Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1

Percentage Accurate: 92.2% → 97.7%

Time: 13.5s

Alternatives: 14

Speedup: 0.7×

• 31.8% 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: 92.2% 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: 97.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(b + \frac{t}{z}\right)\\ \mathbf{if}\;z \leq -7.5 \cdot 10^{+136}:\\ \;\;\;\;z \cdot \left(y + t\_1\right)\\ \mathbf{elif}\;z \leq 5 \cdot 10^{-13}:\\ \;\;\;\;x + \left(y \cdot z + a \cdot \left(t + z \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \left(t\_1 + y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ b (/ t z)))))
   (if (<= z -7.5e+136)
     (* z (+ y t_1))
     (if (<= z 5e-13)
       (+ x (+ (* y z) (* a (+ t (* z b)))))
       (+ x (* z (+ t_1 y)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (b + (t / z));
	double tmp;
	if (z <= -7.5e+136) {
		tmp = z * (y + t_1);
	} else if (z <= 5e-13) {
		tmp = x + ((y * z) + (a * (t + (z * b))));
	} else {
		tmp = x + (z * (t_1 + 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) :: t_1
    real(8) :: tmp
    t_1 = a * (b + (t / z))
    if (z <= (-7.5d+136)) then
        tmp = z * (y + t_1)
    else if (z <= 5d-13) then
        tmp = x + ((y * z) + (a * (t + (z * b))))
    else
        tmp = x + (z * (t_1 + 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 t_1 = a * (b + (t / z));
	double tmp;
	if (z <= -7.5e+136) {
		tmp = z * (y + t_1);
	} else if (z <= 5e-13) {
		tmp = x + ((y * z) + (a * (t + (z * b))));
	} else {
		tmp = x + (z * (t_1 + y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (b + (t / z))
	tmp = 0
	if z <= -7.5e+136:
		tmp = z * (y + t_1)
	elif z <= 5e-13:
		tmp = x + ((y * z) + (a * (t + (z * b))))
	else:
		tmp = x + (z * (t_1 + y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(b + Float64(t / z)))
	tmp = 0.0
	if (z <= -7.5e+136)
		tmp = Float64(z * Float64(y + t_1));
	elseif (z <= 5e-13)
		tmp = Float64(x + Float64(Float64(y * z) + Float64(a * Float64(t + Float64(z * b)))));
	else
		tmp = Float64(x + Float64(z * Float64(t_1 + y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (b + (t / z));
	tmp = 0.0;
	if (z <= -7.5e+136)
		tmp = z * (y + t_1);
	elseif (z <= 5e-13)
		tmp = x + ((y * z) + (a * (t + (z * b))));
	else
		tmp = x + (z * (t_1 + y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(b + N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.5e+136], N[(z * N[(y + t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5e-13], N[(x + N[(N[(y * z), $MachinePrecision] + N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(t$95$1 + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -7.5000000000000002e136

   1. Initial program 87.5%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -7.5000000000000002e136 < z < 4.9999999999999999e-13

   1. Initial program 96.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   if 4.9999999999999999e-13 < z

   1. Initial program 78.1%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 98.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b\\ \mathbf{if}\;t\_1 \leq 2 \cdot 10^{+298}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \left(a \cdot \left(b + \frac{t}{z}\right) + y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (+ (+ x (* y z)) (* t a)) (* (* a z) b))))
   (if (<= t_1 2e+298) t_1 (+ x (* z (+ (* a (+ b (/ t z))) y))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (y * z)) + (t * a)) + ((a * z) * b);
	double tmp;
	if (t_1 <= 2e+298) {
		tmp = t_1;
	} else {
		tmp = x + (z * ((a * (b + (t / z))) + 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) :: t_1
    real(8) :: tmp
    t_1 = ((x + (y * z)) + (t * a)) + ((a * z) * b)
    if (t_1 <= 2d+298) then
        tmp = t_1
    else
        tmp = x + (z * ((a * (b + (t / z))) + 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 t_1 = ((x + (y * z)) + (t * a)) + ((a * z) * b);
	double tmp;
	if (t_1 <= 2e+298) {
		tmp = t_1;
	} else {
		tmp = x + (z * ((a * (b + (t / z))) + y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((x + (y * z)) + (t * a)) + ((a * z) * b)
	tmp = 0
	if t_1 <= 2e+298:
		tmp = t_1
	else:
		tmp = x + (z * ((a * (b + (t / z))) + y))
	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(a * z) * b))
	tmp = 0.0
	if (t_1 <= 2e+298)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(z * Float64(Float64(a * Float64(b + Float64(t / z))) + y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((x + (y * z)) + (t * a)) + ((a * z) * b);
	tmp = 0.0;
	if (t_1 <= 2e+298)
		tmp = t_1;
	else
		tmp = x + (z * ((a * (b + (t / z))) + y));
	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[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 2e+298], t$95$1, N[(x + N[(z * N[(N[(a * N[(b + N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 x (*.f64 y z)) (*.f64 t a)) (*.f64 (*.f64 a z) b)) < 1.9999999999999999e298

   1. Initial program 98.5%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]
   2. Add Preprocessing

   if 1.9999999999999999e298 < (+.f64 (+.f64 (+.f64 x (*.f64 y z)) (*.f64 t a)) (*.f64 (*.f64 a z) b))

   1. Initial program 61.0%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 74.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + b \cdot z\right)\\ \mathbf{if}\;a \leq -1.9 \cdot 10^{+60}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -1.12 \cdot 10^{-66}:\\ \;\;\;\;a \cdot t + z \cdot y\\ \mathbf{elif}\;a \leq -7 \cdot 10^{-92}:\\ \;\;\;\;x + \left(a \cdot z\right) \cdot b\\ \mathbf{elif}\;a \leq 1.2 \cdot 10^{-38}:\\ \;\;\;\;z \cdot y + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* b z)))))
   (if (<= a -1.9e+60)
     t_1
     (if (<= a -1.12e-66)
       (+ (* a t) (* z y))
       (if (<= a -7e-92)
         (+ x (* (* a z) b))
         (if (<= a 1.2e-38) (+ (* z y) x) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (b * z));
	double tmp;
	if (a <= -1.9e+60) {
		tmp = t_1;
	} else if (a <= -1.12e-66) {
		tmp = (a * t) + (z * y);
	} else if (a <= -7e-92) {
		tmp = x + ((a * z) * b);
	} else if (a <= 1.2e-38) {
		tmp = (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 = a * (t + (b * z))
    if (a <= (-1.9d+60)) then
        tmp = t_1
    else if (a <= (-1.12d-66)) then
        tmp = (a * t) + (z * y)
    else if (a <= (-7d-92)) then
        tmp = x + ((a * z) * b)
    else if (a <= 1.2d-38) then
        tmp = (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 = a * (t + (b * z));
	double tmp;
	if (a <= -1.9e+60) {
		tmp = t_1;
	} else if (a <= -1.12e-66) {
		tmp = (a * t) + (z * y);
	} else if (a <= -7e-92) {
		tmp = x + ((a * z) * b);
	} else if (a <= 1.2e-38) {
		tmp = (z * y) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (b * z))
	tmp = 0
	if a <= -1.9e+60:
		tmp = t_1
	elif a <= -1.12e-66:
		tmp = (a * t) + (z * y)
	elif a <= -7e-92:
		tmp = x + ((a * z) * b)
	elif a <= 1.2e-38:
		tmp = (z * y) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(t + Float64(b * z)))
	tmp = 0.0
	if (a <= -1.9e+60)
		tmp = t_1;
	elseif (a <= -1.12e-66)
		tmp = Float64(Float64(a * t) + Float64(z * y));
	elseif (a <= -7e-92)
		tmp = Float64(x + Float64(Float64(a * z) * b));
	elseif (a <= 1.2e-38)
		tmp = 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 = a * (t + (b * z));
	tmp = 0.0;
	if (a <= -1.9e+60)
		tmp = t_1;
	elseif (a <= -1.12e-66)
		tmp = (a * t) + (z * y);
	elseif (a <= -7e-92)
		tmp = x + ((a * z) * b);
	elseif (a <= 1.2e-38)
		tmp = (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[(a * N[(t + N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.9e+60], t$95$1, If[LessEqual[a, -1.12e-66], N[(N[(a * t), $MachinePrecision] + N[(z * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -7e-92], N[(x + N[(N[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.2e-38], N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if a < -1.90000000000000005e60 or 1.20000000000000011e-38 < a

   1. Initial program 81.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.90000000000000005e60 < a < -1.12000000000000004e-66

   1. Initial program 97.2%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.12000000000000004e-66 < a < -7e-92

   1. Initial program 99.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -7e-92 < a < 1.20000000000000011e-38

   1. Initial program 99.0%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 38.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.85 \cdot 10^{-37}:\\ \;\;\;\;b \cdot \left(a \cdot z\right)\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{-289}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-122}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+122}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\left(z \cdot b\right) \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.85e-37)
   (* b (* a z))
   (if (<= z 2.1e-289)
     x
     (if (<= z 4.5e-122) (* a t) (if (<= z 2.8e+122) x (* (* z b) a))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.85e-37) {
		tmp = b * (a * z);
	} else if (z <= 2.1e-289) {
		tmp = x;
	} else if (z <= 4.5e-122) {
		tmp = a * t;
	} else if (z <= 2.8e+122) {
		tmp = x;
	} else {
		tmp = (z * b) * a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-1.85d-37)) then
        tmp = b * (a * z)
    else if (z <= 2.1d-289) then
        tmp = x
    else if (z <= 4.5d-122) then
        tmp = a * t
    else if (z <= 2.8d+122) then
        tmp = x
    else
        tmp = (z * b) * a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.85e-37) {
		tmp = b * (a * z);
	} else if (z <= 2.1e-289) {
		tmp = x;
	} else if (z <= 4.5e-122) {
		tmp = a * t;
	} else if (z <= 2.8e+122) {
		tmp = x;
	} else {
		tmp = (z * b) * a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.85e-37:
		tmp = b * (a * z)
	elif z <= 2.1e-289:
		tmp = x
	elif z <= 4.5e-122:
		tmp = a * t
	elif z <= 2.8e+122:
		tmp = x
	else:
		tmp = (z * b) * a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.85e-37)
		tmp = Float64(b * Float64(a * z));
	elseif (z <= 2.1e-289)
		tmp = x;
	elseif (z <= 4.5e-122)
		tmp = Float64(a * t);
	elseif (z <= 2.8e+122)
		tmp = x;
	else
		tmp = Float64(Float64(z * b) * a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.85e-37)
		tmp = b * (a * z);
	elseif (z <= 2.1e-289)
		tmp = x;
	elseif (z <= 4.5e-122)
		tmp = a * t;
	elseif (z <= 2.8e+122)
		tmp = x;
	else
		tmp = (z * b) * a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.85e-37], N[(b * N[(a * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.1e-289], x, If[LessEqual[z, 4.5e-122], N[(a * t), $MachinePrecision], If[LessEqual[z, 2.8e+122], x, N[(N[(z * b), $MachinePrecision] * a), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -1.85e-37

   1. Initial program 86.3%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.85e-37 < z < 2.0999999999999998e-289 or 4.4999999999999998e-122 < z < 2.8e122

   1. Initial program 99.1%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 2.0999999999999998e-289 < z < 4.4999999999999998e-122

   1. Initial program 99.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 2.8e122 < z

   1. Initial program 62.3%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 5: 38.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.2 \cdot 10^{-39}:\\ \;\;\;\;b \cdot \left(a \cdot z\right)\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-290}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{-122}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;z \leq 2.5 \cdot 10^{+122}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot b\right) \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -3.2e-39)
   (* b (* a z))
   (if (<= z 6e-290)
     x
     (if (<= z 3.8e-122) (* a t) (if (<= z 2.5e+122) x (* (* a b) z))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -3.2e-39) {
		tmp = b * (a * z);
	} else if (z <= 6e-290) {
		tmp = x;
	} else if (z <= 3.8e-122) {
		tmp = a * t;
	} else if (z <= 2.5e+122) {
		tmp = x;
	} else {
		tmp = (a * b) * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-3.2d-39)) then
        tmp = b * (a * z)
    else if (z <= 6d-290) then
        tmp = x
    else if (z <= 3.8d-122) then
        tmp = a * t
    else if (z <= 2.5d+122) then
        tmp = x
    else
        tmp = (a * b) * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -3.2e-39) {
		tmp = b * (a * z);
	} else if (z <= 6e-290) {
		tmp = x;
	} else if (z <= 3.8e-122) {
		tmp = a * t;
	} else if (z <= 2.5e+122) {
		tmp = x;
	} else {
		tmp = (a * b) * z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -3.2e-39:
		tmp = b * (a * z)
	elif z <= 6e-290:
		tmp = x
	elif z <= 3.8e-122:
		tmp = a * t
	elif z <= 2.5e+122:
		tmp = x
	else:
		tmp = (a * b) * z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -3.2e-39)
		tmp = Float64(b * Float64(a * z));
	elseif (z <= 6e-290)
		tmp = x;
	elseif (z <= 3.8e-122)
		tmp = Float64(a * t);
	elseif (z <= 2.5e+122)
		tmp = x;
	else
		tmp = Float64(Float64(a * b) * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -3.2e-39)
		tmp = b * (a * z);
	elseif (z <= 6e-290)
		tmp = x;
	elseif (z <= 3.8e-122)
		tmp = a * t;
	elseif (z <= 2.5e+122)
		tmp = x;
	else
		tmp = (a * b) * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -3.2e-39], N[(b * N[(a * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6e-290], x, If[LessEqual[z, 3.8e-122], N[(a * t), $MachinePrecision], If[LessEqual[z, 2.5e+122], x, N[(N[(a * b), $MachinePrecision] * z), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -3.1999999999999998e-39

   1. Initial program 86.3%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.1999999999999998e-39 < z < 5.99999999999999985e-290 or 3.8000000000000001e-122 < z < 2.49999999999999994e122

   1. Initial program 99.1%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 5.99999999999999985e-290 < z < 3.8000000000000001e-122

   1. Initial program 99.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 2.49999999999999994e122 < z

   1. Initial program 62.3%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 6: 38.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(a \cdot z\right)\\ \mathbf{if}\;z \leq -2.1 \cdot 10^{-36}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{-291}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{-122}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;z \leq 2.4 \cdot 10^{+122}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (* a z))))
   (if (<= z -2.1e-36)
     t_1
     (if (<= z 2.2e-291)
       x
       (if (<= z 4.7e-122) (* a t) (if (<= z 2.4e+122) x t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * (a * z);
	double tmp;
	if (z <= -2.1e-36) {
		tmp = t_1;
	} else if (z <= 2.2e-291) {
		tmp = x;
	} else if (z <= 4.7e-122) {
		tmp = a * t;
	} else if (z <= 2.4e+122) {
		tmp = 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 = b * (a * z)
    if (z <= (-2.1d-36)) then
        tmp = t_1
    else if (z <= 2.2d-291) then
        tmp = x
    else if (z <= 4.7d-122) then
        tmp = a * t
    else if (z <= 2.4d+122) then
        tmp = 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 = b * (a * z);
	double tmp;
	if (z <= -2.1e-36) {
		tmp = t_1;
	} else if (z <= 2.2e-291) {
		tmp = x;
	} else if (z <= 4.7e-122) {
		tmp = a * t;
	} else if (z <= 2.4e+122) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * (a * z)
	tmp = 0
	if z <= -2.1e-36:
		tmp = t_1
	elif z <= 2.2e-291:
		tmp = x
	elif z <= 4.7e-122:
		tmp = a * t
	elif z <= 2.4e+122:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(a * z))
	tmp = 0.0
	if (z <= -2.1e-36)
		tmp = t_1;
	elseif (z <= 2.2e-291)
		tmp = x;
	elseif (z <= 4.7e-122)
		tmp = Float64(a * t);
	elseif (z <= 2.4e+122)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = b * (a * z);
	tmp = 0.0;
	if (z <= -2.1e-36)
		tmp = t_1;
	elseif (z <= 2.2e-291)
		tmp = x;
	elseif (z <= 4.7e-122)
		tmp = a * t;
	elseif (z <= 2.4e+122)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(b * N[(a * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.1e-36], t$95$1, If[LessEqual[z, 2.2e-291], x, If[LessEqual[z, 4.7e-122], N[(a * t), $MachinePrecision], If[LessEqual[z, 2.4e+122], x, t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.09999999999999991e-36 or 2.4000000000000002e122 < z

   1. Initial program 78.2%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.09999999999999991e-36 < z < 2.20000000000000002e-291 or 4.6999999999999999e-122 < z < 2.4000000000000002e122

   1. Initial program 99.1%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 2.20000000000000002e-291 < z < 4.6999999999999999e-122

   1. Initial program 99.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 95.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \left(a \cdot \left(b + \frac{t}{z}\right) + y\right)\\ \mathbf{if}\;z \leq -3.4 \cdot 10^{-62}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 7.2 \cdot 10^{-152}:\\ \;\;\;\;a \cdot t + \left(z \cdot y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* z (+ (* a (+ b (/ t z))) y)))))
   (if (<= z -3.4e-62)
     t_1
     (if (<= z 7.2e-152) (+ (* a t) (+ (* z y) x)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (z * ((a * (b + (t / z))) + y));
	double tmp;
	if (z <= -3.4e-62) {
		tmp = t_1;
	} else if (z <= 7.2e-152) {
		tmp = (a * t) + ((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 = x + (z * ((a * (b + (t / z))) + y))
    if (z <= (-3.4d-62)) then
        tmp = t_1
    else if (z <= 7.2d-152) then
        tmp = (a * t) + ((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 = x + (z * ((a * (b + (t / z))) + y));
	double tmp;
	if (z <= -3.4e-62) {
		tmp = t_1;
	} else if (z <= 7.2e-152) {
		tmp = (a * t) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (z * ((a * (b + (t / z))) + y))
	tmp = 0
	if z <= -3.4e-62:
		tmp = t_1
	elif z <= 7.2e-152:
		tmp = (a * t) + ((z * y) + x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(z * Float64(Float64(a * Float64(b + Float64(t / z))) + y)))
	tmp = 0.0
	if (z <= -3.4e-62)
		tmp = t_1;
	elseif (z <= 7.2e-152)
		tmp = Float64(Float64(a * t) + 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 = x + (z * ((a * (b + (t / z))) + y));
	tmp = 0.0;
	if (z <= -3.4e-62)
		tmp = t_1;
	elseif (z <= 7.2e-152)
		tmp = (a * t) + ((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[(x + N[(z * N[(N[(a * N[(b + N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.4e-62], t$95$1, If[LessEqual[z, 7.2e-152], N[(N[(a * t), $MachinePrecision] + N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -3.39999999999999988e-62 or 7.2e-152 < z

   1. Initial program 86.4%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.39999999999999988e-62 < z < 7.2e-152

   1. Initial program 99.9%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 86.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(y + a \cdot \left(b + \frac{t}{z}\right)\right)\\ \mathbf{if}\;z \leq -1.75 \cdot 10^{-12}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.9 \cdot 10^{+120}:\\ \;\;\;\;a \cdot t + \left(z \cdot y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* z (+ y (* a (+ b (/ t z)))))))
   (if (<= z -1.75e-12)
     t_1
     (if (<= z 3.9e+120) (+ (* a t) (+ (* z y) x)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z * (y + (a * (b + (t / z))));
	double tmp;
	if (z <= -1.75e-12) {
		tmp = t_1;
	} else if (z <= 3.9e+120) {
		tmp = (a * t) + ((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 * (y + (a * (b + (t / z))))
    if (z <= (-1.75d-12)) then
        tmp = t_1
    else if (z <= 3.9d+120) then
        tmp = (a * t) + ((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 * (y + (a * (b + (t / z))));
	double tmp;
	if (z <= -1.75e-12) {
		tmp = t_1;
	} else if (z <= 3.9e+120) {
		tmp = (a * t) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z * (y + (a * (b + (t / z))))
	tmp = 0
	if z <= -1.75e-12:
		tmp = t_1
	elif z <= 3.9e+120:
		tmp = (a * t) + ((z * y) + x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z * Float64(y + Float64(a * Float64(b + Float64(t / z)))))
	tmp = 0.0
	if (z <= -1.75e-12)
		tmp = t_1;
	elseif (z <= 3.9e+120)
		tmp = Float64(Float64(a * t) + 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 * (y + (a * (b + (t / z))));
	tmp = 0.0;
	if (z <= -1.75e-12)
		tmp = t_1;
	elseif (z <= 3.9e+120)
		tmp = (a * t) + ((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[(z * N[(y + N[(a * N[(b + N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.75e-12], t$95$1, If[LessEqual[z, 3.9e+120], N[(N[(a * t), $MachinePrecision] + N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.75e-12 or 3.8999999999999998e120 < z

   1. Initial program 77.7%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.75e-12 < z < 3.8999999999999998e120

   1. Initial program 99.3%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 59.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(a \cdot z\right)\\ \mathbf{if}\;a \leq -1.85 \cdot 10^{+109}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -2.9 \cdot 10^{+37}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;a \leq 6.6 \cdot 10^{+125}:\\ \;\;\;\;z \cdot y + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* b (* a z))))
   (if (<= a -1.85e+109)
     t_1
     (if (<= a -2.9e+37) (* a t) (if (<= a 6.6e+125) (+ (* z y) x) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = b * (a * z);
	double tmp;
	if (a <= -1.85e+109) {
		tmp = t_1;
	} else if (a <= -2.9e+37) {
		tmp = a * t;
	} else if (a <= 6.6e+125) {
		tmp = (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 = b * (a * z)
    if (a <= (-1.85d+109)) then
        tmp = t_1
    else if (a <= (-2.9d+37)) then
        tmp = a * t
    else if (a <= 6.6d+125) then
        tmp = (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 = b * (a * z);
	double tmp;
	if (a <= -1.85e+109) {
		tmp = t_1;
	} else if (a <= -2.9e+37) {
		tmp = a * t;
	} else if (a <= 6.6e+125) {
		tmp = (z * y) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = b * (a * z)
	tmp = 0
	if a <= -1.85e+109:
		tmp = t_1
	elif a <= -2.9e+37:
		tmp = a * t
	elif a <= 6.6e+125:
		tmp = (z * y) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b * Float64(a * z))
	tmp = 0.0
	if (a <= -1.85e+109)
		tmp = t_1;
	elseif (a <= -2.9e+37)
		tmp = Float64(a * t);
	elseif (a <= 6.6e+125)
		tmp = 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 = b * (a * z);
	tmp = 0.0;
	if (a <= -1.85e+109)
		tmp = t_1;
	elseif (a <= -2.9e+37)
		tmp = a * t;
	elseif (a <= 6.6e+125)
		tmp = (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[(b * N[(a * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.85e+109], t$95$1, If[LessEqual[a, -2.9e+37], N[(a * t), $MachinePrecision], If[LessEqual[a, 6.6e+125], N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if a < -1.8500000000000001e109 or 6.60000000000000011e125 < a

   1. Initial program 78.5%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.8500000000000001e109 < a < -2.89999999999999978e37

   1. Initial program 83.7%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.89999999999999978e37 < a < 6.60000000000000011e125

   1. Initial program 98.1%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 83.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + b \cdot z\right)\\ \mathbf{if}\;a \leq -2.2 \cdot 10^{+60}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 8 \cdot 10^{+114}:\\ \;\;\;\;a \cdot t + \left(z \cdot y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* b z)))))
   (if (<= a -2.2e+60) t_1 (if (<= a 8e+114) (+ (* a t) (+ (* z y) x)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (b * z));
	double tmp;
	if (a <= -2.2e+60) {
		tmp = t_1;
	} else if (a <= 8e+114) {
		tmp = (a * t) + ((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 = a * (t + (b * z))
    if (a <= (-2.2d+60)) then
        tmp = t_1
    else if (a <= 8d+114) then
        tmp = (a * t) + ((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 = a * (t + (b * z));
	double tmp;
	if (a <= -2.2e+60) {
		tmp = t_1;
	} else if (a <= 8e+114) {
		tmp = (a * t) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (b * z))
	tmp = 0
	if a <= -2.2e+60:
		tmp = t_1
	elif a <= 8e+114:
		tmp = (a * t) + ((z * y) + x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(t + Float64(b * z)))
	tmp = 0.0
	if (a <= -2.2e+60)
		tmp = t_1;
	elseif (a <= 8e+114)
		tmp = Float64(Float64(a * t) + 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 = a * (t + (b * z));
	tmp = 0.0;
	if (a <= -2.2e+60)
		tmp = t_1;
	elseif (a <= 8e+114)
		tmp = (a * t) + ((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[(a * N[(t + N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.2e+60], t$95$1, If[LessEqual[a, 8e+114], N[(N[(a * t), $MachinePrecision] + N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -2.19999999999999996e60 or 8e114 < a

   1. Initial program 78.2%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.19999999999999996e60 < a < 8e114

   1. Initial program 97.7%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 39.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{+34}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-257}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;x \leq 3300000000000:\\ \;\;\;\;z \cdot y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -3.3e+34)
   x
   (if (<= x -1.25e-257) (* a t) (if (<= x 3300000000000.0) (* z y) x))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -3.3e+34) {
		tmp = x;
	} else if (x <= -1.25e-257) {
		tmp = a * t;
	} else if (x <= 3300000000000.0) {
		tmp = z * y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (x <= (-3.3d+34)) then
        tmp = x
    else if (x <= (-1.25d-257)) then
        tmp = a * t
    else if (x <= 3300000000000.0d0) then
        tmp = z * y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -3.3e+34) {
		tmp = x;
	} else if (x <= -1.25e-257) {
		tmp = a * t;
	} else if (x <= 3300000000000.0) {
		tmp = z * y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -3.3e+34:
		tmp = x
	elif x <= -1.25e-257:
		tmp = a * t
	elif x <= 3300000000000.0:
		tmp = z * y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -3.3e+34)
		tmp = x;
	elseif (x <= -1.25e-257)
		tmp = Float64(a * t);
	elseif (x <= 3300000000000.0)
		tmp = Float64(z * y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -3.3e+34)
		tmp = x;
	elseif (x <= -1.25e-257)
		tmp = a * t;
	elseif (x <= 3300000000000.0)
		tmp = z * y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -3.3e+34], x, If[LessEqual[x, -1.25e-257], N[(a * t), $MachinePrecision], If[LessEqual[x, 3300000000000.0], N[(z * y), $MachinePrecision], x]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.29999999999999988e34 or 3.3e12 < x

   1. Initial program 89.6%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.29999999999999988e34 < x < -1.24999999999999997e-257

   1. Initial program 94.5%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.24999999999999997e-257 < x < 3.3e12

   1. Initial program 90.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 12: 75.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + b \cdot z\right)\\ \mathbf{if}\;a \leq -7.2 \cdot 10^{-40}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 6.8 \cdot 10^{-41}:\\ \;\;\;\;z \cdot y + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* b z)))))
   (if (<= a -7.2e-40) t_1 (if (<= a 6.8e-41) (+ (* z y) x) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (b * z));
	double tmp;
	if (a <= -7.2e-40) {
		tmp = t_1;
	} else if (a <= 6.8e-41) {
		tmp = (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 = a * (t + (b * z))
    if (a <= (-7.2d-40)) then
        tmp = t_1
    else if (a <= 6.8d-41) then
        tmp = (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 = a * (t + (b * z));
	double tmp;
	if (a <= -7.2e-40) {
		tmp = t_1;
	} else if (a <= 6.8e-41) {
		tmp = (z * y) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (b * z))
	tmp = 0
	if a <= -7.2e-40:
		tmp = t_1
	elif a <= 6.8e-41:
		tmp = (z * y) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(t + Float64(b * z)))
	tmp = 0.0
	if (a <= -7.2e-40)
		tmp = t_1;
	elseif (a <= 6.8e-41)
		tmp = 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 = a * (t + (b * z));
	tmp = 0.0;
	if (a <= -7.2e-40)
		tmp = t_1;
	elseif (a <= 6.8e-41)
		tmp = (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[(a * N[(t + N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -7.2e-40], t$95$1, If[LessEqual[a, 6.8e-41], N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -7.2e-40 or 6.7999999999999997e-41 < a

   1. Initial program 84.8%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -7.2e-40 < a < 6.7999999999999997e-41

   1. Initial program 99.1%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 39.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8.2 \cdot 10^{+73}:\\ \;\;\;\;a \cdot t\\ \mathbf{elif}\;t \leq 10500:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;a \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -8.2e+73) (* a t) (if (<= t 10500.0) x (* a t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -8.2e+73) {
		tmp = a * t;
	} else if (t <= 10500.0) {
		tmp = x;
	} 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 <= (-8.2d+73)) then
        tmp = a * t
    else if (t <= 10500.0d0) then
        tmp = x
    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 <= -8.2e+73) {
		tmp = a * t;
	} else if (t <= 10500.0) {
		tmp = x;
	} else {
		tmp = a * t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -8.2e+73:
		tmp = a * t
	elif t <= 10500.0:
		tmp = x
	else:
		tmp = a * t
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -8.2e+73)
		tmp = Float64(a * t);
	elseif (t <= 10500.0)
		tmp = x;
	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 <= -8.2e+73)
		tmp = a * t;
	elseif (t <= 10500.0)
		tmp = x;
	else
		tmp = a * t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -8.2e+73], N[(a * t), $MachinePrecision], If[LessEqual[t, 10500.0], x, N[(a * t), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < -8.1999999999999996e73 or 10500 < t

   1. Initial program 85.9%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -8.1999999999999996e73 < t < 10500

   1. Initial program 94.7%

      \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 27.1% 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

1. Initial program 91.0%

   \[\left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in x around inf 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

Developer target: 97.1% accurate, 0.7× 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 2024110 
(FPCore (x y z t a b)
  :name "Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1"
  :precision binary64

  :alt
  (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)))