Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1

Percentage Accurate: 92.4% → 96.9%

Time: 11.3s

Alternatives: 15

Speedup: 1.2×

• 32.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 92.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 96.9% accurate, 0.0× speedup

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= (+ (+ (+ x (* y z)) (* t a)) (* (* z a) b)) INFINITY)
   (fma (* z a) b (fma a t (fma z y x)))
   (+ x (* z (+ y (* a b))))))

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 96.9% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 3: 85.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(y + a \cdot b\right)\\ t_2 := x + t_1\\ \mathbf{if}\;b \leq -2.1 \cdot 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -2.4 \cdot 10^{-33}:\\ \;\;\;\;t_1 + t \cdot a\\ \mathbf{elif}\;b \leq -8.8 \cdot 10^{-45} \lor \neg \left(b \leq 4.6 \cdot 10^{+89}\right):\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;x + \left(y \cdot z + t \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (+ y (* a b)))) (t_2 (+ x t_1)))
   (if (<= b -2.1e+60)
     t_2
     (if (<= b -2.4e-33)
       (+ t_1 (* t a))
       (if (or (<= b -8.8e-45) (not (<= b 4.6e+89)))
         t_2
         (+ x (+ (* y z) (* t a))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * b));
	double t_2 = x + t_1;
	double tmp;
	if (b <= -2.1e+60) {
		tmp = t_2;
	} else if (b <= -2.4e-33) {
		tmp = t_1 + (t * a);
	} else if ((b <= -8.8e-45) || !(b <= 4.6e+89)) {
		tmp = t_2;
	} else {
		tmp = x + ((y * z) + (t * a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = z * (y + (a * b))
    t_2 = x + t_1
    if (b <= (-2.1d+60)) then
        tmp = t_2
    else if (b <= (-2.4d-33)) then
        tmp = t_1 + (t * a)
    else if ((b <= (-8.8d-45)) .or. (.not. (b <= 4.6d+89))) then
        tmp = t_2
    else
        tmp = x + ((y * z) + (t * a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * b));
	double t_2 = x + t_1;
	double tmp;
	if (b <= -2.1e+60) {
		tmp = t_2;
	} else if (b <= -2.4e-33) {
		tmp = t_1 + (t * a);
	} else if ((b <= -8.8e-45) || !(b <= 4.6e+89)) {
		tmp = t_2;
	} else {
		tmp = x + ((y * z) + (t * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (y + (a * b))
	t_2 = x + t_1
	tmp = 0
	if b <= -2.1e+60:
		tmp = t_2
	elif b <= -2.4e-33:
		tmp = t_1 + (t * a)
	elif (b <= -8.8e-45) or not (b <= 4.6e+89):
		tmp = t_2
	else:
		tmp = x + ((y * z) + (t * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(y + Float64(a * b)))
	t_2 = Float64(x + t_1)
	tmp = 0.0
	if (b <= -2.1e+60)
		tmp = t_2;
	elseif (b <= -2.4e-33)
		tmp = Float64(t_1 + Float64(t * a));
	elseif ((b <= -8.8e-45) || !(b <= 4.6e+89))
		tmp = t_2;
	else
		tmp = Float64(x + Float64(Float64(y * z) + Float64(t * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (y + (a * b));
	t_2 = x + t_1;
	tmp = 0.0;
	if (b <= -2.1e+60)
		tmp = t_2;
	elseif (b <= -2.4e-33)
		tmp = t_1 + (t * a);
	elseif ((b <= -8.8e-45) || ~((b <= 4.6e+89)))
		tmp = t_2;
	else
		tmp = x + ((y * z) + (t * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + t$95$1), $MachinePrecision]}, If[LessEqual[b, -2.1e+60], t$95$2, If[LessEqual[b, -2.4e-33], N[(t$95$1 + N[(t * a), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[b, -8.8e-45], N[Not[LessEqual[b, 4.6e+89]], $MachinePrecision]], t$95$2, N[(x + N[(N[(y * z), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 82.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + z \cdot b\right)\\ \mathbf{if}\;a \leq -5.4 \cdot 10^{-6}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -3.75 \cdot 10^{-65}:\\ \;\;\;\;x + \left(y \cdot z + t \cdot a\right)\\ \mathbf{elif}\;a \leq 7.2 \cdot 10^{-77}:\\ \;\;\;\;x + z \cdot \left(y + a \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot z + t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* z b)))))
   (if (<= a -5.4e-6)
     t_1
     (if (<= a -3.75e-65)
       (+ x (+ (* y z) (* t a)))
       (if (<= a 7.2e-77) (+ x (* z (+ y (* a b)))) (+ (* y z) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (z * b));
	double tmp;
	if (a <= -5.4e-6) {
		tmp = t_1;
	} else if (a <= -3.75e-65) {
		tmp = x + ((y * z) + (t * a));
	} else if (a <= 7.2e-77) {
		tmp = x + (z * (y + (a * b)));
	} else {
		tmp = (y * z) + t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * (t + (z * b))
    if (a <= (-5.4d-6)) then
        tmp = t_1
    else if (a <= (-3.75d-65)) then
        tmp = x + ((y * z) + (t * a))
    else if (a <= 7.2d-77) then
        tmp = x + (z * (y + (a * b)))
    else
        tmp = (y * z) + t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (z * b));
	double tmp;
	if (a <= -5.4e-6) {
		tmp = t_1;
	} else if (a <= -3.75e-65) {
		tmp = x + ((y * z) + (t * a));
	} else if (a <= 7.2e-77) {
		tmp = x + (z * (y + (a * b)));
	} else {
		tmp = (y * z) + t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (z * b))
	tmp = 0
	if a <= -5.4e-6:
		tmp = t_1
	elif a <= -3.75e-65:
		tmp = x + ((y * z) + (t * a))
	elif a <= 7.2e-77:
		tmp = x + (z * (y + (a * b)))
	else:
		tmp = (y * z) + t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(t + Float64(z * b)))
	tmp = 0.0
	if (a <= -5.4e-6)
		tmp = t_1;
	elseif (a <= -3.75e-65)
		tmp = Float64(x + Float64(Float64(y * z) + Float64(t * a)));
	elseif (a <= 7.2e-77)
		tmp = Float64(x + Float64(z * Float64(y + Float64(a * b))));
	else
		tmp = Float64(Float64(y * z) + t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (t + (z * b));
	tmp = 0.0;
	if (a <= -5.4e-6)
		tmp = t_1;
	elseif (a <= -3.75e-65)
		tmp = x + ((y * z) + (t * a));
	elseif (a <= 7.2e-77)
		tmp = x + (z * (y + (a * b)));
	else
		tmp = (y * z) + t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -5.4e-6], t$95$1, If[LessEqual[a, -3.75e-65], N[(x + N[(N[(y * z), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7.2e-77], N[(x + N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y * z), $MachinePrecision] + t$95$1), $MachinePrecision]]]]]

Derivation

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

Alternative 5: 56.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(z \cdot b\right)\\ \mathbf{if}\;a \leq -2.9 \cdot 10^{+232}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -3.8 \cdot 10^{+28}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;a \leq -4.1 \cdot 10^{-5}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -6.2 \cdot 10^{-61} \lor \neg \left(a \leq 5.2 \cdot 10^{+37}\right):\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (* z b))))
   (if (<= a -2.9e+232)
     t_1
     (if (<= a -3.8e+28)
       (* t a)
       (if (<= a -4.1e-5)
         t_1
         (if (or (<= a -6.2e-61) (not (<= a 5.2e+37)))
           (* t a)
           (+ x (* y z))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (z * b);
	double tmp;
	if (a <= -2.9e+232) {
		tmp = t_1;
	} else if (a <= -3.8e+28) {
		tmp = t * a;
	} else if (a <= -4.1e-5) {
		tmp = t_1;
	} else if ((a <= -6.2e-61) || !(a <= 5.2e+37)) {
		tmp = t * a;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * (z * b)
    if (a <= (-2.9d+232)) then
        tmp = t_1
    else if (a <= (-3.8d+28)) then
        tmp = t * a
    else if (a <= (-4.1d-5)) then
        tmp = t_1
    else if ((a <= (-6.2d-61)) .or. (.not. (a <= 5.2d+37))) then
        tmp = t * a
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (z * b);
	double tmp;
	if (a <= -2.9e+232) {
		tmp = t_1;
	} else if (a <= -3.8e+28) {
		tmp = t * a;
	} else if (a <= -4.1e-5) {
		tmp = t_1;
	} else if ((a <= -6.2e-61) || !(a <= 5.2e+37)) {
		tmp = t * a;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (z * b)
	tmp = 0
	if a <= -2.9e+232:
		tmp = t_1
	elif a <= -3.8e+28:
		tmp = t * a
	elif a <= -4.1e-5:
		tmp = t_1
	elif (a <= -6.2e-61) or not (a <= 5.2e+37):
		tmp = t * a
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(z * b))
	tmp = 0.0
	if (a <= -2.9e+232)
		tmp = t_1;
	elseif (a <= -3.8e+28)
		tmp = Float64(t * a);
	elseif (a <= -4.1e-5)
		tmp = t_1;
	elseif ((a <= -6.2e-61) || !(a <= 5.2e+37))
		tmp = Float64(t * a);
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (z * b);
	tmp = 0.0;
	if (a <= -2.9e+232)
		tmp = t_1;
	elseif (a <= -3.8e+28)
		tmp = t * a;
	elseif (a <= -4.1e-5)
		tmp = t_1;
	elseif ((a <= -6.2e-61) || ~((a <= 5.2e+37)))
		tmp = t * a;
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.9e+232], t$95$1, If[LessEqual[a, -3.8e+28], N[(t * a), $MachinePrecision], If[LessEqual[a, -4.1e-5], t$95$1, If[Or[LessEqual[a, -6.2e-61], N[Not[LessEqual[a, 5.2e+37]], $MachinePrecision]], N[(t * a), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 40.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.35 \cdot 10^{-64}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;a \leq -1.36 \cdot 10^{-102}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;a \leq -1.7 \cdot 10^{-300}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 8.4 \cdot 10^{-150}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;a \leq 6.5 \cdot 10^{-77}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -2.35e-64)
   (* t a)
   (if (<= a -1.36e-102)
     (* y z)
     (if (<= a -1.7e-300)
       x
       (if (<= a 8.4e-150) (* y z) (if (<= a 6.5e-77) x (* t a)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -2.35e-64) {
		tmp = t * a;
	} else if (a <= -1.36e-102) {
		tmp = y * z;
	} else if (a <= -1.7e-300) {
		tmp = x;
	} else if (a <= 8.4e-150) {
		tmp = y * z;
	} else if (a <= 6.5e-77) {
		tmp = x;
	} else {
		tmp = t * 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 (a <= (-2.35d-64)) then
        tmp = t * a
    else if (a <= (-1.36d-102)) then
        tmp = y * z
    else if (a <= (-1.7d-300)) then
        tmp = x
    else if (a <= 8.4d-150) then
        tmp = y * z
    else if (a <= 6.5d-77) then
        tmp = x
    else
        tmp = t * 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 (a <= -2.35e-64) {
		tmp = t * a;
	} else if (a <= -1.36e-102) {
		tmp = y * z;
	} else if (a <= -1.7e-300) {
		tmp = x;
	} else if (a <= 8.4e-150) {
		tmp = y * z;
	} else if (a <= 6.5e-77) {
		tmp = x;
	} else {
		tmp = t * a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -2.35e-64:
		tmp = t * a
	elif a <= -1.36e-102:
		tmp = y * z
	elif a <= -1.7e-300:
		tmp = x
	elif a <= 8.4e-150:
		tmp = y * z
	elif a <= 6.5e-77:
		tmp = x
	else:
		tmp = t * a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -2.35e-64)
		tmp = Float64(t * a);
	elseif (a <= -1.36e-102)
		tmp = Float64(y * z);
	elseif (a <= -1.7e-300)
		tmp = x;
	elseif (a <= 8.4e-150)
		tmp = Float64(y * z);
	elseif (a <= 6.5e-77)
		tmp = x;
	else
		tmp = Float64(t * a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -2.35e-64)
		tmp = t * a;
	elseif (a <= -1.36e-102)
		tmp = y * z;
	elseif (a <= -1.7e-300)
		tmp = x;
	elseif (a <= 8.4e-150)
		tmp = y * z;
	elseif (a <= 6.5e-77)
		tmp = x;
	else
		tmp = t * a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -2.35e-64], N[(t * a), $MachinePrecision], If[LessEqual[a, -1.36e-102], N[(y * z), $MachinePrecision], If[LessEqual[a, -1.7e-300], x, If[LessEqual[a, 8.4e-150], N[(y * z), $MachinePrecision], If[LessEqual[a, 6.5e-77], x, N[(t * a), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 62.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + t \cdot a\\ \mathbf{if}\;a \leq -8.8 \cdot 10^{+27}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -0.00145:\\ \;\;\;\;a \cdot \left(z \cdot b\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-65} \lor \neg \left(a \leq 1.42 \cdot 10^{-84}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* t a))))
   (if (<= a -8.8e+27)
     t_1
     (if (<= a -0.00145)
       (* a (* z b))
       (if (or (<= a -2.7e-65) (not (<= a 1.42e-84))) t_1 (+ x (* y z)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (t * a);
	double tmp;
	if (a <= -8.8e+27) {
		tmp = t_1;
	} else if (a <= -0.00145) {
		tmp = a * (z * b);
	} else if ((a <= -2.7e-65) || !(a <= 1.42e-84)) {
		tmp = t_1;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (t * a)
    if (a <= (-8.8d+27)) then
        tmp = t_1
    else if (a <= (-0.00145d0)) then
        tmp = a * (z * b)
    else if ((a <= (-2.7d-65)) .or. (.not. (a <= 1.42d-84))) then
        tmp = t_1
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (t * a);
	double tmp;
	if (a <= -8.8e+27) {
		tmp = t_1;
	} else if (a <= -0.00145) {
		tmp = a * (z * b);
	} else if ((a <= -2.7e-65) || !(a <= 1.42e-84)) {
		tmp = t_1;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (t * a)
	tmp = 0
	if a <= -8.8e+27:
		tmp = t_1
	elif a <= -0.00145:
		tmp = a * (z * b)
	elif (a <= -2.7e-65) or not (a <= 1.42e-84):
		tmp = t_1
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(t * a))
	tmp = 0.0
	if (a <= -8.8e+27)
		tmp = t_1;
	elseif (a <= -0.00145)
		tmp = Float64(a * Float64(z * b));
	elseif ((a <= -2.7e-65) || !(a <= 1.42e-84))
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (t * a);
	tmp = 0.0;
	if (a <= -8.8e+27)
		tmp = t_1;
	elseif (a <= -0.00145)
		tmp = a * (z * b);
	elseif ((a <= -2.7e-65) || ~((a <= 1.42e-84)))
		tmp = t_1;
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(t * a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -8.8e+27], t$95$1, If[LessEqual[a, -0.00145], N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[a, -2.7e-65], N[Not[LessEqual[a, 1.42e-84]], $MachinePrecision]], t$95$1, N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 8: 37.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(z \cdot b\right)\\ \mathbf{if}\;b \leq -2.25 \cdot 10^{-38}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 5.1 \cdot 10^{-236}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;b \leq 4.8 \cdot 10^{-117}:\\ \;\;\;\;x\\ \mathbf{elif}\;b \leq 2.7 \cdot 10^{+75}:\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (* z b))))
   (if (<= b -2.25e-38)
     t_1
     (if (<= b 5.1e-236)
       (* y z)
       (if (<= b 4.8e-117) x (if (<= b 2.7e+75) (* t a) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (z * b);
	double tmp;
	if (b <= -2.25e-38) {
		tmp = t_1;
	} else if (b <= 5.1e-236) {
		tmp = y * z;
	} else if (b <= 4.8e-117) {
		tmp = x;
	} else if (b <= 2.7e+75) {
		tmp = t * a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * (z * b)
    if (b <= (-2.25d-38)) then
        tmp = t_1
    else if (b <= 5.1d-236) then
        tmp = y * z
    else if (b <= 4.8d-117) then
        tmp = x
    else if (b <= 2.7d+75) then
        tmp = t * a
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (z * b);
	double tmp;
	if (b <= -2.25e-38) {
		tmp = t_1;
	} else if (b <= 5.1e-236) {
		tmp = y * z;
	} else if (b <= 4.8e-117) {
		tmp = x;
	} else if (b <= 2.7e+75) {
		tmp = t * a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (z * b)
	tmp = 0
	if b <= -2.25e-38:
		tmp = t_1
	elif b <= 5.1e-236:
		tmp = y * z
	elif b <= 4.8e-117:
		tmp = x
	elif b <= 2.7e+75:
		tmp = t * a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(z * b))
	tmp = 0.0
	if (b <= -2.25e-38)
		tmp = t_1;
	elseif (b <= 5.1e-236)
		tmp = Float64(y * z);
	elseif (b <= 4.8e-117)
		tmp = x;
	elseif (b <= 2.7e+75)
		tmp = Float64(t * a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (z * b);
	tmp = 0.0;
	if (b <= -2.25e-38)
		tmp = t_1;
	elseif (b <= 5.1e-236)
		tmp = y * z;
	elseif (b <= 4.8e-117)
		tmp = x;
	elseif (b <= 2.7e+75)
		tmp = t * a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -2.25e-38], t$95$1, If[LessEqual[b, 5.1e-236], N[(y * z), $MachinePrecision], If[LessEqual[b, 4.8e-117], x, If[LessEqual[b, 2.7e+75], N[(t * a), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 9: 37.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z \cdot a\right) \cdot b\\ \mathbf{if}\;b \leq -2.25 \cdot 10^{-38}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 4.2 \cdot 10^{-237}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;b \leq 1.1 \cdot 10^{-119}:\\ \;\;\;\;x\\ \mathbf{elif}\;b \leq 3.8 \cdot 10^{+75}:\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (* z a) b)))
   (if (<= b -2.25e-38)
     t_1
     (if (<= b 4.2e-237)
       (* y z)
       (if (<= b 1.1e-119) x (if (<= b 3.8e+75) (* t a) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z * a) * b;
	double tmp;
	if (b <= -2.25e-38) {
		tmp = t_1;
	} else if (b <= 4.2e-237) {
		tmp = y * z;
	} else if (b <= 1.1e-119) {
		tmp = x;
	} else if (b <= 3.8e+75) {
		tmp = t * a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z * a) * b
    if (b <= (-2.25d-38)) then
        tmp = t_1
    else if (b <= 4.2d-237) then
        tmp = y * z
    else if (b <= 1.1d-119) then
        tmp = x
    else if (b <= 3.8d+75) then
        tmp = t * a
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z * a) * b;
	double tmp;
	if (b <= -2.25e-38) {
		tmp = t_1;
	} else if (b <= 4.2e-237) {
		tmp = y * z;
	} else if (b <= 1.1e-119) {
		tmp = x;
	} else if (b <= 3.8e+75) {
		tmp = t * a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z * a) * b
	tmp = 0
	if b <= -2.25e-38:
		tmp = t_1
	elif b <= 4.2e-237:
		tmp = y * z
	elif b <= 1.1e-119:
		tmp = x
	elif b <= 3.8e+75:
		tmp = t * a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z * a) * b)
	tmp = 0.0
	if (b <= -2.25e-38)
		tmp = t_1;
	elseif (b <= 4.2e-237)
		tmp = Float64(y * z);
	elseif (b <= 1.1e-119)
		tmp = x;
	elseif (b <= 3.8e+75)
		tmp = Float64(t * a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z * a) * b;
	tmp = 0.0;
	if (b <= -2.25e-38)
		tmp = t_1;
	elseif (b <= 4.2e-237)
		tmp = y * z;
	elseif (b <= 1.1e-119)
		tmp = x;
	elseif (b <= 3.8e+75)
		tmp = t * a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z * a), $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[b, -2.25e-38], t$95$1, If[LessEqual[b, 4.2e-237], N[(y * z), $MachinePrecision], If[LessEqual[b, 1.1e-119], x, If[LessEqual[b, 3.8e+75], N[(t * a), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 10: 82.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.65 \cdot 10^{-6} \lor \neg \left(a \leq 3.1 \cdot 10^{+38}\right):\\ \;\;\;\;a \cdot \left(t + z \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(y \cdot z + t \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -3.65e-6) (not (<= a 3.1e+38)))
   (* a (+ t (* z b)))
   (+ x (+ (* y z) (* t a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -3.65e-6) || !(a <= 3.1e+38)) {
		tmp = a * (t + (z * b));
	} else {
		tmp = x + ((y * z) + (t * 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 ((a <= (-3.65d-6)) .or. (.not. (a <= 3.1d+38))) then
        tmp = a * (t + (z * b))
    else
        tmp = x + ((y * z) + (t * 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 ((a <= -3.65e-6) || !(a <= 3.1e+38)) {
		tmp = a * (t + (z * b));
	} else {
		tmp = x + ((y * z) + (t * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -3.65e-6) or not (a <= 3.1e+38):
		tmp = a * (t + (z * b))
	else:
		tmp = x + ((y * z) + (t * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -3.65e-6) || !(a <= 3.1e+38))
		tmp = Float64(a * Float64(t + Float64(z * b)));
	else
		tmp = Float64(x + Float64(Float64(y * z) + Float64(t * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -3.65e-6) || ~((a <= 3.1e+38)))
		tmp = a * (t + (z * b));
	else
		tmp = x + ((y * z) + (t * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -3.65e-6], N[Not[LessEqual[a, 3.1e+38]], $MachinePrecision]], N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * z), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 85.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -8.2 \cdot 10^{+60} \lor \neg \left(b \leq 1.75 \cdot 10^{+89}\right):\\ \;\;\;\;x + z \cdot \left(y + a \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(y \cdot z + t \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= b -8.2e+60) (not (<= b 1.75e+89)))
   (+ x (* z (+ y (* a b))))
   (+ x (+ (* y z) (* t a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -8.2e+60) || !(b <= 1.75e+89)) {
		tmp = x + (z * (y + (a * b)));
	} else {
		tmp = x + ((y * z) + (t * a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((b <= (-8.2d+60)) .or. (.not. (b <= 1.75d+89))) then
        tmp = x + (z * (y + (a * b)))
    else
        tmp = x + ((y * z) + (t * a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((b <= -8.2e+60) || !(b <= 1.75e+89)) {
		tmp = x + (z * (y + (a * b)));
	} else {
		tmp = x + ((y * z) + (t * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (b <= -8.2e+60) or not (b <= 1.75e+89):
		tmp = x + (z * (y + (a * b)))
	else:
		tmp = x + ((y * z) + (t * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((b <= -8.2e+60) || !(b <= 1.75e+89))
		tmp = Float64(x + Float64(z * Float64(y + Float64(a * b))));
	else
		tmp = Float64(x + Float64(Float64(y * z) + Float64(t * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((b <= -8.2e+60) || ~((b <= 1.75e+89)))
		tmp = x + (z * (y + (a * b)));
	else
		tmp = x + ((y * z) + (t * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[b, -8.2e+60], N[Not[LessEqual[b, 1.75e+89]], $MachinePrecision]], N[(x + N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * z), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 93.8% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 13: 73.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.8 \cdot 10^{-60} \lor \neg \left(a \leq 6.5 \cdot 10^{-77}\right):\\ \;\;\;\;a \cdot \left(t + z \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -3.8e-60) (not (<= a 6.5e-77)))
   (* a (+ t (* z b)))
   (+ x (* y z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -3.8e-60) || !(a <= 6.5e-77)) {
		tmp = a * (t + (z * b));
	} else {
		tmp = x + (y * 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 ((a <= (-3.8d-60)) .or. (.not. (a <= 6.5d-77))) then
        tmp = a * (t + (z * b))
    else
        tmp = x + (y * 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 ((a <= -3.8e-60) || !(a <= 6.5e-77)) {
		tmp = a * (t + (z * b));
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -3.8e-60) or not (a <= 6.5e-77):
		tmp = a * (t + (z * b))
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -3.8e-60) || !(a <= 6.5e-77))
		tmp = Float64(a * Float64(t + Float64(z * b)));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -3.8e-60) || ~((a <= 6.5e-77)))
		tmp = a * (t + (z * b));
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -3.8e-60], N[Not[LessEqual[a, 6.5e-77]], $MachinePrecision]], N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 39.0% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.4 \cdot 10^{-91} \lor \neg \left(a \leq 6 \cdot 10^{-77}\right):\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -2.4e-91) (not (<= a 6e-77))) (* t a) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -2.4e-91) || !(a <= 6e-77)) {
		tmp = t * 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 ((a <= (-2.4d-91)) .or. (.not. (a <= 6d-77))) then
        tmp = t * 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 ((a <= -2.4e-91) || !(a <= 6e-77)) {
		tmp = t * a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -2.4e-91) or not (a <= 6e-77):
		tmp = t * a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -2.4e-91) || !(a <= 6e-77))
		tmp = Float64(t * a);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -2.4e-91) || ~((a <= 6e-77)))
		tmp = t * a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -2.4e-91], N[Not[LessEqual[a, 6e-77]], $MachinePrecision]], N[(t * a), $MachinePrecision], x]

Derivation

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

Alternative 15: 25.7% accurate, 15.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 97.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(b \cdot a + y\right) + \left(x + t \cdot a\right)\\ \mathbf{if}\;z < -11820553527347888000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z < 4.7589743188364287 \cdot 10^{-122}:\\ \;\;\;\;\left(b \cdot z + t\right) \cdot a + \left(z \cdot y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (* z (+ (* b a) y)) (+ x (* t a)))))
   (if (< z -11820553527347888000.0)
     t_1
     (if (< z 4.7589743188364287e-122)
       (+ (* (+ (* b z) t) a) (+ (* z y) x))
       t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z * ((b * a) + y)) + (x + (t * a));
	double tmp;
	if (z < -11820553527347888000.0) {
		tmp = t_1;
	} else if (z < 4.7589743188364287e-122) {
		tmp = (((b * z) + t) * a) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z * ((b * a) + y)) + (x + (t * a))
    if (z < (-11820553527347888000.0d0)) then
        tmp = t_1
    else if (z < 4.7589743188364287d-122) then
        tmp = (((b * z) + t) * a) + ((z * y) + x)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z * ((b * a) + y)) + (x + (t * a));
	double tmp;
	if (z < -11820553527347888000.0) {
		tmp = t_1;
	} else if (z < 4.7589743188364287e-122) {
		tmp = (((b * z) + t) * a) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z * ((b * a) + y)) + (x + (t * a))
	tmp = 0
	if z < -11820553527347888000.0:
		tmp = t_1
	elif z < 4.7589743188364287e-122:
		tmp = (((b * z) + t) * a) + ((z * y) + x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z * Float64(Float64(b * a) + y)) + Float64(x + Float64(t * a)))
	tmp = 0.0
	if (z < -11820553527347888000.0)
		tmp = t_1;
	elseif (z < 4.7589743188364287e-122)
		tmp = Float64(Float64(Float64(Float64(b * z) + t) * a) + Float64(Float64(z * y) + x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z * ((b * a) + y)) + (x + (t * a));
	tmp = 0.0;
	if (z < -11820553527347888000.0)
		tmp = t_1;
	elseif (z < 4.7589743188364287e-122)
		tmp = (((b * z) + t) * a) + ((z * y) + x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z * N[(N[(b * a), $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision] + N[(x + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -11820553527347888000.0], t$95$1, If[Less[z, 4.7589743188364287e-122], N[(N[(N[(N[(b * z), $MachinePrecision] + t), $MachinePrecision] * a), $MachinePrecision] + N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a b)
  :name "Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1"
  :precision binary64

  :herbie-target
  (if (< z -11820553527347888000.0) (+ (* z (+ (* b a) y)) (+ x (* t a))) (if (< z 4.7589743188364287e-122) (+ (* (+ (* b z) t) a) (+ (* z y) x)) (+ (* z (+ (* b a) y)) (+ x (* t a)))))

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