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

Percentage Accurate: 95.0% → 97.3%

Time: 13.3s

Alternatives: 21

Speedup: 0.5×

• 36.1% 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: 95.0% 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: 97.3% accurate, 0.1× speedup

Math

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

FPCore

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

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, (a * (t + -1.0)))));
}

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(a * Float64(t + -1.0)))))
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[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 98.3% accurate, 0.5× speedup

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1
         (+ (+ (+ x (* z (- 1.0 y))) (* a (- 1.0 t))) (* b (- (+ y t) 2.0)))))
   (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))) + (b * ((y + t) - 2.0));
	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))) + (b * ((y + t) - 2.0));
	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))) + (b * ((y + t) - 2.0))
	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(b * Float64(Float64(y + t) - 2.0)))
	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))) + (b * ((y + t) - 2.0));
	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[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $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 3: 59.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := x + a \cdot \left(1 - t\right)\\ t_3 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -1.22 \cdot 10^{+44}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq -4500000000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -5.5 \cdot 10^{-39}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;b \leq -4 \cdot 10^{-59}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 3.55 \cdot 10^{-217}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 2 \cdot 10^{-111}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 3.75 \cdot 10^{+53}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (- 1.0 y)))
        (t_2 (+ x (* a (- 1.0 t))))
        (t_3 (* b (- (+ y t) 2.0))))
   (if (<= b -1.22e+44)
     t_3
     (if (<= b -4500000000.0)
       t_2
       (if (<= b -5.5e-39)
         (* y (- b z))
         (if (<= b -4e-59)
           t_1
           (if (<= b 3.55e-217)
             t_2
             (if (<= b 2e-111) t_1 (if (<= b 3.75e+53) t_2 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 + (a * (1.0 - t));
	double t_3 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -1.22e+44) {
		tmp = t_3;
	} else if (b <= -4500000000.0) {
		tmp = t_2;
	} else if (b <= -5.5e-39) {
		tmp = y * (b - z);
	} else if (b <= -4e-59) {
		tmp = t_1;
	} else if (b <= 3.55e-217) {
		tmp = t_2;
	} else if (b <= 2e-111) {
		tmp = t_1;
	} else if (b <= 3.75e+53) {
		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 = z * (1.0d0 - y)
    t_2 = x + (a * (1.0d0 - t))
    t_3 = b * ((y + t) - 2.0d0)
    if (b <= (-1.22d+44)) then
        tmp = t_3
    else if (b <= (-4500000000.0d0)) then
        tmp = t_2
    else if (b <= (-5.5d-39)) then
        tmp = y * (b - z)
    else if (b <= (-4d-59)) then
        tmp = t_1
    else if (b <= 3.55d-217) then
        tmp = t_2
    else if (b <= 2d-111) then
        tmp = t_1
    else if (b <= 3.75d+53) 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 = z * (1.0 - y);
	double t_2 = x + (a * (1.0 - t));
	double t_3 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -1.22e+44) {
		tmp = t_3;
	} else if (b <= -4500000000.0) {
		tmp = t_2;
	} else if (b <= -5.5e-39) {
		tmp = y * (b - z);
	} else if (b <= -4e-59) {
		tmp = t_1;
	} else if (b <= 3.55e-217) {
		tmp = t_2;
	} else if (b <= 2e-111) {
		tmp = t_1;
	} else if (b <= 3.75e+53) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (1.0 - y)
	t_2 = x + (a * (1.0 - t))
	t_3 = b * ((y + t) - 2.0)
	tmp = 0
	if b <= -1.22e+44:
		tmp = t_3
	elif b <= -4500000000.0:
		tmp = t_2
	elif b <= -5.5e-39:
		tmp = y * (b - z)
	elif b <= -4e-59:
		tmp = t_1
	elif b <= 3.55e-217:
		tmp = t_2
	elif b <= 2e-111:
		tmp = t_1
	elif b <= 3.75e+53:
		tmp = t_2
	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 + Float64(a * Float64(1.0 - t)))
	t_3 = Float64(b * Float64(Float64(y + t) - 2.0))
	tmp = 0.0
	if (b <= -1.22e+44)
		tmp = t_3;
	elseif (b <= -4500000000.0)
		tmp = t_2;
	elseif (b <= -5.5e-39)
		tmp = Float64(y * Float64(b - z));
	elseif (b <= -4e-59)
		tmp = t_1;
	elseif (b <= 3.55e-217)
		tmp = t_2;
	elseif (b <= 2e-111)
		tmp = t_1;
	elseif (b <= 3.75e+53)
		tmp = t_2;
	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 + (a * (1.0 - t));
	t_3 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -1.22e+44)
		tmp = t_3;
	elseif (b <= -4500000000.0)
		tmp = t_2;
	elseif (b <= -5.5e-39)
		tmp = y * (b - z);
	elseif (b <= -4e-59)
		tmp = t_1;
	elseif (b <= 3.55e-217)
		tmp = t_2;
	elseif (b <= 2e-111)
		tmp = t_1;
	elseif (b <= 3.75e+53)
		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[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.22e+44], t$95$3, If[LessEqual[b, -4500000000.0], t$95$2, If[LessEqual[b, -5.5e-39], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -4e-59], t$95$1, If[LessEqual[b, 3.55e-217], t$95$2, If[LessEqual[b, 2e-111], t$95$1, If[LessEqual[b, 3.75e+53], t$95$2, t$95$3]]]]]]]]]]

Derivation

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

Alternative 4: 59.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := b \cdot \left(\left(y + t\right) - 2\right)\\ t_3 := x + a \cdot \left(1 - t\right)\\ \mathbf{if}\;b \leq -1.05 \cdot 10^{+29}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 1.12 \cdot 10^{-111}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 6.2 \cdot 10^{+58}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 2.9 \cdot 10^{+119}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 1.25 \cdot 10^{+129}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 4.55 \cdot 10^{+173}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

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 (* a (- 1.0 t)))))
   (if (<= b -1.05e+29)
     t_2
     (if (<= b 1.12e-111)
       t_1
       (if (<= b 6.2e+58)
         t_3
         (if (<= b 2.9e+119)
           t_1
           (if (<= b 1.25e+129)
             t_3
             (if (<= b 4.55e+173) (* y (- b z)) 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 + (a * (1.0 - t));
	double tmp;
	if (b <= -1.05e+29) {
		tmp = t_2;
	} else if (b <= 1.12e-111) {
		tmp = t_1;
	} else if (b <= 6.2e+58) {
		tmp = t_3;
	} else if (b <= 2.9e+119) {
		tmp = t_1;
	} else if (b <= 1.25e+129) {
		tmp = t_3;
	} else if (b <= 4.55e+173) {
		tmp = y * (b - z);
	} 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 + (a * (1.0d0 - t))
    if (b <= (-1.05d+29)) then
        tmp = t_2
    else if (b <= 1.12d-111) then
        tmp = t_1
    else if (b <= 6.2d+58) then
        tmp = t_3
    else if (b <= 2.9d+119) then
        tmp = t_1
    else if (b <= 1.25d+129) then
        tmp = t_3
    else if (b <= 4.55d+173) then
        tmp = y * (b - z)
    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 + (a * (1.0 - t));
	double tmp;
	if (b <= -1.05e+29) {
		tmp = t_2;
	} else if (b <= 1.12e-111) {
		tmp = t_1;
	} else if (b <= 6.2e+58) {
		tmp = t_3;
	} else if (b <= 2.9e+119) {
		tmp = t_1;
	} else if (b <= 1.25e+129) {
		tmp = t_3;
	} else if (b <= 4.55e+173) {
		tmp = y * (b - z);
	} 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 + (a * (1.0 - t))
	tmp = 0
	if b <= -1.05e+29:
		tmp = t_2
	elif b <= 1.12e-111:
		tmp = t_1
	elif b <= 6.2e+58:
		tmp = t_3
	elif b <= 2.9e+119:
		tmp = t_1
	elif b <= 1.25e+129:
		tmp = t_3
	elif b <= 4.55e+173:
		tmp = y * (b - z)
	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(a * Float64(1.0 - t)))
	tmp = 0.0
	if (b <= -1.05e+29)
		tmp = t_2;
	elseif (b <= 1.12e-111)
		tmp = t_1;
	elseif (b <= 6.2e+58)
		tmp = t_3;
	elseif (b <= 2.9e+119)
		tmp = t_1;
	elseif (b <= 1.25e+129)
		tmp = t_3;
	elseif (b <= 4.55e+173)
		tmp = Float64(y * Float64(b - z));
	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 + (a * (1.0 - t));
	tmp = 0.0;
	if (b <= -1.05e+29)
		tmp = t_2;
	elseif (b <= 1.12e-111)
		tmp = t_1;
	elseif (b <= 6.2e+58)
		tmp = t_3;
	elseif (b <= 2.9e+119)
		tmp = t_1;
	elseif (b <= 1.25e+129)
		tmp = t_3;
	elseif (b <= 4.55e+173)
		tmp = y * (b - z);
	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[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.05e+29], t$95$2, If[LessEqual[b, 1.12e-111], t$95$1, If[LessEqual[b, 6.2e+58], t$95$3, If[LessEqual[b, 2.9e+119], t$95$1, If[LessEqual[b, 1.25e+129], t$95$3, If[LessEqual[b, 4.55e+173], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

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

Alternative 5: 59.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := b \cdot \left(\left(y + t\right) - 2\right)\\ t_3 := x + a \cdot \left(1 - t\right)\\ \mathbf{if}\;b \leq -1.9 \cdot 10^{+29}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 1.1 \cdot 10^{-111}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 3.55 \cdot 10^{+59}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 4.5 \cdot 10^{+119}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 1.05 \cdot 10^{+129}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 4.55 \cdot 10^{+173}:\\ \;\;\;\;y \cdot b - t \cdot a\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

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 (* a (- 1.0 t)))))
   (if (<= b -1.9e+29)
     t_2
     (if (<= b 1.1e-111)
       t_1
       (if (<= b 3.55e+59)
         t_3
         (if (<= b 4.5e+119)
           t_1
           (if (<= b 1.05e+129)
             t_3
             (if (<= b 4.55e+173) (- (* y b) (* t a)) 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 + (a * (1.0 - t));
	double tmp;
	if (b <= -1.9e+29) {
		tmp = t_2;
	} else if (b <= 1.1e-111) {
		tmp = t_1;
	} else if (b <= 3.55e+59) {
		tmp = t_3;
	} else if (b <= 4.5e+119) {
		tmp = t_1;
	} else if (b <= 1.05e+129) {
		tmp = t_3;
	} else if (b <= 4.55e+173) {
		tmp = (y * b) - (t * 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) :: t_3
    real(8) :: tmp
    t_1 = x + (z * (1.0d0 - y))
    t_2 = b * ((y + t) - 2.0d0)
    t_3 = x + (a * (1.0d0 - t))
    if (b <= (-1.9d+29)) then
        tmp = t_2
    else if (b <= 1.1d-111) then
        tmp = t_1
    else if (b <= 3.55d+59) then
        tmp = t_3
    else if (b <= 4.5d+119) then
        tmp = t_1
    else if (b <= 1.05d+129) then
        tmp = t_3
    else if (b <= 4.55d+173) then
        tmp = (y * b) - (t * 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 = x + (z * (1.0 - y));
	double t_2 = b * ((y + t) - 2.0);
	double t_3 = x + (a * (1.0 - t));
	double tmp;
	if (b <= -1.9e+29) {
		tmp = t_2;
	} else if (b <= 1.1e-111) {
		tmp = t_1;
	} else if (b <= 3.55e+59) {
		tmp = t_3;
	} else if (b <= 4.5e+119) {
		tmp = t_1;
	} else if (b <= 1.05e+129) {
		tmp = t_3;
	} else if (b <= 4.55e+173) {
		tmp = (y * b) - (t * a);
	} 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 + (a * (1.0 - t))
	tmp = 0
	if b <= -1.9e+29:
		tmp = t_2
	elif b <= 1.1e-111:
		tmp = t_1
	elif b <= 3.55e+59:
		tmp = t_3
	elif b <= 4.5e+119:
		tmp = t_1
	elif b <= 1.05e+129:
		tmp = t_3
	elif b <= 4.55e+173:
		tmp = (y * b) - (t * a)
	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(a * Float64(1.0 - t)))
	tmp = 0.0
	if (b <= -1.9e+29)
		tmp = t_2;
	elseif (b <= 1.1e-111)
		tmp = t_1;
	elseif (b <= 3.55e+59)
		tmp = t_3;
	elseif (b <= 4.5e+119)
		tmp = t_1;
	elseif (b <= 1.05e+129)
		tmp = t_3;
	elseif (b <= 4.55e+173)
		tmp = Float64(Float64(y * b) - Float64(t * a));
	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 + (a * (1.0 - t));
	tmp = 0.0;
	if (b <= -1.9e+29)
		tmp = t_2;
	elseif (b <= 1.1e-111)
		tmp = t_1;
	elseif (b <= 3.55e+59)
		tmp = t_3;
	elseif (b <= 4.5e+119)
		tmp = t_1;
	elseif (b <= 1.05e+129)
		tmp = t_3;
	elseif (b <= 4.55e+173)
		tmp = (y * b) - (t * a);
	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[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.9e+29], t$95$2, If[LessEqual[b, 1.1e-111], t$95$1, If[LessEqual[b, 3.55e+59], t$95$3, If[LessEqual[b, 4.5e+119], t$95$1, If[LessEqual[b, 1.05e+129], t$95$3, If[LessEqual[b, 4.55e+173], N[(N[(y * b), $MachinePrecision] - N[(t * a), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

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

Alternative 6: 85.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -1.12 \cdot 10^{+75} \lor \neg \left(b \leq 1.36 \cdot 10^{-73}\right):\\ \;\;\;\;\left(x + b \cdot \left(\left(y + t\right) - 2\right)\right) + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -1.12e+75) (not (<= b 1.36e-73)))
   (+ (+ x (* b (- (+ y t) 2.0))) (* a (- 1.0 t)))
   (- x (+ (* a (+ t -1.0)) (* z (+ y -1.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -1.12e+75) || !(b <= 1.36e-73)) {
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	} else {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	}
	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 ((b <= (-1.12d+75)) .or. (.not. (b <= 1.36d-73))) then
        tmp = (x + (b * ((y + t) - 2.0d0))) + (a * (1.0d0 - t))
    else
        tmp = x - ((a * (t + (-1.0d0))) + (z * (y + (-1.0d0))))
    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 ((b <= -1.12e+75) || !(b <= 1.36e-73)) {
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	} else {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -1.12e+75) or not (b <= 1.36e-73):
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t))
	else:
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -1.12e+75) || !(b <= 1.36e-73))
		tmp = Float64(Float64(x + Float64(b * Float64(Float64(y + t) - 2.0))) + Float64(a * Float64(1.0 - t)));
	else
		tmp = Float64(x - Float64(Float64(a * Float64(t + -1.0)) + Float64(z * Float64(y + -1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -1.12e+75) || ~((b <= 1.36e-73)))
		tmp = (x + (b * ((y + t) - 2.0))) + (a * (1.0 - t));
	else
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -1.12e+75], N[Not[LessEqual[b, 1.36e-73]], $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], N[(x - N[(N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision] + N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 35.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.15 \cdot 10^{+266}:\\ \;\;\;\;t \cdot \left(-a\right)\\ \mathbf{elif}\;t \leq -4.5 \cdot 10^{+94}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;t \leq 1.9 \cdot 10^{-307}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 4.5 \cdot 10^{-245}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq 8.8 \cdot 10^{-63}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 1.36 \cdot 10^{+14}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;t \leq 3.5 \cdot 10^{+38}:\\ \;\;\;\;x + a\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2.15e+266)
   (* t (- a))
   (if (<= t -4.5e+94)
     (* t b)
     (if (<= t 1.9e-307)
       (+ x a)
       (if (<= t 4.5e-245)
         (* y b)
         (if (<= t 8.8e-63)
           (+ x a)
           (if (<= t 1.36e+14)
             (* y b)
             (if (<= t 3.5e+38) (+ x a) (* t b)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.15e+266) {
		tmp = t * -a;
	} else if (t <= -4.5e+94) {
		tmp = t * b;
	} else if (t <= 1.9e-307) {
		tmp = x + a;
	} else if (t <= 4.5e-245) {
		tmp = y * b;
	} else if (t <= 8.8e-63) {
		tmp = x + a;
	} else if (t <= 1.36e+14) {
		tmp = y * b;
	} else if (t <= 3.5e+38) {
		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 (t <= (-2.15d+266)) then
        tmp = t * -a
    else if (t <= (-4.5d+94)) then
        tmp = t * b
    else if (t <= 1.9d-307) then
        tmp = x + a
    else if (t <= 4.5d-245) then
        tmp = y * b
    else if (t <= 8.8d-63) then
        tmp = x + a
    else if (t <= 1.36d+14) then
        tmp = y * b
    else if (t <= 3.5d+38) 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 (t <= -2.15e+266) {
		tmp = t * -a;
	} else if (t <= -4.5e+94) {
		tmp = t * b;
	} else if (t <= 1.9e-307) {
		tmp = x + a;
	} else if (t <= 4.5e-245) {
		tmp = y * b;
	} else if (t <= 8.8e-63) {
		tmp = x + a;
	} else if (t <= 1.36e+14) {
		tmp = y * b;
	} else if (t <= 3.5e+38) {
		tmp = x + a;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2.15e+266:
		tmp = t * -a
	elif t <= -4.5e+94:
		tmp = t * b
	elif t <= 1.9e-307:
		tmp = x + a
	elif t <= 4.5e-245:
		tmp = y * b
	elif t <= 8.8e-63:
		tmp = x + a
	elif t <= 1.36e+14:
		tmp = y * b
	elif t <= 3.5e+38:
		tmp = x + a
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2.15e+266)
		tmp = Float64(t * Float64(-a));
	elseif (t <= -4.5e+94)
		tmp = Float64(t * b);
	elseif (t <= 1.9e-307)
		tmp = Float64(x + a);
	elseif (t <= 4.5e-245)
		tmp = Float64(y * b);
	elseif (t <= 8.8e-63)
		tmp = Float64(x + a);
	elseif (t <= 1.36e+14)
		tmp = Float64(y * b);
	elseif (t <= 3.5e+38)
		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 (t <= -2.15e+266)
		tmp = t * -a;
	elseif (t <= -4.5e+94)
		tmp = t * b;
	elseif (t <= 1.9e-307)
		tmp = x + a;
	elseif (t <= 4.5e-245)
		tmp = y * b;
	elseif (t <= 8.8e-63)
		tmp = x + a;
	elseif (t <= 1.36e+14)
		tmp = y * b;
	elseif (t <= 3.5e+38)
		tmp = x + a;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2.15e+266], N[(t * (-a)), $MachinePrecision], If[LessEqual[t, -4.5e+94], N[(t * b), $MachinePrecision], If[LessEqual[t, 1.9e-307], N[(x + a), $MachinePrecision], If[LessEqual[t, 4.5e-245], N[(y * b), $MachinePrecision], If[LessEqual[t, 8.8e-63], N[(x + a), $MachinePrecision], If[LessEqual[t, 1.36e+14], N[(y * b), $MachinePrecision], If[LessEqual[t, 3.5e+38], N[(x + a), $MachinePrecision], N[(t * b), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 8: 50.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -5.1 \cdot 10^{+108}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -8.6 \cdot 10^{-45}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1 \cdot 10^{-284}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 2.4 \cdot 10^{-206}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq 6.5 \cdot 10^{-152}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{+35}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* y (- b z))) (t_2 (* t (- b a))))
   (if (<= t -5.1e+108)
     t_2
     (if (<= t -8.6e-45)
       t_1
       (if (<= t -1e-284)
         (+ x a)
         (if (<= t 2.4e-206)
           (* z (- 1.0 y))
           (if (<= t 6.5e-152) (+ x a) (if (<= t 2.8e+35) 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 <= -5.1e+108) {
		tmp = t_2;
	} else if (t <= -8.6e-45) {
		tmp = t_1;
	} else if (t <= -1e-284) {
		tmp = x + a;
	} else if (t <= 2.4e-206) {
		tmp = z * (1.0 - y);
	} else if (t <= 6.5e-152) {
		tmp = x + a;
	} else if (t <= 2.8e+35) {
		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 <= (-5.1d+108)) then
        tmp = t_2
    else if (t <= (-8.6d-45)) then
        tmp = t_1
    else if (t <= (-1d-284)) then
        tmp = x + a
    else if (t <= 2.4d-206) then
        tmp = z * (1.0d0 - y)
    else if (t <= 6.5d-152) then
        tmp = x + a
    else if (t <= 2.8d+35) 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 <= -5.1e+108) {
		tmp = t_2;
	} else if (t <= -8.6e-45) {
		tmp = t_1;
	} else if (t <= -1e-284) {
		tmp = x + a;
	} else if (t <= 2.4e-206) {
		tmp = z * (1.0 - y);
	} else if (t <= 6.5e-152) {
		tmp = x + a;
	} else if (t <= 2.8e+35) {
		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 <= -5.1e+108:
		tmp = t_2
	elif t <= -8.6e-45:
		tmp = t_1
	elif t <= -1e-284:
		tmp = x + a
	elif t <= 2.4e-206:
		tmp = z * (1.0 - y)
	elif t <= 6.5e-152:
		tmp = x + a
	elif t <= 2.8e+35:
		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 <= -5.1e+108)
		tmp = t_2;
	elseif (t <= -8.6e-45)
		tmp = t_1;
	elseif (t <= -1e-284)
		tmp = Float64(x + a);
	elseif (t <= 2.4e-206)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (t <= 6.5e-152)
		tmp = Float64(x + a);
	elseif (t <= 2.8e+35)
		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 <= -5.1e+108)
		tmp = t_2;
	elseif (t <= -8.6e-45)
		tmp = t_1;
	elseif (t <= -1e-284)
		tmp = x + a;
	elseif (t <= 2.4e-206)
		tmp = z * (1.0 - y);
	elseif (t <= 6.5e-152)
		tmp = x + a;
	elseif (t <= 2.8e+35)
		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, -5.1e+108], t$95$2, If[LessEqual[t, -8.6e-45], t$95$1, If[LessEqual[t, -1e-284], N[(x + a), $MachinePrecision], If[LessEqual[t, 2.4e-206], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.5e-152], N[(x + a), $MachinePrecision], If[LessEqual[t, 2.8e+35], t$95$1, t$95$2]]]]]]]]

Derivation

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

Alternative 9: 50.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - y \cdot z\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -5.1 \cdot 10^{+108}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -8 \cdot 10^{-44}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -8.5 \cdot 10^{-286}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-209}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;t \leq 2 \cdot 10^{-60}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{+35}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- x (* y z))) (t_2 (* t (- b a))))
   (if (<= t -5.1e+108)
     t_2
     (if (<= t -8e-44)
       t_1
       (if (<= t -8.5e-286)
         (+ x a)
         (if (<= t 1.25e-209)
           (* z (- 1.0 y))
           (if (<= t 2e-60) t_1 (if (<= t 3.2e+35) (* y (- b z)) t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x - (y * z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -5.1e+108) {
		tmp = t_2;
	} else if (t <= -8e-44) {
		tmp = t_1;
	} else if (t <= -8.5e-286) {
		tmp = x + a;
	} else if (t <= 1.25e-209) {
		tmp = z * (1.0 - y);
	} else if (t <= 2e-60) {
		tmp = t_1;
	} else if (t <= 3.2e+35) {
		tmp = y * (b - z);
	} 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 - (y * z)
    t_2 = t * (b - a)
    if (t <= (-5.1d+108)) then
        tmp = t_2
    else if (t <= (-8d-44)) then
        tmp = t_1
    else if (t <= (-8.5d-286)) then
        tmp = x + a
    else if (t <= 1.25d-209) then
        tmp = z * (1.0d0 - y)
    else if (t <= 2d-60) then
        tmp = t_1
    else if (t <= 3.2d+35) then
        tmp = y * (b - z)
    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 - (y * z);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -5.1e+108) {
		tmp = t_2;
	} else if (t <= -8e-44) {
		tmp = t_1;
	} else if (t <= -8.5e-286) {
		tmp = x + a;
	} else if (t <= 1.25e-209) {
		tmp = z * (1.0 - y);
	} else if (t <= 2e-60) {
		tmp = t_1;
	} else if (t <= 3.2e+35) {
		tmp = y * (b - z);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x - (y * z)
	t_2 = t * (b - a)
	tmp = 0
	if t <= -5.1e+108:
		tmp = t_2
	elif t <= -8e-44:
		tmp = t_1
	elif t <= -8.5e-286:
		tmp = x + a
	elif t <= 1.25e-209:
		tmp = z * (1.0 - y)
	elif t <= 2e-60:
		tmp = t_1
	elif t <= 3.2e+35:
		tmp = y * (b - z)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x - Float64(y * z))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -5.1e+108)
		tmp = t_2;
	elseif (t <= -8e-44)
		tmp = t_1;
	elseif (t <= -8.5e-286)
		tmp = Float64(x + a);
	elseif (t <= 1.25e-209)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (t <= 2e-60)
		tmp = t_1;
	elseif (t <= 3.2e+35)
		tmp = Float64(y * Float64(b - z));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x - (y * z);
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -5.1e+108)
		tmp = t_2;
	elseif (t <= -8e-44)
		tmp = t_1;
	elseif (t <= -8.5e-286)
		tmp = x + a;
	elseif (t <= 1.25e-209)
		tmp = z * (1.0 - y);
	elseif (t <= 2e-60)
		tmp = t_1;
	elseif (t <= 3.2e+35)
		tmp = y * (b - z);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.1e+108], t$95$2, If[LessEqual[t, -8e-44], t$95$1, If[LessEqual[t, -8.5e-286], N[(x + a), $MachinePrecision], If[LessEqual[t, 1.25e-209], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2e-60], t$95$1, If[LessEqual[t, 3.2e+35], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]

Derivation

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

Alternative 10: 69.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(a + z \cdot \left(1 - y\right)\right)\\ t_2 := b \cdot \left(\left(y + t\right) - 2\right)\\ t_3 := x + t_2\\ \mathbf{if}\;b \leq -1.06 \cdot 10^{+58}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 3.6 \cdot 10^{-64}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 3.3 \cdot 10^{-34}:\\ \;\;\;\;t_2 - t \cdot a\\ \mathbf{elif}\;b \leq 1.2 \cdot 10^{+129}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (+ a (* z (- 1.0 y)))))
        (t_2 (* b (- (+ y t) 2.0)))
        (t_3 (+ x t_2)))
   (if (<= b -1.06e+58)
     t_3
     (if (<= b 3.6e-64)
       t_1
       (if (<= b 3.3e-34) (- t_2 (* t a)) (if (<= b 1.2e+129) t_1 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (a + (z * (1.0 - y)));
	double t_2 = b * ((y + t) - 2.0);
	double t_3 = x + t_2;
	double tmp;
	if (b <= -1.06e+58) {
		tmp = t_3;
	} else if (b <= 3.6e-64) {
		tmp = t_1;
	} else if (b <= 3.3e-34) {
		tmp = t_2 - (t * a);
	} else if (b <= 1.2e+129) {
		tmp = t_1;
	} 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 + (a + (z * (1.0d0 - y)))
    t_2 = b * ((y + t) - 2.0d0)
    t_3 = x + t_2
    if (b <= (-1.06d+58)) then
        tmp = t_3
    else if (b <= 3.6d-64) then
        tmp = t_1
    else if (b <= 3.3d-34) then
        tmp = t_2 - (t * a)
    else if (b <= 1.2d+129) then
        tmp = t_1
    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 + (a + (z * (1.0 - y)));
	double t_2 = b * ((y + t) - 2.0);
	double t_3 = x + t_2;
	double tmp;
	if (b <= -1.06e+58) {
		tmp = t_3;
	} else if (b <= 3.6e-64) {
		tmp = t_1;
	} else if (b <= 3.3e-34) {
		tmp = t_2 - (t * a);
	} else if (b <= 1.2e+129) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (a + (z * (1.0 - y)))
	t_2 = b * ((y + t) - 2.0)
	t_3 = x + t_2
	tmp = 0
	if b <= -1.06e+58:
		tmp = t_3
	elif b <= 3.6e-64:
		tmp = t_1
	elif b <= 3.3e-34:
		tmp = t_2 - (t * a)
	elif b <= 1.2e+129:
		tmp = t_1
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(a + Float64(z * Float64(1.0 - y))))
	t_2 = Float64(b * Float64(Float64(y + t) - 2.0))
	t_3 = Float64(x + t_2)
	tmp = 0.0
	if (b <= -1.06e+58)
		tmp = t_3;
	elseif (b <= 3.6e-64)
		tmp = t_1;
	elseif (b <= 3.3e-34)
		tmp = Float64(t_2 - Float64(t * a));
	elseif (b <= 1.2e+129)
		tmp = t_1;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (a + (z * (1.0 - y)));
	t_2 = b * ((y + t) - 2.0);
	t_3 = x + t_2;
	tmp = 0.0;
	if (b <= -1.06e+58)
		tmp = t_3;
	elseif (b <= 3.6e-64)
		tmp = t_1;
	elseif (b <= 3.3e-34)
		tmp = t_2 - (t * a);
	elseif (b <= 1.2e+129)
		tmp = t_1;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(a + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + t$95$2), $MachinePrecision]}, If[LessEqual[b, -1.06e+58], t$95$3, If[LessEqual[b, 3.6e-64], t$95$1, If[LessEqual[b, 3.3e-34], N[(t$95$2 - N[(t * a), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.2e+129], t$95$1, t$95$3]]]]]]]

Derivation

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

Alternative 11: 83.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -2.1 \cdot 10^{+75} \lor \neg \left(b \leq 2.45 \cdot 10^{+129}\right):\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a \cdot \left(t + -1\right) + z \cdot \left(y + -1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -2.1e+75) (not (<= b 2.45e+129)))
   (+ x (* b (- (+ y t) 2.0)))
   (- x (+ (* a (+ t -1.0)) (* z (+ y -1.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -2.1e+75) || !(b <= 2.45e+129)) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	}
	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 ((b <= (-2.1d+75)) .or. (.not. (b <= 2.45d+129))) then
        tmp = x + (b * ((y + t) - 2.0d0))
    else
        tmp = x - ((a * (t + (-1.0d0))) + (z * (y + (-1.0d0))))
    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 ((b <= -2.1e+75) || !(b <= 2.45e+129)) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -2.1e+75) or not (b <= 2.45e+129):
		tmp = x + (b * ((y + t) - 2.0))
	else:
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -2.1e+75) || !(b <= 2.45e+129))
		tmp = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)));
	else
		tmp = Float64(x - Float64(Float64(a * Float64(t + -1.0)) + Float64(z * Float64(y + -1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -2.1e+75) || ~((b <= 2.45e+129)))
		tmp = x + (b * ((y + t) - 2.0));
	else
		tmp = x - ((a * (t + -1.0)) + (z * (y + -1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -2.1e+75], N[Not[LessEqual[b, 2.45e+129]], $MachinePrecision]], N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a * N[(t + -1.0), $MachinePrecision]), $MachinePrecision] + N[(z * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 48.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(y - 2\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -6.5 \cdot 10^{+91}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -3.7 \cdot 10^{-260}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 2.4 \cdot 10^{-208}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.1 \cdot 10^{-134}:\\ \;\;\;\;x + a\\ \mathbf{elif}\;t \leq 6000000000:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- y 2.0))) (t_2 (* t (- b a))))
   (if (<= t -6.5e+91)
     t_2
     (if (<= t -3.7e-260)
       (+ x a)
       (if (<= t 2.4e-208)
         t_1
         (if (<= t 1.1e-134) (+ x a) (if (<= t 6000000000.0) t_1 t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * (y - 2.0);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -6.5e+91) {
		tmp = t_2;
	} else if (t <= -3.7e-260) {
		tmp = x + a;
	} else if (t <= 2.4e-208) {
		tmp = t_1;
	} else if (t <= 1.1e-134) {
		tmp = x + a;
	} else if (t <= 6000000000.0) {
		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 = b * (y - 2.0d0)
    t_2 = t * (b - a)
    if (t <= (-6.5d+91)) then
        tmp = t_2
    else if (t <= (-3.7d-260)) then
        tmp = x + a
    else if (t <= 2.4d-208) then
        tmp = t_1
    else if (t <= 1.1d-134) then
        tmp = x + a
    else if (t <= 6000000000.0d0) 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 = b * (y - 2.0);
	double t_2 = t * (b - a);
	double tmp;
	if (t <= -6.5e+91) {
		tmp = t_2;
	} else if (t <= -3.7e-260) {
		tmp = x + a;
	} else if (t <= 2.4e-208) {
		tmp = t_1;
	} else if (t <= 1.1e-134) {
		tmp = x + a;
	} else if (t <= 6000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * (y - 2.0)
	t_2 = t * (b - a)
	tmp = 0
	if t <= -6.5e+91:
		tmp = t_2
	elif t <= -3.7e-260:
		tmp = x + a
	elif t <= 2.4e-208:
		tmp = t_1
	elif t <= 1.1e-134:
		tmp = x + a
	elif t <= 6000000000.0:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(y - 2.0))
	t_2 = Float64(t * Float64(b - a))
	tmp = 0.0
	if (t <= -6.5e+91)
		tmp = t_2;
	elseif (t <= -3.7e-260)
		tmp = Float64(x + a);
	elseif (t <= 2.4e-208)
		tmp = t_1;
	elseif (t <= 1.1e-134)
		tmp = Float64(x + a);
	elseif (t <= 6000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * (y - 2.0);
	t_2 = t * (b - a);
	tmp = 0.0;
	if (t <= -6.5e+91)
		tmp = t_2;
	elseif (t <= -3.7e-260)
		tmp = x + a;
	elseif (t <= 2.4e-208)
		tmp = t_1;
	elseif (t <= 1.1e-134)
		tmp = x + a;
	elseif (t <= 6000000000.0)
		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[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -6.5e+91], t$95$2, If[LessEqual[t, -3.7e-260], N[(x + a), $MachinePrecision], If[LessEqual[t, 2.4e-208], t$95$1, If[LessEqual[t, 1.1e-134], N[(x + a), $MachinePrecision], If[LessEqual[t, 6000000000.0], t$95$1, t$95$2]]]]]]]

Derivation

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

Alternative 13: 64.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -1.42 \cdot 10^{+29}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 2.75 \cdot 10^{-111}:\\ \;\;\;\;x + z \cdot \left(1 - y\right)\\ \mathbf{elif}\;b \leq 9 \cdot 10^{+46}:\\ \;\;\;\;x + a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* b (- (+ y t) 2.0)))))
   (if (<= b -1.42e+29)
     t_1
     (if (<= b 2.75e-111)
       (+ x (* z (- 1.0 y)))
       (if (<= b 9e+46) (+ x (* a (- 1.0 t))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (b * ((y + t) - 2.0));
	double tmp;
	if (b <= -1.42e+29) {
		tmp = t_1;
	} else if (b <= 2.75e-111) {
		tmp = x + (z * (1.0 - y));
	} else if (b <= 9e+46) {
		tmp = x + (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 = x + (b * ((y + t) - 2.0d0))
    if (b <= (-1.42d+29)) then
        tmp = t_1
    else if (b <= 2.75d-111) then
        tmp = x + (z * (1.0d0 - y))
    else if (b <= 9d+46) then
        tmp = x + (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 = x + (b * ((y + t) - 2.0));
	double tmp;
	if (b <= -1.42e+29) {
		tmp = t_1;
	} else if (b <= 2.75e-111) {
		tmp = x + (z * (1.0 - y));
	} else if (b <= 9e+46) {
		tmp = x + (a * (1.0 - t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (b * ((y + t) - 2.0))
	tmp = 0
	if b <= -1.42e+29:
		tmp = t_1
	elif b <= 2.75e-111:
		tmp = x + (z * (1.0 - y))
	elif b <= 9e+46:
		tmp = x + (a * (1.0 - t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)))
	tmp = 0.0
	if (b <= -1.42e+29)
		tmp = t_1;
	elseif (b <= 2.75e-111)
		tmp = Float64(x + Float64(z * Float64(1.0 - y)));
	elseif (b <= 9e+46)
		tmp = Float64(x + 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 = x + (b * ((y + t) - 2.0));
	tmp = 0.0;
	if (b <= -1.42e+29)
		tmp = t_1;
	elseif (b <= 2.75e-111)
		tmp = x + (z * (1.0 - y));
	elseif (b <= 9e+46)
		tmp = x + (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[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.42e+29], t$95$1, If[LessEqual[b, 2.75e-111], N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 9e+46], N[(x + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 14: 37.9% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(y - 2\right)\\ t_2 := a \cdot \left(1 - t\right)\\ \mathbf{if}\;a \leq -3.15 \cdot 10^{+103}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -11.5:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 3.5 \cdot 10^{-292}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;a \leq 3.9 \cdot 10^{+37}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- y 2.0))) (t_2 (* a (- 1.0 t))))
   (if (<= a -3.15e+103)
     t_2
     (if (<= a -11.5)
       t_1
       (if (<= a 3.5e-292) (* t b) (if (<= a 3.9e+37) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * (y - 2.0);
	double t_2 = a * (1.0 - t);
	double tmp;
	if (a <= -3.15e+103) {
		tmp = t_2;
	} else if (a <= -11.5) {
		tmp = t_1;
	} else if (a <= 3.5e-292) {
		tmp = t * b;
	} else if (a <= 3.9e+37) {
		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 = b * (y - 2.0d0)
    t_2 = a * (1.0d0 - t)
    if (a <= (-3.15d+103)) then
        tmp = t_2
    else if (a <= (-11.5d0)) then
        tmp = t_1
    else if (a <= 3.5d-292) then
        tmp = t * b
    else if (a <= 3.9d+37) 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 = b * (y - 2.0);
	double t_2 = a * (1.0 - t);
	double tmp;
	if (a <= -3.15e+103) {
		tmp = t_2;
	} else if (a <= -11.5) {
		tmp = t_1;
	} else if (a <= 3.5e-292) {
		tmp = t * b;
	} else if (a <= 3.9e+37) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * (y - 2.0)
	t_2 = a * (1.0 - t)
	tmp = 0
	if a <= -3.15e+103:
		tmp = t_2
	elif a <= -11.5:
		tmp = t_1
	elif a <= 3.5e-292:
		tmp = t * b
	elif a <= 3.9e+37:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(y - 2.0))
	t_2 = Float64(a * Float64(1.0 - t))
	tmp = 0.0
	if (a <= -3.15e+103)
		tmp = t_2;
	elseif (a <= -11.5)
		tmp = t_1;
	elseif (a <= 3.5e-292)
		tmp = Float64(t * b);
	elseif (a <= 3.9e+37)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * (y - 2.0);
	t_2 = a * (1.0 - t);
	tmp = 0.0;
	if (a <= -3.15e+103)
		tmp = t_2;
	elseif (a <= -11.5)
		tmp = t_1;
	elseif (a <= 3.5e-292)
		tmp = t * b;
	elseif (a <= 3.9e+37)
		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[(b * N[(y - 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.15e+103], t$95$2, If[LessEqual[a, -11.5], t$95$1, If[LessEqual[a, 3.5e-292], N[(t * b), $MachinePrecision], If[LessEqual[a, 3.9e+37], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 15: 53.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{if}\;b \leq -3.75 \cdot 10^{+28}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 2.9 \cdot 10^{-111}:\\ \;\;\;\;x - y \cdot z\\ \mathbf{elif}\;b \leq 7.2 \cdot 10^{+46}:\\ \;\;\;\;a \cdot \left(1 - t\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (- (+ y t) 2.0))))
   (if (<= b -3.75e+28)
     t_1
     (if (<= b 2.9e-111)
       (- x (* y z))
       (if (<= b 7.2e+46) (* a (- 1.0 t)) 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 <= -3.75e+28) {
		tmp = t_1;
	} else if (b <= 2.9e-111) {
		tmp = x - (y * z);
	} else if (b <= 7.2e+46) {
		tmp = 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 = b * ((y + t) - 2.0d0)
    if (b <= (-3.75d+28)) then
        tmp = t_1
    else if (b <= 2.9d-111) then
        tmp = x - (y * z)
    else if (b <= 7.2d+46) then
        tmp = 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 = b * ((y + t) - 2.0);
	double tmp;
	if (b <= -3.75e+28) {
		tmp = t_1;
	} else if (b <= 2.9e-111) {
		tmp = x - (y * z);
	} else if (b <= 7.2e+46) {
		tmp = a * (1.0 - t);
	} 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 <= -3.75e+28:
		tmp = t_1
	elif b <= 2.9e-111:
		tmp = x - (y * z)
	elif b <= 7.2e+46:
		tmp = a * (1.0 - t)
	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 <= -3.75e+28)
		tmp = t_1;
	elseif (b <= 2.9e-111)
		tmp = Float64(x - Float64(y * z));
	elseif (b <= 7.2e+46)
		tmp = 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 = b * ((y + t) - 2.0);
	tmp = 0.0;
	if (b <= -3.75e+28)
		tmp = t_1;
	elseif (b <= 2.9e-111)
		tmp = x - (y * z);
	elseif (b <= 7.2e+46)
		tmp = 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[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -3.75e+28], t$95$1, If[LessEqual[b, 2.9e-111], N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 7.2e+46], N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 16: 70.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -1.7 \cdot 10^{+55} \lor \neg \left(b \leq 1.04 \cdot 10^{+129}\right):\\ \;\;\;\;x + b \cdot \left(\left(y + t\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(a + z \cdot \left(1 - y\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -1.7e+55) (not (<= b 1.04e+129)))
   (+ x (* b (- (+ y t) 2.0)))
   (+ x (+ a (* z (- 1.0 y))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -1.7e+55) || !(b <= 1.04e+129)) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x + (a + (z * (1.0 - y)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((b <= (-1.7d+55)) .or. (.not. (b <= 1.04d+129))) then
        tmp = x + (b * ((y + t) - 2.0d0))
    else
        tmp = x + (a + (z * (1.0d0 - y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -1.7e+55) || !(b <= 1.04e+129)) {
		tmp = x + (b * ((y + t) - 2.0));
	} else {
		tmp = x + (a + (z * (1.0 - y)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -1.7e+55) or not (b <= 1.04e+129):
		tmp = x + (b * ((y + t) - 2.0))
	else:
		tmp = x + (a + (z * (1.0 - y)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -1.7e+55) || !(b <= 1.04e+129))
		tmp = Float64(x + Float64(b * Float64(Float64(y + t) - 2.0)));
	else
		tmp = Float64(x + Float64(a + Float64(z * Float64(1.0 - y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -1.7e+55) || ~((b <= 1.04e+129)))
		tmp = x + (b * ((y + t) - 2.0));
	else
		tmp = x + (a + (z * (1.0 - y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -1.7e+55], N[Not[LessEqual[b, 1.04e+129]], $MachinePrecision]], N[(x + N[(b * N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 27.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.3 \cdot 10^{+92}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;t \leq -2.05 \cdot 10^{-190}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 4 \cdot 10^{-103}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 1.8 \cdot 10^{+37}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -1.3e+92)
   (* t b)
   (if (<= t -2.05e-190)
     x
     (if (<= t 4e-103) a (if (<= t 1.8e+37) x (* t b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -1.3e+92) {
		tmp = t * b;
	} else if (t <= -2.05e-190) {
		tmp = x;
	} else if (t <= 4e-103) {
		tmp = a;
	} else if (t <= 1.8e+37) {
		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 <= (-1.3d+92)) then
        tmp = t * b
    else if (t <= (-2.05d-190)) then
        tmp = x
    else if (t <= 4d-103) then
        tmp = a
    else if (t <= 1.8d+37) 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 <= -1.3e+92) {
		tmp = t * b;
	} else if (t <= -2.05e-190) {
		tmp = x;
	} else if (t <= 4e-103) {
		tmp = a;
	} else if (t <= 1.8e+37) {
		tmp = x;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -1.3e+92:
		tmp = t * b
	elif t <= -2.05e-190:
		tmp = x
	elif t <= 4e-103:
		tmp = a
	elif t <= 1.8e+37:
		tmp = x
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -1.3e+92)
		tmp = Float64(t * b);
	elseif (t <= -2.05e-190)
		tmp = x;
	elseif (t <= 4e-103)
		tmp = a;
	elseif (t <= 1.8e+37)
		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 <= -1.3e+92)
		tmp = t * b;
	elseif (t <= -2.05e-190)
		tmp = x;
	elseif (t <= 4e-103)
		tmp = a;
	elseif (t <= 1.8e+37)
		tmp = x;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -1.3e+92], N[(t * b), $MachinePrecision], If[LessEqual[t, -2.05e-190], x, If[LessEqual[t, 4e-103], a, If[LessEqual[t, 1.8e+37], x, N[(t * b), $MachinePrecision]]]]]

Derivation

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

Alternative 18: 51.2% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.8 \cdot 10^{+15} \lor \neg \left(y \leq 11500000000000\right):\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(b - a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -7.8e+15) (not (<= y 11500000000000.0)))
   (* y (- b z))
   (* t (- b a))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -7.8e+15) || !(y <= 11500000000000.0)) {
		tmp = y * (b - z);
	} else {
		tmp = t * (b - 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 ((y <= (-7.8d+15)) .or. (.not. (y <= 11500000000000.0d0))) then
        tmp = y * (b - z)
    else
        tmp = t * (b - 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 ((y <= -7.8e+15) || !(y <= 11500000000000.0)) {
		tmp = y * (b - z);
	} else {
		tmp = t * (b - a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -7.8e+15) or not (y <= 11500000000000.0):
		tmp = y * (b - z)
	else:
		tmp = t * (b - a)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -7.8e+15) || !(y <= 11500000000000.0))
		tmp = Float64(y * Float64(b - z));
	else
		tmp = Float64(t * Float64(b - a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -7.8e+15) || ~((y <= 11500000000000.0)))
		tmp = y * (b - z);
	else
		tmp = t * (b - a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -7.8e+15], N[Not[LessEqual[y, 11500000000000.0]], $MachinePrecision]], N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision], N[(t * N[(b - a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 19: 27.9% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.5 \cdot 10^{+19} \lor \neg \left(y \leq 7 \cdot 10^{+14}\right):\\ \;\;\;\;y \cdot b\\ \mathbf{else}:\\ \;\;\;\;t \cdot b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -1.5e+19) (not (<= y 7e+14))) (* y b) (* t b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -1.5e+19) || !(y <= 7e+14)) {
		tmp = y * b;
	} 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 ((y <= (-1.5d+19)) .or. (.not. (y <= 7d+14))) then
        tmp = y * b
    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 ((y <= -1.5e+19) || !(y <= 7e+14)) {
		tmp = y * b;
	} else {
		tmp = t * b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -1.5e+19) or not (y <= 7e+14):
		tmp = y * b
	else:
		tmp = t * b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -1.5e+19) || !(y <= 7e+14))
		tmp = Float64(y * b);
	else
		tmp = Float64(t * b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -1.5e+19) || ~((y <= 7e+14)))
		tmp = y * b;
	else
		tmp = t * b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -1.5e+19], N[Not[LessEqual[y, 7e+14]], $MachinePrecision]], N[(y * b), $MachinePrecision], N[(t * b), $MachinePrecision]]

Derivation

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

Alternative 20: 21.4% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.4 \cdot 10^{+60}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{+85}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -1.4e+60) x (if (<= x 2.2e+85) a x)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -1.4e+60) {
		tmp = x;
	} else if (x <= 2.2e+85) {
		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 <= (-1.4d+60)) then
        tmp = x
    else if (x <= 2.2d+85) 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 <= -1.4e+60) {
		tmp = x;
	} else if (x <= 2.2e+85) {
		tmp = a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -1.4e+60:
		tmp = x
	elif x <= 2.2e+85:
		tmp = a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -1.4e+60)
		tmp = x;
	elseif (x <= 2.2e+85)
		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 <= -1.4e+60)
		tmp = x;
	elseif (x <= 2.2e+85)
		tmp = a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -1.4e+60], x, If[LessEqual[x, 2.2e+85], a, x]]

Derivation

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

Alternative 21: 11.6% 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 2024008 
(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)))