Numeric.SpecFunctions:incompleteBetaApprox from math-functions-0.1.5.2, B

Percentage Accurate: 95.8% → 99.3%

Time: 15.2s

Alternatives: 15

Speedup: 0.8×

• 41.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return x * exp(((y * (log(z) - t)) + (a * (log((1.0 - z)) - 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 * exp(((y * (log(z) - t)) + (a * (log((1.0d0 - z)) - b))))
end function

Java

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

Python

def code(x, y, z, t, a, b):
	return x * math.exp(((y * (math.log(z) - t)) + (a * (math.log((1.0 - z)) - b))))

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 95.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return x * exp(((y * (log(z) - t)) + (a * (log((1.0 - z)) - 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 * exp(((y * (log(z) - t)) + (a * (log((1.0d0 - z)) - b))))
end function

Java

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

Python

def code(x, y, z, t, a, b):
	return x * math.exp(((y * (math.log(z) - t)) + (a * (math.log((1.0 - z)) - b))))

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ x \cdot e^{\mathsf{fma}\left(y, \log z - t, a \cdot \left(\mathsf{log1p}\left(-z\right) - b\right)\right)} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (* x (exp (fma y (- (log z) t) (* a (- (log1p (- z)) b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return x * exp(fma(y, (log(z) - t), (a * (log1p(-z) - b))));
}

Julia

function code(x, y, z, t, a, b)
	return Float64(x * exp(fma(y, Float64(log(z) - t), Float64(a * Float64(log1p(Float64(-z)) - b)))))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(x * N[Exp[N[(y * N[(N[Log[z], $MachinePrecision] - t), $MachinePrecision] + N[(a * N[(N[Log[1 + (-z)], $MachinePrecision] - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 95.4% accurate, 1.5× speedup

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (* x (exp (- (* y (- (log z) t)) (* a b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return x * exp(((y * (log(z) - t)) - (a * 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 * exp(((y * (log(z) - t)) - (a * b)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return x * Math.exp(((y * (Math.log(z) - t)) - (a * b)));
}

Python

def code(x, y, z, t, a, b):
	return x * math.exp(((y * (math.log(z) - t)) - (a * b)))

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 3: 86.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.4 \cdot 10^{+106}:\\ \;\;\;\;x \cdot e^{t \cdot \left(-y\right) - a \cdot b}\\ \mathbf{else}:\\ \;\;\;\;x \cdot {\left(\frac{z}{e^{t}}\right)}^{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y 3.4e+106)
   (* x (exp (- (* t (- y)) (* a b))))
   (* x (pow (/ z (exp t)) y))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 3.4e+106) {
		tmp = x * exp(((t * -y) - (a * b)));
	} else {
		tmp = x * pow((z / exp(t)), y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= 3.4d+106) then
        tmp = x * exp(((t * -y) - (a * b)))
    else
        tmp = x * ((z / exp(t)) ** y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 3.4e+106) {
		tmp = x * Math.exp(((t * -y) - (a * b)));
	} else {
		tmp = x * Math.pow((z / Math.exp(t)), y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= 3.4e+106:
		tmp = x * math.exp(((t * -y) - (a * b)))
	else:
		tmp = x * math.pow((z / math.exp(t)), y)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= 3.4e+106)
		tmp = Float64(x * exp(Float64(Float64(t * Float64(-y)) - Float64(a * b))));
	else
		tmp = Float64(x * (Float64(z / exp(t)) ^ y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= 3.4e+106)
		tmp = x * exp(((t * -y) - (a * b)));
	else
		tmp = x * ((z / exp(t)) ^ y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, 3.4e+106], N[(x * N[Exp[N[(N[(t * (-y)), $MachinePrecision] - N[(a * b), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(x * N[Power[N[(z / N[Exp[t], $MachinePrecision]), $MachinePrecision], y], $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 4: 73.1% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot e^{t \cdot \left(-y\right)}\\ \mathbf{if}\;t \leq -2.65 \cdot 10^{+44}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 5.2 \cdot 10^{-272}:\\ \;\;\;\;x \cdot e^{a \cdot \left(-\left(z + b\right)\right)}\\ \mathbf{elif}\;t \leq 4.6 \cdot 10^{-182}:\\ \;\;\;\;x \cdot {z}^{y}\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{+35}:\\ \;\;\;\;x \cdot e^{a \cdot \left(-b\right)}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* x (exp (* t (- y))))))
   (if (<= t -2.65e+44)
     t_1
     (if (<= t 5.2e-272)
       (* x (exp (* a (- (+ z b)))))
       (if (<= t 4.6e-182)
         (* x (pow z y))
         (if (<= t 6.2e+35) (* x (exp (* a (- b)))) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x * exp((t * -y));
	double tmp;
	if (t <= -2.65e+44) {
		tmp = t_1;
	} else if (t <= 5.2e-272) {
		tmp = x * exp((a * -(z + b)));
	} else if (t <= 4.6e-182) {
		tmp = x * pow(z, y);
	} else if (t <= 6.2e+35) {
		tmp = x * exp((a * -b));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * exp((t * -y))
    if (t <= (-2.65d+44)) then
        tmp = t_1
    else if (t <= 5.2d-272) then
        tmp = x * exp((a * -(z + b)))
    else if (t <= 4.6d-182) then
        tmp = x * (z ** y)
    else if (t <= 6.2d+35) then
        tmp = x * exp((a * -b))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x * Math.exp((t * -y));
	double tmp;
	if (t <= -2.65e+44) {
		tmp = t_1;
	} else if (t <= 5.2e-272) {
		tmp = x * Math.exp((a * -(z + b)));
	} else if (t <= 4.6e-182) {
		tmp = x * Math.pow(z, y);
	} else if (t <= 6.2e+35) {
		tmp = x * Math.exp((a * -b));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x * math.exp((t * -y))
	tmp = 0
	if t <= -2.65e+44:
		tmp = t_1
	elif t <= 5.2e-272:
		tmp = x * math.exp((a * -(z + b)))
	elif t <= 4.6e-182:
		tmp = x * math.pow(z, y)
	elif t <= 6.2e+35:
		tmp = x * math.exp((a * -b))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x * exp(Float64(t * Float64(-y))))
	tmp = 0.0
	if (t <= -2.65e+44)
		tmp = t_1;
	elseif (t <= 5.2e-272)
		tmp = Float64(x * exp(Float64(a * Float64(-Float64(z + b)))));
	elseif (t <= 4.6e-182)
		tmp = Float64(x * (z ^ y));
	elseif (t <= 6.2e+35)
		tmp = Float64(x * exp(Float64(a * Float64(-b))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x * exp((t * -y));
	tmp = 0.0;
	if (t <= -2.65e+44)
		tmp = t_1;
	elseif (t <= 5.2e-272)
		tmp = x * exp((a * -(z + b)));
	elseif (t <= 4.6e-182)
		tmp = x * (z ^ y);
	elseif (t <= 6.2e+35)
		tmp = x * exp((a * -b));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x * N[Exp[N[(t * (-y)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.65e+44], t$95$1, If[LessEqual[t, 5.2e-272], N[(x * N[Exp[N[(a * (-N[(z + b), $MachinePrecision])), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4.6e-182], N[(x * N[Power[z, y], $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.2e+35], N[(x * N[Exp[N[(a * (-b)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 5: 68.7% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.5 \cdot 10^{+163} \lor \neg \left(y \leq 4.3 \cdot 10^{+110}\right):\\ \;\;\;\;x \cdot {z}^{y}\\ \mathbf{else}:\\ \;\;\;\;x \cdot e^{a \cdot \left(-b\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -2.5e+163) (not (<= y 4.3e+110)))
   (* x (pow z y))
   (* x (exp (* a (- b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -2.5e+163) || !(y <= 4.3e+110)) {
		tmp = x * pow(z, y);
	} else {
		tmp = x * exp((a * -b));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-2.5d+163)) .or. (.not. (y <= 4.3d+110))) then
        tmp = x * (z ** y)
    else
        tmp = x * exp((a * -b))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -2.5e+163) || !(y <= 4.3e+110)) {
		tmp = x * Math.pow(z, y);
	} else {
		tmp = x * Math.exp((a * -b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -2.5e+163) or not (y <= 4.3e+110):
		tmp = x * math.pow(z, y)
	else:
		tmp = x * math.exp((a * -b))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -2.5e+163) || !(y <= 4.3e+110))
		tmp = Float64(x * (z ^ y));
	else
		tmp = Float64(x * exp(Float64(a * Float64(-b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -2.5e+163) || ~((y <= 4.3e+110)))
		tmp = x * (z ^ y);
	else
		tmp = x * exp((a * -b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -2.5e+163], N[Not[LessEqual[y, 4.3e+110]], $MachinePrecision]], N[(x * N[Power[z, y], $MachinePrecision]), $MachinePrecision], N[(x * N[Exp[N[(a * (-b)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 70.6% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -9.2 \cdot 10^{-5} \lor \neg \left(b \leq 6.5 \cdot 10^{-22}\right):\\ \;\;\;\;x \cdot e^{a \cdot \left(-b\right)}\\ \mathbf{else}:\\ \;\;\;\;x \cdot e^{t \cdot \left(-y\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -9.2e-5) (not (<= b 6.5e-22)))
   (* x (exp (* a (- b))))
   (* x (exp (* t (- y))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -9.2e-5) || !(b <= 6.5e-22)) {
		tmp = x * exp((a * -b));
	} else {
		tmp = x * exp((t * -y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((b <= (-9.2d-5)) .or. (.not. (b <= 6.5d-22))) then
        tmp = x * exp((a * -b))
    else
        tmp = x * exp((t * -y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -9.2e-5) || !(b <= 6.5e-22)) {
		tmp = x * Math.exp((a * -b));
	} else {
		tmp = x * Math.exp((t * -y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -9.2e-5) or not (b <= 6.5e-22):
		tmp = x * math.exp((a * -b))
	else:
		tmp = x * math.exp((t * -y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -9.2e-5) || !(b <= 6.5e-22))
		tmp = Float64(x * exp(Float64(a * Float64(-b))));
	else
		tmp = Float64(x * exp(Float64(t * Float64(-y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -9.2e-5) || ~((b <= 6.5e-22)))
		tmp = x * exp((a * -b));
	else
		tmp = x * exp((t * -y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -9.2e-5], N[Not[LessEqual[b, 6.5e-22]], $MachinePrecision]], N[(x * N[Exp[N[(a * (-b)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(x * N[Exp[N[(t * (-y)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 83.5% accurate, 2.9× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return x * exp(((t * -y) - (a * 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 * exp(((t * -y) - (a * b)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return x * Math.exp(((t * -y) - (a * b)));
}

Python

def code(x, y, z, t, a, b):
	return x * math.exp(((t * -y) - (a * b)))

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 8: 56.3% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.2 \cdot 10^{-41} \lor \neg \left(y \leq 3.4 \cdot 10^{-5}\right):\\ \;\;\;\;x \cdot {z}^{y}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - a \cdot \left(z + b\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -6.2e-41) (not (<= y 3.4e-5)))
   (* x (pow z y))
   (* x (- 1.0 (* a (+ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -6.2e-41) || !(y <= 3.4e-5)) {
		tmp = x * pow(z, y);
	} else {
		tmp = x * (1.0 - (a * (z + 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 <= (-6.2d-41)) .or. (.not. (y <= 3.4d-5))) then
        tmp = x * (z ** y)
    else
        tmp = x * (1.0d0 - (a * (z + 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 <= -6.2e-41) || !(y <= 3.4e-5)) {
		tmp = x * Math.pow(z, y);
	} else {
		tmp = x * (1.0 - (a * (z + b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -6.2e-41) or not (y <= 3.4e-5):
		tmp = x * math.pow(z, y)
	else:
		tmp = x * (1.0 - (a * (z + b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -6.2e-41) || !(y <= 3.4e-5))
		tmp = Float64(x * (z ^ y));
	else
		tmp = Float64(x * Float64(1.0 - Float64(a * Float64(z + b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -6.2e-41) || ~((y <= 3.4e-5)))
		tmp = x * (z ^ y);
	else
		tmp = x * (1.0 - (a * (z + b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -6.2e-41], N[Not[LessEqual[y, 3.4e-5]], $MachinePrecision]], N[(x * N[Power[z, y], $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - N[(a * N[(z + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 27.3% accurate, 22.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(x \cdot \left(-z\right)\right)\\ \mathbf{if}\;y \leq -1.65 \cdot 10^{-8}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-41}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{+107}:\\ \;\;\;\;b \cdot \left(x \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{+230}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-b\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (* x (- z)))))
   (if (<= y -1.65e-8)
     t_1
     (if (<= y 4.8e-41)
       x
       (if (<= y 1.12e+107)
         (* b (* x (- a)))
         (if (<= y 2.8e+230) t_1 (* a (* x (- b)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (x * -z);
	double tmp;
	if (y <= -1.65e-8) {
		tmp = t_1;
	} else if (y <= 4.8e-41) {
		tmp = x;
	} else if (y <= 1.12e+107) {
		tmp = b * (x * -a);
	} else if (y <= 2.8e+230) {
		tmp = t_1;
	} else {
		tmp = a * (x * -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) :: t_1
    real(8) :: tmp
    t_1 = a * (x * -z)
    if (y <= (-1.65d-8)) then
        tmp = t_1
    else if (y <= 4.8d-41) then
        tmp = x
    else if (y <= 1.12d+107) then
        tmp = b * (x * -a)
    else if (y <= 2.8d+230) then
        tmp = t_1
    else
        tmp = a * (x * -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 t_1 = a * (x * -z);
	double tmp;
	if (y <= -1.65e-8) {
		tmp = t_1;
	} else if (y <= 4.8e-41) {
		tmp = x;
	} else if (y <= 1.12e+107) {
		tmp = b * (x * -a);
	} else if (y <= 2.8e+230) {
		tmp = t_1;
	} else {
		tmp = a * (x * -b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (x * -z)
	tmp = 0
	if y <= -1.65e-8:
		tmp = t_1
	elif y <= 4.8e-41:
		tmp = x
	elif y <= 1.12e+107:
		tmp = b * (x * -a)
	elif y <= 2.8e+230:
		tmp = t_1
	else:
		tmp = a * (x * -b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(x * Float64(-z)))
	tmp = 0.0
	if (y <= -1.65e-8)
		tmp = t_1;
	elseif (y <= 4.8e-41)
		tmp = x;
	elseif (y <= 1.12e+107)
		tmp = Float64(b * Float64(x * Float64(-a)));
	elseif (y <= 2.8e+230)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(x * Float64(-b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (x * -z);
	tmp = 0.0;
	if (y <= -1.65e-8)
		tmp = t_1;
	elseif (y <= 4.8e-41)
		tmp = x;
	elseif (y <= 1.12e+107)
		tmp = b * (x * -a);
	elseif (y <= 2.8e+230)
		tmp = t_1;
	else
		tmp = a * (x * -b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(x * (-z)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.65e-8], t$95$1, If[LessEqual[y, 4.8e-41], x, If[LessEqual[y, 1.12e+107], N[(b * N[(x * (-a)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.8e+230], t$95$1, N[(a * N[(x * (-b)), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 10: 27.9% accurate, 22.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(a \cdot \left(-b\right)\right)\\ \mathbf{if}\;y \leq -1.05 \cdot 10^{-10}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 4.6 \cdot 10^{-41}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{+128}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+230}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-b\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* x (* a (- b)))))
   (if (<= y -1.05e-10)
     t_1
     (if (<= y 4.6e-41)
       x
       (if (<= y 1.7e+128)
         t_1
         (if (<= y 1.15e+230) (* a (* x (- z))) (* a (* x (- b)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x * (a * -b);
	double tmp;
	if (y <= -1.05e-10) {
		tmp = t_1;
	} else if (y <= 4.6e-41) {
		tmp = x;
	} else if (y <= 1.7e+128) {
		tmp = t_1;
	} else if (y <= 1.15e+230) {
		tmp = a * (x * -z);
	} else {
		tmp = a * (x * -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) :: t_1
    real(8) :: tmp
    t_1 = x * (a * -b)
    if (y <= (-1.05d-10)) then
        tmp = t_1
    else if (y <= 4.6d-41) then
        tmp = x
    else if (y <= 1.7d+128) then
        tmp = t_1
    else if (y <= 1.15d+230) then
        tmp = a * (x * -z)
    else
        tmp = a * (x * -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 t_1 = x * (a * -b);
	double tmp;
	if (y <= -1.05e-10) {
		tmp = t_1;
	} else if (y <= 4.6e-41) {
		tmp = x;
	} else if (y <= 1.7e+128) {
		tmp = t_1;
	} else if (y <= 1.15e+230) {
		tmp = a * (x * -z);
	} else {
		tmp = a * (x * -b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x * (a * -b)
	tmp = 0
	if y <= -1.05e-10:
		tmp = t_1
	elif y <= 4.6e-41:
		tmp = x
	elif y <= 1.7e+128:
		tmp = t_1
	elif y <= 1.15e+230:
		tmp = a * (x * -z)
	else:
		tmp = a * (x * -b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x * Float64(a * Float64(-b)))
	tmp = 0.0
	if (y <= -1.05e-10)
		tmp = t_1;
	elseif (y <= 4.6e-41)
		tmp = x;
	elseif (y <= 1.7e+128)
		tmp = t_1;
	elseif (y <= 1.15e+230)
		tmp = Float64(a * Float64(x * Float64(-z)));
	else
		tmp = Float64(a * Float64(x * Float64(-b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x * (a * -b);
	tmp = 0.0;
	if (y <= -1.05e-10)
		tmp = t_1;
	elseif (y <= 4.6e-41)
		tmp = x;
	elseif (y <= 1.7e+128)
		tmp = t_1;
	elseif (y <= 1.15e+230)
		tmp = a * (x * -z);
	else
		tmp = a * (x * -b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x * N[(a * (-b)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.05e-10], t$95$1, If[LessEqual[y, 4.6e-41], x, If[LessEqual[y, 1.7e+128], t$95$1, If[LessEqual[y, 1.15e+230], N[(a * N[(x * (-z)), $MachinePrecision]), $MachinePrecision], N[(a * N[(x * (-b)), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 11: 28.4% accurate, 22.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(a \cdot \left(-b\right)\right)\\ \mathbf{if}\;y \leq -7.2 \cdot 10^{-10}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 7.6 \cdot 10^{-42}:\\ \;\;\;\;x \cdot \left(1 - z \cdot a\right)\\ \mathbf{elif}\;y \leq 2.4 \cdot 10^{+127}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{+230}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-b\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* x (* a (- b)))))
   (if (<= y -7.2e-10)
     t_1
     (if (<= y 7.6e-42)
       (* x (- 1.0 (* z a)))
       (if (<= y 2.4e+127)
         t_1
         (if (<= y 2.9e+230) (* a (* x (- z))) (* a (* x (- b)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x * (a * -b);
	double tmp;
	if (y <= -7.2e-10) {
		tmp = t_1;
	} else if (y <= 7.6e-42) {
		tmp = x * (1.0 - (z * a));
	} else if (y <= 2.4e+127) {
		tmp = t_1;
	} else if (y <= 2.9e+230) {
		tmp = a * (x * -z);
	} else {
		tmp = a * (x * -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) :: t_1
    real(8) :: tmp
    t_1 = x * (a * -b)
    if (y <= (-7.2d-10)) then
        tmp = t_1
    else if (y <= 7.6d-42) then
        tmp = x * (1.0d0 - (z * a))
    else if (y <= 2.4d+127) then
        tmp = t_1
    else if (y <= 2.9d+230) then
        tmp = a * (x * -z)
    else
        tmp = a * (x * -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 t_1 = x * (a * -b);
	double tmp;
	if (y <= -7.2e-10) {
		tmp = t_1;
	} else if (y <= 7.6e-42) {
		tmp = x * (1.0 - (z * a));
	} else if (y <= 2.4e+127) {
		tmp = t_1;
	} else if (y <= 2.9e+230) {
		tmp = a * (x * -z);
	} else {
		tmp = a * (x * -b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x * (a * -b)
	tmp = 0
	if y <= -7.2e-10:
		tmp = t_1
	elif y <= 7.6e-42:
		tmp = x * (1.0 - (z * a))
	elif y <= 2.4e+127:
		tmp = t_1
	elif y <= 2.9e+230:
		tmp = a * (x * -z)
	else:
		tmp = a * (x * -b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x * Float64(a * Float64(-b)))
	tmp = 0.0
	if (y <= -7.2e-10)
		tmp = t_1;
	elseif (y <= 7.6e-42)
		tmp = Float64(x * Float64(1.0 - Float64(z * a)));
	elseif (y <= 2.4e+127)
		tmp = t_1;
	elseif (y <= 2.9e+230)
		tmp = Float64(a * Float64(x * Float64(-z)));
	else
		tmp = Float64(a * Float64(x * Float64(-b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x * (a * -b);
	tmp = 0.0;
	if (y <= -7.2e-10)
		tmp = t_1;
	elseif (y <= 7.6e-42)
		tmp = x * (1.0 - (z * a));
	elseif (y <= 2.4e+127)
		tmp = t_1;
	elseif (y <= 2.9e+230)
		tmp = a * (x * -z);
	else
		tmp = a * (x * -b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x * N[(a * (-b)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -7.2e-10], t$95$1, If[LessEqual[y, 7.6e-42], N[(x * N[(1.0 - N[(z * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.4e+127], t$95$1, If[LessEqual[y, 2.9e+230], N[(a * N[(x * (-z)), $MachinePrecision]), $MachinePrecision], N[(a * N[(x * (-b)), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 12: 32.4% accurate, 25.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.3:\\ \;\;\;\;x \cdot \left(1 - a \cdot b\right)\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+49}:\\ \;\;\;\;x \cdot \left(z \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+126}:\\ \;\;\;\;x \cdot \left(a \cdot \left(-b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-z\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y 2.3)
   (* x (- 1.0 (* a b)))
   (if (<= y 1.35e+49)
     (* x (* z (- a)))
     (if (<= y 2.5e+126) (* x (* a (- b))) (* a (* x (- z)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 2.3) {
		tmp = x * (1.0 - (a * b));
	} else if (y <= 1.35e+49) {
		tmp = x * (z * -a);
	} else if (y <= 2.5e+126) {
		tmp = x * (a * -b);
	} else {
		tmp = a * (x * -z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= 2.3d0) then
        tmp = x * (1.0d0 - (a * b))
    else if (y <= 1.35d+49) then
        tmp = x * (z * -a)
    else if (y <= 2.5d+126) then
        tmp = x * (a * -b)
    else
        tmp = a * (x * -z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 2.3) {
		tmp = x * (1.0 - (a * b));
	} else if (y <= 1.35e+49) {
		tmp = x * (z * -a);
	} else if (y <= 2.5e+126) {
		tmp = x * (a * -b);
	} else {
		tmp = a * (x * -z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= 2.3:
		tmp = x * (1.0 - (a * b))
	elif y <= 1.35e+49:
		tmp = x * (z * -a)
	elif y <= 2.5e+126:
		tmp = x * (a * -b)
	else:
		tmp = a * (x * -z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= 2.3)
		tmp = Float64(x * Float64(1.0 - Float64(a * b)));
	elseif (y <= 1.35e+49)
		tmp = Float64(x * Float64(z * Float64(-a)));
	elseif (y <= 2.5e+126)
		tmp = Float64(x * Float64(a * Float64(-b)));
	else
		tmp = Float64(a * Float64(x * Float64(-z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= 2.3)
		tmp = x * (1.0 - (a * b));
	elseif (y <= 1.35e+49)
		tmp = x * (z * -a);
	elseif (y <= 2.5e+126)
		tmp = x * (a * -b);
	else
		tmp = a * (x * -z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, 2.3], N[(x * N[(1.0 - N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.35e+49], N[(x * N[(z * (-a)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.5e+126], N[(x * N[(a * (-b)), $MachinePrecision]), $MachinePrecision], N[(a * N[(x * (-z)), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 13: 32.9% accurate, 25.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 11:\\ \;\;\;\;x \cdot \left(1 - a \cdot \left(z + b\right)\right)\\ \mathbf{elif}\;y \leq 5.8 \cdot 10^{+52}:\\ \;\;\;\;x \cdot \left(z \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{+120}:\\ \;\;\;\;x \cdot \left(a \cdot \left(-b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(x \cdot \left(-z\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y 11.0)
   (* x (- 1.0 (* a (+ z b))))
   (if (<= y 5.8e+52)
     (* x (* z (- a)))
     (if (<= y 2.7e+120) (* x (* a (- b))) (* a (* x (- z)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 11.0) {
		tmp = x * (1.0 - (a * (z + b)));
	} else if (y <= 5.8e+52) {
		tmp = x * (z * -a);
	} else if (y <= 2.7e+120) {
		tmp = x * (a * -b);
	} else {
		tmp = a * (x * -z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= 11.0d0) then
        tmp = x * (1.0d0 - (a * (z + b)))
    else if (y <= 5.8d+52) then
        tmp = x * (z * -a)
    else if (y <= 2.7d+120) then
        tmp = x * (a * -b)
    else
        tmp = a * (x * -z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 11.0) {
		tmp = x * (1.0 - (a * (z + b)));
	} else if (y <= 5.8e+52) {
		tmp = x * (z * -a);
	} else if (y <= 2.7e+120) {
		tmp = x * (a * -b);
	} else {
		tmp = a * (x * -z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= 11.0:
		tmp = x * (1.0 - (a * (z + b)))
	elif y <= 5.8e+52:
		tmp = x * (z * -a)
	elif y <= 2.7e+120:
		tmp = x * (a * -b)
	else:
		tmp = a * (x * -z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= 11.0)
		tmp = Float64(x * Float64(1.0 - Float64(a * Float64(z + b))));
	elseif (y <= 5.8e+52)
		tmp = Float64(x * Float64(z * Float64(-a)));
	elseif (y <= 2.7e+120)
		tmp = Float64(x * Float64(a * Float64(-b)));
	else
		tmp = Float64(a * Float64(x * Float64(-z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= 11.0)
		tmp = x * (1.0 - (a * (z + b)));
	elseif (y <= 5.8e+52)
		tmp = x * (z * -a);
	elseif (y <= 2.7e+120)
		tmp = x * (a * -b);
	else
		tmp = a * (x * -z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, 11.0], N[(x * N[(1.0 - N[(a * N[(z + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.8e+52], N[(x * N[(z * (-a)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.7e+120], N[(x * N[(a * (-b)), $MachinePrecision]), $MachinePrecision], N[(a * N[(x * (-z)), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 14: 27.7% accurate, 31.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{-8} \lor \neg \left(y \leq 8.8 \cdot 10^{-41}\right):\\ \;\;\;\;a \cdot \left(x \cdot \left(-z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -1.1e-8) (not (<= y 8.8e-41))) (* a (* x (- z))) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -1.1e-8) || !(y <= 8.8e-41)) {
		tmp = a * (x * -z);
	} 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 ((y <= (-1.1d-8)) .or. (.not. (y <= 8.8d-41))) then
        tmp = a * (x * -z)
    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 ((y <= -1.1e-8) || !(y <= 8.8e-41)) {
		tmp = a * (x * -z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -1.1e-8) or not (y <= 8.8e-41):
		tmp = a * (x * -z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -1.1e-8) || !(y <= 8.8e-41))
		tmp = Float64(a * Float64(x * Float64(-z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -1.1e-8) || ~((y <= 8.8e-41)))
		tmp = a * (x * -z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -1.1e-8], N[Not[LessEqual[y, 8.8e-41]], $MachinePrecision]], N[(a * N[(x * (-z)), $MachinePrecision]), $MachinePrecision], x]

Derivation

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

Alternative 15: 19.6% accurate, 315.0× 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 2023350 
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:incompleteBetaApprox from math-functions-0.1.5.2, B"
  :precision binary64
  (* x (exp (+ (* y (- (log z) t)) (* a (- (log (- 1.0 z)) b))))))