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

Percentage Accurate: 95.2% → 98.0%

Time: 15.8s

Alternatives: 21

Speedup: 0.5×

• 35.6% 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: 95.2% 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: 98.0% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (1.0 - t);
	double t_2 = x + (z * (1.0 - y));
	double tmp;
	if (((t_1 + t_2) + (b * ((y + t) - 2.0))) <= Double.POSITIVE_INFINITY) {
		tmp = ((b * (t + (y + -2.0))) + t_2) + t_1;
	} else {
		tmp = t * (b - a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (1.0 - t)
	t_2 = x + (z * (1.0 - y))
	tmp = 0
	if ((t_1 + t_2) + (b * ((y + t) - 2.0))) <= math.inf:
		tmp = ((b * (t + (y + -2.0))) + t_2) + t_1
	else:
		tmp = t * (b - a)
	return tmp

Julia

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

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (1.0 - t);
	t_2 = x + (z * (1.0 - y));
	tmp = 0.0;
	if (((t_1 + t_2) + (b * ((y + t) - 2.0))) <= Inf)
		tmp = ((b * (t + (y + -2.0))) + t_2) + t_1;
	else
		tmp = t * (b - a);
	end
	tmp_2 = tmp;
end

Wolfram

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

Derivation

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

Alternative 2: 97.6% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 3: 98.0% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 4: 58.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := x + t_1\\ t_3 := t \cdot \left(b - a\right)\\ t_4 := a + \left(x + y \cdot b\right)\\ \mathbf{if}\;t \leq -1.75 \cdot 10^{+94}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -1.06 \cdot 10^{+70}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -5.6 \cdot 10^{+33}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -2 \cdot 10^{-117}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t \leq 1.95 \cdot 10^{-277}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 7.8 \cdot 10^{-47}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t \leq 3.8 \cdot 10^{+86}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 6.1 \cdot 10^{+94}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y)))
        (t_2 (+ x t_1))
        (t_3 (* t (- b a)))
        (t_4 (+ a (+ x (* y b)))))
   (if (<= t -1.75e+94)
     t_3
     (if (<= t -1.06e+70)
       t_1
       (if (<= t -5.6e+33)
         t_3
         (if (<= t -2e-117)
           t_4
           (if (<= t 1.95e-277)
             t_2
             (if (<= t 7.8e-47)
               t_4
               (if (<= t 3.8e+86)
                 t_2
                 (if (<= t 6.1e+94) (* y (- b z)) 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 = x + t_1;
	double t_3 = t * (b - a);
	double t_4 = a + (x + (y * b));
	double tmp;
	if (t <= -1.75e+94) {
		tmp = t_3;
	} else if (t <= -1.06e+70) {
		tmp = t_1;
	} else if (t <= -5.6e+33) {
		tmp = t_3;
	} else if (t <= -2e-117) {
		tmp = t_4;
	} else if (t <= 1.95e-277) {
		tmp = t_2;
	} else if (t <= 7.8e-47) {
		tmp = t_4;
	} else if (t <= 3.8e+86) {
		tmp = t_2;
	} else if (t <= 6.1e+94) {
		tmp = y * (b - z);
	} 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 = x + t_1
    t_3 = t * (b - a)
    t_4 = a + (x + (y * b))
    if (t <= (-1.75d+94)) then
        tmp = t_3
    else if (t <= (-1.06d+70)) then
        tmp = t_1
    else if (t <= (-5.6d+33)) then
        tmp = t_3
    else if (t <= (-2d-117)) then
        tmp = t_4
    else if (t <= 1.95d-277) then
        tmp = t_2
    else if (t <= 7.8d-47) then
        tmp = t_4
    else if (t <= 3.8d+86) then
        tmp = t_2
    else if (t <= 6.1d+94) then
        tmp = y * (b - z)
    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 = x + t_1;
	double t_3 = t * (b - a);
	double t_4 = a + (x + (y * b));
	double tmp;
	if (t <= -1.75e+94) {
		tmp = t_3;
	} else if (t <= -1.06e+70) {
		tmp = t_1;
	} else if (t <= -5.6e+33) {
		tmp = t_3;
	} else if (t <= -2e-117) {
		tmp = t_4;
	} else if (t <= 1.95e-277) {
		tmp = t_2;
	} else if (t <= 7.8e-47) {
		tmp = t_4;
	} else if (t <= 3.8e+86) {
		tmp = t_2;
	} else if (t <= 6.1e+94) {
		tmp = y * (b - z);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = x + t_1
	t_3 = t * (b - a)
	t_4 = a + (x + (y * b))
	tmp = 0
	if t <= -1.75e+94:
		tmp = t_3
	elif t <= -1.06e+70:
		tmp = t_1
	elif t <= -5.6e+33:
		tmp = t_3
	elif t <= -2e-117:
		tmp = t_4
	elif t <= 1.95e-277:
		tmp = t_2
	elif t <= 7.8e-47:
		tmp = t_4
	elif t <= 3.8e+86:
		tmp = t_2
	elif t <= 6.1e+94:
		tmp = y * (b - z)
	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(x + t_1)
	t_3 = Float64(t * Float64(b - a))
	t_4 = Float64(a + Float64(x + Float64(y * b)))
	tmp = 0.0
	if (t <= -1.75e+94)
		tmp = t_3;
	elseif (t <= -1.06e+70)
		tmp = t_1;
	elseif (t <= -5.6e+33)
		tmp = t_3;
	elseif (t <= -2e-117)
		tmp = t_4;
	elseif (t <= 1.95e-277)
		tmp = t_2;
	elseif (t <= 7.8e-47)
		tmp = t_4;
	elseif (t <= 3.8e+86)
		tmp = t_2;
	elseif (t <= 6.1e+94)
		tmp = Float64(y * Float64(b - z));
	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 = x + t_1;
	t_3 = t * (b - a);
	t_4 = a + (x + (y * b));
	tmp = 0.0;
	if (t <= -1.75e+94)
		tmp = t_3;
	elseif (t <= -1.06e+70)
		tmp = t_1;
	elseif (t <= -5.6e+33)
		tmp = t_3;
	elseif (t <= -2e-117)
		tmp = t_4;
	elseif (t <= 1.95e-277)
		tmp = t_2;
	elseif (t <= 7.8e-47)
		tmp = t_4;
	elseif (t <= 3.8e+86)
		tmp = t_2;
	elseif (t <= 6.1e+94)
		tmp = y * (b - z);
	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[(x + t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(a + N[(x + N[(y * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.75e+94], t$95$3, If[LessEqual[t, -1.06e+70], t$95$1, If[LessEqual[t, -5.6e+33], t$95$3, If[LessEqual[t, -2e-117], t$95$4, If[LessEqual[t, 1.95e-277], t$95$2, If[LessEqual[t, 7.8e-47], t$95$4, If[LessEqual[t, 3.8e+86], t$95$2, If[LessEqual[t, 6.1e+94], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]]]]]]

Derivation

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

Alternative 5: 59.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := b \cdot \left(\left(y + t\right) - 2\right)\\ t_3 := x - \left(t + -1\right) \cdot a\\ \mathbf{if}\;b \leq -3.2 \cdot 10^{+101}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -4.1 \cdot 10^{+55}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -2.6 \cdot 10^{-19}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -6.3 \cdot 10^{-80}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 1.42 \cdot 10^{-235}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 5.7 \cdot 10^{-8}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 7.8 \cdot 10^{+92}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* z (- 1.0 y))))
        (t_2 (* b (- (+ y t) 2.0)))
        (t_3 (- x (* (+ t -1.0) a))))
   (if (<= b -3.2e+101)
     t_2
     (if (<= b -4.1e+55)
       t_1
       (if (<= b -2.6e-19)
         t_2
         (if (<= b -6.3e-80)
           t_3
           (if (<= b 1.42e-235)
             t_1
             (if (<= b 5.7e-8) t_3 (if (<= b 7.8e+92) 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));
	double t_2 = b * ((y + t) - 2.0);
	double t_3 = x - ((t + -1.0) * a);
	double tmp;
	if (b <= -3.2e+101) {
		tmp = t_2;
	} else if (b <= -4.1e+55) {
		tmp = t_1;
	} else if (b <= -2.6e-19) {
		tmp = t_2;
	} else if (b <= -6.3e-80) {
		tmp = t_3;
	} else if (b <= 1.42e-235) {
		tmp = t_1;
	} else if (b <= 5.7e-8) {
		tmp = t_3;
	} else if (b <= 7.8e+92) {
		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))
    t_2 = b * ((y + t) - 2.0d0)
    t_3 = x - ((t + (-1.0d0)) * a)
    if (b <= (-3.2d+101)) then
        tmp = t_2
    else if (b <= (-4.1d+55)) then
        tmp = t_1
    else if (b <= (-2.6d-19)) then
        tmp = t_2
    else if (b <= (-6.3d-80)) then
        tmp = t_3
    else if (b <= 1.42d-235) then
        tmp = t_1
    else if (b <= 5.7d-8) then
        tmp = t_3
    else if (b <= 7.8d+92) 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));
	double t_2 = b * ((y + t) - 2.0);
	double t_3 = x - ((t + -1.0) * a);
	double tmp;
	if (b <= -3.2e+101) {
		tmp = t_2;
	} else if (b <= -4.1e+55) {
		tmp = t_1;
	} else if (b <= -2.6e-19) {
		tmp = t_2;
	} else if (b <= -6.3e-80) {
		tmp = t_3;
	} else if (b <= 1.42e-235) {
		tmp = t_1;
	} else if (b <= 5.7e-8) {
		tmp = t_3;
	} else if (b <= 7.8e+92) {
		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))
	t_2 = b * ((y + t) - 2.0)
	t_3 = x - ((t + -1.0) * a)
	tmp = 0
	if b <= -3.2e+101:
		tmp = t_2
	elif b <= -4.1e+55:
		tmp = t_1
	elif b <= -2.6e-19:
		tmp = t_2
	elif b <= -6.3e-80:
		tmp = t_3
	elif b <= 1.42e-235:
		tmp = t_1
	elif b <= 5.7e-8:
		tmp = t_3
	elif b <= 7.8e+92:
		tmp = t_1
	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(b * Float64(Float64(y + t) - 2.0))
	t_3 = Float64(x - Float64(Float64(t + -1.0) * a))
	tmp = 0.0
	if (b <= -3.2e+101)
		tmp = t_2;
	elseif (b <= -4.1e+55)
		tmp = t_1;
	elseif (b <= -2.6e-19)
		tmp = t_2;
	elseif (b <= -6.3e-80)
		tmp = t_3;
	elseif (b <= 1.42e-235)
		tmp = t_1;
	elseif (b <= 5.7e-8)
		tmp = t_3;
	elseif (b <= 7.8e+92)
		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));
	t_2 = b * ((y + t) - 2.0);
	t_3 = x - ((t + -1.0) * a);
	tmp = 0.0;
	if (b <= -3.2e+101)
		tmp = t_2;
	elseif (b <= -4.1e+55)
		tmp = t_1;
	elseif (b <= -2.6e-19)
		tmp = t_2;
	elseif (b <= -6.3e-80)
		tmp = t_3;
	elseif (b <= 1.42e-235)
		tmp = t_1;
	elseif (b <= 5.7e-8)
		tmp = t_3;
	elseif (b <= 7.8e+92)
		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[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x - N[(N[(t + -1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -3.2e+101], t$95$2, If[LessEqual[b, -4.1e+55], t$95$1, If[LessEqual[b, -2.6e-19], t$95$2, If[LessEqual[b, -6.3e-80], t$95$3, If[LessEqual[b, 1.42e-235], t$95$1, If[LessEqual[b, 5.7e-8], t$95$3, If[LessEqual[b, 7.8e+92], t$95$1, t$95$2]]]]]]]]]]

Derivation

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

Alternative 6: 40.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(1 - t\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -2.9 \cdot 10^{+22}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -2.45 \cdot 10^{-89}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -8.6 \cdot 10^{-279}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{-294}:\\ \;\;\;\;z \cdot \left(-y\right)\\ \mathbf{elif}\;t \leq 4.7 \cdot 10^{-278}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq 8.2 \cdot 10^{-168}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{+65}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (- 1.0 t))) (t_2 (* t (- b a))))
   (if (<= t -2.9e+22)
     t_2
     (if (<= t -2.45e-89)
       t_1
       (if (<= t -8.6e-279)
         x
         (if (<= t 7.2e-294)
           (* z (- y))
           (if (<= t 4.7e-278)
             z
             (if (<= t 8.2e-168) t_1 (if (<= t 7.2e+65) x t_2)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (1.0 - t);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -2.9e+22) {
		tmp = t_2;
	} else if (t <= -2.45e-89) {
		tmp = t_1;
	} else if (t <= -8.6e-279) {
		tmp = x;
	} else if (t <= 7.2e-294) {
		tmp = z * -y;
	} else if (t <= 4.7e-278) {
		tmp = z;
	} else if (t <= 8.2e-168) {
		tmp = t_1;
	} else if (t <= 7.2e+65) {
		tmp = x;
	} 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 * (1.0d0 - t)
    t_2 = t * (b - a)
    if (t <= (-2.9d+22)) then
        tmp = t_2
    else if (t <= (-2.45d-89)) then
        tmp = t_1
    else if (t <= (-8.6d-279)) then
        tmp = x
    else if (t <= 7.2d-294) then
        tmp = z * -y
    else if (t <= 4.7d-278) then
        tmp = z
    else if (t <= 8.2d-168) then
        tmp = t_1
    else if (t <= 7.2d+65) then
        tmp = x
    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 * (1.0 - t);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -2.9e+22) {
		tmp = t_2;
	} else if (t <= -2.45e-89) {
		tmp = t_1;
	} else if (t <= -8.6e-279) {
		tmp = x;
	} else if (t <= 7.2e-294) {
		tmp = z * -y;
	} else if (t <= 4.7e-278) {
		tmp = z;
	} else if (t <= 8.2e-168) {
		tmp = t_1;
	} else if (t <= 7.2e+65) {
		tmp = x;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (1.0 - t)
	t_2 = t * (b - a)
	tmp = 0
	if t <= -2.9e+22:
		tmp = t_2
	elif t <= -2.45e-89:
		tmp = t_1
	elif t <= -8.6e-279:
		tmp = x
	elif t <= 7.2e-294:
		tmp = z * -y
	elif t <= 4.7e-278:
		tmp = z
	elif t <= 8.2e-168:
		tmp = t_1
	elif t <= 7.2e+65:
		tmp = x
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(1.0 - t))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -2.9e+22)
		tmp = t_2;
	elseif (t <= -2.45e-89)
		tmp = t_1;
	elseif (t <= -8.6e-279)
		tmp = x;
	elseif (t <= 7.2e-294)
		tmp = Float64(z * Float64(-y));
	elseif (t <= 4.7e-278)
		tmp = z;
	elseif (t <= 8.2e-168)
		tmp = t_1;
	elseif (t <= 7.2e+65)
		tmp = x;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (1.0 - t);
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -2.9e+22)
		tmp = t_2;
	elseif (t <= -2.45e-89)
		tmp = t_1;
	elseif (t <= -8.6e-279)
		tmp = x;
	elseif (t <= 7.2e-294)
		tmp = z * -y;
	elseif (t <= 4.7e-278)
		tmp = z;
	elseif (t <= 8.2e-168)
		tmp = t_1;
	elseif (t <= 7.2e+65)
		tmp = x;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.9e+22], t$95$2, If[LessEqual[t, -2.45e-89], t$95$1, If[LessEqual[t, -8.6e-279], x, If[LessEqual[t, 7.2e-294], N[(z * (-y)), $MachinePrecision], If[LessEqual[t, 4.7e-278], z, If[LessEqual[t, 8.2e-168], t$95$1, If[LessEqual[t, 7.2e+65], x, t$95$2]]]]]]]]]

Derivation

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

Alternative 7: 85.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+60} \lor \neg \left(z \leq 3.2 \cdot 10^{-39}\right):\\ \;\;\;\;x + \left(z \cdot \left(1 - y\right) - \left(t + -1\right) \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + a \cdot \left(1 - t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -2.2e+60) (not (<= z 3.2e-39)))
   (+ x (- (* z (- 1.0 y)) (* (+ t -1.0) a)))
   (+ (+ x (* b (- (+ y t) 2.0))) (* a (- 1.0 t)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -2.2e+60) || !(z <= 3.2e-39)) {
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a));
	} else {
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-2.2d+60)) .or. (.not. (z <= 3.2d-39))) then
        tmp = x + ((z * (1.0d0 - y)) - ((t + (-1.0d0)) * a))
    else
        tmp = (x + (b * ((y + t) - 2.0d0))) + (a * (1.0d0 - t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -2.2e+60) || !(z <= 3.2e-39)) {
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a));
	} else {
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -2.2e+60) or not (z <= 3.2e-39):
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a))
	else:
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -2.2e+60) || !(z <= 3.2e-39))
		tmp = Float64(x + Float64(Float64(z * Float64(1.0 - y)) - Float64(Float64(t + -1.0) * a)));
	else
		tmp = Float64(Float64(x + Float64(b * Float64(Float64(y + t) - 2.0))) + Float64(a * Float64(1.0 - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -2.2e+60) || ~((z <= 3.2e-39)))
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a));
	else
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -2.2e+60], N[Not[LessEqual[z, 3.2e-39]], $MachinePrecision]], N[(x + N[(N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision] - N[(N[(t + -1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 86.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ \mathbf{if}\;b \leq -1.02 \cdot 10^{-20} \lor \neg \left(b \leq 3.5 \cdot 10^{-7}\right):\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + t_1\\ \mathbf{else}:\\ \;\;\;\;x + \left(t_1 - \left(t + -1\right) \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y))))
   (if (or (<= b -1.02e-20) (not (<= b 3.5e-7)))
     (+ (+ x (* b (- (+ y t) 2.0))) t_1)
     (+ x (- t_1 (* (+ t -1.0) a))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (1.0 - y);
	double tmp;
	if ((b <= -1.02e-20) || !(b <= 3.5e-7)) {
		tmp = (x + (b * ((y + t) - 2.0))) + t_1;
	} else {
		tmp = x + (t_1 - ((t + -1.0) * 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) :: t_1
    real(8) :: tmp
    t_1 = z * (1.0d0 - y)
    if ((b <= (-1.02d-20)) .or. (.not. (b <= 3.5d-7))) then
        tmp = (x + (b * ((y + t) - 2.0d0))) + t_1
    else
        tmp = x + (t_1 - ((t + (-1.0d0)) * 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 t_1 = z * (1.0 - y);
	double tmp;
	if ((b <= -1.02e-20) || !(b <= 3.5e-7)) {
		tmp = (x + (b * ((y + t) - 2.0))) + t_1;
	} else {
		tmp = x + (t_1 - ((t + -1.0) * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	tmp = 0
	if (b <= -1.02e-20) or not (b <= 3.5e-7):
		tmp = (x + (b * ((y + t) - 2.0))) + t_1
	else:
		tmp = x + (t_1 - ((t + -1.0) * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(1.0 - y))
	tmp = 0.0
	if ((b <= -1.02e-20) || !(b <= 3.5e-7))
		tmp = Float64(Float64(x + Float64(b * Float64(Float64(y + t) - 2.0))) + t_1);
	else
		tmp = Float64(x + Float64(t_1 - Float64(Float64(t + -1.0) * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (1.0 - y);
	tmp = 0.0;
	if ((b <= -1.02e-20) || ~((b <= 3.5e-7)))
		tmp = (x + (b * ((y + t) - 2.0))) + t_1;
	else
		tmp = x + (t_1 - ((t + -1.0) * a));
	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]}, If[Or[LessEqual[b, -1.02e-20], N[Not[LessEqual[b, 3.5e-7]], $MachinePrecision]], N[(N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision], N[(x + N[(t$95$1 - N[(N[(t + -1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 9: 46.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := a \cdot \left(1 - t\right)\\ t_3 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -9 \cdot 10^{+96}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -1.06 \cdot 10^{+70}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -5.4 \cdot 10^{-65}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 8.2 \cdot 10^{-277}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{-213}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 5.8 \cdot 10^{+97}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y))) (t_2 (* a (- 1.0 t))) (t_3 (* t (- b a))))
   (if (<= t -9e+96)
     t_3
     (if (<= t -1.06e+70)
       t_1
       (if (<= t -5.4e-65)
         t_2
         (if (<= t 8.2e-277)
           t_1
           (if (<= t 2.8e-213) t_2 (if (<= t 5.8e+97) (* y (- b z)) 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 * (1.0 - t);
	double t_3 = t * (b - a);
	double tmp;
	if (t <= -9e+96) {
		tmp = t_3;
	} else if (t <= -1.06e+70) {
		tmp = t_1;
	} else if (t <= -5.4e-65) {
		tmp = t_2;
	} else if (t <= 8.2e-277) {
		tmp = t_1;
	} else if (t <= 2.8e-213) {
		tmp = t_2;
	} else if (t <= 5.8e+97) {
		tmp = y * (b - z);
	} 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 * (1.0d0 - t)
    t_3 = t * (b - a)
    if (t <= (-9d+96)) then
        tmp = t_3
    else if (t <= (-1.06d+70)) then
        tmp = t_1
    else if (t <= (-5.4d-65)) then
        tmp = t_2
    else if (t <= 8.2d-277) then
        tmp = t_1
    else if (t <= 2.8d-213) then
        tmp = t_2
    else if (t <= 5.8d+97) then
        tmp = y * (b - z)
    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 * (1.0 - t);
	double t_3 = t * (b - a);
	double tmp;
	if (t <= -9e+96) {
		tmp = t_3;
	} else if (t <= -1.06e+70) {
		tmp = t_1;
	} else if (t <= -5.4e-65) {
		tmp = t_2;
	} else if (t <= 8.2e-277) {
		tmp = t_1;
	} else if (t <= 2.8e-213) {
		tmp = t_2;
	} else if (t <= 5.8e+97) {
		tmp = y * (b - z);
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = a * (1.0 - t)
	t_3 = t * (b - a)
	tmp = 0
	if t <= -9e+96:
		tmp = t_3
	elif t <= -1.06e+70:
		tmp = t_1
	elif t <= -5.4e-65:
		tmp = t_2
	elif t <= 8.2e-277:
		tmp = t_1
	elif t <= 2.8e-213:
		tmp = t_2
	elif t <= 5.8e+97:
		tmp = y * (b - z)
	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(1.0 - t))
	t_3 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -9e+96)
		tmp = t_3;
	elseif (t <= -1.06e+70)
		tmp = t_1;
	elseif (t <= -5.4e-65)
		tmp = t_2;
	elseif (t <= 8.2e-277)
		tmp = t_1;
	elseif (t <= 2.8e-213)
		tmp = t_2;
	elseif (t <= 5.8e+97)
		tmp = Float64(y * Float64(b - z));
	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 * (1.0 - t);
	t_3 = t * (b - a);
	tmp = 0.0;
	if (t <= -9e+96)
		tmp = t_3;
	elseif (t <= -1.06e+70)
		tmp = t_1;
	elseif (t <= -5.4e-65)
		tmp = t_2;
	elseif (t <= 8.2e-277)
		tmp = t_1;
	elseif (t <= 2.8e-213)
		tmp = t_2;
	elseif (t <= 5.8e+97)
		tmp = y * (b - z);
	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[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -9e+96], t$95$3, If[LessEqual[t, -1.06e+70], t$95$1, If[LessEqual[t, -5.4e-65], t$95$2, If[LessEqual[t, 8.2e-277], t$95$1, If[LessEqual[t, 2.8e-213], t$95$2, If[LessEqual[t, 5.8e+97], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]]]

Derivation

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

Alternative 10: 66.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x + z\right) + a \cdot \left(1 - t\right)\\ t_2 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -2.9 \cdot 10^{+86}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{-112}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{-149}:\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{elif}\;y \leq 6.9 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (+ x z) (* a (- 1.0 t)))) (t_2 (* y (- b z))))
   (if (<= y -2.9e+86)
     t_2
     (if (<= y -3.5e-112)
       t_1
       (if (<= y -8.5e-149)
         (* b (- (+ y t) 2.0))
         (if (<= y 6.9e+16) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + z) + (a * (1.0 - t));
	double t_2 = y * (b - z);
	double tmp;
	if (y <= -2.9e+86) {
		tmp = t_2;
	} else if (y <= -3.5e-112) {
		tmp = t_1;
	} else if (y <= -8.5e-149) {
		tmp = b * ((y + t) - 2.0);
	} else if (y <= 6.9e+16) {
		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 = (x + z) + (a * (1.0d0 - t))
    t_2 = y * (b - z)
    if (y <= (-2.9d+86)) then
        tmp = t_2
    else if (y <= (-3.5d-112)) then
        tmp = t_1
    else if (y <= (-8.5d-149)) then
        tmp = b * ((y + t) - 2.0d0)
    else if (y <= 6.9d+16) 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) + (a * (1.0 - t));
	double t_2 = y * (b - z);
	double tmp;
	if (y <= -2.9e+86) {
		tmp = t_2;
	} else if (y <= -3.5e-112) {
		tmp = t_1;
	} else if (y <= -8.5e-149) {
		tmp = b * ((y + t) - 2.0);
	} else if (y <= 6.9e+16) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + z) + (a * (1.0 - t))
	t_2 = y * (b - z)
	tmp = 0
	if y <= -2.9e+86:
		tmp = t_2
	elif y <= -3.5e-112:
		tmp = t_1
	elif y <= -8.5e-149:
		tmp = b * ((y + t) - 2.0)
	elif y <= 6.9e+16:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + z) + Float64(a * Float64(1.0 - t)))
	t_2 = Float64(y * Float64(b - z))
	tmp = 0.0
	if (y <= -2.9e+86)
		tmp = t_2;
	elseif (y <= -3.5e-112)
		tmp = t_1;
	elseif (y <= -8.5e-149)
		tmp = Float64(b * Float64(Float64(y + t) - 2.0));
	elseif (y <= 6.9e+16)
		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) + (a * (1.0 - t));
	t_2 = y * (b - z);
	tmp = 0.0;
	if (y <= -2.9e+86)
		tmp = t_2;
	elseif (y <= -3.5e-112)
		tmp = t_1;
	elseif (y <= -8.5e-149)
		tmp = b * ((y + t) - 2.0);
	elseif (y <= 6.9e+16)
		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[(N[(x + z), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.9e+86], t$95$2, If[LessEqual[y, -3.5e-112], t$95$1, If[LessEqual[y, -8.5e-149], N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.9e+16], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 11: 66.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x + z\right) + a \cdot \left(1 - t\right)\\ t_2 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -7.4 \cdot 10^{+84}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{+18}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{-211}:\\ \;\;\;\;x + \left(z + \left(t + -2\right) \cdot b\right)\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (+ x z) (* a (- 1.0 t)))) (t_2 (* y (- b z))))
   (if (<= y -7.4e+84)
     t_2
     (if (<= y -2.4e+18)
       t_1
       (if (<= y -1.75e-211)
         (+ x (+ z (* (+ t -2.0) b)))
         (if (<= y 5.5e+17) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + z) + (a * (1.0 - t));
	double t_2 = y * (b - z);
	double tmp;
	if (y <= -7.4e+84) {
		tmp = t_2;
	} else if (y <= -2.4e+18) {
		tmp = t_1;
	} else if (y <= -1.75e-211) {
		tmp = x + (z + ((t + -2.0) * b));
	} else if (y <= 5.5e+17) {
		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 = (x + z) + (a * (1.0d0 - t))
    t_2 = y * (b - z)
    if (y <= (-7.4d+84)) then
        tmp = t_2
    else if (y <= (-2.4d+18)) then
        tmp = t_1
    else if (y <= (-1.75d-211)) then
        tmp = x + (z + ((t + (-2.0d0)) * b))
    else if (y <= 5.5d+17) 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) + (a * (1.0 - t));
	double t_2 = y * (b - z);
	double tmp;
	if (y <= -7.4e+84) {
		tmp = t_2;
	} else if (y <= -2.4e+18) {
		tmp = t_1;
	} else if (y <= -1.75e-211) {
		tmp = x + (z + ((t + -2.0) * b));
	} else if (y <= 5.5e+17) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + z) + (a * (1.0 - t))
	t_2 = y * (b - z)
	tmp = 0
	if y <= -7.4e+84:
		tmp = t_2
	elif y <= -2.4e+18:
		tmp = t_1
	elif y <= -1.75e-211:
		tmp = x + (z + ((t + -2.0) * b))
	elif y <= 5.5e+17:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + z) + Float64(a * Float64(1.0 - t)))
	t_2 = Float64(y * Float64(b - z))
	tmp = 0.0
	if (y <= -7.4e+84)
		tmp = t_2;
	elseif (y <= -2.4e+18)
		tmp = t_1;
	elseif (y <= -1.75e-211)
		tmp = Float64(x + Float64(z + Float64(Float64(t + -2.0) * b)));
	elseif (y <= 5.5e+17)
		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) + (a * (1.0 - t));
	t_2 = y * (b - z);
	tmp = 0.0;
	if (y <= -7.4e+84)
		tmp = t_2;
	elseif (y <= -2.4e+18)
		tmp = t_1;
	elseif (y <= -1.75e-211)
		tmp = x + (z + ((t + -2.0) * b));
	elseif (y <= 5.5e+17)
		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[(N[(x + z), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -7.4e+84], t$95$2, If[LessEqual[y, -2.4e+18], t$95$1, If[LessEqual[y, -1.75e-211], N[(x + N[(z + N[(N[(t + -2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.5e+17], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 12: 72.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -3.5 \cdot 10^{+76}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 0.0016:\\ \;\;\;\;\left(x - y \cdot z\right) + a \cdot \left(1 - t\right)\\ \mathbf{elif}\;b \leq 1.05 \cdot 10^{+111}:\\ \;\;\;\;x + \left(z + \left(t + -2\right) \cdot b\right)\\ \mathbf{elif}\;b \leq 9.2 \cdot 10^{+170}:\\ \;\;\;\;a + \left(x + b \cdot \left(y + -2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- (+ y t) 2.0))))
   (if (<= b -3.5e+76)
     t_1
     (if (<= b 0.0016)
       (+ (- x (* y z)) (* a (- 1.0 t)))
       (if (<= b 1.05e+111)
         (+ x (+ z (* (+ t -2.0) b)))
         (if (<= b 9.2e+170) (+ a (+ x (* b (+ y -2.0)))) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -3.5e+76) {
		tmp = t_1;
	} else if (b <= 0.0016) {
		tmp = (x - (y * z)) + (a * (1.0 - t));
	} else if (b <= 1.05e+111) {
		tmp = x + (z + ((t + -2.0) * b));
	} else if (b <= 9.2e+170) {
		tmp = a + (x + (b * (y + -2.0)));
	} 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 = b * ((y + t) - 2.0d0)
    if (b <= (-3.5d+76)) then
        tmp = t_1
    else if (b <= 0.0016d0) then
        tmp = (x - (y * z)) + (a * (1.0d0 - t))
    else if (b <= 1.05d+111) then
        tmp = x + (z + ((t + (-2.0d0)) * b))
    else if (b <= 9.2d+170) then
        tmp = a + (x + (b * (y + (-2.0d0))))
    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 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -3.5e+76) {
		tmp = t_1;
	} else if (b <= 0.0016) {
		tmp = (x - (y * z)) + (a * (1.0 - t));
	} else if (b <= 1.05e+111) {
		tmp = x + (z + ((t + -2.0) * b));
	} else if (b <= 9.2e+170) {
		tmp = a + (x + (b * (y + -2.0)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -3.5e+76:
		tmp = t_1
	elif b <= 0.0016:
		tmp = (x - (y * z)) + (a * (1.0 - t))
	elif b <= 1.05e+111:
		tmp = x + (z + ((t + -2.0) * b))
	elif b <= 9.2e+170:
		tmp = a + (x + (b * (y + -2.0)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -3.5e+76)
		tmp = t_1;
	elseif (b <= 0.0016)
		tmp = Float64(Float64(x - Float64(y * z)) + Float64(a * Float64(1.0 - t)));
	elseif (b <= 1.05e+111)
		tmp = Float64(x + Float64(z + Float64(Float64(t + -2.0) * b)));
	elseif (b <= 9.2e+170)
		tmp = Float64(a + Float64(x + Float64(b * Float64(y + -2.0))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -3.5e+76)
		tmp = t_1;
	elseif (b <= 0.0016)
		tmp = (x - (y * z)) + (a * (1.0 - t));
	elseif (b <= 1.05e+111)
		tmp = x + (z + ((t + -2.0) * b));
	elseif (b <= 9.2e+170)
		tmp = a + (x + (b * (y + -2.0)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -3.5e+76], t$95$1, If[LessEqual[b, 0.0016], N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.05e+111], N[(x + N[(z + N[(N[(t + -2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 9.2e+170], N[(a + N[(x + N[(b * N[(y + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 13: 81.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -9.6 \cdot 10^{+100} \lor \neg \left(b \leq 1.5 \cdot 10^{+173}\right):\\ \;\;\;\;b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(z \cdot \left(1 - y\right) - \left(t + -1\right) \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -9.6e+100) (not (<= b 1.5e+173)))
   (* b (- (+ y t) 2.0))
   (+ x (- (* z (- 1.0 y)) (* (+ t -1.0) a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -9.6e+100) || !(b <= 1.5e+173)) {
		tmp = b * ((y + t) - 2.0);
	} else {
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * 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 <= (-9.6d+100)) .or. (.not. (b <= 1.5d+173))) then
        tmp = b * ((y + t) - 2.0d0)
    else
        tmp = x + ((z * (1.0d0 - y)) - ((t + (-1.0d0)) * 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 <= -9.6e+100) || !(b <= 1.5e+173)) {
		tmp = b * ((y + t) - 2.0);
	} else {
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -9.6e+100) or not (b <= 1.5e+173):
		tmp = b * ((y + t) - 2.0)
	else:
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -9.6e+100) || !(b <= 1.5e+173))
		tmp = Float64(b * Float64(Float64(y + t) - 2.0));
	else
		tmp = Float64(x + Float64(Float64(z * Float64(1.0 - y)) - Float64(Float64(t + -1.0) * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -9.6e+100) || ~((b <= 1.5e+173)))
		tmp = b * ((y + t) - 2.0);
	else
		tmp = x + ((z * (1.0 - y)) - ((t + -1.0) * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -9.6e+100], N[Not[LessEqual[b, 1.5e+173]], $MachinePrecision]], N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision] - N[(N[(t + -1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 50.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(1 - t\right)\\ t_2 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -2.6 \cdot 10^{-19}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -1.5 \cdot 10^{-94}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 1.7 \cdot 10^{-236}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;b \leq 9.5:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (- 1.0 t))) (t_2 (* b (- (+ y t) 2.0))))
   (if (<= b -2.6e-19)
     t_2
     (if (<= b -1.5e-94)
       t_1
       (if (<= b 1.7e-236) (* z (- 1.0 y)) (if (<= b 9.5) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (1.0 - t);
	double t_2 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -2.6e-19) {
		tmp = t_2;
	} else if (b <= -1.5e-94) {
		tmp = t_1;
	} else if (b <= 1.7e-236) {
		tmp = z * (1.0 - y);
	} else if (b <= 9.5) {
		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 = a * (1.0d0 - t)
    t_2 = b * ((y + t) - 2.0d0)
    if (b <= (-2.6d-19)) then
        tmp = t_2
    else if (b <= (-1.5d-94)) then
        tmp = t_1
    else if (b <= 1.7d-236) then
        tmp = z * (1.0d0 - y)
    else if (b <= 9.5d0) 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 = a * (1.0 - t);
	double t_2 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -2.6e-19) {
		tmp = t_2;
	} else if (b <= -1.5e-94) {
		tmp = t_1;
	} else if (b <= 1.7e-236) {
		tmp = z * (1.0 - y);
	} else if (b <= 9.5) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (1.0 - t)
	t_2 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -2.6e-19:
		tmp = t_2
	elif b <= -1.5e-94:
		tmp = t_1
	elif b <= 1.7e-236:
		tmp = z * (1.0 - y)
	elif b <= 9.5:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(1.0 - t))
	t_2 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -2.6e-19)
		tmp = t_2;
	elseif (b <= -1.5e-94)
		tmp = t_1;
	elseif (b <= 1.7e-236)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (b <= 9.5)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (1.0 - t);
	t_2 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -2.6e-19)
		tmp = t_2;
	elseif (b <= -1.5e-94)
		tmp = t_1;
	elseif (b <= 1.7e-236)
		tmp = z * (1.0 - y);
	elseif (b <= 9.5)
		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[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -2.6e-19], t$95$2, If[LessEqual[b, -1.5e-94], t$95$1, If[LessEqual[b, 1.7e-236], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 9.5], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 15: 57.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \left(t + -1\right) \cdot a\\ t_2 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -4.5 \cdot 10^{+78}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -2.05 \cdot 10^{-198}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 8 \cdot 10^{-236}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;b \leq 0.022:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- x (* (+ t -1.0) a))) (t_2 (* b (- (+ y t) 2.0))))
   (if (<= b -4.5e+78)
     t_2
     (if (<= b -2.05e-198)
       t_1
       (if (<= b 8e-236) (* z (- 1.0 y)) (if (<= b 0.022) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x - ((t + -1.0) * a);
	double t_2 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -4.5e+78) {
		tmp = t_2;
	} else if (b <= -2.05e-198) {
		tmp = t_1;
	} else if (b <= 8e-236) {
		tmp = z * (1.0 - y);
	} else if (b <= 0.022) {
		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 = x - ((t + (-1.0d0)) * a)
    t_2 = b * ((y + t) - 2.0d0)
    if (b <= (-4.5d+78)) then
        tmp = t_2
    else if (b <= (-2.05d-198)) then
        tmp = t_1
    else if (b <= 8d-236) then
        tmp = z * (1.0d0 - y)
    else if (b <= 0.022d0) 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 - ((t + -1.0) * a);
	double t_2 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -4.5e+78) {
		tmp = t_2;
	} else if (b <= -2.05e-198) {
		tmp = t_1;
	} else if (b <= 8e-236) {
		tmp = z * (1.0 - y);
	} else if (b <= 0.022) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x - ((t + -1.0) * a)
	t_2 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -4.5e+78:
		tmp = t_2
	elif b <= -2.05e-198:
		tmp = t_1
	elif b <= 8e-236:
		tmp = z * (1.0 - y)
	elif b <= 0.022:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x - Float64(Float64(t + -1.0) * a))
	t_2 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -4.5e+78)
		tmp = t_2;
	elseif (b <= -2.05e-198)
		tmp = t_1;
	elseif (b <= 8e-236)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (b <= 0.022)
		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 - ((t + -1.0) * a);
	t_2 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -4.5e+78)
		tmp = t_2;
	elseif (b <= -2.05e-198)
		tmp = t_1;
	elseif (b <= 8e-236)
		tmp = z * (1.0 - y);
	elseif (b <= 0.022)
		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[(t + -1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -4.5e+78], t$95$2, If[LessEqual[b, -2.05e-198], t$95$1, If[LessEqual[b, 8e-236], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 0.022], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 16: 27.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(-y\right)\\ \mathbf{if}\;y \leq -3.4 \cdot 10^{+76}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -53:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{-130}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 1.75 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 44000000:\\ \;\;\;\;t \cdot b\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- y))))
   (if (<= y -3.4e+76)
     t_1
     (if (<= y -53.0)
       x
       (if (<= y -5.8e-130)
         z
         (if (<= y 1.75e-85) x (if (<= y 44000000.0) (* t b) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * -y;
	double tmp;
	if (y <= -3.4e+76) {
		tmp = t_1;
	} else if (y <= -53.0) {
		tmp = x;
	} else if (y <= -5.8e-130) {
		tmp = z;
	} else if (y <= 1.75e-85) {
		tmp = x;
	} else if (y <= 44000000.0) {
		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 = z * -y
    if (y <= (-3.4d+76)) then
        tmp = t_1
    else if (y <= (-53.0d0)) then
        tmp = x
    else if (y <= (-5.8d-130)) then
        tmp = z
    else if (y <= 1.75d-85) then
        tmp = x
    else if (y <= 44000000.0d0) 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 = z * -y;
	double tmp;
	if (y <= -3.4e+76) {
		tmp = t_1;
	} else if (y <= -53.0) {
		tmp = x;
	} else if (y <= -5.8e-130) {
		tmp = z;
	} else if (y <= 1.75e-85) {
		tmp = x;
	} else if (y <= 44000000.0) {
		tmp = t * b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * -y
	tmp = 0
	if y <= -3.4e+76:
		tmp = t_1
	elif y <= -53.0:
		tmp = x
	elif y <= -5.8e-130:
		tmp = z
	elif y <= 1.75e-85:
		tmp = x
	elif y <= 44000000.0:
		tmp = t * b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(-y))
	tmp = 0.0
	if (y <= -3.4e+76)
		tmp = t_1;
	elseif (y <= -53.0)
		tmp = x;
	elseif (y <= -5.8e-130)
		tmp = z;
	elseif (y <= 1.75e-85)
		tmp = x;
	elseif (y <= 44000000.0)
		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 = z * -y;
	tmp = 0.0;
	if (y <= -3.4e+76)
		tmp = t_1;
	elseif (y <= -53.0)
		tmp = x;
	elseif (y <= -5.8e-130)
		tmp = z;
	elseif (y <= 1.75e-85)
		tmp = x;
	elseif (y <= 44000000.0)
		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[(z * (-y)), $MachinePrecision]}, If[LessEqual[y, -3.4e+76], t$95$1, If[LessEqual[y, -53.0], x, If[LessEqual[y, -5.8e-130], z, If[LessEqual[y, 1.75e-85], x, If[LessEqual[y, 44000000.0], N[(t * b), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 17: 48.7% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -2 \cdot 10^{+94}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 5.5 \cdot 10^{-302}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.05 \cdot 10^{-212}:\\ \;\;\;\;a \cdot \left(1 - t\right)\\ \mathbf{elif}\;t \leq 4.7 \cdot 10^{+94}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* y (- b z))) (t_2 (* t (- b a))))
   (if (<= t -2e+94)
     t_2
     (if (<= t 5.5e-302)
       t_1
       (if (<= t 2.05e-212) (* a (- 1.0 t)) (if (<= t 4.7e+94) t_1 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 <= -2e+94) {
		tmp = t_2;
	} else if (t <= 5.5e-302) {
		tmp = t_1;
	} else if (t <= 2.05e-212) {
		tmp = a * (1.0 - t);
	} else if (t <= 4.7e+94) {
		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 = y * (b - z)
    t_2 = t * (b - a)
    if (t <= (-2d+94)) then
        tmp = t_2
    else if (t <= 5.5d-302) then
        tmp = t_1
    else if (t <= 2.05d-212) then
        tmp = a * (1.0d0 - t)
    else if (t <= 4.7d+94) 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 = y * (b - z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -2e+94) {
		tmp = t_2;
	} else if (t <= 5.5e-302) {
		tmp = t_1;
	} else if (t <= 2.05e-212) {
		tmp = a * (1.0 - t);
	} else if (t <= 4.7e+94) {
		tmp = t_1;
	} 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 <= -2e+94:
		tmp = t_2
	elif t <= 5.5e-302:
		tmp = t_1
	elif t <= 2.05e-212:
		tmp = a * (1.0 - t)
	elif t <= 4.7e+94:
		tmp = t_1
	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 <= -2e+94)
		tmp = t_2;
	elseif (t <= 5.5e-302)
		tmp = t_1;
	elseif (t <= 2.05e-212)
		tmp = Float64(a * Float64(1.0 - t));
	elseif (t <= 4.7e+94)
		tmp = t_1;
	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 <= -2e+94)
		tmp = t_2;
	elseif (t <= 5.5e-302)
		tmp = t_1;
	elseif (t <= 2.05e-212)
		tmp = a * (1.0 - t);
	elseif (t <= 4.7e+94)
		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[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2e+94], t$95$2, If[LessEqual[t, 5.5e-302], t$95$1, If[LessEqual[t, 2.05e-212], N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4.7e+94], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 18: 34.1% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.1 \cdot 10^{+224}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq -1.06 \cdot 10^{+151} \lor \neg \left(z \leq 3.4 \cdot 10^{-15}\right):\\ \;\;\;\;z \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(1 - t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -3.1e+224)
   z
   (if (or (<= z -1.06e+151) (not (<= z 3.4e-15)))
     (* z (- y))
     (* a (- 1.0 t)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -3.1e+224) {
		tmp = z;
	} else if ((z <= -1.06e+151) || !(z <= 3.4e-15)) {
		tmp = z * -y;
	} else {
		tmp = a * (1.0 - t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-3.1d+224)) then
        tmp = z
    else if ((z <= (-1.06d+151)) .or. (.not. (z <= 3.4d-15))) then
        tmp = z * -y
    else
        tmp = a * (1.0d0 - t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -3.1e+224) {
		tmp = z;
	} else if ((z <= -1.06e+151) || !(z <= 3.4e-15)) {
		tmp = z * -y;
	} else {
		tmp = a * (1.0 - t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -3.1e+224:
		tmp = z
	elif (z <= -1.06e+151) or not (z <= 3.4e-15):
		tmp = z * -y
	else:
		tmp = a * (1.0 - t)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -3.1e+224)
		tmp = z;
	elseif ((z <= -1.06e+151) || !(z <= 3.4e-15))
		tmp = Float64(z * Float64(-y));
	else
		tmp = Float64(a * Float64(1.0 - t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -3.1e+224)
		tmp = z;
	elseif ((z <= -1.06e+151) || ~((z <= 3.4e-15)))
		tmp = z * -y;
	else
		tmp = a * (1.0 - t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -3.1e+224], z, If[Or[LessEqual[z, -1.06e+151], N[Not[LessEqual[z, 3.4e-15]], $MachinePrecision]], N[(z * (-y)), $MachinePrecision], N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 19: 24.1% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.5 \cdot 10^{+110}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;t \leq -3.6 \cdot 10^{-67}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+110}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2.5e+110)
   (* t b)
   (if (<= t -3.6e-67) z (if (<= t 1.65e+110) x (* t b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.5e+110) {
		tmp = t * b;
	} else if (t <= -3.6e-67) {
		tmp = z;
	} else if (t <= 1.65e+110) {
		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 <= (-2.5d+110)) then
        tmp = t * b
    else if (t <= (-3.6d-67)) then
        tmp = z
    else if (t <= 1.65d+110) 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 <= -2.5e+110) {
		tmp = t * b;
	} else if (t <= -3.6e-67) {
		tmp = z;
	} else if (t <= 1.65e+110) {
		tmp = x;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2.5e+110:
		tmp = t * b
	elif t <= -3.6e-67:
		tmp = z
	elif t <= 1.65e+110:
		tmp = x
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2.5e+110)
		tmp = Float64(t * b);
	elseif (t <= -3.6e-67)
		tmp = z;
	elseif (t <= 1.65e+110)
		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 <= -2.5e+110)
		tmp = t * b;
	elseif (t <= -3.6e-67)
		tmp = z;
	elseif (t <= 1.65e+110)
		tmp = x;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2.5e+110], N[(t * b), $MachinePrecision], If[LessEqual[t, -3.6e-67], z, If[LessEqual[t, 1.65e+110], x, N[(t * b), $MachinePrecision]]]]

Derivation

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

Alternative 20: 21.0% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.5 \cdot 10^{+144}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{+58}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -2.5e+144) z (if (<= z 4.7e+58) x z)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.5e+144) {
		tmp = z;
	} else if (z <= 4.7e+58) {
		tmp = x;
	} else {
		tmp = z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-2.5d+144)) then
        tmp = z
    else if (z <= 4.7d+58) then
        tmp = x
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.5e+144) {
		tmp = z;
	} else if (z <= 4.7e+58) {
		tmp = x;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.5e+144:
		tmp = z
	elif z <= 4.7e+58:
		tmp = x
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.5e+144)
		tmp = z;
	elseif (z <= 4.7e+58)
		tmp = x;
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.5e+144)
		tmp = z;
	elseif (z <= 4.7e+58)
		tmp = x;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -2.5e+144], z, If[LessEqual[z, 4.7e+58], x, z]]

Derivation

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

Alternative 21: 15.3% accurate, 21.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Reproduce

herbie shell --seed 2023342 
(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)))