Diagrams.Segment:$catParam from diagrams-lib-1.3.0.3, B

Percentage Accurate: 99.7% → 99.7%

Time: 5.5s

Alternatives: 4

Speedup: 1.0×

• 24.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x \cdot 3\right) \cdot y\right) \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* (* x 3.0) y) y))

C

double code(double x, double y) {
	return ((x * 3.0) * y) * y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * 3.0d0) * y) * y
end function

Java

public static double code(double x, double y) {
	return ((x * 3.0) * y) * y;
}

Python

def code(x, y):
	return ((x * 3.0) * y) * y

Julia

function code(x, y)
	return Float64(Float64(Float64(x * 3.0) * y) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = ((x * 3.0) * y) * y;
end

Wolfram

code[x_, y_] := N[(N[(N[(x * 3.0), $MachinePrecision] * y), $MachinePrecision] * y), $MachinePrecision]

Initial Program: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x \cdot 3\right) \cdot y\right) \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* (* x 3.0) y) y))

C

double code(double x, double y) {
	return ((x * 3.0) * y) * y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * 3.0d0) * y) * y
end function

Java

public static double code(double x, double y) {
	return ((x * 3.0) * y) * y;
}

Python

def code(x, y):
	return ((x * 3.0) * y) * y

Julia

function code(x, y)
	return Float64(Float64(Float64(x * 3.0) * y) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = ((x * 3.0) * y) * y;
end

Wolfram

code[x_, y_] := N[(N[(N[(x * 3.0), $MachinePrecision] * y), $MachinePrecision] * y), $MachinePrecision]

Alternative 1: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y \cdot \left(x \cdot \left(3 \cdot y\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* y (* x (* 3.0 y))))

C

double code(double x, double y) {
	return y * (x * (3.0 * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y * (x * (3.0d0 * y))
end function

Java

public static double code(double x, double y) {
	return y * (x * (3.0 * y));
}

Python

def code(x, y):
	return y * (x * (3.0 * y))

Julia

function code(x, y)
	return Float64(y * Float64(x * Float64(3.0 * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = y * (x * (3.0 * y));
end

Wolfram

code[x_, y_] := N[(y * N[(x * N[(3.0 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\left(x \cdot 3\right) \cdot y\right) \cdot y \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.7%

   \[\leadsto \color{blue}{\left(3 \cdot \left(x \cdot y\right)\right)} \cdot y \]
4. Step-by-step derivation

   1. associate-*r* 99.7%

      \[\leadsto \color{blue}{\left(\left(3 \cdot x\right) \cdot y\right)} \cdot y \]

   2. *-commutative 99.7%

      \[\leadsto \left(\color{blue}{\left(x \cdot 3\right)} \cdot y\right) \cdot y \]

   3. associate-*r* 99.7%

      \[\leadsto \color{blue}{\left(x \cdot \left(3 \cdot y\right)\right)} \cdot y \]

5. Simplified 99.7%

   \[\leadsto \color{blue}{\left(x \cdot \left(3 \cdot y\right)\right)} \cdot y \]

6. Final simplification 99.7%

   \[\leadsto y \cdot \left(x \cdot \left(3 \cdot y\right)\right) \]
7. Add Preprocessing

Alternative 2: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y \cdot \left(3 \cdot \left(x \cdot y\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* y (* 3.0 (* x y))))

C

double code(double x, double y) {
	return y * (3.0 * (x * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y * (3.0d0 * (x * y))
end function

Java

public static double code(double x, double y) {
	return y * (3.0 * (x * y));
}

Python

def code(x, y):
	return y * (3.0 * (x * y))

Julia

function code(x, y)
	return Float64(y * Float64(3.0 * Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = y * (3.0 * (x * y));
end

Wolfram

code[x_, y_] := N[(y * N[(3.0 * N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\left(x \cdot 3\right) \cdot y\right) \cdot y \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.7%

   \[\leadsto \color{blue}{\left(3 \cdot \left(x \cdot y\right)\right)} \cdot y \]

4. Final simplification 99.7%

   \[\leadsto y \cdot \left(3 \cdot \left(x \cdot y\right)\right) \]
5. Add Preprocessing

Alternative 3: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(y \cdot \left(x \cdot y\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 3.0 (* y (* x y))))

C

double code(double x, double y) {
	return 3.0 * (y * (x * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 3.0d0 * (y * (x * y))
end function

Java

public static double code(double x, double y) {
	return 3.0 * (y * (x * y));
}

Python

def code(x, y):
	return 3.0 * (y * (x * y))

Julia

function code(x, y)
	return Float64(3.0 * Float64(y * Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = 3.0 * (y * (x * y));
end

Wolfram

code[x_, y_] := N[(3.0 * N[(y * N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\left(x \cdot 3\right) \cdot y\right) \cdot y \]
2. Step-by-step derivation

   1. associate-*l* 86.9%

      \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot \left(y \cdot y\right)} \]

   2. associate-*l* 86.9%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(y \cdot y\right)\right)} \]

3. Simplified 86.9%

   \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(y \cdot y\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 86.8%

   \[\leadsto \color{blue}{3 \cdot \left(x \cdot {y}^{2}\right)} \]
6. Step-by-step derivation

   1. add-sqr-sqrt 57.3%

      \[\leadsto 3 \cdot \color{blue}{\left(\sqrt{x \cdot {y}^{2}} \cdot \sqrt{x \cdot {y}^{2}}\right)} \]

   2. pow2 57.3%

      \[\leadsto 3 \cdot \color{blue}{{\left(\sqrt{x \cdot {y}^{2}}\right)}^{2}} \]

   3. *-commutative 57.3%

      \[\leadsto 3 \cdot {\left(\sqrt{\color{blue}{{y}^{2} \cdot x}}\right)}^{2} \]

   4. sqrt-prod 45.0%

      \[\leadsto 3 \cdot {\color{blue}{\left(\sqrt{{y}^{2}} \cdot \sqrt{x}\right)}}^{2} \]

   5. sqrt-pow1 52.1%

      \[\leadsto 3 \cdot {\left(\color{blue}{{y}^{\left(\frac{2}{2}\right)}} \cdot \sqrt{x}\right)}^{2} \]

   6. metadata-eval 52.1%

      \[\leadsto 3 \cdot {\left({y}^{\color{blue}{1}} \cdot \sqrt{x}\right)}^{2} \]

   7. pow1 52.1%

      \[\leadsto 3 \cdot {\left(\color{blue}{y} \cdot \sqrt{x}\right)}^{2} \]

7. Applied egg-rr 52.1%

   \[\leadsto 3 \cdot \color{blue}{{\left(y \cdot \sqrt{x}\right)}^{2}} \]
8. Step-by-step derivation

   1. unpow2 52.1%

      \[\leadsto 3 \cdot \color{blue}{\left(\left(y \cdot \sqrt{x}\right) \cdot \left(y \cdot \sqrt{x}\right)\right)} \]

   2. swap-sqr 45.0%

      \[\leadsto 3 \cdot \color{blue}{\left(\left(y \cdot y\right) \cdot \left(\sqrt{x} \cdot \sqrt{x}\right)\right)} \]

   3. add-sqr-sqrt 86.8%

      \[\leadsto 3 \cdot \left(\left(y \cdot y\right) \cdot \color{blue}{x}\right) \]

   4. *-commutative 86.8%

      \[\leadsto 3 \cdot \color{blue}{\left(x \cdot \left(y \cdot y\right)\right)} \]

   5. associate-*r* 99.7%

      \[\leadsto 3 \cdot \color{blue}{\left(\left(x \cdot y\right) \cdot y\right)} \]

   6. *-commutative 99.7%

      \[\leadsto 3 \cdot \left(\color{blue}{\left(y \cdot x\right)} \cdot y\right) \]

9. Applied egg-rr 99.7%

   \[\leadsto 3 \cdot \color{blue}{\left(\left(y \cdot x\right) \cdot y\right)} \]

10. Final simplification 99.7%

    \[\leadsto 3 \cdot \left(y \cdot \left(x \cdot y\right)\right) \]
11. Add Preprocessing

Alternative 4: 87.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(x \cdot \left(y \cdot y\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 3.0 (* x (* y y))))

C

double code(double x, double y) {
	return 3.0 * (x * (y * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 3.0d0 * (x * (y * y))
end function

Java

public static double code(double x, double y) {
	return 3.0 * (x * (y * y));
}

Python

def code(x, y):
	return 3.0 * (x * (y * y))

Julia

function code(x, y)
	return Float64(3.0 * Float64(x * Float64(y * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = 3.0 * (x * (y * y));
end

Wolfram

code[x_, y_] := N[(3.0 * N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\left(x \cdot 3\right) \cdot y\right) \cdot y \]
2. Step-by-step derivation

   1. associate-*l* 86.9%

      \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot \left(y \cdot y\right)} \]

   2. associate-*l* 86.9%

      \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(y \cdot y\right)\right)} \]

3. Simplified 86.9%

   \[\leadsto \color{blue}{x \cdot \left(3 \cdot \left(y \cdot y\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 86.8%

   \[\leadsto \color{blue}{3 \cdot \left(x \cdot {y}^{2}\right)} \]
6. Step-by-step derivation

   1. unpow2 86.8%

      \[\leadsto 3 \cdot \left(x \cdot \color{blue}{\left(y \cdot y\right)}\right) \]

7. Applied egg-rr 86.8%

   \[\leadsto 3 \cdot \left(x \cdot \color{blue}{\left(y \cdot y\right)}\right) \]
8. Add Preprocessing

Developer Target 1: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \left(3 \cdot y\right)\right) \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* x (* 3.0 y)) y))

C

double code(double x, double y) {
	return (x * (3.0 * y)) * y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * (3.0d0 * y)) * y
end function

Java

public static double code(double x, double y) {
	return (x * (3.0 * y)) * y;
}

Python

def code(x, y):
	return (x * (3.0 * y)) * y

Julia

function code(x, y)
	return Float64(Float64(x * Float64(3.0 * y)) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = (x * (3.0 * y)) * y;
end

Wolfram

code[x_, y_] := N[(N[(x * N[(3.0 * y), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]

Reproduce

herbie shell --seed 2024128 
(FPCore (x y)
  :name "Diagrams.Segment:$catParam from diagrams-lib-1.3.0.3, B"
  :precision binary64

  :alt
  (! :herbie-platform default (* (* x (* 3 y)) y))

  (* (* (* x 3.0) y) y))