b parameter of renormalized beta distribution

Average Error: 0.1 → 0.3

Time: 4.7s

Precision: binary64

Cost: 836

\[\left(0 < m \land 0 < v\right) \land v < 0.25\]

Math

\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right) \]

↓

\[\begin{array}{l} \mathbf{if}\;m \leq 2.3277668791324443 \cdot 10^{-25}:\\ \;\;\;\;m + \left(-1 + \frac{m}{v}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{m \cdot \left(1 - m\right)}{\frac{v}{1 - m}}\\ \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (* (- (/ (* m (- 1.0 m)) v) 1.0) (- 1.0 m)))

↓

(FPCore (m v)
 :precision binary64
 (if (<= m 2.3277668791324443e-25)
   (+ m (+ -1.0 (/ m v)))
   (/ (* m (- 1.0 m)) (/ v (- 1.0 m)))))

C

double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * (1.0 - m);
}

↓

double code(double m, double v) {
	double tmp;
	if (m <= 2.3277668791324443e-25) {
		tmp = m + (-1.0 + (m / v));
	} else {
		tmp = (m * (1.0 - m)) / (v / (1.0 - m));
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    code = (((m * (1.0d0 - m)) / v) - 1.0d0) * (1.0d0 - m)
end function

↓

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if (m <= 2.3277668791324443d-25) then
        tmp = m + ((-1.0d0) + (m / v))
    else
        tmp = (m * (1.0d0 - m)) / (v / (1.0d0 - m))
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * (1.0 - m);
}

↓

public static double code(double m, double v) {
	double tmp;
	if (m <= 2.3277668791324443e-25) {
		tmp = m + (-1.0 + (m / v));
	} else {
		tmp = (m * (1.0 - m)) / (v / (1.0 - m));
	}
	return tmp;
}

Python

def code(m, v):
	return (((m * (1.0 - m)) / v) - 1.0) * (1.0 - m)

↓

def code(m, v):
	tmp = 0
	if m <= 2.3277668791324443e-25:
		tmp = m + (-1.0 + (m / v))
	else:
		tmp = (m * (1.0 - m)) / (v / (1.0 - m))
	return tmp

Julia

function code(m, v)
	return Float64(Float64(Float64(Float64(m * Float64(1.0 - m)) / v) - 1.0) * Float64(1.0 - m))
end

↓

function code(m, v)
	tmp = 0.0
	if (m <= 2.3277668791324443e-25)
		tmp = Float64(m + Float64(-1.0 + Float64(m / v)));
	else
		tmp = Float64(Float64(m * Float64(1.0 - m)) / Float64(v / Float64(1.0 - m)));
	end
	return tmp
end

MATLAB

function tmp = code(m, v)
	tmp = (((m * (1.0 - m)) / v) - 1.0) * (1.0 - m);
end

↓

function tmp_2 = code(m, v)
	tmp = 0.0;
	if (m <= 2.3277668791324443e-25)
		tmp = m + (-1.0 + (m / v));
	else
		tmp = (m * (1.0 - m)) / (v / (1.0 - m));
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := N[(N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * N[(1.0 - m), $MachinePrecision]), $MachinePrecision]

↓

code[m_, v_] := If[LessEqual[m, 2.3277668791324443e-25], N[(m + N[(-1.0 + N[(m / v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / N[(v / N[(1.0 - m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 2.32776687913244428e-25

   1. Initial program 0.0

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right) \]

   2. Taylor expanded in m around 0 0.0

      \[\leadsto \color{blue}{\left(\frac{m}{v} - 1\right)} \cdot \left(1 - m\right) \]

   3. Simplified 0.0

      \[\leadsto \color{blue}{\left(-1 + \frac{m}{v}\right)} \cdot \left(1 - m\right) \]

   4. Taylor expanded in m around 0 0.1

      \[\leadsto \color{blue}{\left(1 + \frac{1}{v}\right) \cdot m - 1} \]

   5. Simplified 0.0

      \[\leadsto \color{blue}{m + \left(-1 + \frac{m}{v}\right)} \]

   if 2.32776687913244428e-25 < m

   1. Initial program 0.4

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right) \]

   2. Simplified 0.4

      \[\leadsto \color{blue}{\left(1 - m\right) \cdot \mathsf{fma}\left(1 - m, \frac{m}{v}, -1\right)} \]

   3. Taylor expanded in m around inf 1.5

      \[\leadsto \left(1 - m\right) \cdot \color{blue}{\left(\frac{m}{v} + -1 \cdot \frac{{m}^{2}}{v}\right)} \]

   4. Simplified 1.6

      \[\leadsto \left(1 - m\right) \cdot \color{blue}{\left(m \cdot \frac{1 - m}{v}\right)} \]

   5. Applied egg-rr 1.5

      \[\leadsto \left(1 - m\right) \cdot \color{blue}{\frac{m}{\frac{v}{1 - m}}} \]

   6. Applied egg-rr 1.5

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

4. Final simplification 0.3

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \leq 2.3277668791324443 \cdot 10^{-25}:\\ \;\;\;\;m + \left(-1 + \frac{m}{v}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{m \cdot \left(1 - m\right)}{\frac{v}{1 - m}}\\ \end{array} \]

Alternatives

Alternative 1
Error	0.1
Cost	832

\[\left(1 - m\right) \cdot \left(-1 + \frac{m}{v} \cdot \left(1 - m\right)\right) \]

Alternative 2
Error	0.2
Cost	832

\[\left(1 - m\right) \cdot \left(-1 + \frac{1 - m}{\frac{v}{m}}\right) \]

Alternative 3
Error	17.0
Cost	712

\[\begin{array}{l} \mathbf{if}\;m \leq 1.0391889848568815 \cdot 10^{-157}:\\ \;\;\;\;-1\\ \mathbf{elif}\;m \leq 0.005520585486508588:\\ \;\;\;\;\frac{m}{v}\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v} \cdot \left(m \cdot m\right)\\ \end{array} \]

Alternative 4
Error	16.9
Cost	712

\[\begin{array}{l} \mathbf{if}\;m \leq 1.0391889848568815 \cdot 10^{-157}:\\ \;\;\;\;-1\\ \mathbf{elif}\;m \leq 0.005520585486508588:\\ \;\;\;\;\frac{m \cdot \left(1 - m\right)}{v}\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v} \cdot \left(m \cdot m\right)\\ \end{array} \]

Alternative 5
Error	1.9
Cost	708

\[\begin{array}{l} \mathbf{if}\;m \leq 0.005520585486508588:\\ \;\;\;\;m + \left(-1 + \frac{m}{v}\right)\\ \mathbf{else}:\\ \;\;\;\;m \cdot \left(m \cdot \frac{m + -2}{v}\right)\\ \end{array} \]

Alternative 6
Error	1.9
Cost	708

\[\begin{array}{l} \mathbf{if}\;m \leq 0.005520585486508588:\\ \;\;\;\;m + \left(-1 + \frac{m}{v}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(m \cdot m\right) \cdot \left(m + -2\right)}{v}\\ \end{array} \]

Alternative 7
Error	1.9
Cost	708

\[\begin{array}{l} \mathbf{if}\;m \leq 0.005520585486508588:\\ \;\;\;\;\left(1 - m\right) \cdot \left(-1 + \frac{m}{v}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(m \cdot m\right) \cdot \left(m + -2\right)}{v}\\ \end{array} \]

Alternative 8
Error	2.4
Cost	580

\[\begin{array}{l} \mathbf{if}\;m \leq 0.005520585486508588:\\ \;\;\;\;m + \left(-1 + \frac{m}{v}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v} \cdot \left(m \cdot m\right)\\ \end{array} \]

Alternative 9
Error	24.6
Cost	324

\[\begin{array}{l} \mathbf{if}\;m \leq 1.0391889848568815 \cdot 10^{-157}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v}\\ \end{array} \]

Alternative 10
Error	61.7
Cost	64

\[m \]

Alternative 11
Error	38.1
Cost	64

\[-1 \]

Reproduce

herbie shell --seed 2022217 
(FPCore (m v)
  :name "b parameter of renormalized beta distribution"
  :precision binary64
  :pre (and (and (< 0.0 m) (< 0.0 v)) (< v 0.25))
  (* (- (/ (* m (- 1.0 m)) v) 1.0) (- 1.0 m)))