Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1

Average Accuracy: 97.0% → 99.6%

Time: 15.1s

Precision: binary64

Cost: 1225

Math

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

↓

\[\begin{array}{l} t_1 := x + y \cdot z\\ \mathbf{if}\;z \leq -1 \cdot 10^{-50} \lor \neg \left(z \leq 5 \cdot 10^{-71}\right):\\ \;\;\;\;z \cdot \left(b \cdot a\right) + \left(t \cdot a + t_1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot \left(b \cdot z\right) + t \cdot a\right) + t_1\\ \end{array} \]

FPCore

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

↓

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* y z))))
   (if (or (<= z -1e-50) (not (<= z 5e-71)))
     (+ (* z (* b a)) (+ (* t a) t_1))
     (+ (+ (* a (* b z)) (* t a)) t_1))))

C

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

↓

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (y * z);
	double tmp;
	if ((z <= -1e-50) || !(z <= 5e-71)) {
		tmp = (z * (b * a)) + ((t * a) + t_1);
	} else {
		tmp = ((a * (b * z)) + (t * a)) + 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
    code = ((x + (y * z)) + (t * a)) + ((a * z) * b)
end function

↓

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)
    if ((z <= (-1d-50)) .or. (.not. (z <= 5d-71))) then
        tmp = (z * (b * a)) + ((t * a) + t_1)
    else
        tmp = ((a * (b * z)) + (t * a)) + 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) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * b);
}

↓

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (y * z);
	double tmp;
	if ((z <= -1e-50) || !(z <= 5e-71)) {
		tmp = (z * (b * a)) + ((t * a) + t_1);
	} else {
		tmp = ((a * (b * z)) + (t * a)) + t_1;
	}
	return tmp;
}

Python

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

↓

def code(x, y, z, t, a, b):
	t_1 = x + (y * z)
	tmp = 0
	if (z <= -1e-50) or not (z <= 5e-71):
		tmp = (z * (b * a)) + ((t * a) + t_1)
	else:
		tmp = ((a * (b * z)) + (t * a)) + t_1
	return tmp

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

↓

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(y * z))
	tmp = 0.0
	if ((z <= -1e-50) || !(z <= 5e-71))
		tmp = Float64(Float64(z * Float64(b * a)) + Float64(Float64(t * a) + t_1));
	else
		tmp = Float64(Float64(Float64(a * Float64(b * z)) + Float64(t * a)) + t_1);
	end
	return tmp
end

MATLAB

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

↓

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (y * z);
	tmp = 0.0;
	if ((z <= -1e-50) || ~((z <= 5e-71)))
		tmp = (z * (b * a)) + ((t * a) + t_1);
	else
		tmp = ((a * (b * z)) + (t * a)) + t_1;
	end
	tmp_2 = tmp;
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]

↓

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[z, -1e-50], N[Not[LessEqual[z, 5e-71]], $MachinePrecision]], N[(N[(z * N[(b * a), $MachinePrecision]), $MachinePrecision] + N[(N[(t * a), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision], N[(N[(N[(a * N[(b * z), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]]]

Target

Original	97.0%
Target	99.5%
Herbie	99.6%

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

Derivation

1. Split input into 2 regimes

2. if z < -1.00000000000000001e-50 or 4.99999999999999998e-71 < z

   1. Initial program 94.8%

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

   2. Taylor expanded in a around 0 91.8%

      \[\leadsto \left(\left(x + y \cdot z\right) + t \cdot a\right) + \color{blue}{a \cdot \left(b \cdot z\right)} \]

   3. Simplified 99.3%

      \[\leadsto \left(\left(x + y \cdot z\right) + t \cdot a\right) + \color{blue}{z \cdot \left(a \cdot b\right)} \]
      Proof

      [Start] 91.8	\[ \left(\left(x + y \cdot z\right) + t \cdot a\right) + a \cdot \left(b \cdot z\right) \]
      associate-*r* [=>] 99.3	\[ \left(\left(x + y \cdot z\right) + t \cdot a\right) + \color{blue}{\left(a \cdot b\right) \cdot z} \]
      *-commutative [=>] 99.3	\[ \left(\left(x + y \cdot z\right) + t \cdot a\right) + \color{blue}{z \cdot \left(a \cdot b\right)} \]

   if -1.00000000000000001e-50 < z < 4.99999999999999998e-71

   1. Initial program 99.3%

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

   2. Simplified 99.9%

      \[\leadsto \color{blue}{\left(x + y \cdot z\right) + \left(t \cdot a + a \cdot \left(z \cdot b\right)\right)} \]
      Proof

      [Start] 99.3	\[ \left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \]
      associate-+l+ [=>] 99.3	\[ \color{blue}{\left(x + y \cdot z\right) + \left(t \cdot a + \left(a \cdot z\right) \cdot b\right)} \]
      associate-*l* [=>] 99.9	\[ \left(x + y \cdot z\right) + \left(t \cdot a + \color{blue}{a \cdot \left(z \cdot b\right)}\right) \]

3. Recombined 2 regimes into one program.

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{-50} \lor \neg \left(z \leq 5 \cdot 10^{-71}\right):\\ \;\;\;\;z \cdot \left(b \cdot a\right) + \left(t \cdot a + \left(x + y \cdot z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot \left(b \cdot z\right) + t \cdot a\right) + \left(x + y \cdot z\right)\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	98.7%
Cost	13641

\[\begin{array}{l} \mathbf{if}\;b \leq -5 \cdot 10^{+161} \lor \neg \left(b \leq 5 \cdot 10^{+51}\right):\\ \;\;\;\;b \cdot \left(z \cdot a\right) + \left(t \cdot a + \left(x + y \cdot z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(a, t + b \cdot z, \mathsf{fma}\left(y, z, x\right)\right)\\ \end{array} \]

Alternative 2
Accuracy	62.8%
Cost	1768

\[\begin{array}{l} t_1 := x + t \cdot a\\ t_2 := a \cdot \left(t + b \cdot z\right)\\ t_3 := x + y \cdot z\\ t_4 := z \cdot \left(y + b \cdot a\right)\\ \mathbf{if}\;x \leq -3 \cdot 10^{-16}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq -2.6 \cdot 10^{-49}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -3.5 \cdot 10^{-178}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq -1.7 \cdot 10^{-239}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{-234}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{-191}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{-102}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq 4.1 \cdot 10^{+14}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 9 \cdot 10^{+74}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{+158}:\\ \;\;\;\;x + b \cdot \left(z \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \]

Alternative 3
Accuracy	63.4%
Cost	1640

\[\begin{array}{l} t_1 := x + t \cdot a\\ t_2 := a \cdot \left(t + b \cdot z\right)\\ t_3 := x + y \cdot z\\ t_4 := z \cdot \left(y + b \cdot a\right)\\ \mathbf{if}\;x \leq -1.6 \cdot 10^{-16}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq -3.15 \cdot 10^{-49}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -2.1 \cdot 10^{-178}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq -1.7 \cdot 10^{-239}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 5.9 \cdot 10^{-236}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{-191}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{-103}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq 9 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 6 \cdot 10^{+61}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x \leq 10^{+196}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \]

Alternative 4
Accuracy	62.0%
Cost	1636

\[\begin{array}{l} t_1 := x + t \cdot a\\ t_2 := a \cdot \left(t + b \cdot z\right)\\ t_3 := z \cdot \left(y + b \cdot a\right)\\ \mathbf{if}\;x \leq -4 \cdot 10^{-15}:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;x \leq -9 \cdot 10^{-50}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -1.18 \cdot 10^{-179}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq -1.95 \cdot 10^{-239}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{-234}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq 10^{-190}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 7.8 \cdot 10^{-103}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq 2.65 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 9 \cdot 10^{+74}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \left(b \cdot z\right)\\ \end{array} \]

Alternative 5
Accuracy	46.1%
Cost	1380

\[\begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{+143}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -1.9 \cdot 10^{+86}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq -13600000000000:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -3.1 \cdot 10^{-49}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq -8 \cdot 10^{-162}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq -9.5 \cdot 10^{-240}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{-75}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 1.9 \cdot 10^{+16}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 9.5 \cdot 10^{+75}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6
Accuracy	54.2%
Cost	1380

\[\begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{+143} \lor \neg \left(x \leq -1.9 \cdot 10^{+86}\right) \land \left(x \leq -7.4 \cdot 10^{-50} \lor \neg \left(x \leq -7.5 \cdot 10^{-162}\right) \land \left(x \leq -1.6 \cdot 10^{-239} \lor \neg \left(x \leq 5.4 \cdot 10^{-106}\right) \land \left(x \leq 3.5 \cdot 10^{+16} \lor \neg \left(x \leq 2.35 \cdot 10^{+60}\right)\right)\right)\right):\\ \;\;\;\;x + t \cdot a\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \]

Alternative 7
Accuracy	63.4%
Cost	1376

\[\begin{array}{l} t_1 := x + t \cdot a\\ t_2 := z \cdot \left(y + b \cdot a\right)\\ t_3 := x + y \cdot z\\ \mathbf{if}\;x \leq -3.6 \cdot 10^{+25}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq -1.55 \cdot 10^{-49}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.65 \cdot 10^{-178}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -1.9 \cdot 10^{-239}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 1.52 \cdot 10^{-103}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 2.25 \cdot 10^{+15}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+61}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{+197}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \]

Alternative 8
Accuracy	67.8%
Cost	1368

\[\begin{array}{l} t_1 := t \cdot a + y \cdot z\\ \mathbf{if}\;x \leq -19000000:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;x \leq -8 \cdot 10^{-50}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.3 \cdot 10^{-163}:\\ \;\;\;\;z \cdot \left(y + b \cdot a\right)\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{-75}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 9 \cdot 10^{+14}:\\ \;\;\;\;x + t \cdot a\\ \mathbf{elif}\;x \leq 9 \cdot 10^{+74}:\\ \;\;\;\;z \cdot \left(b \cdot a\right) + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \left(b \cdot z\right)\\ \end{array} \]

Alternative 9
Accuracy	68.0%
Cost	1368

\[\begin{array}{l} t_1 := t \cdot a + y \cdot z\\ \mathbf{if}\;x \leq -5800000:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;x \leq -7.8 \cdot 10^{-50}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -4.4 \cdot 10^{-158}:\\ \;\;\;\;b \cdot \left(z \cdot a\right) + y \cdot z\\ \mathbf{elif}\;x \leq 6.5 \cdot 10^{-80}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{+16}:\\ \;\;\;\;x + t \cdot a\\ \mathbf{elif}\;x \leq 9 \cdot 10^{+74}:\\ \;\;\;\;z \cdot \left(b \cdot a\right) + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \left(b \cdot z\right)\\ \end{array} \]

Alternative 10
Accuracy	67.8%
Cost	1240

\[\begin{array}{l} t_1 := t \cdot a + y \cdot z\\ t_2 := z \cdot \left(y + b \cdot a\right)\\ \mathbf{if}\;x \leq -90000000:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;x \leq -1.1 \cdot 10^{-49}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -4.1 \cdot 10^{-165}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 9.5 \cdot 10^{-78}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 5.1 \cdot 10^{+16}:\\ \;\;\;\;x + t \cdot a\\ \mathbf{elif}\;x \leq 1.95 \cdot 10^{+75}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \left(b \cdot z\right)\\ \end{array} \]

Alternative 11
Accuracy	76.7%
Cost	1236

\[\begin{array}{l} t_1 := t \cdot a + y \cdot z\\ t_2 := x + z \cdot \left(y + b \cdot a\right)\\ \mathbf{if}\;z \leq -1.05 \cdot 10^{+111}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -3.5 \cdot 10^{+48}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -0.029:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.55 \cdot 10^{-94}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{-82}:\\ \;\;\;\;x + t \cdot a\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 12
Accuracy	85.2%
Cost	1236

\[\begin{array}{l} t_1 := z \cdot \left(y + b \cdot a\right) + t \cdot a\\ t_2 := t \cdot a + \left(x + y \cdot z\right)\\ \mathbf{if}\;x \leq -1.1 \cdot 10^{-49}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-146}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{+15}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 3.55 \cdot 10^{+75}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{+127}:\\ \;\;\;\;x + a \cdot \left(b \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 13
Accuracy	95.6%
Cost	1225

\[\begin{array}{l} \mathbf{if}\;a \leq -6.4 \cdot 10^{-146} \lor \neg \left(a \leq 9.5 \cdot 10^{-136}\right):\\ \;\;\;\;\left(a \cdot \left(b \cdot z\right) + t \cdot a\right) + \left(x + y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \left(y + b \cdot a\right)\\ \end{array} \]

Alternative 14
Accuracy	98.9%
Cost	1225

\[\begin{array}{l} t_1 := t \cdot a + \left(x + y \cdot z\right)\\ \mathbf{if}\;b \leq -2.7 \cdot 10^{+87} \lor \neg \left(b \leq 1.12 \cdot 10^{+136}\right):\\ \;\;\;\;b \cdot \left(z \cdot a\right) + t_1\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(b \cdot a\right) + t_1\\ \end{array} \]

Alternative 15
Accuracy	87.7%
Cost	841

\[\begin{array}{l} \mathbf{if}\;t \leq -7.2 \cdot 10^{-20} \lor \neg \left(t \leq 1.4 \cdot 10^{-106}\right):\\ \;\;\;\;t \cdot a + \left(x + y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \left(y + b \cdot a\right)\\ \end{array} \]

Alternative 16
Accuracy	67.5%
Cost	585

\[\begin{array}{l} \mathbf{if}\;t \leq -5 \cdot 10^{-42} \lor \neg \left(t \leq 2.8 \cdot 10^{+121}\right):\\ \;\;\;\;x + t \cdot a\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 17
Accuracy	48.5%
Cost	456

\[\begin{array}{l} \mathbf{if}\;x \leq -190000000000:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{-48}:\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 18
Accuracy	37.1%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023151 
(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)))