Data.Spline.Key:interpolateKeys from smoothie-0.4.0.2

Percentage Accurate: 99.8% → 99.8%

Time: 6.9s

Alternatives: 5

Speedup: 1.0×

• 33.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot \left(3 - x \cdot 2\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* x x) (- 3.0 (* x 2.0))))

C

double code(double x) {
	return (x * x) * (3.0 - (x * 2.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) * (3.0d0 - (x * 2.0d0))
end function

Java

public static double code(double x) {
	return (x * x) * (3.0 - (x * 2.0));
}

Python

def code(x):
	return (x * x) * (3.0 - (x * 2.0))

Julia

function code(x)
	return Float64(Float64(x * x) * Float64(3.0 - Float64(x * 2.0)))
end

MATLAB

function tmp = code(x)
	tmp = (x * x) * (3.0 - (x * 2.0));
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * N[(3.0 - N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot \left(3 - x \cdot 2\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* x x) (- 3.0 (* x 2.0))))

C

double code(double x) {
	return (x * x) * (3.0 - (x * 2.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) * (3.0d0 - (x * 2.0d0))
end function

Java

public static double code(double x) {
	return (x * x) * (3.0 - (x * 2.0));
}

Python

def code(x):
	return (x * x) * (3.0 - (x * 2.0))

Julia

function code(x)
	return Float64(Float64(x * x) * Float64(3.0 - Float64(x * 2.0)))
end

MATLAB

function tmp = code(x)
	tmp = (x * x) * (3.0 - (x * 2.0));
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * N[(3.0 - N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot 3, x, -2 \cdot {x}^{3}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma (* x 3.0) x (* -2.0 (pow x 3.0))))

C

double code(double x) {
	return fma((x * 3.0), x, (-2.0 * pow(x, 3.0)));
}

Julia

function code(x)
	return fma(Float64(x * 3.0), x, Float64(-2.0 * (x ^ 3.0)))
end

Wolfram

code[x_] := N[(N[(x * 3.0), $MachinePrecision] * x + N[(-2.0 * N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

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

   1. associate-*l* 99.8%

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

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 88.2%

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

   1. add-exp-log 72.2%

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

   2. fma-def 72.2%

      \[\leadsto e^{\log \color{blue}{\left(\mathsf{fma}\left(-2, {x}^{3}, 3 \cdot {x}^{2}\right)\right)}} \]

7. Applied egg-rr 72.2%

   \[\leadsto \color{blue}{e^{\log \left(\mathsf{fma}\left(-2, {x}^{3}, 3 \cdot {x}^{2}\right)\right)}} \]
8. Step-by-step derivation

   1. rem-exp-log 88.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-2, {x}^{3}, 3 \cdot {x}^{2}\right)} \]

   2. fma-udef 88.2%

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

   3. +-commutative 88.2%

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

   4. *-commutative 88.2%

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

   5. metadata-eval 88.2%

      \[\leadsto {x}^{2} \cdot \color{blue}{\frac{1}{0.3333333333333333}} + -2 \cdot {x}^{3} \]

   6. div-inv 88.1%

      \[\leadsto \color{blue}{\frac{{x}^{2}}{0.3333333333333333}} + -2 \cdot {x}^{3} \]

   7. unpow2 88.1%

      \[\leadsto \frac{\color{blue}{x \cdot x}}{0.3333333333333333} + -2 \cdot {x}^{3} \]

   8. associate-*r/ 88.1%

      \[\leadsto \color{blue}{x \cdot \frac{x}{0.3333333333333333}} + -2 \cdot {x}^{3} \]

   9. *-commutative 88.1%

      \[\leadsto \color{blue}{\frac{x}{0.3333333333333333} \cdot x} + -2 \cdot {x}^{3} \]

   10. fma-def 99.9%

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{0.3333333333333333}, x, -2 \cdot {x}^{3}\right)} \]

   11. div-inv 99.9%

       \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot \frac{1}{0.3333333333333333}}, x, -2 \cdot {x}^{3}\right) \]

   12. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x \cdot \color{blue}{3}, x, -2 \cdot {x}^{3}\right) \]

9. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot 3, x, -2 \cdot {x}^{3}\right)} \]

10. Final simplification 99.9%

    \[\leadsto \mathsf{fma}\left(x \cdot 3, x, -2 \cdot {x}^{3}\right) \]
11. Add Preprocessing

Alternative 2: 99.8% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(3, x, -2 \cdot {x}^{2}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (fma 3.0 x (* -2.0 (pow x 2.0)))))

C

double code(double x) {
	return x * fma(3.0, x, (-2.0 * pow(x, 2.0)));
}

Julia

function code(x)
	return Float64(x * fma(3.0, x, Float64(-2.0 * (x ^ 2.0))))
end

Wolfram

code[x_] := N[(x * N[(3.0 * x + N[(-2.0 * N[Power[x, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

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

   1. associate-*l* 99.8%

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

3. Simplified 99.8%

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

   1. flip-- 99.8%

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

   2. *-commutative 99.8%

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

   3. metadata-eval 99.8%

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

   4. cancel-sign-sub-inv 99.8%

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

   5. *-commutative 99.8%

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

   6. associate-*r/ 99.8%

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

6. Applied egg-rr 51.7%

   \[\leadsto x \cdot \color{blue}{\frac{x \cdot \left(9 + {x}^{2} \cdot 4\right)}{\mathsf{fma}\left(x, 2, 3\right)}} \]
7. Step-by-step derivation

   1. associate-/l* 51.7%

      \[\leadsto x \cdot \color{blue}{\frac{x}{\frac{\mathsf{fma}\left(x, 2, 3\right)}{9 + {x}^{2} \cdot 4}}} \]

8. Simplified 51.7%

   \[\leadsto x \cdot \color{blue}{\frac{x}{\frac{\mathsf{fma}\left(x, 2, 3\right)}{9 + {x}^{2} \cdot 4}}} \]

9. Taylor expanded in x around 0 99.8%

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

    1. +-commutative 99.8%

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

    2. fma-def 99.8%

       \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(3, x, -2 \cdot {x}^{2}\right)} \]

    3. *-commutative 99.8%

       \[\leadsto x \cdot \mathsf{fma}\left(3, x, \color{blue}{{x}^{2} \cdot -2}\right) \]

11. Simplified 99.8%

    \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(3, x, {x}^{2} \cdot -2\right)} \]

12. Final simplification 99.8%

    \[\leadsto x \cdot \mathsf{fma}\left(3, x, -2 \cdot {x}^{2}\right) \]
13. Add Preprocessing

Alternative 3: 75.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.5:\\ \;\;\;\;x \cdot \left(x \cdot 3\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot -3\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 1.5) (* x (* x 3.0)) (* x (* x -3.0))))

C

double code(double x) {
	double tmp;
	if (x <= 1.5) {
		tmp = x * (x * 3.0);
	} else {
		tmp = x * (x * -3.0);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 1.5d0) then
        tmp = x * (x * 3.0d0)
    else
        tmp = x * (x * (-3.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 1.5) {
		tmp = x * (x * 3.0);
	} else {
		tmp = x * (x * -3.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 1.5:
		tmp = x * (x * 3.0)
	else:
		tmp = x * (x * -3.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 1.5)
		tmp = Float64(x * Float64(x * 3.0));
	else
		tmp = Float64(x * Float64(x * -3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 1.5)
		tmp = x * (x * 3.0);
	else
		tmp = x * (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 1.5], N[(x * N[(x * 3.0), $MachinePrecision]), $MachinePrecision], N[(x * N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.5

   1. Initial program 99.8%

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

      1. associate-*l* 99.8%

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

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 83.1%

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

   if 1.5 < x

   1. Initial program 99.9%

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

      1. associate-*l* 99.9%

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

   3. Simplified 99.9%

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

      1. flip-- 99.9%

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

      2. *-commutative 99.9%

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

      3. metadata-eval 99.9%

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

      4. cancel-sign-sub-inv 99.9%

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

      5. *-commutative 99.9%

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

      6. associate-*r/ 99.9%

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

   6. Applied egg-rr 0.2%

      \[\leadsto x \cdot \color{blue}{\frac{x \cdot \left(9 + {x}^{2} \cdot 4\right)}{\mathsf{fma}\left(x, 2, 3\right)}} \]
   7. Step-by-step derivation

      1. associate-/l* 0.2%

         \[\leadsto x \cdot \color{blue}{\frac{x}{\frac{\mathsf{fma}\left(x, 2, 3\right)}{9 + {x}^{2} \cdot 4}}} \]

   8. Simplified 0.2%

      \[\leadsto x \cdot \color{blue}{\frac{x}{\frac{\mathsf{fma}\left(x, 2, 3\right)}{9 + {x}^{2} \cdot 4}}} \]

   9. Taylor expanded in x around 0 0.2%

      \[\leadsto x \cdot \frac{x}{\color{blue}{0.3333333333333333}} \]
   10. Step-by-step derivation

       1. clear-num 0.2%

          \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{0.3333333333333333}{x}}} \]

       2. un-div-inv 0.2%

          \[\leadsto \color{blue}{\frac{x}{\frac{0.3333333333333333}{x}}} \]

   11. Applied egg-rr 0.2%

       \[\leadsto \color{blue}{\frac{x}{\frac{0.3333333333333333}{x}}} \]
   12. Step-by-step derivation

       1. frac-2neg 0.2%

          \[\leadsto \frac{x}{\color{blue}{\frac{-0.3333333333333333}{-x}}} \]

       2. metadata-eval 0.2%

          \[\leadsto \frac{x}{\frac{\color{blue}{-0.3333333333333333}}{-x}} \]

       3. associate-/l* 0.2%

          \[\leadsto \color{blue}{\frac{x \cdot \left(-x\right)}{-0.3333333333333333}} \]

       4. *-commutative 0.2%

          \[\leadsto \frac{\color{blue}{\left(-x\right) \cdot x}}{-0.3333333333333333} \]

       5. div-inv 0.2%

          \[\leadsto \color{blue}{\left(\left(-x\right) \cdot x\right) \cdot \frac{1}{-0.3333333333333333}} \]

       6. *-commutative 0.2%

          \[\leadsto \color{blue}{\left(x \cdot \left(-x\right)\right)} \cdot \frac{1}{-0.3333333333333333} \]

       7. associate-*l* 0.2%

          \[\leadsto \color{blue}{x \cdot \left(\left(-x\right) \cdot \frac{1}{-0.3333333333333333}\right)} \]

       8. add-sqr-sqrt 0.0%

          \[\leadsto x \cdot \left(\color{blue}{\left(\sqrt{-x} \cdot \sqrt{-x}\right)} \cdot \frac{1}{-0.3333333333333333}\right) \]

       9. sqrt-unprod 50.9%

          \[\leadsto x \cdot \left(\color{blue}{\sqrt{\left(-x\right) \cdot \left(-x\right)}} \cdot \frac{1}{-0.3333333333333333}\right) \]

       10. sqr-neg 50.9%

           \[\leadsto x \cdot \left(\sqrt{\color{blue}{x \cdot x}} \cdot \frac{1}{-0.3333333333333333}\right) \]

       11. sqrt-prod 50.9%

           \[\leadsto x \cdot \left(\color{blue}{\left(\sqrt{x} \cdot \sqrt{x}\right)} \cdot \frac{1}{-0.3333333333333333}\right) \]

       12. add-sqr-sqrt 50.9%

           \[\leadsto x \cdot \left(\color{blue}{x} \cdot \frac{1}{-0.3333333333333333}\right) \]

       13. metadata-eval 50.9%

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

   13. Applied egg-rr 50.9%

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

4. Final simplification 75.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.5:\\ \;\;\;\;x \cdot \left(x \cdot 3\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot -3\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (* x (- 3.0 (* x 2.0)))))

C

double code(double x) {
	return x * (x * (3.0 - (x * 2.0)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (x * (3.0d0 - (x * 2.0d0)))
end function

Java

public static double code(double x) {
	return x * (x * (3.0 - (x * 2.0)));
}

Python

def code(x):
	return x * (x * (3.0 - (x * 2.0)))

Julia

function code(x)
	return Float64(x * Float64(x * Float64(3.0 - Float64(x * 2.0))))
end

MATLAB

function tmp = code(x)
	tmp = x * (x * (3.0 - (x * 2.0)));
end

Wolfram

code[x_] := N[(x * N[(x * N[(3.0 - N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

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

   1. associate-*l* 99.8%

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

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right)} \]
4. Add Preprocessing

5. Final simplification 99.8%

   \[\leadsto x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right) \]
6. Add Preprocessing

Alternative 5: 61.8% accurate, 1.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.8%

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

   1. associate-*l* 99.8%

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

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 62.4%

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

6. Final simplification 62.4%

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

Developer target: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot \left(3 - x \cdot 2\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (* x (- 3.0 (* x 2.0)))))

C

double code(double x) {
	return x * (x * (3.0 - (x * 2.0)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (x * (3.0d0 - (x * 2.0d0)))
end function

Java

public static double code(double x) {
	return x * (x * (3.0 - (x * 2.0)));
}

Python

def code(x):
	return x * (x * (3.0 - (x * 2.0)))

Julia

function code(x)
	return Float64(x * Float64(x * Float64(3.0 - Float64(x * 2.0))))
end

MATLAB

function tmp = code(x)
	tmp = x * (x * (3.0 - (x * 2.0)));
end

Wolfram

code[x_] := N[(x * N[(x * N[(3.0 - N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024029 
(FPCore (x)
  :name "Data.Spline.Key:interpolateKeys from smoothie-0.4.0.2"
  :precision binary64

  :herbie-target
  (* x (* x (- 3.0 (* x 2.0))))

  (* (* x x) (- 3.0 (* x 2.0))))