Development.Shake.Progress:decay from shake-0.15.5

Percentage Accurate: 65.9% → 90.2%

Time: 25.9s

Precision: binary64

Cost: 12817

Math

\[\frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]

↓

\[\begin{array}{l} t_1 := y + z \cdot \left(b - y\right)\\ t_2 := z \cdot \left(t - a\right)\\ t_3 := \frac{t_2 + x \cdot y}{t_1}\\ \mathbf{if}\;t_3 \leq -\infty:\\ \;\;\;\;\frac{\frac{a - t}{\frac{z + -1}{z}} - \frac{x \cdot \left(z \cdot b\right)}{{\left(z + -1\right)}^{2}}}{y} - \frac{x}{z + -1}\\ \mathbf{elif}\;t_3 \leq -2 \cdot 10^{-284}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t_3 \leq 0 \lor \neg \left(t_3 \leq 10^{+271}\right):\\ \;\;\;\;\frac{\frac{y}{\frac{b - y}{x}} + \frac{a - t}{\frac{{\left(b - y\right)}^{2}}{y}}}{z} + \frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, t_2\right)}{t_1}\\ \end{array} \]

FPCore

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

↓

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (* z (- b y))))
        (t_2 (* z (- t a)))
        (t_3 (/ (+ t_2 (* x y)) t_1)))
   (if (<= t_3 (- INFINITY))
     (-
      (/
       (- (/ (- a t) (/ (+ z -1.0) z)) (/ (* x (* z b)) (pow (+ z -1.0) 2.0)))
       y)
      (/ x (+ z -1.0)))
     (if (<= t_3 -2e-284)
       t_3
       (if (or (<= t_3 0.0) (not (<= t_3 1e+271)))
         (+
          (/ (+ (/ y (/ (- b y) x)) (/ (- a t) (/ (pow (- b y) 2.0) y))) z)
          (/ (- t a) (- b y)))
         (/ (fma x y t_2) t_1))))))

C

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

↓

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (z * (b - y));
	double t_2 = z * (t - a);
	double t_3 = (t_2 + (x * y)) / t_1;
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = ((((a - t) / ((z + -1.0) / z)) - ((x * (z * b)) / pow((z + -1.0), 2.0))) / y) - (x / (z + -1.0));
	} else if (t_3 <= -2e-284) {
		tmp = t_3;
	} else if ((t_3 <= 0.0) || !(t_3 <= 1e+271)) {
		tmp = (((y / ((b - y) / x)) + ((a - t) / (pow((b - y), 2.0) / y))) / z) + ((t - a) / (b - y));
	} else {
		tmp = fma(x, y, t_2) / t_1;
	}
	return tmp;
}

Julia

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

↓

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(z * Float64(b - y)))
	t_2 = Float64(z * Float64(t - a))
	t_3 = Float64(Float64(t_2 + Float64(x * y)) / t_1)
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(Float64(Float64(a - t) / Float64(Float64(z + -1.0) / z)) - Float64(Float64(x * Float64(z * b)) / (Float64(z + -1.0) ^ 2.0))) / y) - Float64(x / Float64(z + -1.0)));
	elseif (t_3 <= -2e-284)
		tmp = t_3;
	elseif ((t_3 <= 0.0) || !(t_3 <= 1e+271))
		tmp = Float64(Float64(Float64(Float64(y / Float64(Float64(b - y) / x)) + Float64(Float64(a - t) / Float64((Float64(b - y) ^ 2.0) / y))) / z) + Float64(Float64(t - a) / Float64(b - y)));
	else
		tmp = Float64(fma(x, y, t_2) / t_1);
	end
	return tmp
end

Wolfram

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

↓

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t$95$2 + N[(x * y), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], N[(N[(N[(N[(N[(a - t), $MachinePrecision] / N[(N[(z + -1.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - N[(N[(x * N[(z * b), $MachinePrecision]), $MachinePrecision] / N[Power[N[(z + -1.0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision] - N[(x / N[(z + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, -2e-284], t$95$3, If[Or[LessEqual[t$95$3, 0.0], N[Not[LessEqual[t$95$3, 1e+271]], $MachinePrecision]], N[(N[(N[(N[(y / N[(N[(b - y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] + N[(N[(a - t), $MachinePrecision] / N[(N[Power[N[(b - y), $MachinePrecision], 2.0], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x * y + t$95$2), $MachinePrecision] / t$95$1), $MachinePrecision]]]]]]]

Target

Original	65.9%
Target	73.0%
Herbie	90.2%

\[\frac{z \cdot t + y \cdot x}{y + z \cdot \left(b - y\right)} - \frac{a}{\left(b - y\right) + \frac{y}{z}} \]

Derivation

1. Split input into 4 regimes

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

   1. Initial program 28.7%

      \[\frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]

   2. Applied egg-rr 28.7%

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

      [Start] 28.7%	\[ \frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]
      div-inv [=>] 28.7%	\[ \color{blue}{\left(x \cdot y + z \cdot \left(t - a\right)\right) \cdot \frac{1}{y + z \cdot \left(b - y\right)}} \]
      fma-def [=>] 28.7%	\[ \color{blue}{\mathsf{fma}\left(x, y, z \cdot \left(t - a\right)\right)} \cdot \frac{1}{y + z \cdot \left(b - y\right)} \]
      +-commutative [=>] 28.7%	\[ \mathsf{fma}\left(x, y, z \cdot \left(t - a\right)\right) \cdot \frac{1}{\color{blue}{z \cdot \left(b - y\right) + y}} \]
      fma-def [=>] 28.7%	\[ \mathsf{fma}\left(x, y, z \cdot \left(t - a\right)\right) \cdot \frac{1}{\color{blue}{\mathsf{fma}\left(z, b - y, y\right)}} \]

   3. Taylor expanded in y around -inf 60.1%

      \[\leadsto \color{blue}{-1 \cdot \frac{x}{z - 1} + -1 \cdot \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y}} \]

   4. Simplified 83.4%

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

      [Start] 60.1%	\[ -1 \cdot \frac{x}{z - 1} + -1 \cdot \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y} \]
      mul-1-neg [=>] 60.1%	\[ -1 \cdot \frac{x}{z - 1} + \color{blue}{\left(-\frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y}\right)} \]
      unsub-neg [=>] 60.1%	\[ \color{blue}{-1 \cdot \frac{x}{z - 1} - \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y}} \]
      associate-*r/ [=>] 60.1%	\[ \color{blue}{\frac{-1 \cdot x}{z - 1}} - \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y} \]
      mul-1-neg [=>] 60.1%	\[ \frac{\color{blue}{-x}}{z - 1} - \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y} \]
      sub-neg [=>] 60.1%	\[ \frac{-x}{\color{blue}{z + \left(-1\right)}} - \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y} \]
      metadata-eval [=>] 60.1%	\[ \frac{-x}{z + \color{blue}{-1}} - \frac{\frac{\left(t - a\right) \cdot z}{z - 1} - -1 \cdot \frac{z \cdot \left(b \cdot x\right)}{{\left(z - 1\right)}^{2}}}{y} \]

   if -inf.0 < (/.f64 (+.f64 (*.f64 x y) (*.f64 z (-.f64 t a))) (+.f64 y (*.f64 z (-.f64 b y)))) < -2.00000000000000007e-284

   1. Initial program 99.5%

      \[\frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]

   if -2.00000000000000007e-284 < (/.f64 (+.f64 (*.f64 x y) (*.f64 z (-.f64 t a))) (+.f64 y (*.f64 z (-.f64 b y)))) < -0.0 or 9.99999999999999953e270 < (/.f64 (+.f64 (*.f64 x y) (*.f64 z (-.f64 t a))) (+.f64 y (*.f64 z (-.f64 b y))))

   1. Initial program 14.0%

      \[\frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]

   2. Taylor expanded in z around -inf 50.7%

      \[\leadsto \color{blue}{\left(\frac{t}{b - y} + -1 \cdot \frac{-1 \cdot \frac{y \cdot x}{b - y} - -1 \cdot \frac{\left(t - a\right) \cdot y}{{\left(b - y\right)}^{2}}}{z}\right) - \frac{a}{b - y}} \]

   3. Simplified 88.7%

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

      [Start] 50.7%	\[ \left(\frac{t}{b - y} + -1 \cdot \frac{-1 \cdot \frac{y \cdot x}{b - y} - -1 \cdot \frac{\left(t - a\right) \cdot y}{{\left(b - y\right)}^{2}}}{z}\right) - \frac{a}{b - y} \]
      +-commutative [=>] 50.7%	\[ \color{blue}{\left(-1 \cdot \frac{-1 \cdot \frac{y \cdot x}{b - y} - -1 \cdot \frac{\left(t - a\right) \cdot y}{{\left(b - y\right)}^{2}}}{z} + \frac{t}{b - y}\right)} - \frac{a}{b - y} \]
      associate--l+ [=>] 50.7%	\[ \color{blue}{-1 \cdot \frac{-1 \cdot \frac{y \cdot x}{b - y} - -1 \cdot \frac{\left(t - a\right) \cdot y}{{\left(b - y\right)}^{2}}}{z} + \left(\frac{t}{b - y} - \frac{a}{b - y}\right)} \]

   if -0.0 < (/.f64 (+.f64 (*.f64 x y) (*.f64 z (-.f64 t a))) (+.f64 y (*.f64 z (-.f64 b y)))) < 9.99999999999999953e270

   1. Initial program 99.5%

      \[\frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]

   2. Simplified 99.6%

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

      [Start] 99.5%	\[ \frac{x \cdot y + z \cdot \left(t - a\right)}{y + z \cdot \left(b - y\right)} \]
      fma-def [=>] 99.6%	\[ \frac{\color{blue}{\mathsf{fma}\left(x, y, z \cdot \left(t - a\right)\right)}}{y + z \cdot \left(b - y\right)} \]

3. Recombined 4 regimes into one program.

4. Final simplification 94.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot \left(b - y\right)} \leq -\infty:\\ \;\;\;\;\frac{\frac{a - t}{\frac{z + -1}{z}} - \frac{x \cdot \left(z \cdot b\right)}{{\left(z + -1\right)}^{2}}}{y} - \frac{x}{z + -1}\\ \mathbf{elif}\;\frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot \left(b - y\right)} \leq -2 \cdot 10^{-284}:\\ \;\;\;\;\frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot \left(b - y\right)}\\ \mathbf{elif}\;\frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot \left(b - y\right)} \leq 0 \lor \neg \left(\frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot \left(b - y\right)} \leq 10^{+271}\right):\\ \;\;\;\;\frac{\frac{y}{\frac{b - y}{x}} + \frac{a - t}{\frac{{\left(b - y\right)}^{2}}{y}}}{z} + \frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, z \cdot \left(t - a\right)\right)}{y + z \cdot \left(b - y\right)}\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	90.2%
Cost	12817

\[\begin{array}{l} t_1 := y + z \cdot \left(b - y\right)\\ t_2 := z \cdot \left(t - a\right)\\ t_3 := \frac{t_2 + x \cdot y}{t_1}\\ \mathbf{if}\;t_3 \leq -\infty:\\ \;\;\;\;\frac{\frac{a - t}{\frac{z + -1}{z}} - \frac{x \cdot \left(z \cdot b\right)}{{\left(z + -1\right)}^{2}}}{y} - \frac{x}{z + -1}\\ \mathbf{elif}\;t_3 \leq -2 \cdot 10^{-284}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t_3 \leq 0 \lor \neg \left(t_3 \leq 10^{+271}\right):\\ \;\;\;\;\frac{\frac{y}{\frac{b - y}{x}} + \frac{a - t}{\frac{{\left(b - y\right)}^{2}}{y}}}{z} + \frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, t_2\right)}{t_1}\\ \end{array} \]

Alternative 2
Accuracy	85.4%
Cost	11984

\[\begin{array}{l} t_1 := y + z \cdot \left(b - y\right)\\ t_2 := z \cdot \left(t - a\right)\\ t_3 := \frac{t_2 + x \cdot y}{t_1}\\ t_4 := \frac{a - t}{y} - \frac{x}{z + -1}\\ \mathbf{if}\;t_3 \leq -\infty:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t_3 \leq -2 \cdot 10^{-284}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t_3 \leq 0:\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{elif}\;t_3 \leq 10^{+288}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, t_2\right)}{t_1}\\ \mathbf{else}:\\ \;\;\;\;t_4\\ \end{array} \]

Alternative 3
Accuracy	85.1%
Cost	11984

\[\begin{array}{l} t_1 := y + z \cdot \left(b - y\right)\\ t_2 := \frac{x}{z + -1}\\ t_3 := z \cdot \left(t - a\right)\\ t_4 := \frac{t_3 + x \cdot y}{t_1}\\ \mathbf{if}\;t_4 \leq -\infty:\\ \;\;\;\;\frac{\frac{a - t}{\frac{z + -1}{z}} - \frac{x \cdot \left(z \cdot b\right)}{{\left(z + -1\right)}^{2}}}{y} - t_2\\ \mathbf{elif}\;t_4 \leq -2 \cdot 10^{-284}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t_4 \leq 0:\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{elif}\;t_4 \leq 10^{+288}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, t_3\right)}{t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{a - t}{y} - t_2\\ \end{array} \]

Alternative 4
Accuracy	85.4%
Cost	5712

\[\begin{array}{l} t_1 := \frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot \left(b - y\right)}\\ t_2 := \frac{a - t}{y} - \frac{x}{z + -1}\\ \mathbf{if}\;t_1 \leq -\infty:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_1 \leq -2 \cdot 10^{-284}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_1 \leq 0:\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{elif}\;t_1 \leq 10^{+288}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 5
Accuracy	68.4%
Cost	1496

\[\begin{array}{l} t_1 := x - z \cdot \frac{a}{y}\\ t_2 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -1.12 \cdot 10^{-40}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -9.2 \cdot 10^{-183}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.32 \cdot 10^{-151}:\\ \;\;\;\;x + \frac{z \cdot t}{y}\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-126}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5.9 \cdot 10^{-90}:\\ \;\;\;\;\frac{t - a}{b}\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-28}:\\ \;\;\;\;\frac{x \cdot y}{y + z \cdot \left(b - y\right)}\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-22}:\\ \;\;\;\;x + z \cdot \frac{t}{y}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 6
Accuracy	65.3%
Cost	1496

\[\begin{array}{l} t_1 := \frac{t - a}{b - y}\\ t_2 := \frac{a - t}{y} - \frac{x}{z + -1}\\ t_3 := x + \frac{\left(t - a\right) - x \cdot b}{\frac{y}{z}}\\ \mathbf{if}\;y \leq -3.4 \cdot 10^{+110}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.5 \cdot 10^{-5}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.85 \cdot 10^{-61}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -1.1 \cdot 10^{-96}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -4 \cdot 10^{-109}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+67}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 7
Accuracy	70.1%
Cost	1492

\[\begin{array}{l} t_1 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -7 \cdot 10^{-39}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.1 \cdot 10^{-175}:\\ \;\;\;\;x - z \cdot \frac{a}{y}\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{-149}:\\ \;\;\;\;x + \frac{z \cdot t}{y}\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{-125}:\\ \;\;\;\;\frac{z \cdot \left(t - a\right) + x \cdot y}{z \cdot b}\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-22}:\\ \;\;\;\;x + \frac{\left(t - a\right) - x \cdot b}{\frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 8
Accuracy	82.8%
Cost	1488

\[\begin{array}{l} t_1 := \frac{z \cdot \left(t - a\right) + x \cdot y}{y + z \cdot b}\\ t_2 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -280000000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.7 \cdot 10^{-75}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-99}:\\ \;\;\;\;x - z \cdot \frac{a}{y}\\ \mathbf{elif}\;z \leq 5 \cdot 10^{+14}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 9
Accuracy	69.2%
Cost	1241

\[\begin{array}{l} t_1 := x - z \cdot \frac{a}{y}\\ t_2 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -1.45 \cdot 10^{-38}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -8.2 \cdot 10^{-183}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{-149}:\\ \;\;\;\;x + \frac{z \cdot t}{y}\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-126} \lor \neg \left(z \leq 2 \cdot 10^{-110}\right) \land z \leq 2.7 \cdot 10^{-22}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \]

Alternative 10
Accuracy	69.4%
Cost	978

\[\begin{array}{l} \mathbf{if}\;z \leq -3.6 \cdot 10^{-73} \lor \neg \left(z \leq 3.7 \cdot 10^{-126} \lor \neg \left(z \leq 2.8 \cdot 10^{-110}\right) \land z \leq 2.7 \cdot 10^{-22}\right):\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z \cdot t}{y}\\ \end{array} \]

Alternative 11
Accuracy	66.4%
Cost	969

\[\begin{array}{l} \mathbf{if}\;y \leq -2.75 \cdot 10^{+110} \lor \neg \left(y \leq 2 \cdot 10^{+65}\right):\\ \;\;\;\;\frac{a - t}{y} - \frac{x}{z + -1}\\ \mathbf{else}:\\ \;\;\;\;\frac{t - a}{b - y}\\ \end{array} \]

Alternative 12
Accuracy	51.7%
Cost	848

\[\begin{array}{l} t_1 := \frac{x}{1 - z}\\ \mathbf{if}\;y \leq -6 \cdot 10^{+81}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.5 \cdot 10^{-21}:\\ \;\;\;\;\frac{-a}{b - y}\\ \mathbf{elif}\;y \leq -3.9 \cdot 10^{-160}:\\ \;\;\;\;x + z \cdot \frac{t}{y}\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{+64}:\\ \;\;\;\;\frac{t - a}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 13
Accuracy	52.0%
Cost	848

\[\begin{array}{l} t_1 := \frac{x}{1 - z}\\ \mathbf{if}\;y \leq -5.6 \cdot 10^{+81}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.32 \cdot 10^{-21}:\\ \;\;\;\;\frac{-a}{b - y}\\ \mathbf{elif}\;y \leq -3.9 \cdot 10^{-160}:\\ \;\;\;\;x + \frac{z \cdot t}{y}\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{+64}:\\ \;\;\;\;\frac{t - a}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 14
Accuracy	40.5%
Cost	585

\[\begin{array}{l} \mathbf{if}\;y \leq -2.7 \cdot 10^{-219} \lor \neg \left(y \leq 0.000125\right):\\ \;\;\;\;\frac{x}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{-a}{b}\\ \end{array} \]

Alternative 15
Accuracy	54.0%
Cost	585

\[\begin{array}{l} \mathbf{if}\;y \leq -8 \cdot 10^{+80} \lor \neg \left(y \leq 1.05 \cdot 10^{+64}\right):\\ \;\;\;\;\frac{x}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{t - a}{b}\\ \end{array} \]

Alternative 16
Accuracy	32.3%
Cost	520

\[\begin{array}{l} \mathbf{if}\;y \leq -2.7 \cdot 10^{-219}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 10^{+41}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 17
Accuracy	32.3%
Cost	520

\[\begin{array}{l} \mathbf{if}\;y \leq -5.6 \cdot 10^{-220}:\\ \;\;\;\;x + x \cdot z\\ \mathbf{elif}\;y \leq 10^{+41}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 18
Accuracy	34.0%
Cost	457

\[\begin{array}{l} \mathbf{if}\;z \leq -1020000 \lor \neg \left(z \leq 6 \cdot 10^{+15}\right):\\ \;\;\;\;\frac{a}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 19
Accuracy	25.2%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023263 
(FPCore (x y z t a b)
  :name "Development.Shake.Progress:decay from shake-0.15.5"
  :precision binary64

  :herbie-target
  (- (/ (+ (* z t) (* y x)) (+ y (* z (- b y)))) (/ a (+ (- b y) (/ y z))))

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