Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2

Percentage Accurate: 95.0% → 97.8%

Time: 16.7s

Precision: binary64

Cost: 2756

• 35.1% of points produce a very large (infinite) output. You may want to add a precondition.

Math

\[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

↓

\[\begin{array}{l} t_1 := \left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + b \cdot \left(\left(t + y\right) - 2\right)\\ \mathbf{if}\;t_1 \leq \infty:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))

↓

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1
         (+ (+ (+ x (* z (- 1.0 y))) (* a (- 1.0 t))) (* b (- (+ t y) 2.0)))))
   (if (<= t_1 INFINITY) t_1 (* y (- b z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

↓

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((t + y) - 2.0));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
}

↓

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((t + y) - 2.0));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = y * (b - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	return ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b)

↓

def code(x, y, z, t, a, b):
	t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((t + y) - 2.0))
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = y * (b - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x - Float64(Float64(y - 1.0) * z)) - Float64(Float64(t - 1.0) * a)) + Float64(Float64(Float64(y + t) - 2.0) * b))
end

↓

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(x + Float64(z * Float64(1.0 - y))) + Float64(a * Float64(1.0 - t))) + Float64(b * Float64(Float64(t + y) - 2.0)))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(y * Float64(b - z));
	end
	return tmp
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x - ((y - 1.0) * z)) - ((t - 1.0) * a)) + (((y + t) - 2.0) * b);
end

↓

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((x + (z * (1.0 - y))) + (a * (1.0 - t))) + (b * ((t + y) - 2.0));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = y * (b - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x - N[(N[(y - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(N[(t - 1.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y + t), $MachinePrecision] - 2.0), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(x + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(b * N[(N[(t + y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(y * N[(b - z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y 1) z)) (*.f64 (-.f64 t 1) a)) (*.f64 (-.f64 (+.f64 y t) 2) b)) < +inf.0

   1. Initial program 100.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   if +inf.0 < (+.f64 (-.f64 (-.f64 x (*.f64 (-.f64 y 1) z)) (*.f64 (-.f64 t 1) a)) (*.f64 (-.f64 (+.f64 y t) 2) b))

   1. Initial program 0.0%

      \[\left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]

   2. Simplified 0.0%

      \[\leadsto \color{blue}{\left(x - \left(y + -1\right) \cdot z\right) - \left(\left(t + -1\right) \cdot a - \left(y + \left(t - 2\right)\right) \cdot b\right)} \]
      Step-by-step derivation

      [Start] 0.0%	\[ \left(\left(x - \left(y - 1\right) \cdot z\right) - \left(t - 1\right) \cdot a\right) + \left(\left(y + t\right) - 2\right) \cdot b \]
      associate-+l- [=>] 0.0%	\[ \color{blue}{\left(x - \left(y - 1\right) \cdot z\right) - \left(\left(t - 1\right) \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right)} \]
      *-commutative [=>] 0.0%	\[ \left(x - \color{blue}{z \cdot \left(y - 1\right)}\right) - \left(\left(t - 1\right) \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      *-commutative [<=] 0.0%	\[ \left(x - \color{blue}{\left(y - 1\right) \cdot z}\right) - \left(\left(t - 1\right) \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      sub-neg [=>] 0.0%	\[ \left(x - \color{blue}{\left(y + \left(-1\right)\right)} \cdot z\right) - \left(\left(t - 1\right) \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      metadata-eval [=>] 0.0%	\[ \left(x - \left(y + \color{blue}{-1}\right) \cdot z\right) - \left(\left(t - 1\right) \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      remove-double-neg [<=] 0.0%	\[ \left(x - \left(y + -1\right) \cdot z\right) - \left(\color{blue}{\left(-\left(-\left(t - 1\right)\right)\right)} \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      remove-double-neg [=>] 0.0%	\[ \left(x - \left(y + -1\right) \cdot z\right) - \left(\color{blue}{\left(t - 1\right)} \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      sub-neg [=>] 0.0%	\[ \left(x - \left(y + -1\right) \cdot z\right) - \left(\color{blue}{\left(t + \left(-1\right)\right)} \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      metadata-eval [=>] 0.0%	\[ \left(x - \left(y + -1\right) \cdot z\right) - \left(\left(t + \color{blue}{-1}\right) \cdot a - \left(\left(y + t\right) - 2\right) \cdot b\right) \]
      associate--l+ [=>] 0.0%	\[ \left(x - \left(y + -1\right) \cdot z\right) - \left(\left(t + -1\right) \cdot a - \color{blue}{\left(y + \left(t - 2\right)\right)} \cdot b\right) \]

   3. Taylor expanded in y around inf 86.1%

      \[\leadsto \color{blue}{\left(b - z\right) \cdot y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + b \cdot \left(\left(t + y\right) - 2\right) \leq \infty:\\ \;\;\;\;\left(\left(x + z \cdot \left(1 - y\right)\right) + a \cdot \left(1 - t\right)\right) + b \cdot \left(\left(t + y\right) - 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	97.6%
Cost	20160

\[\mathsf{fma}\left(a, 1 - t, \mathsf{fma}\left(y + \left(t + -2\right), b, \mathsf{fma}\left(z, 1 - y, x\right)\right)\right) \]

Alternative 2
Accuracy	97.4%
Cost	13888

\[\mathsf{fma}\left(t + \left(y - 2\right), b, x - \mathsf{fma}\left(y + -1, z, a \cdot \left(t + -1\right)\right)\right) \]

Alternative 3
Accuracy	79.5%
Cost	1756

\[\begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := t_1 + t \cdot \left(b - a\right)\\ t_3 := b \cdot \left(\left(t + y\right) - 2\right)\\ \mathbf{if}\;b \leq -1.35 \cdot 10^{+217}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq -3.4 \cdot 10^{+159}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -3.8 \cdot 10^{+79}:\\ \;\;\;\;a + \left(x + b \cdot \left(y - 2\right)\right)\\ \mathbf{elif}\;b \leq -3.7 \cdot 10^{-30}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 1.85 \cdot 10^{-86}:\\ \;\;\;\;t_1 + \left(a - a \cdot t\right)\\ \mathbf{elif}\;b \leq 6.8 \cdot 10^{-13}:\\ \;\;\;\;t_1 + y \cdot b\\ \mathbf{elif}\;b \leq 3.8 \cdot 10^{+101}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \]

Alternative 4
Accuracy	59.7%
Cost	1505

\[\begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := b \cdot \left(\left(t + y\right) - 2\right)\\ t_3 := x - a \cdot \left(t + -1\right)\\ \mathbf{if}\;b \leq -1.06 \cdot 10^{+80}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -3.75 \cdot 10^{-280}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 1.6 \cdot 10^{-295}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 4 \cdot 10^{-225}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 5.5 \cdot 10^{-149}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;b \leq 7.9 \cdot 10^{-38} \lor \neg \left(b \leq 6.8 \cdot 10^{+14}\right) \land b \leq 1.4 \cdot 10^{+101}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 5
Accuracy	71.5%
Cost	1496

\[\begin{array}{l} t_1 := x - y \cdot z\\ t_2 := t \cdot \left(a - b\right)\\ t_3 := y \cdot \left(b - z\right)\\ t_4 := \left(z + x\right) - t_2\\ \mathbf{if}\;y \leq -3.8 \cdot 10^{+159}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{+19}:\\ \;\;\;\;t_1 - t_2\\ \mathbf{elif}\;y \leq -1.85 \cdot 10^{-137}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;y \leq -1.86 \cdot 10^{-174}:\\ \;\;\;\;a + \left(x + b \cdot \left(y - 2\right)\right)\\ \mathbf{elif}\;y \leq 9 \cdot 10^{-19}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;y \leq 6.3 \cdot 10^{+153}:\\ \;\;\;\;t_1 - \left(a \cdot t - a\right)\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \]

Alternative 6
Accuracy	71.5%
Cost	1364

\[\begin{array}{l} t_1 := t \cdot \left(a - b\right)\\ t_2 := \left(z + x\right) - t_1\\ \mathbf{if}\;y \leq -1.7 \cdot 10^{+159}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{+19}:\\ \;\;\;\;\left(x - y \cdot z\right) - t_1\\ \mathbf{elif}\;y \leq -1.82 \cdot 10^{-137}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.86 \cdot 10^{-174}:\\ \;\;\;\;a + \left(x + b \cdot \left(y - 2\right)\right)\\ \mathbf{elif}\;y \leq 170000:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\left(x + z \cdot \left(1 - y\right)\right) + y \cdot b\\ \end{array} \]

Alternative 7
Accuracy	72.3%
Cost	1360

\[\begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := t_1 + t \cdot \left(b - a\right)\\ \mathbf{if}\;y \leq -8.5 \cdot 10^{+159}:\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{elif}\;y \leq -1.82 \cdot 10^{-137}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{-174}:\\ \;\;\;\;a + \left(x + b \cdot \left(y - 2\right)\right)\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+14}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1 + y \cdot b\\ \end{array} \]

Alternative 8
Accuracy	59.2%
Cost	1241

\[\begin{array}{l} t_1 := x + z \cdot \left(1 - y\right)\\ t_2 := b \cdot \left(\left(t + y\right) - 2\right)\\ \mathbf{if}\;b \leq -1.4 \cdot 10^{+79}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq 8.2 \cdot 10^{-225}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq 9 \cdot 10^{-163}:\\ \;\;\;\;x + t \cdot \left(b - a\right)\\ \mathbf{elif}\;b \leq 7.9 \cdot 10^{-38} \lor \neg \left(b \leq 7 \cdot 10^{+14}\right) \land b \leq 1.05 \cdot 10^{+101}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 9
Accuracy	57.1%
Cost	1240

\[\begin{array}{l} t_1 := x + t \cdot \left(b - a\right)\\ t_2 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -1.15 \cdot 10^{+127}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{-300}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.56 \cdot 10^{-191}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-162}:\\ \;\;\;\;-2 \cdot b\\ \mathbf{elif}\;y \leq 1.52 \cdot 10^{-118}:\\ \;\;\;\;z - a \cdot t\\ \mathbf{elif}\;y \leq 3.35 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 10
Accuracy	70.3%
Cost	1236

\[\begin{array}{l} t_1 := t \cdot \left(a - b\right)\\ t_2 := y \cdot \left(b - z\right)\\ t_3 := \left(z + x\right) - t_1\\ \mathbf{if}\;y \leq -1.3 \cdot 10^{+160}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{+19}:\\ \;\;\;\;\left(x - y \cdot z\right) - t_1\\ \mathbf{elif}\;y \leq -1.82 \cdot 10^{-137}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -1.86 \cdot 10^{-174}:\\ \;\;\;\;a + \left(x + b \cdot \left(y - 2\right)\right)\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+18}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 11
Accuracy	85.3%
Cost	1225

\[\begin{array}{l} \mathbf{if}\;z \leq -5.6 \cdot 10^{+91} \lor \neg \left(z \leq 3.35 \cdot 10^{-34}\right):\\ \;\;\;\;\left(x + z \cdot \left(1 - y\right)\right) + t \cdot \left(b - a\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x + b \cdot \left(\left(t + y\right) - 2\right)\right) + a \cdot \left(1 - t\right)\\ \end{array} \]

Alternative 12
Accuracy	68.5%
Cost	1104

\[\begin{array}{l} t_1 := \left(z + x\right) - t \cdot \left(a - b\right)\\ t_2 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -1.15 \cdot 10^{+127}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.82 \cdot 10^{-137}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.86 \cdot 10^{-174}:\\ \;\;\;\;a + \left(x + b \cdot \left(y - 2\right)\right)\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+15}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 13
Accuracy	35.7%
Cost	1049

\[\begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+55}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;y \leq -3.2 \cdot 10^{-95}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq -4.2 \cdot 10^{-174}:\\ \;\;\;\;b \cdot \left(t - 2\right)\\ \mathbf{elif}\;y \leq 28000000:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+155} \lor \neg \left(y \leq 4 \cdot 10^{+228}\right):\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot b\\ \end{array} \]

Alternative 14
Accuracy	60.6%
Cost	977

\[\begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -1 \cdot 10^{+60}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{+43}:\\ \;\;\;\;x - a \cdot \left(t + -1\right)\\ \mathbf{elif}\;y \leq -3.1 \cdot 10^{+20} \lor \neg \left(y \leq 9.6 \cdot 10^{+15}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\left(z + x\right) - a \cdot t\\ \end{array} \]

Alternative 15
Accuracy	26.6%
Cost	852

\[\begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{+38}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;y \leq -5.4 \cdot 10^{-39}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;y \leq -2.2 \cdot 10^{-94}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq -3.9 \cdot 10^{-174}:\\ \;\;\;\;-2 \cdot b\\ \mathbf{elif}\;y \leq 2100000:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;y \cdot b\\ \end{array} \]

Alternative 16
Accuracy	47.0%
Cost	848

\[\begin{array}{l} t_1 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -130000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 8.5 \cdot 10^{-284}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-261}:\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{elif}\;t \leq 2.1 \cdot 10^{+15}:\\ \;\;\;\;z + x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 17
Accuracy	45.6%
Cost	848

\[\begin{array}{l} t_1 := z \cdot \left(1 - y\right)\\ t_2 := t \cdot \left(b - a\right)\\ \mathbf{if}\;t \leq -12500000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 2.4 \cdot 10^{-138}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 7.8 \cdot 10^{-90}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{+105}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 18
Accuracy	50.3%
Cost	848

\[\begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -3.8 \cdot 10^{+19}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -5.2 \cdot 10^{-94}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq -6 \cdot 10^{-174}:\\ \;\;\;\;b \cdot \left(t - 2\right)\\ \mathbf{elif}\;y \leq 17000000:\\ \;\;\;\;z + x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 19
Accuracy	50.5%
Cost	848

\[\begin{array}{l} t_1 := y \cdot \left(b - z\right)\\ \mathbf{if}\;y \leq -1.48 \cdot 10^{+19}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -7.4 \cdot 10^{-96}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq -3.4 \cdot 10^{-174}:\\ \;\;\;\;b \cdot \left(t - 2\right)\\ \mathbf{elif}\;y \leq 4800:\\ \;\;\;\;z - a \cdot t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 20
Accuracy	69.1%
Cost	841

\[\begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{+127} \lor \neg \left(y \leq 4.5 \cdot 10^{+18}\right):\\ \;\;\;\;y \cdot \left(b - z\right)\\ \mathbf{else}:\\ \;\;\;\;\left(z + x\right) - t \cdot \left(a - b\right)\\ \end{array} \]

Alternative 21
Accuracy	36.0%
Cost	785

\[\begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+55}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;y \leq 13500000:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq 5.3 \cdot 10^{+156} \lor \neg \left(y \leq 4 \cdot 10^{+228}\right):\\ \;\;\;\;y \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot b\\ \end{array} \]

Alternative 22
Accuracy	34.4%
Cost	720

\[\begin{array}{l} \mathbf{if}\;y \leq -1.95 \cdot 10^{+55}:\\ \;\;\;\;y \cdot b\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-99}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq -6 \cdot 10^{-174}:\\ \;\;\;\;-2 \cdot b\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{+18}:\\ \;\;\;\;z + x\\ \mathbf{else}:\\ \;\;\;\;y \cdot b\\ \end{array} \]

Alternative 23
Accuracy	23.2%
Cost	460

\[\begin{array}{l} \mathbf{if}\;x \leq -1.9 \cdot 10^{+131}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2 \cdot 10^{-275}:\\ \;\;\;\;t \cdot b\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{+49}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 24
Accuracy	21.3%
Cost	328

\[\begin{array}{l} \mathbf{if}\;x \leq -14000000000000:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.02 \cdot 10^{-12}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 25
Accuracy	21.2%
Cost	328

\[\begin{array}{l} \mathbf{if}\;x \leq -2.3 \cdot 10^{-54}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.1 \cdot 10^{+49}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 26
Accuracy	11.3%
Cost	64

\[a \]

Reproduce

herbie shell --seed 2023164 
(FPCore (x y z t a b)
  :name "Statistics.Distribution.Beta:$centropy from math-functions-0.1.5.2"
  :precision binary64
  (+ (- (- x (* (- y 1.0) z)) (* (- t 1.0) a)) (* (- (+ y t) 2.0) b)))