Data.Metrics.Snapshot:quantile from metrics-0.3.0.2

Average Accuracy: 65.9% → 65.9%

Time: 15.6s

Precision: binary64

Cost: 576

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((y - z) * (t - x))
end function

↓

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((y - z) * (t - x))
end function

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Target

Original	65.9%
Target	65.9%
Herbie	65.9%

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

Derivation

1. Initial program 66.8%

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

2. Final simplification 66.8%

   \[\leadsto x + \left(y - z\right) \cdot \left(t - x\right) \]

Alternatives

Alternative 1
Accuracy	52.4%
Cost	1108

\[\begin{array}{l} t_1 := x + \left(y - z\right) \cdot t\\ t_2 := z \cdot \left(x - t\right)\\ \mathbf{if}\;z \leq -4.4 \cdot 10^{+16}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 3.1 \cdot 10^{-169}:\\ \;\;\;\;x - y \cdot \left(x - t\right)\\ \mathbf{elif}\;z \leq 1920000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{+14}:\\ \;\;\;\;x \cdot \left(\left(z + 1\right) - y\right)\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+63}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 2
Accuracy	52.5%
Cost	1108

\[\begin{array}{l} t_1 := x + \left(y - z\right) \cdot t\\ t_2 := z \cdot \left(x - t\right)\\ \mathbf{if}\;z \leq -1.85 \cdot 10^{-25}:\\ \;\;\;\;x + t_2\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-169}:\\ \;\;\;\;x - y \cdot \left(x - t\right)\\ \mathbf{elif}\;z \leq 3900000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 6.4 \cdot 10^{+21}:\\ \;\;\;\;x \cdot \left(\left(z + 1\right) - y\right)\\ \mathbf{elif}\;z \leq 1.52 \cdot 10^{+63}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 3
Accuracy	32.7%
Cost	716

\[\begin{array}{l} t_1 := x + x \cdot z\\ \mathbf{if}\;x \leq -3.2 \cdot 10^{+87}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -3.8 \cdot 10^{-39}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{-98}:\\ \;\;\;\;z \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 4
Accuracy	54.3%
Cost	713

\[\begin{array}{l} \mathbf{if}\;t \leq -6.8 \cdot 10^{-153} \lor \neg \left(t \leq 5.5 \cdot 10^{-131}\right):\\ \;\;\;\;x + \left(y - z\right) \cdot t\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(\left(z + 1\right) - y\right)\\ \end{array} \]

Alternative 5
Accuracy	27.1%
Cost	653

\[\begin{array}{l} \mathbf{if}\;z \leq -1.15 \cdot 10^{+216}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -340000 \lor \neg \left(z \leq 11.5\right):\\ \;\;\;\;z \cdot \left(-t\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6
Accuracy	36.5%
Cost	585

\[\begin{array}{l} \mathbf{if}\;z \leq -2.2 \lor \neg \left(z \leq 8.2 \cdot 10^{-5}\right):\\ \;\;\;\;z \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 7
Accuracy	47.7%
Cost	585

\[\begin{array}{l} \mathbf{if}\;z \leq -8.5 \cdot 10^{-9} \lor \neg \left(z \leq 0.0142\right):\\ \;\;\;\;z \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot t\\ \end{array} \]

Alternative 8
Accuracy	26.6%
Cost	456

\[\begin{array}{l} \mathbf{if}\;z \leq -2.4 \cdot 10^{-8}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq 1:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \]

Alternative 9
Accuracy	18.0%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023157 
(FPCore (x y z t)
  :name "Data.Metrics.Snapshot:quantile from metrics-0.3.0.2"
  :precision binary64

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

  (+ x (* (- y z) (- t x))))