a parameter of renormalized beta distribution

Percentage Accurate: 99.8% → 99.8%

Time: 6.3s

Alternatives: 12

Speedup: 1.1×

• 41.6% 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, 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]

Derivation

1. Initial program 99.9%

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

Alternative 2: 75.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+38}:\\ \;\;\;\;\frac{\left(-m\right) \cdot m}{m}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* m (- 1.0 m)) v) 1.0) m) -2e+38)
   (/ (* (- m) m) m)
   (* (- (/ m v) 1.0) m)))

C

double code(double m, double v) {
	double tmp;
	if (((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38) {
		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 (((((m * (1.0d0 - m)) / v) - 1.0d0) * m) <= (-2d+38)) 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 (((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38) {
		tmp = (-m * m) / m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if ((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38:
		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(m * Float64(1.0 - m)) / v) - 1.0) * m) <= -2e+38)
		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 (((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38)
		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[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e+38], 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) < -1.99999999999999995e38

   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 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 5.5

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

   5. Applied rewrites 5.5%

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

   7. Applied rewrites 45.8%

      \[\leadsto \frac{\left(-m\right) \cdot m}{\color{blue}{0 + m}} \]

   if -1.99999999999999995e38 < (*.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 98.2

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

   5. Applied rewrites 98.2%

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

4. Final simplification 73.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+38}:\\ \;\;\;\;\frac{\left(-m\right) \cdot m}{m}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 52.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+38}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* m (- 1.0 m)) v) 1.0) m) -2e+38)
   (- m)
   (* (- (/ m v) 1.0) m)))

C

double code(double m, double v) {
	double tmp;
	if (((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38) {
		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 (((((m * (1.0d0 - m)) / v) - 1.0d0) * m) <= (-2d+38)) 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 (((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38) {
		tmp = -m;
	} else {
		tmp = ((m / v) - 1.0) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if ((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38:
		tmp = -m
	else:
		tmp = ((m / v) - 1.0) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(m * Float64(1.0 - m)) / v) - 1.0) * m) <= -2e+38)
		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 (((((m * (1.0 - m)) / v) - 1.0) * m) <= -2e+38)
		tmp = -m;
	else
		tmp = ((m / v) - 1.0) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -2e+38], (-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) < -1.99999999999999995e38

   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 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 5.5

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

   5. Applied rewrites 5.5%

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

   if -1.99999999999999995e38 < (*.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 98.2

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

   5. Applied rewrites 98.2%

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

4. Final simplification 54.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \leq -2 \cdot 10^{+38}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 49.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m \leq -1 \cdot 10^{-303}:\\ \;\;\;\;-m\\ \mathbf{else}:\\ \;\;\;\;\frac{m}{v} \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= (* (- (/ (* m (- 1.0 m)) v) 1.0) m) -1e-303) (- m) (* (/ m v) m)))

C

double code(double m, double v) {
	double tmp;
	if (((((m * (1.0 - m)) / v) - 1.0) * m) <= -1e-303) {
		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 (((((m * (1.0d0 - m)) / v) - 1.0d0) * m) <= (-1d-303)) 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 (((((m * (1.0 - m)) / v) - 1.0) * m) <= -1e-303) {
		tmp = -m;
	} else {
		tmp = (m / v) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if ((((m * (1.0 - m)) / v) - 1.0) * m) <= -1e-303:
		tmp = -m
	else:
		tmp = (m / v) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(m * Float64(1.0 - m)) / v) - 1.0) * m) <= -1e-303)
		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 (((((m * (1.0 - m)) / v) - 1.0) * m) <= -1e-303)
		tmp = -m;
	else
		tmp = (m / v) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[N[(N[(N[(N[(m * N[(1.0 - m), $MachinePrecision]), $MachinePrecision] / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], -1e-303], (-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) < -9.99999999999999931e-304

   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 m around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 40.1

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

   5. Applied rewrites 40.1%

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

   if -9.99999999999999931e-304 < (*.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. Taylor expanded in m around inf

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

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

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

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

      6. associate-/r* N/A

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

      7. associate-*r/ N/A

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

      8. rgt-mult-inverse N/A

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

      9. associate-*r/ N/A

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

      10. *-rgt-identity N/A

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

      11. div-sub N/A

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

      12. lower-/.f64 N/A

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

      13. lower--.f64 94.1

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

   5. Applied rewrites 94.1%

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

   7. Applied rewrites 94.3%

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

   8. Taylor expanded in m around 0

      \[\leadsto \frac{m}{\color{blue}{v}} \cdot m \]
   9. Step-by-step derivation

   10. Applied rewrites 90.7%

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

Alternative 5: 98.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 5.2 \cdot 10^{-63}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 - m\right) \cdot m}{v} \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= m 5.2e-63) (* (- (/ m v) 1.0) m) (* (/ (* (- 1.0 m) m) v) m)))

C

double code(double m, double v) {
	double tmp;
	if (m <= 5.2e-63) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = (((1.0 - m) * 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 (m <= 5.2d-63) then
        tmp = ((m / v) - 1.0d0) * m
    else
        tmp = (((1.0d0 - m) * m) / v) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if (m <= 5.2e-63) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = (((1.0 - m) * m) / v) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if m <= 5.2e-63:
		tmp = ((m / v) - 1.0) * m
	else:
		tmp = (((1.0 - m) * m) / v) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (m <= 5.2e-63)
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	else
		tmp = Float64(Float64(Float64(Float64(1.0 - m) * m) / v) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if (m <= 5.2e-63)
		tmp = ((m / v) - 1.0) * m;
	else
		tmp = (((1.0 - m) * m) / v) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[m, 5.2e-63], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], N[(N[(N[(N[(1.0 - m), $MachinePrecision] * m), $MachinePrecision] / v), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 5.2000000000000003e-63

   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 0

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

      1. lower-/.f64 99.9

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

   5. Applied rewrites 99.9%

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

   if 5.2000000000000003e-63 < m

   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

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

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

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

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

      6. associate-/r* N/A

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

      7. associate-*r/ N/A

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

      8. rgt-mult-inverse N/A

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

      9. associate-*r/ N/A

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

      10. *-rgt-identity N/A

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

      11. div-sub N/A

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

      12. lower-/.f64 N/A

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

      13. lower--.f64 99.8

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

   5. Applied rewrites 99.8%

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

   7. Applied rewrites 99.9%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \leq 5.2 \cdot 10^{-63}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 - m\right) \cdot m}{v} \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 1.05 \cdot 10^{-21}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{1 - m}{v} \cdot m\right) \cdot m\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= m 1.05e-21) (* (- (/ m v) 1.0) m) (* (* (/ (- 1.0 m) v) m) m)))

C

double code(double m, double v) {
	double tmp;
	if (m <= 1.05e-21) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = (((1.0 - m) / v) * m) * m;
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if (m <= 1.05d-21) then
        tmp = ((m / v) - 1.0d0) * m
    else
        tmp = (((1.0d0 - m) / v) * m) * m
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if (m <= 1.05e-21) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = (((1.0 - m) / v) * m) * m;
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if m <= 1.05e-21:
		tmp = ((m / v) - 1.0) * m
	else:
		tmp = (((1.0 - m) / v) * m) * m
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (m <= 1.05e-21)
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	else
		tmp = Float64(Float64(Float64(Float64(1.0 - m) / v) * m) * m);
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if (m <= 1.05e-21)
		tmp = ((m / v) - 1.0) * m;
	else
		tmp = (((1.0 - m) / v) * m) * m;
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[m, 1.05e-21], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], N[(N[(N[(N[(1.0 - m), $MachinePrecision] / v), $MachinePrecision] * m), $MachinePrecision] * m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 1.05000000000000006e-21

   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 1.05000000000000006e-21 < m

   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 m around inf

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

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

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

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

      6. associate-/r* N/A

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

      7. associate-*r/ N/A

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

      8. rgt-mult-inverse N/A

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

      9. associate-*r/ N/A

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

      10. *-rgt-identity N/A

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

      11. div-sub N/A

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

      12. lower-/.f64 N/A

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

      13. lower--.f64 99.9

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

   5. Applied rewrites 99.9%

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

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \leq 1.05 \cdot 10^{-21}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{1 - m}{v} \cdot m\right) \cdot m\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;m \leq 1.05 \cdot 10^{-21}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - m}{v} \cdot \left(m \cdot m\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (m v)
 :precision binary64
 (if (<= m 1.05e-21) (* (- (/ m v) 1.0) m) (* (/ (- 1.0 m) v) (* m m))))

C

double code(double m, double v) {
	double tmp;
	if (m <= 1.05e-21) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = ((1.0 - m) / v) * (m * m);
	}
	return tmp;
}

Fortran

real(8) function code(m, v)
    real(8), intent (in) :: m
    real(8), intent (in) :: v
    real(8) :: tmp
    if (m <= 1.05d-21) then
        tmp = ((m / v) - 1.0d0) * m
    else
        tmp = ((1.0d0 - m) / v) * (m * m)
    end if
    code = tmp
end function

Java

public static double code(double m, double v) {
	double tmp;
	if (m <= 1.05e-21) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = ((1.0 - m) / v) * (m * m);
	}
	return tmp;
}

Python

def code(m, v):
	tmp = 0
	if m <= 1.05e-21:
		tmp = ((m / v) - 1.0) * m
	else:
		tmp = ((1.0 - m) / v) * (m * m)
	return tmp

Julia

function code(m, v)
	tmp = 0.0
	if (m <= 1.05e-21)
		tmp = Float64(Float64(Float64(m / v) - 1.0) * m);
	else
		tmp = Float64(Float64(Float64(1.0 - m) / v) * Float64(m * m));
	end
	return tmp
end

MATLAB

function tmp_2 = code(m, v)
	tmp = 0.0;
	if (m <= 1.05e-21)
		tmp = ((m / v) - 1.0) * m;
	else
		tmp = ((1.0 - m) / v) * (m * m);
	end
	tmp_2 = tmp;
end

Wolfram

code[m_, v_] := If[LessEqual[m, 1.05e-21], N[(N[(N[(m / v), $MachinePrecision] - 1.0), $MachinePrecision] * m), $MachinePrecision], N[(N[(N[(1.0 - m), $MachinePrecision] / v), $MachinePrecision] * N[(m * m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if m < 1.05000000000000006e-21

   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 1.05000000000000006e-21 < m

   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 m around inf

      \[\leadsto \color{blue}{{m}^{3} \cdot \left(\frac{1}{m \cdot v} - \frac{1}{v}\right)} \]
   4. Step-by-step derivation

      1. unpow3 N/A

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

      2. unpow2 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

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

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

      7. associate-/r* N/A

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

      8. associate-*r/ N/A

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

      9. rgt-mult-inverse N/A

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

      10. associate-*r/ N/A

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

      11. *-rgt-identity N/A

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

      12. div-sub N/A

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

      13. lower-/.f64 N/A

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

      14. lower--.f64 N/A

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

      15. unpow2 N/A

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

      16. lower-*.f64 99.8

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

   5. Applied rewrites 99.8%

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

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;m \leq 1.05 \cdot 10^{-21}:\\ \;\;\;\;\left(\frac{m}{v} - 1\right) \cdot m\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - m}{v} \cdot \left(m \cdot m\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 98.0% accurate, 0.9× speedup

Math

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

FPCore

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

C

double code(double m, double v) {
	double tmp;
	if (m <= 1.0) {
		tmp = ((m / v) - 1.0) * m;
	} else {
		tmp = ((-m * 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 (m <= 1.0d0) then
        tmp = ((m / v) - 1.0d0) * m
    else
        tmp = ((-m * m) / v) * m
    end if
    code = tmp
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if m < 1

   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 98.2

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

   5. Applied rewrites 98.2%

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

   if 1 < m

   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. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. unpow2 N/A

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

      4. associate-*r* N/A

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

      5. lower-*.f64 N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 96.3

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

   5. Applied rewrites 96.3%

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

4. Final simplification 97.3%

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

Alternative 9: 98.0% accurate, 0.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if m < 1

   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 98.2

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

   5. Applied rewrites 98.2%

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

   if 1 < m

   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. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. unpow2 N/A

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

      4. associate-*r* N/A

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

      5. lower-*.f64 N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 96.3

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

   5. Applied rewrites 96.3%

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

   7. Applied rewrites 96.2%

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

4. Final simplification 97.3%

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

Alternative 10: 99.8% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

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

   6. associate-/l* N/A

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

   7. *-commutative N/A

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

   8. lower-fma.f64 N/A

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

   9. lower-/.f64 N/A

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

   10. metadata-eval 99.9

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

4. Applied rewrites 99.9%

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

Alternative 11: 27.9% 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 m around 0

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 31.4

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

5. Applied rewrites 31.4%

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

Alternative 12: 3.1% accurate, 28.0× 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 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 m around 0

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 31.4

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

5. Applied rewrites 31.4%

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

7. Applied rewrites 30.1%

   \[\leadsto \frac{\left(-m\right) \cdot m}{\color{blue}{0 + m}} \]
8. Step-by-step derivation

9. Applied rewrites 3.2%

   \[\leadsto m \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024319 
(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))