a parameter of renormalized beta distribution

Average Error: 0.2 → 0.2

Time: 2.2s

Precision: binary64

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

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

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) * m
end function

↓

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

Java

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

↓

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

Python

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

↓

def code(m, v):
	return m * (-1.0 + math.pow(((v / (1.0 - m)) / m), -1.0))

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.2

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

2. Applied egg-rr 0.2

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

3. Final simplification 0.2

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

Reproduce

herbie shell --seed 2022214 
(FPCore (m v)
  :name "a 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) m))