	t_3 = b * (y - 2.0)
	tmp = 0
	if t <= -3.8e+76:
		tmp = t_2
	elif t <= -1.45e-227:
		tmp = t_1
	elif t <= 1.45e-301:
		tmp = a + t_3
	elif t <= 1.15e-220:
		tmp = t_1
	elif t <= 1.2e+37:
		tmp = x + t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a + Float64(z * Float64(1.0 - y)))
	t_2 = Float64(t * Float64(b - a))
	t_3 = Float64(b * Float64(y - 2.0))
	tmp = 0.0
	if (t <= -3.8e+76)
		tmp = t_2;
	elseif (t <= -1.45e-227)
		tmp = t_1;
	elseif (t <= 1.45e-301)
		tmp = Float64(a + t_3);
	elseif (t <= 1.15e-220)
		tmp = t_1;
	elseif (t <= 1.2e+37)
		tmp = Float64(x + t_3);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a + (z * (1.0 - y));
	t_2 = t * (b - a);
	t_3 = b * (y - 2.0);
	tmp = 0.0;
	if (t <= -3.8e+76)
		tmp = t_2;
	elseif (t <= -1.45e-227)
		tmp = t_1;
	elseif (t <= 1.45e-301)
		tmp = a + t_3;
	elseif (t <= 1.15e-220)
		tmp = t_1;
	elseif (t <= 1.2e+37)
		tmp = x + t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.8e+76], t$95$2, If[LessEqual[t, -1.45e-227], t$95$1, If[LessEqual[t, 1.45e-301], N[(a + t$95$3), $MachinePrecision], If[LessEqual[t, 1.15e-220], t$95$1, If[LessEqual[t, 1.2e+37], N[(x + t$95$3), $MachinePrecision], t$95$2]]]]]]]]

Derivation

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

Alternative 17: 30.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -2.05 \cdot 10^{+117}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;b \leq 9 \cdot 10^{-234}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;b \leq 1.1 \cdot 10^{-76}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;b \leq 1.4 \cdot 10^{+69}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;b \leq 9 \cdot 10^{+82}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;b \leq 6 \cdot 10^{+145}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= b -2.05e+117)
   (* t b)
   (if (<= b 9e-234)
     (+ x a)
     (if (<= b 1.1e-76)
       (+ x z)
       (if (<= b 1.4e+69)
         (* t b)
         (if (<= b 9e+82) (+ x z) (if (<= b 6e+145) (+ x a) (* t b))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (b <= -2.05e+117) {
		tmp = t * b;
	} else if (b <= 9e-234) {
		tmp = x + a;
	} else if (b <= 1.1e-76) {
		tmp = x + z;
	} else if (b <= 1.4e+69) {
		tmp = t * b;
	} else if (b <= 9e+82) {
		tmp = x + z;
	} else if (b <= 6e+145) {
		tmp = x + a;
	} 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 (b <= (-2.05d+117)) then
        tmp = t * b
    else if (b <= 9d-234) then
        tmp = x + a
    else if (b <= 1.1d-76) then
        tmp = x + z
    else if (b <= 1.4d+69) then
        tmp = t * b
    else if (b <= 9d+82) then
        tmp = x + z
    else if (b <= 6d+145) then
        tmp = x + a
    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 (b <= -2.05e+117) {
		tmp = t * b;
	} else if (b <= 9e-234) {
		tmp = x + a;
	} else if (b <= 1.1e-76) {
		tmp = x + z;
	} else if (b <= 1.4e+69) {
		tmp = t * b;
	} else if (b <= 9e+82) {
		tmp = x + z;
	} else if (b <= 6e+145) {
		tmp = x + a;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if b <= -2.05e+117:
		tmp = t * b
	elif b <= 9e-234:
		tmp = x + a
	elif b <= 1.1e-76:
		tmp = x + z
	elif b <= 1.4e+69:
		tmp = t * b
	elif b <= 9e+82:
		tmp = x + z
	elif b <= 6e+145:
		tmp = x + a
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (b <= -2.05e+117)
		tmp = Float64(t * b);
	elseif (b <= 9e-234)
		tmp = Float64(x + a);
	elseif (b <= 1.1e-76)
		tmp = Float64(x + z);
	elseif (b <= 1.4e+69)
		tmp = Float64(t * b);
	elseif (b <= 9e+82)
		tmp = Float64(x + z);
	elseif (b <= 6e+145)
		tmp = Float64(x + a);
	else
		tmp = Float64(t * b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (b <= -2.05e+117)
		tmp = t * b;
	elseif (b <= 9e-234)
		tmp = x + a;
	elseif (b <= 1.1e-76)
		tmp = x + z;
	elseif (b <= 1.4e+69)
		tmp = t * b;
	elseif (b <= 9e+82)
		tmp = x + z;
	elseif (b <= 6e+145)
		tmp = x + a;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[b, -2.05e+117], N[(t * b), $MachinePrecision], If[LessEqual[b, 9e-234], N[(x + a), $MachinePrecision], If[LessEqual[b, 1.1e-76], N[(x + z), $MachinePrecision], If[LessEqual[b, 1.4e+69], N[(t * b), $MachinePrecision], If[LessEqual[b, 9e+82], N[(x + z), $MachinePrecision], If[LessEqual[b, 6e+145], N[(x + a), $MachinePrecision], N[(t * b), $MachinePrecision]]]]]]]

Derivation

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

Alternative 18: 36.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ t_2 := y \cdot \left(-z\right)\\ \mathbf{if}\;y \leq -6.5 \cdot 10^{+45}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -2.45 \cdot 10^{-163}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-226}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.5 \cdot 10^{-268}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;y \leq 2.22 \cdot 10^{+86}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- b a))) (t_2 (* y (- z))))
   (if (<= y -6.5e+45)
     t_2
     (if (<= y -2.45e-163)
       (+ x a)
       (if (<= y -1.15e-226)
         t_1
         (if (<= y -2.5e-268) (+ x a) (if (<= y 2.22e+86) t_1 t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double t_2 = y * -z;
	double tmp;
	if (y <= -6.5e+45) {
		tmp = t_2;
	} else if (y <= -2.45e-163) {
		tmp = x + a;
	} else if (y <= -1.15e-226) {
		tmp = t_1;
	} else if (y <= -2.5e-268) {
		tmp = x + a;
	} else if (y <= 2.22e+86) {
		tmp = t_1;
	} 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 = t * (b - a)
    t_2 = y * -z
    if (y <= (-6.5d+45)) then
        tmp = t_2
    else if (y <= (-2.45d-163)) then
        tmp = x + a
    else if (y <= (-1.15d-226)) then
        tmp = t_1
    else if (y <= (-2.5d-268)) then
        tmp = x + a
    else if (y <= 2.22d+86) then
        tmp = t_1
    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 = t * (b - a);
	double t_2 = y * -z;
	double tmp;
	if (y <= -6.5e+45) {
		tmp = t_2;
	} else if (y <= -2.45e-163) {
		tmp = x + a;
	} else if (y <= -1.15e-226) {
		tmp = t_1;
	} else if (y <= -2.5e-268) {
		tmp = x + a;
	} else if (y <= 2.22e+86) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	t_2 = y * -z
	tmp = 0
	if y <= -6.5e+45:
		tmp = t_2
	elif y <= -2.45e-163:
		tmp = x + a
	elif y <= -1.15e-226:
		tmp = t_1
	elif y <= -2.5e-268:
		tmp = x + a
	elif y <= 2.22e+86:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	t_2 = Float64(y * Float64(-z))
	tmp = 0.0
	if (y <= -6.5e+45)
		tmp = t_2;
	elseif (y <= -2.45e-163)
		tmp = Float64(x + a);
	elseif (y <= -1.15e-226)
		tmp = t_1;
	elseif (y <= -2.5e-268)
		tmp = Float64(x + a);
	elseif (y <= 2.22e+86)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	t_2 = y * -z;
	tmp = 0.0;
	if (y <= -6.5e+45)
		tmp = t_2;
	elseif (y <= -2.45e-163)
		tmp = x + a;
	elseif (y <= -1.15e-226)
		tmp = t_1;
	elseif (y <= -2.5e-268)
		tmp = x + a;
	elseif (y <= 2.22e+86)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * (-z)), $MachinePrecision]}, If[LessEqual[y, -6.5e+45], t$95$2, If[LessEqual[y, -2.45e-163], N[(x + a), $MachinePrecision], If[LessEqual[y, -1.15e-226], t$95$1, If[LessEqual[y, -2.5e-268], N[(x + a), $MachinePrecision], If[LessEqual[y, 2.22e+86], t$95$1, t$95$2]]]]]]]

Derivation

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

Alternative 19: 49.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -1.12 \cdot 10^{+51}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -1.06 \cdot 10^{+21}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.05 \cdot 10^{-39}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t \leq 1.05 \cdot 10^{-159}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 3.6 \cdot 10^{+18}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* y (- b z))) (t_2 (* t (- b a))))
   (if (<= t -1.12e+51)
     t_2
     (if (<= t -1.06e+21)
       t_1
       (if (<= t -1.05e-39)
         (+ x z)
         (if (<= t 1.05e-159) t_1 (if (<= t 3.6e+18) (+ x a) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -1.12e+51) {
		tmp = t_2;
	} else if (t <= -1.06e+21) {
		tmp = t_1;
	} else if (t <= -1.05e-39) {
		tmp = x + z;
	} else if (t <= 1.05e-159) {
		tmp = t_1;
	} else if (t <= 3.6e+18) {
		tmp = x + a;
	} 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 = y * (b - z)
    t_2 = t * (b - a)
    if (t <= (-1.12d+51)) then
        tmp = t_2
    else if (t <= (-1.06d+21)) then
        tmp = t_1
    else if (t <= (-1.05d-39)) then
        tmp = x + z
    else if (t <= 1.05d-159) then
        tmp = t_1
    else if (t <= 3.6d+18) then
        tmp = x + 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 b) {
	double t_1 = y * (b - z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -1.12e+51) {
		tmp = t_2;
	} else if (t <= -1.06e+21) {
		tmp = t_1;
	} else if (t <= -1.05e-39) {
		tmp = x + z;
	} else if (t <= 1.05e-159) {
		tmp = t_1;
	} else if (t <= 3.6e+18) {
		tmp = x + a;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y * (b - z)
	t_2 = t * (b - a)
	tmp = 0
	if t <= -1.12e+51:
		tmp = t_2
	elif t <= -1.06e+21:
		tmp = t_1
	elif t <= -1.05e-39:
		tmp = x + z
	elif t <= 1.05e-159:
		tmp = t_1
	elif t <= 3.6e+18:
		tmp = x + a
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y * Float64(b - z))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -1.12e+51)
		tmp = t_2;
	elseif (t <= -1.06e+21)
		tmp = t_1;
	elseif (t <= -1.05e-39)
		tmp = Float64(x + z);
	elseif (t <= 1.05e-159)
		tmp = t_1;
	elseif (t <= 3.6e+18)
		tmp = Float64(x + a);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y * (b - z);
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -1.12e+51)
		tmp = t_2;
	elseif (t <= -1.06e+21)
		tmp = t_1;
	elseif (t <= -1.05e-39)
		tmp = x + z;
	elseif (t <= 1.05e-159)
		tmp = t_1;
	elseif (t <= 3.6e+18)
		tmp = x + a;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.12e+51], t$95$2, If[LessEqual[t, -1.06e+21], t$95$1, If[LessEqual[t, -1.05e-39], N[(x + z), $MachinePrecision], If[LessEqual[t, 1.05e-159], t$95$1, If[LessEqual[t, 3.6e+18], N[(x + a), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 20: 48.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -3.2 \cdot 10^{+76}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.55 \cdot 10^{-75}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq -1.02 \cdot 10^{-199}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 1.8 \cdot 10^{-158}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* t (- b a))))
   (if (<= t -3.2e+76)
     t_1
     (if (<= t -3.55e-75)
       (* z (- 1.0 y))
       (if (<= t -1.02e-199)
         (+ x a)
         (if (<= t 1.8e-158) (* y (- b z)) (if (<= t 5e+18) (+ x a) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -3.2e+76) {
		tmp = t_1;
	} else if (t <= -3.55e-75) {
		tmp = z * (1.0 - y);
	} else if (t <= -1.02e-199) {
		tmp = x + a;
	} else if (t <= 1.8e-158) {
		tmp = y * (b - z);
	} else if (t <= 5e+18) {
		tmp = x + a;
	} 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 = t * (b - a)
    if (t <= (-3.2d+76)) then
        tmp = t_1
    else if (t <= (-3.55d-75)) then
        tmp = z * (1.0d0 - y)
    else if (t <= (-1.02d-199)) then
        tmp = x + a
    else if (t <= 1.8d-158) then
        tmp = y * (b - z)
    else if (t <= 5d+18) then
        tmp = x + 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 b) {
	double t_1 = t * (b - a);
	double tmp;
	if (t <= -3.2e+76) {
		tmp = t_1;
	} else if (t <= -3.55e-75) {
		tmp = z * (1.0 - y);
	} else if (t <= -1.02e-199) {
		tmp = x + a;
	} else if (t <= 1.8e-158) {
		tmp = y * (b - z);
	} else if (t <= 5e+18) {
		tmp = x + a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = t * (b - a)
	tmp = 0
	if t <= -3.2e+76:
		tmp = t_1
	elif t <= -3.55e-75:
		tmp = z * (1.0 - y)
	elif t <= -1.02e-199:
		tmp = x + a
	elif t <= 1.8e-158:
		tmp = y * (b - z)
	elif t <= 5e+18:
		tmp = x + a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -3.2e+76)
		tmp = t_1;
	elseif (t <= -3.55e-75)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (t <= -1.02e-199)
		tmp = Float64(x + a);
	elseif (t <= 1.8e-158)
		tmp = Float64(y * Float64(b - z));
	elseif (t <= 5e+18)
		tmp = Float64(x + a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = t * (b - a);
	tmp = 0.0;
	if (t <= -3.2e+76)
		tmp = t_1;
	elseif (t <= -3.55e-75)
		tmp = z * (1.0 - y);
	elseif (t <= -1.02e-199)
		tmp = x + a;
	elseif (t <= 1.8e-158)
		tmp = y * (b - z);
	elseif (t <= 5e+18)
		tmp = x + a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.2e+76], t$95$1, If[LessEqual[t, -3.55e-75], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -1.02e-199], N[(x + a), $MachinePrecision], If[LessEqual[t, 1.8e-158], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 5e+18], N[(x + a), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 21: 31.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(-z\right)\\ \mathbf{if}\;y \leq -6.8 \cdot 10^{+54}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{-271}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-123}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{-63}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;y \leq 1.14 \cdot 10^{+108}:\\ \;\;\;\;t \cdot b\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* y (- z))))
   (if (<= y -6.8e+54)
     t_1
     (if (<= y -1.75e-271)
       (+ x a)
       (if (<= y 3e-123)
         (* t b)
         (if (<= y 7.2e-63) (+ x z) (if (<= y 1.14e+108) (* t b) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * -z;
	double tmp;
	if (y <= -6.8e+54) {
		tmp = t_1;
	} else if (y <= -1.75e-271) {
		tmp = x + a;
	} else if (y <= 3e-123) {
		tmp = t * b;
	} else if (y <= 7.2e-63) {
		tmp = x + z;
	} else if (y <= 1.14e+108) {
		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 = y * -z
    if (y <= (-6.8d+54)) then
        tmp = t_1
    else if (y <= (-1.75d-271)) then
        tmp = x + a
    else if (y <= 3d-123) then
        tmp = t * b
    else if (y <= 7.2d-63) then
        tmp = x + z
    else if (y <= 1.14d+108) 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 = y * -z;
	double tmp;
	if (y <= -6.8e+54) {
		tmp = t_1;
	} else if (y <= -1.75e-271) {
		tmp = x + a;
	} else if (y <= 3e-123) {
		tmp = t * b;
	} else if (y <= 7.2e-63) {
		tmp = x + z;
	} else if (y <= 1.14e+108) {
		tmp = t * b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y * -z
	tmp = 0
	if y <= -6.8e+54:
		tmp = t_1
	elif y <= -1.75e-271:
		tmp = x + a
	elif y <= 3e-123:
		tmp = t * b
	elif y <= 7.2e-63:
		tmp = x + z
	elif y <= 1.14e+108:
		tmp = t * b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y * Float64(-z))
	tmp = 0.0
	if (y <= -6.8e+54)
		tmp = t_1;
	elseif (y <= -1.75e-271)
		tmp = Float64(x + a);
	elseif (y <= 3e-123)
		tmp = Float64(t * b);
	elseif (y <= 7.2e-63)
		tmp = Float64(x + z);
	elseif (y <= 1.14e+108)
		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 = y * -z;
	tmp = 0.0;
	if (y <= -6.8e+54)
		tmp = t_1;
	elseif (y <= -1.75e-271)
		tmp = x + a;
	elseif (y <= 3e-123)
		tmp = t * b;
	elseif (y <= 7.2e-63)
		tmp = x + z;
	elseif (y <= 1.14e+108)
		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[(y * (-z)), $MachinePrecision]}, If[LessEqual[y, -6.8e+54], t$95$1, If[LessEqual[y, -1.75e-271], N[(x + a), $MachinePrecision], If[LessEqual[y, 3e-123], N[(t * b), $MachinePrecision], If[LessEqual[y, 7.2e-63], N[(x + z), $MachinePrecision], If[LessEqual[y, 1.14e+108], N[(t * b), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 22: 65.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+74} \lor \neg \left(y \leq 7 \cdot 10^{+89}\right):\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z + b \cdot \left(t + -2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -1.2e+74) (not (<= y 7e+89)))
   (* y (- b z))
   (+ x (+ z (* b (+ t -2.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -1.2e+74) || !(y <= 7e+89)) {
		tmp = y * (b - z);
	} else {
		tmp = x + (z + (b * (t + -2.0)));
	}
	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 <= (-1.2d+74)) .or. (.not. (y <= 7d+89))) then
        tmp = y * (b - z)
    else
        tmp = x + (z + (b * (t + (-2.0d0))))
    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 <= -1.2e+74) || !(y <= 7e+89)) {
		tmp = y * (b - z);
	} else {
		tmp = x + (z + (b * (t + -2.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -1.2e+74) or not (y <= 7e+89):
		tmp = y * (b - z)
	else:
		tmp = x + (z + (b * (t + -2.0)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -1.2e+74) || !(y <= 7e+89))
		tmp = Float64(y * Float64(b - z));
	else
		tmp = Float64(x + Float64(z + Float64(b * Float64(t + -2.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -1.2e+74) || ~((y <= 7e+89)))
		tmp = y * (b - z);
	else
		tmp = x + (z + (b * (t + -2.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -1.2e+74], N[Not[LessEqual[y, 7e+89]], $MachinePrecision]], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], N[(x + N[(z + N[(b * N[(t + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 23: 72.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -17000 \lor \neg \left(y \leq 7.8 \cdot 10^{+83}\right):\\ \;\;\;\;a + \left(x + y \cdot \left(b - z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z + b \cdot \left(t + -2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -17000.0) (not (<= y 7.8e+83)))
   (+ a (+ x (* y (- b z))))
   (+ x (+ z (* b (+ t -2.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -17000.0) || !(y <= 7.8e+83)) {
		tmp = a + (x + (y * (b - z)));
	} else {
		tmp = x + (z + (b * (t + -2.0)));
	}
	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 <= (-17000.0d0)) .or. (.not. (y <= 7.8d+83))) then
        tmp = a + (x + (y * (b - z)))
    else
        tmp = x + (z + (b * (t + (-2.0d0))))
    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 <= -17000.0) || !(y <= 7.8e+83)) {
		tmp = a + (x + (y * (b - z)));
	} else {
		tmp = x + (z + (b * (t + -2.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -17000.0) or not (y <= 7.8e+83):
		tmp = a + (x + (y * (b - z)))
	else:
		tmp = x + (z + (b * (t + -2.0)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -17000.0) || !(y <= 7.8e+83))
		tmp = Float64(a + Float64(x + Float64(y * Float64(b - z))));
	else
		tmp = Float64(x + Float64(z + Float64(b * Float64(t + -2.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -17000.0) || ~((y <= 7.8e+83)))
		tmp = a + (x + (y * (b - z)));
	else
		tmp = x + (z + (b * (t + -2.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -17000.0], N[Not[LessEqual[y, 7.8e+83]], $MachinePrecision]], N[(a + N[(x + N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z + N[(b * N[(t + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 24: 54.2% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -3.5 \cdot 10^{+49}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -4.5 \cdot 10^{-266}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{+83}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* y (- b z))))
   (if (<= y -3.5e+49)
     t_1
     (if (<= y -4.5e-266)
       (+ x (* a (- 1.0 t)))
       (if (<= y 8.2e+83) (* t (- b a)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y * (b - z);
	double tmp;
	if (y <= -3.5e+49) {
		tmp = t_1;
	} else if (y <= -4.5e-266) {
		tmp = x + (a * (1.0 - t));
	} else if (y <= 8.2e+83) {
		tmp = t * (b - a);
	} 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 = y * (b - z)
    if (y <= (-3.5d+49)) then
        tmp = t_1
    else if (y <= (-4.5d-266)) then
        tmp = x + (a * (1.0d0 - t))
    else if (y <= 8.2d+83) then
        tmp = t * (b - 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 b) {
	double t_1 = y * (b - z);
	double tmp;
	if (y <= -3.5e+49) {
		tmp = t_1;
	} else if (y <= -4.5e-266) {
		tmp = x + (a * (1.0 - t));
	} else if (y <= 8.2e+83) {
		tmp = t * (b - a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y * (b - z)
	tmp = 0
	if y <= -3.5e+49:
		tmp = t_1
	elif y <= -4.5e-266:
		tmp = x + (a * (1.0 - t))
	elif y <= 8.2e+83:
		tmp = t * (b - a)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y * Float64(b - z))
	tmp = 0.0
	if (y <= -3.5e+49)
		tmp = t_1;
	elseif (y <= -4.5e-266)
		tmp = Float64(x + Float64(a * Float64(1.0 - t)));
	elseif (y <= 8.2e+83)
		tmp = Float64(t * Float64(b - a));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y * (b - z);
	tmp = 0.0;
	if (y <= -3.5e+49)
		tmp = t_1;
	elseif (y <= -4.5e-266)
		tmp = x + (a * (1.0 - t));
	elseif (y <= 8.2e+83)
		tmp = t * (b - a);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.5e+49], t$95$1, If[LessEqual[y, -4.5e-266], N[(x + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 8.2e+83], N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 25: 25.5% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.085:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;t \leq 1.3 \cdot 10^{-192}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{+22}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -0.085)
   (* t b)
   (if (<= t 1.3e-192) a (if (<= t 2.3e+22) x (* t b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -0.085) {
		tmp = t * b;
	} else if (t <= 1.3e-192) {
		tmp = a;
	} else if (t <= 2.3e+22) {
		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 (t <= (-0.085d0)) then
        tmp = t * b
    else if (t <= 1.3d-192) then
        tmp = a
    else if (t <= 2.3d+22) 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 (t <= -0.085) {
		tmp = t * b;
	} else if (t <= 1.3e-192) {
		tmp = a;
	} else if (t <= 2.3e+22) {
		tmp = x;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -0.085:
		tmp = t * b
	elif t <= 1.3e-192:
		tmp = a
	elif t <= 2.3e+22:
		tmp = x
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -0.085)
		tmp = Float64(t * b);
	elseif (t <= 1.3e-192)
		tmp = a;
	elseif (t <= 2.3e+22)
		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 (t <= -0.085)
		tmp = t * b;
	elseif (t <= 1.3e-192)
		tmp = a;
	elseif (t <= 2.3e+22)
		tmp = x;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -0.085], N[(t * b), $MachinePrecision], If[LessEqual[t, 1.3e-192], a, If[LessEqual[t, 2.3e+22], x, N[(t * b), $MachinePrecision]]]]

Derivation

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

Alternative 26: 30.6% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -2.05 \cdot 10^{+117} \lor \neg \left(b \leq 1.1 \cdot 10^{-76}\right):\\ \;\;\;\;t \cdot b\\ \mathbf{else}:\\ \;\;\;\;x + a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -2.05e+117) (not (<= b 1.1e-76))) (* t b) (+ x a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -2.05e+117) || !(b <= 1.1e-76)) {
		tmp = t * b;
	} else {
		tmp = x + a;
	}
	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 ((b <= (-2.05d+117)) .or. (.not. (b <= 1.1d-76))) then
        tmp = t * b
    else
        tmp = x + a
    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 ((b <= -2.05e+117) || !(b <= 1.1e-76)) {
		tmp = t * b;
	} else {
		tmp = x + a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -2.05e+117) or not (b <= 1.1e-76):
		tmp = t * b
	else:
		tmp = x + a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -2.05e+117) || !(b <= 1.1e-76))
		tmp = Float64(t * b);
	else
		tmp = Float64(x + a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -2.05e+117) || ~((b <= 1.1e-76)))
		tmp = t * b;
	else
		tmp = x + a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -2.05e+117], N[Not[LessEqual[b, 1.1e-76]], $MachinePrecision]], N[(t * b), $MachinePrecision], N[(x + a), $MachinePrecision]]

Derivation

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

Alternative 27: 20.3% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.7 \cdot 10^{+151}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 3.4 \cdot 10^{-95}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -4.7e+151) x (if (<= x 3.4e-95) a x)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -4.7e+151) {
		tmp = x;
	} else if (x <= 3.4e-95) {
		tmp = a;
	} 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 (x <= (-4.7d+151)) then
        tmp = x
    else if (x <= 3.4d-95) then
        tmp = a
    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 (x <= -4.7e+151) {
		tmp = x;
	} else if (x <= 3.4e-95) {
		tmp = a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -4.7e+151:
		tmp = x
	elif x <= 3.4e-95:
		tmp = a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -4.7e+151)
		tmp = x;
	elseif (x <= 3.4e-95)
		tmp = a;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -4.7e+151)
		tmp = x;
	elseif (x <= 3.4e-95)
		tmp = a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -4.7e+151], x, If[LessEqual[x, 3.4e-95], a, x]]

Derivation

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

Alternative 28: 11.2% accurate, 21.0× speedup

Math

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

FPCore

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

C

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

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 = a
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Reproduce

herbie shell --seed 2023347 
(FPCore (x y z t a b)
  :name "Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2"
  :precision binary64
  (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))