a parameter of renormalized beta distribution

Percentage Accurate: 99.8% → 99.8%

Time: 7.4s

Alternatives: 9

Speedup: 1.0×

• 41.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

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

Math

\[\begin{array}{l} \\ \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \end{array} \]

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \end{array} \]

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 1.6 \cdot 10^{-16}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\left(m \cdot m\right) \cdot \frac{1 - m}{v}\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= m 1.6e-16) (* (- (/ m v) 1.0) m) (* (* m m) (/ (- 1.0 m) v))))

C

double code(double m, double v) {
	double tmp;
	if (m <= 1.6e-16) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = (m * m) * ((1.0 - m) / v);
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if (m <= 1.6d-16) then
        tmp = ((m / v) - 1.0d0) * m
    else
        tmp = (m * m) * ((1.0d0 - m) / v)
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if (m <= 1.6e-16) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = (m * m) * ((1.0 - m) / v);
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if m <= 1.6e-16:
		tmp = ((m / v) - 1.0) * m
	else:
		tmp = (m * m) * ((1.0 - m) / v)
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (m <= 1.6e-16)
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	else
		tmp = Float64(Float64(m * m) * Float64(Float64(1.0 - m) / v));
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if (m <= 1.6e-16)
		tmp = ((m / v) - 1.0) * m;
	else
		tmp = (m * m) * ((1.0 - m) / v);
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[m, 1.6e-16], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], N[(N[(m * m), $MachinePrecision] * N[(N[(1.0 - m), $MachinePrecision] / v), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 1.60000000000000011e-16

   1. Initial program 99.8%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
   4. Step-by-step derivation

      1. lower-/.f64 99.8

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

   5. Applied rewrites 99.8%

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

   if 1.60000000000000011e-16 < m

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift--.f64 N/A

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

      4. sub-neg N/A

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

      5. distribute-rgt-in N/A

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

      6. lift-/.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. associate-/l* N/A

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

      9. *-commutative N/A

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

      10. associate-*l* N/A

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

      11. metadata-eval N/A

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

      12. neg-mul-1 N/A

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

      13. lower-fma.f64 N/A

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

      14. lower-/.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lower-neg.f64 99.9

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

   4. Applied rewrites 99.9%

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

   5. Taylor expanded in v around 0

      \[\leadsto \color{blue}{\frac{{m}^{2} \cdot \left(1 - m\right)}{v}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 99.9

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

   7. Applied rewrites 99.9%

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

   8. Taylor expanded in v around 0

      \[\leadsto \color{blue}{\frac{{m}^{2} \cdot \left(1 - m\right)}{v}} \]
   9. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{m}^{2} \cdot \frac{1 - m}{v}} \]

      2. div-sub N/A

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

      3. sub-neg N/A

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

      4. mul-1-neg N/A

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

      5. +-commutative N/A

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

      6. +-commutative N/A

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

      7. mul-1-neg N/A

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

      8. sub-neg N/A

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

      9. *-rgt-identity N/A

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

      10. associate-*r/ N/A

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

      11. rgt-mult-inverse N/A

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

      12. associate-*r/ N/A

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

      13. associate-/r* N/A

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

      14. distribute-lft-out-- N/A

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

      15. *-commutative N/A

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

      16. lower-*.f64 N/A

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

   10. Applied rewrites 99.9%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \leq 1.6 \cdot 10^{-16}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\left(m \cdot m\right) \cdot \frac{1 - m}{v}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 97.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;\frac{\left(-m\right) \cdot m}{v} \cdot m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* (- 1.0 m) m) v) 1.0) m) -2e+86)
   (* (/ (* (- m) m) v) m)
   (* (- (/ m v) 1.0) m)))

C

double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = ((-m * m) / v) * m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if ((((((1.0d0 - m) * m) / v) - 1.0d0) * m) <= (-2d+86)) then
        tmp = ((-m * m) / v) * m
    else
        tmp = ((m / v) - 1.0d0) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = ((-m * m) / v) * m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if (((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86:
		tmp = ((-m * m) / v) * m
	else:
		tmp = ((m / v) - 1.0) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(Float64(1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = Float64(Float64(Float64(Float64(-m) * m) / v) * m);
	else
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = ((-m * m) / v) * m;
	else
		tmp = ((m / v) - 1.0) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(N[(1.0 - m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e+86], N[(N[(N[((-m) * m), $MachinePrecision] / v), $MachinePrecision] * m), $MachinePrecision], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -2e86

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around inf

      \[\leadsto \color{blue}{\left(-1 \cdot \frac{{m}^{2}}{v}\right)} \cdot m \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-/l* N/A

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

      3. associate-*r* N/A

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

      4. associate-*r/ N/A

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

      5. associate-*l/ N/A

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

      6. associate-*r/ N/A

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

      7. lower-*.f64 N/A

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

      8. associate-*r/ N/A

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

      9. lower-/.f64 N/A

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

      10. mul-1-neg N/A

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

      11. lower-neg.f64 99.3

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

   5. Applied rewrites 99.3%

      \[\leadsto \color{blue}{\left(\frac{-m}{v} \cdot m\right)} \cdot m \]
   6. Step-by-step derivation

   7. Applied rewrites 99.3%

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

   if -2e86 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.8%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
   4. Step-by-step derivation

      1. lower-/.f64 97.7

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

   5. Applied rewrites 97.7%

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

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;\frac{\left(-m\right) \cdot m}{v} \cdot m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 97.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;\frac{\left(\left(-m\right) \cdot m\right) \cdot m}{v}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* (- 1.0 m) m) v) 1.0) m) -2e+86)
   (/ (* (* (- m) m) m) v)
   (* (- (/ m v) 1.0) m)))

C

double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = ((-m * m) * m) / v;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if ((((((1.0d0 - m) * m) / v) - 1.0d0) * m) <= (-2d+86)) then
        tmp = ((-m * m) * m) / v
    else
        tmp = ((m / v) - 1.0d0) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = ((-m * m) * m) / v;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if (((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86:
		tmp = ((-m * m) * m) / v
	else:
		tmp = ((m / v) - 1.0) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(Float64(1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = Float64(Float64(Float64(Float64(-m) * m) * m) / v);
	else
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = ((-m * m) * m) / v;
	else
		tmp = ((m / v) - 1.0) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(N[(1.0 - m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e+86], N[(N[(N[((-m) * m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -2e86

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift--.f64 N/A

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

      4. sub-neg N/A

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

      5. distribute-rgt-in N/A

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

      6. lift-/.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. associate-/l* N/A

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

      9. *-commutative N/A

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

      10. associate-*l* N/A

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

      11. metadata-eval N/A

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

      12. neg-mul-1 N/A

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

      13. lower-fma.f64 N/A

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

      14. lower-/.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lower-neg.f64 99.9

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

   4. Applied rewrites 99.9%

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

   5. Taylor expanded in v around 0

      \[\leadsto \color{blue}{\frac{{m}^{2} \cdot \left(1 - m\right)}{v}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 99.9

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

   7. Applied rewrites 99.9%

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

   8. Taylor expanded in m around inf

      \[\leadsto \frac{\left(\left(-1 \cdot m\right) \cdot m\right) \cdot m}{v} \]
   9. Step-by-step derivation

   10. Applied rewrites 99.3%

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

   if -2e86 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.8%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
   4. Step-by-step derivation

      1. lower-/.f64 97.7

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

   5. Applied rewrites 97.7%

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

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;\frac{\left(\left(-m\right) \cdot m\right) \cdot m}{v}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 75.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;\frac{\left(-m\right) \cdot m}{m}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* (- 1.0 m) m) v) 1.0) m) -2e+86)
   (/ (* (- m) m) m)
   (* (- (/ m v) 1.0) m)))

C

double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = (-m * m) / m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if ((((((1.0d0 - m) * m) / v) - 1.0d0) * m) <= (-2d+86)) then
        tmp = (-m * m) / m
    else
        tmp = ((m / v) - 1.0d0) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = (-m * m) / m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if (((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86:
		tmp = (-m * m) / m
	else:
		tmp = ((m / v) - 1.0) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(Float64(1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = Float64(Float64(Float64(-m) * m) / m);
	else
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = (-m * m) / m;
	else
		tmp = ((m / v) - 1.0) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(N[(1.0 - m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e+86], N[(N[((-m) * m), $MachinePrecision] / m), $MachinePrecision], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -2e86

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in v around inf

      \[\leadsto \color{blue}{-1 \cdot m} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

      2. lower-neg.f64 5.7

         \[\leadsto \color{blue}{-m} \]

   5. Applied rewrites 5.7%

      \[\leadsto \color{blue}{-m} \]
   6. Step-by-step derivation

   7. Applied rewrites 53.8%

      \[\leadsto \frac{0 - m \cdot m}{\color{blue}{0 + m}} \]

   if -2e86 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.8%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
   4. Step-by-step derivation

      1. lower-/.f64 97.7

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

   5. Applied rewrites 97.7%

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

4. Final simplification 74.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;\frac{\left(-m\right) \cdot m}{m}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 51.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* (- 1.0 m) m) v) 1.0) m) -2e+86)
   (- m)
   (* (- (/ m v) 1.0) m)))

C

double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = -m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if ((((((1.0d0 - m) * m) / v) - 1.0d0) * m) <= (-2d+86)) then
        tmp = -m
    else
        tmp = ((m / v) - 1.0d0) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86) {
		tmp = -m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if (((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86:
		tmp = -m
	else:
		tmp = ((m / v) - 1.0) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(Float64(1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = Float64(-m);
	else
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e+86)
		tmp = -m;
	else
		tmp = ((m / v) - 1.0) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(N[(1.0 - m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e+86], (-m), N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -2e86

   1. Initial program 99.9%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in v around inf

      \[\leadsto \color{blue}{-1 \cdot m} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

      2. lower-neg.f64 5.7

         \[\leadsto \color{blue}{-m} \]

   5. Applied rewrites 5.7%

      \[\leadsto \color{blue}{-m} \]

   if -2e86 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.8%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in m around 0

      \[\leadsto \left(\color{blue}{\frac{m}{v}} - 1\right) \cdot m \]
   4. Step-by-step derivation

      1. lower-/.f64 97.7

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

   5. Applied rewrites 97.7%

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

4. Final simplification 48.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+86}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 49.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{-305}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v} \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* (- 1.0 m) m) v) 1.0) m) -2e-305) (- m) (* (/ m v) m)))

C

double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e-305) {
		tmp = -m;
	} else {
		tmp = (m / v) * m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if ((((((1.0d0 - m) * m) / v) - 1.0d0) * m) <= (-2d-305)) then
        tmp = -m
    else
        tmp = (m / v) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e-305) {
		tmp = -m;
	} else {
		tmp = (m / v) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if (((((1.0 - m) * m) / v) - 1.0) * m) <= -2e-305:
		tmp = -m
	else:
		tmp = (m / v) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(Float64(1.0 - m) * m) / v) - 1.0) * m) <= -2e-305)
		tmp = Float64(-m);
	else
		tmp = Float64(Float64(m / v) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if ((((((1.0 - m) * m) / v) - 1.0) * m) <= -2e-305)
		tmp = -m;
	else
		tmp = (m / v) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(N[(1.0 - m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e-305], (-m), N[(N[(m / v), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m) < -1.99999999999999999e-305

   1. Initial program 100.0%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing

   3. Taylor expanded in v around inf

      \[\leadsto \color{blue}{-1 \cdot m} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

      2. lower-neg.f64 36.6

         \[\leadsto \color{blue}{-m} \]

   5. Applied rewrites 36.6%

      \[\leadsto \color{blue}{-m} \]

   if -1.99999999999999999e-305 < (*.f64 (-.f64 (/.f64 (*.f64 m (-.f64 #s(literal 1 binary64) m)) v) #s(literal 1 binary64)) m)

   1. Initial program 99.6%

      \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift--.f64 N/A

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

      4. sub-neg N/A

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

      5. distribute-rgt-in N/A

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

      6. lift-/.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. associate-/l* N/A

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

      9. *-commutative N/A

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

      10. associate-*l* N/A

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

      11. metadata-eval N/A

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

      12. neg-mul-1 N/A

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

      13. lower-fma.f64 N/A

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

      14. lower-/.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lower-neg.f64 77.8

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

   4. Applied rewrites 77.8%

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

   5. Taylor expanded in v around 0

      \[\leadsto \color{blue}{\frac{{m}^{2} \cdot \left(1 - m\right)}{v}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 76.6

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

   7. Applied rewrites 76.6%

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

   9. Applied rewrites 91.9%

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

   10. Taylor expanded in m around 0

       \[\leadsto \frac{{m}^{2}}{\color{blue}{v}} \]
   11. Step-by-step derivation

   12. Applied rewrites 92.2%

       \[\leadsto \frac{m}{v} \cdot \color{blue}{m} \]
3. Recombined 2 regimes into one program.

4. Final simplification 47.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \leq -2 \cdot 10^{-305}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v} \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto \left(\frac{\left(1 - m\right) \cdot m}{v} - 1\right) \cdot m \]
4. Add Preprocessing

Alternative 8: 99.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{1 - m}{v}, m, -1\right) \cdot m \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

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

   2. sub-neg N/A

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

   3. lift-/.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. associate-/l* N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f64 N/A

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

   8. lower-/.f64 N/A

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

   9. metadata-eval 99.8

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

4. Applied rewrites 99.8%

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

Alternative 9: 26.9% accurate, 9.3× speedup

Math

\[\begin{array}{l} \\ -m \end{array} \]

FPCore

(FPCore (m v) :precision binary64 (- m))

C

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

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    code = -m
end function

Java

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

Python

def code(m, v):
	return -m

Julia

function code(m, v)
	return Float64(-m)
end

MATLAB

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

Wolfram

code[m_, v_] := (-m)

Derivation

1. Initial program 99.9%

   \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \]
2. Add Preprocessing

3. Taylor expanded in v around inf

   \[\leadsto \color{blue}{-1 \cdot m} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(m\right)} \]

   2. lower-neg.f64 30.0

      \[\leadsto \color{blue}{-m} \]

5. Applied rewrites 30.0%

   \[\leadsto \color{blue}{-m} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024235 
(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))