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

Percentage Accurate: 99.8% → 99.9%

Time: 5.6s

Alternatives: 6

Speedup: 1.0×

• 25.6% 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

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

FPCore

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

C

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

Julia

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

Wolfram

code[x_] := N[(x * N[(N[(x * 9.0), $MachinePrecision] - 12.0), $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. *-commutative 99.8%

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

   3. distribute-lft-out-- 99.8%

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

   4. associate-*l* 99.8%

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

   5. metadata-eval 99.8%

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

   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. *-commutative 99.8%

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

   9. fma-neg 99.8%

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

   10. metadata-eval 99.8%

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

3. Simplified 99.8%

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

4. Taylor expanded in x around 0 99.9%

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

5. Final simplification 99.9%

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

Alternative 2: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(3.0 * N[(1.0 + N[(N[(x * N[(x * 3.0), $MachinePrecision]), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $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. Final simplification 99.8%

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

Alternative 3: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(3.0 * N[(N[(x * N[(x * 3.0), $MachinePrecision]), $MachinePrecision] + N[(1.0 - N[(x * 4.0), $MachinePrecision]), $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. Step-by-step derivation

   1. associate-+l- 99.8%

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

   2. associate-*l* 99.8%

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

3. Simplified 99.8%

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

4. Final simplification 99.8%

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

Alternative 4: 98.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.56 \lor \neg \left(x \leq 0.58\right):\\ \;\;\;\;x \cdot \left(x \cdot 9 + -12\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -0.56) (not (<= x 0.58)))
   (* x (+ (* x 9.0) -12.0))
   (+ 3.0 (* x -12.0))))

C

double code(double x) {
	double tmp;
	if ((x <= -0.56) || !(x <= 0.58)) {
		tmp = x * ((x * 9.0) + -12.0);
	} else {
		tmp = 3.0 + (x * -12.0);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-0.56d0)) .or. (.not. (x <= 0.58d0))) then
        tmp = x * ((x * 9.0d0) + (-12.0d0))
    else
        tmp = 3.0d0 + (x * (-12.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -0.56) || !(x <= 0.58)) {
		tmp = x * ((x * 9.0) + -12.0);
	} else {
		tmp = 3.0 + (x * -12.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -0.56) or not (x <= 0.58):
		tmp = x * ((x * 9.0) + -12.0)
	else:
		tmp = 3.0 + (x * -12.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -0.56) || !(x <= 0.58))
		tmp = Float64(x * Float64(Float64(x * 9.0) + -12.0));
	else
		tmp = Float64(3.0 + Float64(x * -12.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -0.56) || ~((x <= 0.58)))
		tmp = x * ((x * 9.0) + -12.0);
	else
		tmp = 3.0 + (x * -12.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -0.56], N[Not[LessEqual[x, 0.58]], $MachinePrecision]], N[(x * N[(N[(x * 9.0), $MachinePrecision] + -12.0), $MachinePrecision]), $MachinePrecision], N[(3.0 + N[(x * -12.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -0.56000000000000005 or 0.57999999999999996 < x

   1. Initial program 99.6%

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

      1. associate-+l- 99.6%

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

      2. associate-*l* 99.6%

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

   3. Simplified 99.6%

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

   4. Taylor expanded in x around inf 97.8%

      \[\leadsto 3 \cdot \color{blue}{\left(-4 \cdot x + 3 \cdot {x}^{2}\right)} \]
   5. Step-by-step derivation

      1. unpow2 97.8%

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

      2. associate-*r* 97.7%

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

      3. *-commutative 97.7%

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

      4. distribute-rgt-in 97.7%

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

      5. +-commutative 97.7%

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

      6. *-commutative 97.7%

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

      7. fma-udef 97.7%

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

   6. Simplified 97.7%

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

      1. fma-udef 97.7%

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

      2. distribute-rgt-in 97.7%

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

      3. *-commutative 97.7%

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

   8. Applied egg-rr 97.7%

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

   9. Taylor expanded in x around 0 97.8%

      \[\leadsto 3 \cdot \left(\color{blue}{3 \cdot {x}^{2}} + -4 \cdot x\right) \]
   10. Step-by-step derivation

       1. *-commutative 97.8%

          \[\leadsto 3 \cdot \left(\color{blue}{{x}^{2} \cdot 3} + -4 \cdot x\right) \]

   11. Simplified 97.8%

       \[\leadsto 3 \cdot \left(\color{blue}{{x}^{2} \cdot 3} + -4 \cdot x\right) \]

   12. Taylor expanded in x around 0 97.8%

       \[\leadsto \color{blue}{-12 \cdot x + 9 \cdot {x}^{2}} \]
   13. Step-by-step derivation

       1. +-commutative 97.8%

          \[\leadsto \color{blue}{9 \cdot {x}^{2} + -12 \cdot x} \]

       2. *-commutative 97.8%

          \[\leadsto \color{blue}{{x}^{2} \cdot 9} + -12 \cdot x \]

       3. *-commutative 97.8%

          \[\leadsto {x}^{2} \cdot 9 + \color{blue}{x \cdot -12} \]

       4. unpow2 97.8%

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

       5. associate-*r* 97.8%

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

       6. *-commutative 97.8%

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

       7. distribute-lft-out 97.8%

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

       8. *-commutative 97.8%

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

   14. Simplified 97.8%

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

   if -0.56000000000000005 < x < 0.57999999999999996

   1. Initial program 99.9%

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

      1. associate-+l- 100.0%

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

      2. associate-*l* 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.4%

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

      1. *-commutative 98.4%

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

   6. Simplified 98.4%

      \[\leadsto \color{blue}{3 + x \cdot -12} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.56 \lor \neg \left(x \leq 0.58\right):\\ \;\;\;\;x \cdot \left(x \cdot 9 + -12\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \]

Alternative 5: 51.1% 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. Step-by-step derivation

   1. associate-+l- 99.8%

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

   2. associate-*l* 99.8%

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

3. Simplified 99.8%

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

4. Taylor expanded in x around 0 49.7%

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

   1. *-commutative 49.7%

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

6. Simplified 49.7%

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

7. Final simplification 49.7%

   \[\leadsto 3 + x \cdot -12 \]

Alternative 6: 50.6% 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. Step-by-step derivation

   1. associate-+l- 99.8%

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

   2. associate-*l* 99.8%

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

3. Simplified 99.8%

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

4. Taylor expanded in x around 0 49.4%

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

5. Final simplification 49.4%

   \[\leadsto 3 \]

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 2023333 
(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)))