Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D

Percentage Accurate: 99.8% → 99.9%

Time: 7.8s

Alternatives: 5

Speedup: 1.0×

• 24.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))

C

double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * ((((x * 3.0d0) * x) - (x * 4.0d0)) + 1.0d0)
end function

Java

public static double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Python

def code(x):
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0)

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(Float64(x * 3.0) * x) - Float64(x * 4.0)) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))

C

double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * ((((x * 3.0d0) * x) - (x * 4.0d0)) + 1.0d0)
end function

Java

public static double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

Python

def code(x):
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0)

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(Float64(x * 3.0) * x) - Float64(x * 4.0)) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, -12 + x \cdot 9, 3\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma x (+ -12.0 (* x 9.0)) 3.0))

C

double code(double x) {
	return fma(x, (-12.0 + (x * 9.0)), 3.0);
}

Julia

function code(x)
	return fma(x, Float64(-12.0 + Float64(x * 9.0)), 3.0)
end

Wolfram

code[x_] := N[(x * N[(-12.0 + N[(x * 9.0), $MachinePrecision]), $MachinePrecision] + 3.0), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Step-by-step derivation

   1. distribute-rgt-in 99.8%

      \[\leadsto \color{blue}{\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) \cdot 3 + 1 \cdot 3} \]

   2. metadata-eval 99.8%

      \[\leadsto \left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) \cdot 3 + \color{blue}{3} \]

   3. *-commutative 99.8%

      \[\leadsto \left(\color{blue}{x \cdot \left(x \cdot 3\right)} - x \cdot 4\right) \cdot 3 + 3 \]

   4. distribute-lft-out-- 99.8%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot 3 - 4\right)\right)} \cdot 3 + 3 \]

   5. associate-*l* 99.8%

      \[\leadsto \color{blue}{x \cdot \left(\left(x \cdot 3 - 4\right) \cdot 3\right)} + 3 \]

   6. fma-def 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, \left(x \cdot 3 - 4\right) \cdot 3, 3\right)} \]

   7. *-commutative 99.8%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{3 \cdot \left(x \cdot 3 - 4\right)}, 3\right) \]

   8. sub-neg 99.8%

      \[\leadsto \mathsf{fma}\left(x, 3 \cdot \color{blue}{\left(x \cdot 3 + \left(-4\right)\right)}, 3\right) \]

   9. +-commutative 99.8%

      \[\leadsto \mathsf{fma}\left(x, 3 \cdot \color{blue}{\left(\left(-4\right) + x \cdot 3\right)}, 3\right) \]

   10. distribute-rgt-in 99.9%

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{\left(-4\right) \cdot 3 + \left(x \cdot 3\right) \cdot 3}, 3\right) \]

   11. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{-4} \cdot 3 + \left(x \cdot 3\right) \cdot 3, 3\right) \]

   12. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{-12} + \left(x \cdot 3\right) \cdot 3, 3\right) \]

   13. associate-*l* 99.9%

       \[\leadsto \mathsf{fma}\left(x, -12 + \color{blue}{x \cdot \left(3 \cdot 3\right)}, 3\right) \]

   14. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, -12 + x \cdot \color{blue}{9}, 3\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, -12 + x \cdot 9, 3\right)} \]
4. Add Preprocessing

5. Final simplification 99.9%

   \[\leadsto \mathsf{fma}\left(x, -12 + x \cdot 9, 3\right) \]
6. Add Preprocessing

Alternative 2: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(\left(x \cdot \left(x \cdot 3\right) - x \cdot 4\right) + 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* x (* x 3.0)) (* x 4.0)) 1.0)))

C

double code(double x) {
	return 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 * (((x * (x * 3.0d0)) - (x * 4.0d0)) + 1.0d0)
end function

Java

public static double code(double x) {
	return 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0);
}

Python

def code(x):
	return 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0)

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(x * Float64(x * 3.0)) - Float64(x * 4.0)) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 * (((x * (x * 3.0)) - (x * 4.0)) + 1.0);
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(x * N[(x * 3.0), $MachinePrecision]), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Add Preprocessing

3. Final simplification 99.8%

   \[\leadsto 3 \cdot \left(\left(x \cdot \left(x \cdot 3\right) - x \cdot 4\right) + 1\right) \]
4. Add Preprocessing

Alternative 3: 97.8% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ 3 + x \cdot \left(x \cdot 9\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ 3.0 (* x (* x 9.0))))

C

double code(double x) {
	return 3.0 + (x * (x * 9.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 + (x * (x * 9.0d0))
end function

Java

public static double code(double x) {
	return 3.0 + (x * (x * 9.0));
}

Python

def code(x):
	return 3.0 + (x * (x * 9.0))

Julia

function code(x)
	return Float64(3.0 + Float64(x * Float64(x * 9.0)))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 + (x * (x * 9.0));
end

Wolfram

code[x_] := N[(3.0 + N[(x * N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.9%

   \[\leadsto \color{blue}{3 + \left(-12 \cdot x + 9 \cdot {x}^{2}\right)} \]

4. Taylor expanded in x around inf 97.4%

   \[\leadsto 3 + \color{blue}{9 \cdot {x}^{2}} \]
5. Step-by-step derivation

   1. *-commutative 97.4%

      \[\leadsto 3 + \color{blue}{{x}^{2} \cdot 9} \]

   2. unpow2 97.4%

      \[\leadsto 3 + \color{blue}{\left(x \cdot x\right)} \cdot 9 \]

   3. metadata-eval 97.4%

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

   4. swap-sqr 97.3%

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

   5. unpow2 97.3%

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

6. Simplified 97.3%

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

   1. unpow2 97.3%

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

   2. *-commutative 97.3%

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

   3. associate-*r* 97.4%

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

8. Applied egg-rr 97.4%

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

9. Taylor expanded in x around 0 97.4%

   \[\leadsto 3 + \color{blue}{\left(9 \cdot x\right)} \cdot x \]

10. Final simplification 97.4%

    \[\leadsto 3 + x \cdot \left(x \cdot 9\right) \]
11. Add Preprocessing

Alternative 4: 51.6% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ 3 + x \cdot -12 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ 3.0 (* x -12.0)))

C

double code(double x) {
	return 3.0 + (x * -12.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 + (x * (-12.0d0))
end function

Java

public static double code(double x) {
	return 3.0 + (x * -12.0);
}

Python

def code(x):
	return 3.0 + (x * -12.0)

Julia

function code(x)
	return Float64(3.0 + Float64(x * -12.0))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 + (x * -12.0);
end

Wolfram

code[x_] := N[(3.0 + N[(x * -12.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 52.1%

   \[\leadsto \color{blue}{3 + -12 \cdot x} \]
4. Step-by-step derivation

   1. *-commutative 52.1%

      \[\leadsto 3 + \color{blue}{x \cdot -12} \]

5. Simplified 52.1%

   \[\leadsto \color{blue}{3 + x \cdot -12} \]

6. Final simplification 52.1%

   \[\leadsto 3 + x \cdot -12 \]
7. Add Preprocessing

Alternative 5: 51.1% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 3 \end{array} \]

FPCore

(FPCore (x) :precision binary64 3.0)

C

double code(double x) {
	return 3.0;
}

Fortran

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

Java

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

Python

def code(x):
	return 3.0

Julia

function code(x)
	return 3.0
end

MATLAB

function tmp = code(x)
	tmp = 3.0;
end

Wolfram

code[x_] := 3.0

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 51.5%

   \[\leadsto \color{blue}{3} \]

4. Final simplification 51.5%

   \[\leadsto 3 \]
5. Add Preprocessing

Developer target: 99.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ 3 + \left(\left(9 \cdot x\right) \cdot x - 12 \cdot x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ 3.0 (- (* (* 9.0 x) x) (* 12.0 x))))

C

double code(double x) {
	return 3.0 + (((9.0 * x) * x) - (12.0 * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0 + (((9.0d0 * x) * x) - (12.0d0 * x))
end function

Java

public static double code(double x) {
	return 3.0 + (((9.0 * x) * x) - (12.0 * x));
}

Python

def code(x):
	return 3.0 + (((9.0 * x) * x) - (12.0 * x))

Julia

function code(x)
	return Float64(3.0 + Float64(Float64(Float64(9.0 * x) * x) - Float64(12.0 * x)))
end

MATLAB

function tmp = code(x)
	tmp = 3.0 + (((9.0 * x) * x) - (12.0 * x));
end

Wolfram

code[x_] := N[(3.0 + N[(N[(N[(9.0 * x), $MachinePrecision] * x), $MachinePrecision] - N[(12.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024024 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D"
  :precision binary64

  :herbie-target
  (+ 3.0 (- (* (* 9.0 x) x) (* 12.0 x)))

  (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))