Statistics.Distribution.Binomial:$cvariance from math-functions-0.1.5.2

Average Error: 0.1 → 0.1

Time: 33.8s

Precision: binary64

Cost: 712

Math

\[\left(x \cdot y\right) \cdot \left(1 - y\right) \]

↓

\[\begin{array}{l} t_0 := y \cdot \left(x \cdot \left(-y\right)\right)\\ \mathbf{if}\;y \leq -1 \cdot 10^{+136}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+61}:\\ \;\;\;\;x \cdot \left(y \cdot \left(1 - y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* x y) (- 1.0 y)))

↓

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (* x (- y)))))
   (if (<= y -1e+136) t_0 (if (<= y 4e+61) (* x (* y (- 1.0 y))) t_0))))

C

double code(double x, double y) {
	return (x * y) * (1.0 - y);
}

↓

double code(double x, double y) {
	double t_0 = y * (x * -y);
	double tmp;
	if (y <= -1e+136) {
		tmp = t_0;
	} else if (y <= 4e+61) {
		tmp = x * (y * (1.0 - y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * y) * (1.0d0 - y)
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * (x * -y)
    if (y <= (-1d+136)) then
        tmp = t_0
    else if (y <= 4d+61) then
        tmp = x * (y * (1.0d0 - y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	return (x * y) * (1.0 - y);
}

↓

public static double code(double x, double y) {
	double t_0 = y * (x * -y);
	double tmp;
	if (y <= -1e+136) {
		tmp = t_0;
	} else if (y <= 4e+61) {
		tmp = x * (y * (1.0 - y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	return (x * y) * (1.0 - y)

↓

def code(x, y):
	t_0 = y * (x * -y)
	tmp = 0
	if y <= -1e+136:
		tmp = t_0
	elif y <= 4e+61:
		tmp = x * (y * (1.0 - y))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	return Float64(Float64(x * y) * Float64(1.0 - y))
end

↓

function code(x, y)
	t_0 = Float64(y * Float64(x * Float64(-y)))
	tmp = 0.0
	if (y <= -1e+136)
		tmp = t_0;
	elseif (y <= 4e+61)
		tmp = Float64(x * Float64(y * Float64(1.0 - y)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp = code(x, y)
	tmp = (x * y) * (1.0 - y);
end

↓

function tmp_2 = code(x, y)
	t_0 = y * (x * -y);
	tmp = 0.0;
	if (y <= -1e+136)
		tmp = t_0;
	elseif (y <= 4e+61)
		tmp = x * (y * (1.0 - y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := N[(N[(x * y), $MachinePrecision] * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := Block[{t$95$0 = N[(y * N[(x * (-y)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1e+136], t$95$0, If[LessEqual[y, 4e+61], N[(x * N[(y * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.00000000000000006e136 or 3.9999999999999998e61 < y

   1. Initial program 0.2

      \[\left(x \cdot y\right) \cdot \left(1 - y\right) \]

   2. Simplified 0.2

      \[\leadsto \color{blue}{y \cdot \left(x \cdot \left(1 - y\right)\right)} \]
      Proof
      (*.f64 y (*.f64 x (-.f64 1 y))): 0 points increase in error, 0 points decrease in error
      (Rewrite<= associate-*l*_binary64 (*.f64 (*.f64 y x) (-.f64 1 y))): 2 points increase in error, 6 points decrease in error
      (*.f64 (Rewrite<= *-commutative_binary64 (*.f64 x y)) (-.f64 1 y)): 0 points increase in error, 0 points decrease in error

   3. Taylor expanded in y around inf 0.2

      \[\leadsto y \cdot \color{blue}{\left(-1 \cdot \left(y \cdot x\right)\right)} \]

   4. Simplified 0.2

      \[\leadsto y \cdot \color{blue}{\left(y \cdot \left(-x\right)\right)} \]
      Proof
      (*.f64 y (neg.f64 x)): 0 points increase in error, 0 points decrease in error
      (Rewrite<= distribute-rgt-neg-in_binary64 (neg.f64 (*.f64 y x))): 0 points increase in error, 0 points decrease in error
      (Rewrite<= mul-1-neg_binary64 (*.f64 -1 (*.f64 y x))): 0 points increase in error, 0 points decrease in error

   if -1.00000000000000006e136 < y < 3.9999999999999998e61

   1. Initial program 0.1

      \[\left(x \cdot y\right) \cdot \left(1 - y\right) \]

   2. Simplified 0.1

      \[\leadsto \color{blue}{x \cdot \left(y - y \cdot y\right)} \]
      Proof
      (*.f64 x (-.f64 y (*.f64 y y))): 0 points increase in error, 0 points decrease in error
      (Rewrite<= distribute-lft-out--_binary64 (-.f64 (*.f64 x y) (*.f64 x (*.f64 y y)))): 4 points increase in error, 2 points decrease in error
      (-.f64 (Rewrite<= *-rgt-identity_binary64 (*.f64 (*.f64 x y) 1)) (*.f64 x (*.f64 y y))): 0 points increase in error, 0 points decrease in error
      (-.f64 (*.f64 (*.f64 x y) 1) (Rewrite<= associate-*l*_binary64 (*.f64 (*.f64 x y) y))): 11 points increase in error, 36 points decrease in error
      (Rewrite=> distribute-lft-out--_binary64 (*.f64 (*.f64 x y) (-.f64 1 y))): 2 points increase in error, 2 points decrease in error

   3. Applied egg-rr 0.1

      \[\leadsto x \cdot \color{blue}{\left(y \cdot \left(1 - y\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 0.1

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1 \cdot 10^{+136}:\\ \;\;\;\;y \cdot \left(x \cdot \left(-y\right)\right)\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+61}:\\ \;\;\;\;x \cdot \left(y \cdot \left(1 - y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x \cdot \left(-y\right)\right)\\ \end{array} \]

Alternatives

Alternative 1
Error	1.5
Cost	648

\[\begin{array}{l} t_0 := y \cdot \left(x \cdot \left(-y\right)\right)\\ \mathbf{if}\;y \leq -1:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 2
Error	0.1
Cost	448

\[\left(1 - y\right) \cdot \left(x \cdot y\right) \]

Alternative 3
Error	0.1
Cost	448

\[y \cdot \left(x \cdot \left(1 - y\right)\right) \]

Alternative 4
Error	21.1
Cost	192

\[x \cdot y \]

Reproduce

herbie shell --seed 2022325 
(FPCore (x y)
  :name "Statistics.Distribution.Binomial:$cvariance from math-functions-0.1.5.2"
  :precision binary64
  (* (* x y) (- 1.0 y)))