Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1

Percentage Accurate: 92.8% → 95.5%

Time: 10.2s

Alternatives: 15

Speedup: 0.9×

• 31.7% 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: 92.8% 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: 95.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x + y \cdot z\right) + a \cdot t\right) + b \cdot \left(z \cdot a\right)\\ \mathbf{if}\;t_1 \leq 2 \cdot 10^{+297}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t + z \cdot b\right) + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (+ (+ x (* y z)) (* a t)) (* b (* z a)))))
   (if (<= t_1 2e+297) t_1 (+ (* a (+ t (* z b))) (* y z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (y * z)) + (a * t)) + (b * (z * a));
	double tmp;
	if (t_1 <= 2e+297) {
		tmp = t_1;
	} else {
		tmp = (a * (t + (z * b))) + (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 + (y * z)) + (a * t)) + (b * (z * a))
    if (t_1 <= 2d+297) then
        tmp = t_1
    else
        tmp = (a * (t + (z * b))) + (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 + (y * z)) + (a * t)) + (b * (z * a));
	double tmp;
	if (t_1 <= 2e+297) {
		tmp = t_1;
	} else {
		tmp = (a * (t + (z * b))) + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((x + (y * z)) + (a * t)) + (b * (z * a))
	tmp = 0
	if t_1 <= 2e+297:
		tmp = t_1
	else:
		tmp = (a * (t + (z * b))) + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(x + Float64(y * z)) + Float64(a * t)) + Float64(b * Float64(z * a)))
	tmp = 0.0
	if (t_1 <= 2e+297)
		tmp = t_1;
	else
		tmp = Float64(Float64(a * Float64(t + Float64(z * b))) + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((x + (y * z)) + (a * t)) + (b * (z * a));
	tmp = 0.0;
	if (t_1 <= 2e+297)
		tmp = t_1;
	else
		tmp = (a * (t + (z * b))) + (y * z);
	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[(a * t), $MachinePrecision]), $MachinePrecision] + N[(b * N[(z * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 2e+297], t$95$1, N[(N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 94.2% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 3: 37.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(z \cdot b\right)\\ \mathbf{if}\;b \leq -1.4 \cdot 10^{+114}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -6.8 \cdot 10^{-27}:\\ \;\;\;\;x\\ \mathbf{elif}\;b \leq -1.25 \cdot 10^{-206}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;b \leq -9 \cdot 10^{-252}:\\ \;\;\;\;x\\ \mathbf{elif}\;b \leq 3.6 \cdot 10^{-290}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;b \leq 1.5 \cdot 10^{-151}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;b \leq 1.5 \cdot 10^{+98}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (* z b))))
   (if (<= b -1.4e+114)
     t_1
     (if (<= b -6.8e-27)
       x
       (if (<= b -1.25e-206)
         (* y z)
         (if (<= b -9e-252)
           x
           (if (<= b 3.6e-290)
             (* y z)
             (if (<= b 1.5e-151)
               (* a t)
               (if (<= b 1.5e+98) (* y z) 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 <= -1.4e+114) {
		tmp = t_1;
	} else if (b <= -6.8e-27) {
		tmp = x;
	} else if (b <= -1.25e-206) {
		tmp = y * z;
	} else if (b <= -9e-252) {
		tmp = x;
	} else if (b <= 3.6e-290) {
		tmp = y * z;
	} else if (b <= 1.5e-151) {
		tmp = a * t;
	} else if (b <= 1.5e+98) {
		tmp = y * z;
	} 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 <= (-1.4d+114)) then
        tmp = t_1
    else if (b <= (-6.8d-27)) then
        tmp = x
    else if (b <= (-1.25d-206)) then
        tmp = y * z
    else if (b <= (-9d-252)) then
        tmp = x
    else if (b <= 3.6d-290) then
        tmp = y * z
    else if (b <= 1.5d-151) then
        tmp = a * t
    else if (b <= 1.5d+98) then
        tmp = y * z
    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 <= -1.4e+114) {
		tmp = t_1;
	} else if (b <= -6.8e-27) {
		tmp = x;
	} else if (b <= -1.25e-206) {
		tmp = y * z;
	} else if (b <= -9e-252) {
		tmp = x;
	} else if (b <= 3.6e-290) {
		tmp = y * z;
	} else if (b <= 1.5e-151) {
		tmp = a * t;
	} else if (b <= 1.5e+98) {
		tmp = y * z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (z * b)
	tmp = 0
	if b <= -1.4e+114:
		tmp = t_1
	elif b <= -6.8e-27:
		tmp = x
	elif b <= -1.25e-206:
		tmp = y * z
	elif b <= -9e-252:
		tmp = x
	elif b <= 3.6e-290:
		tmp = y * z
	elif b <= 1.5e-151:
		tmp = a * t
	elif b <= 1.5e+98:
		tmp = y * z
	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 <= -1.4e+114)
		tmp = t_1;
	elseif (b <= -6.8e-27)
		tmp = x;
	elseif (b <= -1.25e-206)
		tmp = Float64(y * z);
	elseif (b <= -9e-252)
		tmp = x;
	elseif (b <= 3.6e-290)
		tmp = Float64(y * z);
	elseif (b <= 1.5e-151)
		tmp = Float64(a * t);
	elseif (b <= 1.5e+98)
		tmp = Float64(y * z);
	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 <= -1.4e+114)
		tmp = t_1;
	elseif (b <= -6.8e-27)
		tmp = x;
	elseif (b <= -1.25e-206)
		tmp = y * z;
	elseif (b <= -9e-252)
		tmp = x;
	elseif (b <= 3.6e-290)
		tmp = y * z;
	elseif (b <= 1.5e-151)
		tmp = a * t;
	elseif (b <= 1.5e+98)
		tmp = y * z;
	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, -1.4e+114], t$95$1, If[LessEqual[b, -6.8e-27], x, If[LessEqual[b, -1.25e-206], N[(y * z), $MachinePrecision], If[LessEqual[b, -9e-252], x, If[LessEqual[b, 3.6e-290], N[(y * z), $MachinePrecision], If[LessEqual[b, 1.5e-151], N[(a * t), $MachinePrecision], If[LessEqual[b, 1.5e+98], N[(y * z), $MachinePrecision], t$95$1]]]]]]]]

Derivation

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

Alternative 4: 81.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(y + a \cdot b\right)\\ \mathbf{if}\;z \leq -1.6 \cdot 10^{+144}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.6 \cdot 10^{+65} \lor \neg \left(z \leq -2.1 \cdot 10^{-61}\right) \land z \leq 6.5 \cdot 10^{+83}:\\ \;\;\;\;x + a \cdot \left(t + z \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;t_1 + a \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (+ y (* a b)))))
   (if (<= z -1.6e+144)
     t_1
     (if (or (<= z -6.6e+65) (and (not (<= z -2.1e-61)) (<= z 6.5e+83)))
       (+ x (* a (+ t (* z b))))
       (+ t_1 (* a t))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * b));
	double tmp;
	if (z <= -1.6e+144) {
		tmp = t_1;
	} else if ((z <= -6.6e+65) || (!(z <= -2.1e-61) && (z <= 6.5e+83))) {
		tmp = x + (a * (t + (z * b)));
	} else {
		tmp = t_1 + (a * t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = z * (y + (a * b))
    if (z <= (-1.6d+144)) then
        tmp = t_1
    else if ((z <= (-6.6d+65)) .or. (.not. (z <= (-2.1d-61))) .and. (z <= 6.5d+83)) then
        tmp = x + (a * (t + (z * b)))
    else
        tmp = t_1 + (a * t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * b));
	double tmp;
	if (z <= -1.6e+144) {
		tmp = t_1;
	} else if ((z <= -6.6e+65) || (!(z <= -2.1e-61) && (z <= 6.5e+83))) {
		tmp = x + (a * (t + (z * b)));
	} else {
		tmp = t_1 + (a * t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (y + (a * b))
	tmp = 0
	if z <= -1.6e+144:
		tmp = t_1
	elif (z <= -6.6e+65) or (not (z <= -2.1e-61) and (z <= 6.5e+83)):
		tmp = x + (a * (t + (z * b)))
	else:
		tmp = t_1 + (a * t)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(y + Float64(a * b)))
	tmp = 0.0
	if (z <= -1.6e+144)
		tmp = t_1;
	elseif ((z <= -6.6e+65) || (!(z <= -2.1e-61) && (z <= 6.5e+83)))
		tmp = Float64(x + Float64(a * Float64(t + Float64(z * b))));
	else
		tmp = Float64(t_1 + Float64(a * t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z * (y + (a * b));
	tmp = 0.0;
	if (z <= -1.6e+144)
		tmp = t_1;
	elseif ((z <= -6.6e+65) || (~((z <= -2.1e-61)) && (z <= 6.5e+83)))
		tmp = x + (a * (t + (z * b)));
	else
		tmp = t_1 + (a * t);
	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]}, If[LessEqual[z, -1.6e+144], t$95$1, If[Or[LessEqual[z, -6.6e+65], And[N[Not[LessEqual[z, -2.1e-61]], $MachinePrecision], LessEqual[z, 6.5e+83]]], N[(x + N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t$95$1 + N[(a * t), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 5: 37.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -1.95 \cdot 10^{+65}:\\ \;\;\;\;z \cdot \left(a \cdot b\right)\\ \mathbf{elif}\;b \leq -1.5 \cdot 10^{-209}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;b \leq -9 \cdot 10^{-253}:\\ \;\;\;\;x\\ \mathbf{elif}\;b \leq 1.6 \cdot 10^{-290}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;b \leq 6.4 \cdot 10^{-154}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;b \leq 1.7 \cdot 10^{+98}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(z \cdot b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= b -1.95e+65)
   (* z (* a b))
   (if (<= b -1.5e-209)
     (* y z)
     (if (<= b -9e-253)
       x
       (if (<= b 1.6e-290)
         (* y z)
         (if (<= b 6.4e-154)
           (* a t)
           (if (<= b 1.7e+98) (* y z) (* a (* z b)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (b <= -1.95e+65) {
		tmp = z * (a * b);
	} else if (b <= -1.5e-209) {
		tmp = y * z;
	} else if (b <= -9e-253) {
		tmp = x;
	} else if (b <= 1.6e-290) {
		tmp = y * z;
	} else if (b <= 6.4e-154) {
		tmp = a * t;
	} else if (b <= 1.7e+98) {
		tmp = y * z;
	} else {
		tmp = 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 (b <= (-1.95d+65)) then
        tmp = z * (a * b)
    else if (b <= (-1.5d-209)) then
        tmp = y * z
    else if (b <= (-9d-253)) then
        tmp = x
    else if (b <= 1.6d-290) then
        tmp = y * z
    else if (b <= 6.4d-154) then
        tmp = a * t
    else if (b <= 1.7d+98) then
        tmp = y * z
    else
        tmp = 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 (b <= -1.95e+65) {
		tmp = z * (a * b);
	} else if (b <= -1.5e-209) {
		tmp = y * z;
	} else if (b <= -9e-253) {
		tmp = x;
	} else if (b <= 1.6e-290) {
		tmp = y * z;
	} else if (b <= 6.4e-154) {
		tmp = a * t;
	} else if (b <= 1.7e+98) {
		tmp = y * z;
	} else {
		tmp = a * (z * b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if b <= -1.95e+65:
		tmp = z * (a * b)
	elif b <= -1.5e-209:
		tmp = y * z
	elif b <= -9e-253:
		tmp = x
	elif b <= 1.6e-290:
		tmp = y * z
	elif b <= 6.4e-154:
		tmp = a * t
	elif b <= 1.7e+98:
		tmp = y * z
	else:
		tmp = a * (z * b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (b <= -1.95e+65)
		tmp = Float64(z * Float64(a * b));
	elseif (b <= -1.5e-209)
		tmp = Float64(y * z);
	elseif (b <= -9e-253)
		tmp = x;
	elseif (b <= 1.6e-290)
		tmp = Float64(y * z);
	elseif (b <= 6.4e-154)
		tmp = Float64(a * t);
	elseif (b <= 1.7e+98)
		tmp = Float64(y * z);
	else
		tmp = Float64(a * Float64(z * b));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (b <= -1.95e+65)
		tmp = z * (a * b);
	elseif (b <= -1.5e-209)
		tmp = y * z;
	elseif (b <= -9e-253)
		tmp = x;
	elseif (b <= 1.6e-290)
		tmp = y * z;
	elseif (b <= 6.4e-154)
		tmp = a * t;
	elseif (b <= 1.7e+98)
		tmp = y * z;
	else
		tmp = a * (z * b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[b, -1.95e+65], N[(z * N[(a * b), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -1.5e-209], N[(y * z), $MachinePrecision], If[LessEqual[b, -9e-253], x, If[LessEqual[b, 1.6e-290], N[(y * z), $MachinePrecision], If[LessEqual[b, 6.4e-154], N[(a * t), $MachinePrecision], If[LessEqual[b, 1.7e+98], N[(y * z), $MachinePrecision], N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 6: 80.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.55 \cdot 10^{+144} \lor \neg \left(z \leq -1.3 \cdot 10^{+62} \lor \neg \left(z \leq -1.9 \cdot 10^{+28}\right) \land z \leq 1.9 \cdot 10^{+87}\right):\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \left(t + z \cdot b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -1.55e+144)
         (not (or (<= z -1.3e+62) (and (not (<= z -1.9e+28)) (<= z 1.9e+87)))))
   (* z (+ y (* a b)))
   (+ x (* a (+ t (* z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -1.55e+144) || !((z <= -1.3e+62) || (!(z <= -1.9e+28) && (z <= 1.9e+87)))) {
		tmp = z * (y + (a * b));
	} else {
		tmp = x + (a * (t + (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 ((z <= (-1.55d+144)) .or. (.not. (z <= (-1.3d+62)) .or. (.not. (z <= (-1.9d+28))) .and. (z <= 1.9d+87))) then
        tmp = z * (y + (a * b))
    else
        tmp = x + (a * (t + (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 ((z <= -1.55e+144) || !((z <= -1.3e+62) || (!(z <= -1.9e+28) && (z <= 1.9e+87)))) {
		tmp = z * (y + (a * b));
	} else {
		tmp = x + (a * (t + (z * b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -1.55e+144) or not ((z <= -1.3e+62) or (not (z <= -1.9e+28) and (z <= 1.9e+87))):
		tmp = z * (y + (a * b))
	else:
		tmp = x + (a * (t + (z * b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -1.55e+144) || !((z <= -1.3e+62) || (!(z <= -1.9e+28) && (z <= 1.9e+87))))
		tmp = Float64(z * Float64(y + Float64(a * b)));
	else
		tmp = Float64(x + Float64(a * Float64(t + Float64(z * b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -1.55e+144) || ~(((z <= -1.3e+62) || (~((z <= -1.9e+28)) && (z <= 1.9e+87)))))
		tmp = z * (y + (a * b));
	else
		tmp = x + (a * (t + (z * b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -1.55e+144], N[Not[Or[LessEqual[z, -1.3e+62], And[N[Not[LessEqual[z, -1.9e+28]], $MachinePrecision], LessEqual[z, 1.9e+87]]]], $MachinePrecision]], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 93.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.4 \cdot 10^{+218}:\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x + y \cdot z\right) + \left(a \cdot t + a \cdot \left(z \cdot b\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.4e+218)
   (* z (+ y (* a b)))
   (+ (+ x (* y z)) (+ (* a t) (* a (* z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.4e+218) {
		tmp = z * (y + (a * b));
	} else {
		tmp = (x + (y * z)) + ((a * t) + (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 (z <= (-1.4d+218)) then
        tmp = z * (y + (a * b))
    else
        tmp = (x + (y * z)) + ((a * t) + (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 (z <= -1.4e+218) {
		tmp = z * (y + (a * b));
	} else {
		tmp = (x + (y * z)) + ((a * t) + (a * (z * b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.4e+218:
		tmp = z * (y + (a * b))
	else:
		tmp = (x + (y * z)) + ((a * t) + (a * (z * b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.4e+218)
		tmp = Float64(z * Float64(y + Float64(a * b)));
	else
		tmp = Float64(Float64(x + Float64(y * z)) + Float64(Float64(a * t) + Float64(a * Float64(z * b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.4e+218)
		tmp = z * (y + (a * b));
	else
		tmp = (x + (y * z)) + ((a * t) + (a * (z * b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.4e+218], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(N[(a * t), $MachinePrecision] + N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 70.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7.5 \cdot 10^{+131} \lor \neg \left(z \leq -2.7 \cdot 10^{+67}\right) \land \left(z \leq -1.1 \cdot 10^{-60} \lor \neg \left(z \leq 4.4 \cdot 10^{+83}\right)\right):\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -7.5e+131)
         (and (not (<= z -2.7e+67)) (or (<= z -1.1e-60) (not (<= z 4.4e+83)))))
   (* z (+ y (* a b)))
   (+ x (* a t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -7.5e+131) || (!(z <= -2.7e+67) && ((z <= -1.1e-60) || !(z <= 4.4e+83)))) {
		tmp = z * (y + (a * b));
	} else {
		tmp = x + (a * t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-7.5d+131)) .or. (.not. (z <= (-2.7d+67))) .and. (z <= (-1.1d-60)) .or. (.not. (z <= 4.4d+83))) then
        tmp = z * (y + (a * b))
    else
        tmp = x + (a * t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -7.5e+131) || (!(z <= -2.7e+67) && ((z <= -1.1e-60) || !(z <= 4.4e+83)))) {
		tmp = z * (y + (a * b));
	} else {
		tmp = x + (a * t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -7.5e+131) or (not (z <= -2.7e+67) and ((z <= -1.1e-60) or not (z <= 4.4e+83))):
		tmp = z * (y + (a * b))
	else:
		tmp = x + (a * t)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -7.5e+131) || (!(z <= -2.7e+67) && ((z <= -1.1e-60) || !(z <= 4.4e+83))))
		tmp = Float64(z * Float64(y + Float64(a * b)));
	else
		tmp = Float64(x + Float64(a * t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -7.5e+131) || (~((z <= -2.7e+67)) && ((z <= -1.1e-60) || ~((z <= 4.4e+83)))))
		tmp = z * (y + (a * b));
	else
		tmp = x + (a * t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -7.5e+131], And[N[Not[LessEqual[z, -2.7e+67]], $MachinePrecision], Or[LessEqual[z, -1.1e-60], N[Not[LessEqual[z, 4.4e+83]], $MachinePrecision]]]], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a * t), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 73.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + z \cdot b\right)\\ \mathbf{if}\;a \leq -64000000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 4 \cdot 10^{-76}:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;a \leq 2.05 \cdot 10^{+24}:\\ \;\;\;\;x + a \cdot t\\ \mathbf{elif}\;a \leq 2.15 \cdot 10^{+38}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* z b)))))
   (if (<= a -64000000000.0)
     t_1
     (if (<= a 4e-76)
       (+ x (* y z))
       (if (<= a 2.05e+24) (+ x (* a t)) (if (<= a 2.15e+38) (* 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 <= -64000000000.0) {
		tmp = t_1;
	} else if (a <= 4e-76) {
		tmp = x + (y * z);
	} else if (a <= 2.05e+24) {
		tmp = x + (a * t);
	} else if (a <= 2.15e+38) {
		tmp = y * z;
	} 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 * (t + (z * b))
    if (a <= (-64000000000.0d0)) then
        tmp = t_1
    else if (a <= 4d-76) then
        tmp = x + (y * z)
    else if (a <= 2.05d+24) then
        tmp = x + (a * t)
    else if (a <= 2.15d+38) then
        tmp = y * z
    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 * (t + (z * b));
	double tmp;
	if (a <= -64000000000.0) {
		tmp = t_1;
	} else if (a <= 4e-76) {
		tmp = x + (y * z);
	} else if (a <= 2.05e+24) {
		tmp = x + (a * t);
	} else if (a <= 2.15e+38) {
		tmp = y * z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (z * b))
	tmp = 0
	if a <= -64000000000.0:
		tmp = t_1
	elif a <= 4e-76:
		tmp = x + (y * z)
	elif a <= 2.05e+24:
		tmp = x + (a * t)
	elif a <= 2.15e+38:
		tmp = y * z
	else:
		tmp = 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 <= -64000000000.0)
		tmp = t_1;
	elseif (a <= 4e-76)
		tmp = Float64(x + Float64(y * z));
	elseif (a <= 2.05e+24)
		tmp = Float64(x + Float64(a * t));
	elseif (a <= 2.15e+38)
		tmp = Float64(y * z);
	else
		tmp = 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 <= -64000000000.0)
		tmp = t_1;
	elseif (a <= 4e-76)
		tmp = x + (y * z);
	elseif (a <= 2.05e+24)
		tmp = x + (a * t);
	elseif (a <= 2.15e+38)
		tmp = y * z;
	else
		tmp = 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, -64000000000.0], t$95$1, If[LessEqual[a, 4e-76], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.05e+24], N[(x + N[(a * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.15e+38], N[(y * z), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 10: 82.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + z \cdot b\right)\\ \mathbf{if}\;y \leq -7 \cdot 10^{+69}:\\ \;\;\;\;z \cdot \left(y + a \cdot b\right) + a \cdot t\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{+53}:\\ \;\;\;\;x + t_1\\ \mathbf{else}:\\ \;\;\;\;t_1 + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* z b)))))
   (if (<= y -7e+69)
     (+ (* z (+ y (* a b))) (* a t))
     (if (<= y 7.2e+53) (+ x t_1) (+ t_1 (* y z))))))

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 (y <= -7e+69) {
		tmp = (z * (y + (a * b))) + (a * t);
	} else if (y <= 7.2e+53) {
		tmp = x + t_1;
	} else {
		tmp = t_1 + (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 * (t + (z * b))
    if (y <= (-7d+69)) then
        tmp = (z * (y + (a * b))) + (a * t)
    else if (y <= 7.2d+53) then
        tmp = x + t_1
    else
        tmp = t_1 + (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 * (t + (z * b));
	double tmp;
	if (y <= -7e+69) {
		tmp = (z * (y + (a * b))) + (a * t);
	} else if (y <= 7.2e+53) {
		tmp = x + t_1;
	} else {
		tmp = t_1 + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (z * b))
	tmp = 0
	if y <= -7e+69:
		tmp = (z * (y + (a * b))) + (a * t)
	elif y <= 7.2e+53:
		tmp = x + t_1
	else:
		tmp = t_1 + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(t + Float64(z * b)))
	tmp = 0.0
	if (y <= -7e+69)
		tmp = Float64(Float64(z * Float64(y + Float64(a * b))) + Float64(a * t));
	elseif (y <= 7.2e+53)
		tmp = Float64(x + t_1);
	else
		tmp = Float64(t_1 + Float64(y * z));
	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 (y <= -7e+69)
		tmp = (z * (y + (a * b))) + (a * t);
	elseif (y <= 7.2e+53)
		tmp = x + t_1;
	else
		tmp = t_1 + (y * z);
	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[y, -7e+69], N[(N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.2e+53], N[(x + t$95$1), $MachinePrecision], N[(t$95$1 + N[(y * z), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 11: 39.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.9 \cdot 10^{+54}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{-126}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 2.1 \cdot 10^{+145}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;a \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -4.9e+54)
   (* a t)
   (if (<= t 2.8e-126) x (if (<= t 2.1e+145) (* y z) (* a t)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -4.9e+54) {
		tmp = a * t;
	} else if (t <= 2.8e-126) {
		tmp = x;
	} else if (t <= 2.1e+145) {
		tmp = y * z;
	} else {
		tmp = a * t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-4.9d+54)) then
        tmp = a * t
    else if (t <= 2.8d-126) then
        tmp = x
    else if (t <= 2.1d+145) then
        tmp = y * z
    else
        tmp = a * t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -4.9e+54) {
		tmp = a * t;
	} else if (t <= 2.8e-126) {
		tmp = x;
	} else if (t <= 2.1e+145) {
		tmp = y * z;
	} else {
		tmp = a * t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -4.9e+54:
		tmp = a * t
	elif t <= 2.8e-126:
		tmp = x
	elif t <= 2.1e+145:
		tmp = y * z
	else:
		tmp = a * t
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -4.9e+54)
		tmp = Float64(a * t);
	elseif (t <= 2.8e-126)
		tmp = x;
	elseif (t <= 2.1e+145)
		tmp = Float64(y * z);
	else
		tmp = Float64(a * t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -4.9e+54)
		tmp = a * t;
	elseif (t <= 2.8e-126)
		tmp = x;
	elseif (t <= 2.1e+145)
		tmp = y * z;
	else
		tmp = a * t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -4.9e+54], N[(a * t), $MachinePrecision], If[LessEqual[t, 2.8e-126], x, If[LessEqual[t, 2.1e+145], N[(y * z), $MachinePrecision], N[(a * t), $MachinePrecision]]]]

Derivation

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

Alternative 12: 63.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.2 \cdot 10^{+85} \lor \neg \left(t \leq 2.3 \cdot 10^{+145}\right):\\ \;\;\;\;x + a \cdot t\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -9.2e+85) (not (<= t 2.3e+145))) (+ x (* a t)) (+ x (* y z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -9.2e+85) || !(t <= 2.3e+145)) {
		tmp = x + (a * t);
	} 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 ((t <= (-9.2d+85)) .or. (.not. (t <= 2.3d+145))) then
        tmp = x + (a * t)
    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 ((t <= -9.2e+85) || !(t <= 2.3e+145)) {
		tmp = x + (a * t);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -9.2e+85) or not (t <= 2.3e+145):
		tmp = x + (a * t)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -9.2e+85) || !(t <= 2.3e+145))
		tmp = Float64(x + Float64(a * t));
	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 ((t <= -9.2e+85) || ~((t <= 2.3e+145)))
		tmp = x + (a * t);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -9.2e+85], N[Not[LessEqual[t, 2.3e+145]], $MachinePrecision]], N[(x + N[(a * t), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 58.1% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -8.2 \cdot 10^{+202}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+41}:\\ \;\;\;\;x + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(z \cdot b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -8.2e+202)
   (* a t)
   (if (<= a 1.02e+41) (+ x (* y z)) (* a (* z b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -8.2e+202) {
		tmp = a * t;
	} else if (a <= 1.02e+41) {
		tmp = x + (y * z);
	} else {
		tmp = 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 (a <= (-8.2d+202)) then
        tmp = a * t
    else if (a <= 1.02d+41) then
        tmp = x + (y * z)
    else
        tmp = 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 (a <= -8.2e+202) {
		tmp = a * t;
	} else if (a <= 1.02e+41) {
		tmp = x + (y * z);
	} else {
		tmp = a * (z * b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -8.2e+202:
		tmp = a * t
	elif a <= 1.02e+41:
		tmp = x + (y * z)
	else:
		tmp = a * (z * b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -8.2e+202)
		tmp = Float64(a * t);
	elseif (a <= 1.02e+41)
		tmp = Float64(x + Float64(y * z));
	else
		tmp = Float64(a * Float64(z * b));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -8.2e+202)
		tmp = a * t;
	elseif (a <= 1.02e+41)
		tmp = x + (y * z);
	else
		tmp = a * (z * b);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -8.2e+202], N[(a * t), $MachinePrecision], If[LessEqual[a, 1.02e+41], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 14: 40.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.6 \cdot 10^{+44} \lor \neg \left(t \leq 6 \cdot 10^{+20}\right):\\ \;\;\;\;a \cdot t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -3.6e+44) (not (<= t 6e+20))) (* a t) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -3.6e+44) || !(t <= 6e+20)) {
		tmp = a * t;
	} 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 ((t <= (-3.6d+44)) .or. (.not. (t <= 6d+20))) then
        tmp = a * t
    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 ((t <= -3.6e+44) || !(t <= 6e+20)) {
		tmp = a * t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -3.6e+44) or not (t <= 6e+20):
		tmp = a * t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -3.6e+44) || !(t <= 6e+20))
		tmp = Float64(a * t);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((t <= -3.6e+44) || ~((t <= 6e+20)))
		tmp = a * t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -3.6e+44], N[Not[LessEqual[t, 6e+20]], $MachinePrecision]], N[(a * t), $MachinePrecision], x]

Derivation

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

Alternative 15: 26.7% 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: 97.7% 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 2023350 
(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)))