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

Percentage Accurate: 99.7% → 99.9%

Time: 14.1s

Alternatives: 5

Speedup: 1.4×

• 25.0% 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: 99.7% 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, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(3.0 + N[(x * N[(N[(x * 9.0), $MachinePrecision] - 12.0), $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. Step-by-step derivation

   1. *-commutative 99.8%

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

   2. distribute-lft-out-- 99.8%

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

   3. fma-define 99.8%

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

   4. *-commutative 99.8%

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

   5. fma-neg 99.8%

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

   6. metadata-eval 99.8%

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

3. Simplified 99.8%

   \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 99.9%

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

6. Final simplification 99.9%

   \[\leadsto 3 + x \cdot \left(x \cdot 9 - 12\right) \]
7. Add Preprocessing

Alternative 2: 98.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -1.55) (not (<= x 1.0))) (* 9.0 (* x x)) (+ 3.0 (* x -12.0))))

C

double code(double x) {
	double tmp;
	if ((x <= -1.55) || !(x <= 1.0)) {
		tmp = 9.0 * (x * x);
	} 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 <= (-1.55d0)) .or. (.not. (x <= 1.0d0))) then
        tmp = 9.0d0 * (x * x)
    else
        tmp = 3.0d0 + (x * (-12.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -1.55) || !(x <= 1.0)) {
		tmp = 9.0 * (x * x);
	} else {
		tmp = 3.0 + (x * -12.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -1.55) or not (x <= 1.0):
		tmp = 9.0 * (x * x)
	else:
		tmp = 3.0 + (x * -12.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -1.55) || !(x <= 1.0))
		tmp = Float64(9.0 * Float64(x * x));
	else
		tmp = Float64(3.0 + Float64(x * -12.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -1.55) || ~((x <= 1.0)))
		tmp = 9.0 * (x * x);
	else
		tmp = 3.0 + (x * -12.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -1.55], N[Not[LessEqual[x, 1.0]], $MachinePrecision]], N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], N[(3.0 + N[(x * -12.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.55000000000000004 or 1 < x

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. distribute-lft-out-- 99.7%

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

      3. fma-define 99.7%

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

      4. *-commutative 99.7%

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

      5. fma-neg 99.7%

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

      6. metadata-eval 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 99.0%

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

      1. *-commutative 99.0%

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

   7. Simplified 99.0%

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

      1. unpow2 99.0%

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

   9. Applied egg-rr 99.0%

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

   if -1.55000000000000004 < x < 1

   1. Initial program 100.0%

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

      1. *-commutative 100.0%

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

      2. distribute-lft-out-- 100.0%

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

      3. fma-define 100.0%

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

      4. *-commutative 100.0%

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

      5. fma-neg 100.0%

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

      6. metadata-eval 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 99.6%

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

      1. *-commutative 99.6%

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

   7. Simplified 99.6%

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

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.55 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 97.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.58 \lor \neg \left(x \leq 1.7\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -0.58) (not (<= x 1.7))) (* 9.0 (* x x)) 3.0))

C

double code(double x) {
	double tmp;
	if ((x <= -0.58) || !(x <= 1.7)) {
		tmp = 9.0 * (x * x);
	} else {
		tmp = 3.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-0.58d0)) .or. (.not. (x <= 1.7d0))) then
        tmp = 9.0d0 * (x * x)
    else
        tmp = 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -0.58) || !(x <= 1.7)) {
		tmp = 9.0 * (x * x);
	} else {
		tmp = 3.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -0.58) or not (x <= 1.7):
		tmp = 9.0 * (x * x)
	else:
		tmp = 3.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -0.58) || !(x <= 1.7))
		tmp = Float64(9.0 * Float64(x * x));
	else
		tmp = 3.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -0.58) || ~((x <= 1.7)))
		tmp = 9.0 * (x * x);
	else
		tmp = 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -0.58], N[Not[LessEqual[x, 1.7]], $MachinePrecision]], N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], 3.0]

Derivation

1. Split input into 2 regimes

2. if x < -0.57999999999999996 or 1.69999999999999996 < x

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. distribute-lft-out-- 99.7%

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

      3. fma-define 99.7%

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

      4. *-commutative 99.7%

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

      5. fma-neg 99.7%

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

      6. metadata-eval 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 99.0%

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

      1. *-commutative 99.0%

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

   7. Simplified 99.0%

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

      1. unpow2 99.0%

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

   9. Applied egg-rr 99.0%

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

   if -0.57999999999999996 < x < 1.69999999999999996

   1. Initial program 100.0%

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

      1. *-commutative 100.0%

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

      2. distribute-lft-out-- 100.0%

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

      3. fma-define 100.0%

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

      4. *-commutative 100.0%

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

      5. fma-neg 100.0%

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

      6. metadata-eval 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 98.6%

      \[\leadsto \color{blue}{3} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.58 \lor \neg \left(x \leq 1.7\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 97.7% 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. Step-by-step derivation

   1. *-commutative 99.8%

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

   2. distribute-lft-out-- 99.8%

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

   3. fma-define 99.8%

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

   4. *-commutative 99.8%

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

   5. fma-neg 99.8%

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

   6. metadata-eval 99.8%

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

3. Simplified 99.8%

   \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 99.9%

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

6. Taylor expanded in x around inf 98.8%

   \[\leadsto 3 + x \cdot \color{blue}{\left(9 \cdot x\right)} \]
7. Step-by-step derivation

   1. *-commutative 98.8%

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

8. Simplified 98.8%

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

Alternative 5: 50.7% 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. *-commutative 99.8%

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

   2. distribute-lft-out-- 99.8%

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

   3. fma-define 99.8%

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

   4. *-commutative 99.8%

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

   5. fma-neg 99.8%

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

   6. metadata-eval 99.8%

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

3. Simplified 99.8%

   \[\leadsto \color{blue}{3 \cdot \mathsf{fma}\left(x, \mathsf{fma}\left(3, x, -4\right), 1\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 50.9%

   \[\leadsto \color{blue}{3} \]
6. Add Preprocessing

Developer Target 1: 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 2024143 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D"
  :precision binary64

  :alt
  (! :herbie-platform default (+ 3 (- (* (* 9 x) x) (* 12 x))))

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