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

Percentage Accurate: 99.8% → 99.8%

Time: 1.3s

Alternatives: 8

Speedup: 1.0×

• 25.2% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

\[\mathsf{TRUE}\left(\right)\]

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: 99.8% 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]

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. Add Preprocessing

Alternative 2: 59.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x \cdot 3\right) \cdot x\\ \mathbf{if}\;t\_0 - x \cdot 4 \leq 0.5:\\ \;\;\;\;\left(t\_0 + 1\right) + 1\\ \mathbf{else}:\\ \;\;\;\;3 \cdot t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* (* x 3.0) x)))
   (if (<= (- t_0 (* x 4.0)) 0.5) (+ (+ t_0 1.0) 1.0) (* 3.0 t_0))))

C

double code(double x) {
	double t_0 = (x * 3.0) * x;
	double tmp;
	if ((t_0 - (x * 4.0)) <= 0.5) {
		tmp = (t_0 + 1.0) + 1.0;
	} else {
		tmp = 3.0 * t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x * 3.0d0) * x
    if ((t_0 - (x * 4.0d0)) <= 0.5d0) then
        tmp = (t_0 + 1.0d0) + 1.0d0
    else
        tmp = 3.0d0 * t_0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = (x * 3.0) * x;
	double tmp;
	if ((t_0 - (x * 4.0)) <= 0.5) {
		tmp = (t_0 + 1.0) + 1.0;
	} else {
		tmp = 3.0 * t_0;
	}
	return tmp;
}

Python

def code(x):
	t_0 = (x * 3.0) * x
	tmp = 0
	if (t_0 - (x * 4.0)) <= 0.5:
		tmp = (t_0 + 1.0) + 1.0
	else:
		tmp = 3.0 * t_0
	return tmp

Julia

function code(x)
	t_0 = Float64(Float64(x * 3.0) * x)
	tmp = 0.0
	if (Float64(t_0 - Float64(x * 4.0)) <= 0.5)
		tmp = Float64(Float64(t_0 + 1.0) + 1.0);
	else
		tmp = Float64(3.0 * t_0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = (x * 3.0) * x;
	tmp = 0.0;
	if ((t_0 - (x * 4.0)) <= 0.5)
		tmp = (t_0 + 1.0) + 1.0;
	else
		tmp = 3.0 * t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[N[(t$95$0 - N[(x * 4.0), $MachinePrecision]), $MachinePrecision], 0.5], N[(N[(t$95$0 + 1.0), $MachinePrecision] + 1.0), $MachinePrecision], N[(3.0 * t$95$0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (*.f64 (*.f64 x #s(literal 3 binary64)) x) (*.f64 x #s(literal 4 binary64))) < 0.5

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 17.8%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 17.7%

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

   9. Taylor expanded in x around 0

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

   11. Applied rewrites 20.1%

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

   if 0.5 < (-.f64 (*.f64 (*.f64 x #s(literal 3 binary64)) x) (*.f64 x #s(literal 4 binary64)))

   1. Initial program 99.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto 3 \cdot \color{blue}{1} \]

   5. Applied rewrites 98.1%

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

Alternative 3: 57.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x \cdot 3\right) \cdot x\\ \mathbf{if}\;t\_0 - x \cdot 4 \leq 0.5:\\ \;\;\;\;t\_0 + 1\\ \mathbf{else}:\\ \;\;\;\;3 \cdot t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* (* x 3.0) x)))
   (if (<= (- t_0 (* x 4.0)) 0.5) (+ t_0 1.0) (* 3.0 t_0))))

C

double code(double x) {
	double t_0 = (x * 3.0) * x;
	double tmp;
	if ((t_0 - (x * 4.0)) <= 0.5) {
		tmp = t_0 + 1.0;
	} else {
		tmp = 3.0 * t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x * 3.0d0) * x
    if ((t_0 - (x * 4.0d0)) <= 0.5d0) then
        tmp = t_0 + 1.0d0
    else
        tmp = 3.0d0 * t_0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = (x * 3.0) * x;
	double tmp;
	if ((t_0 - (x * 4.0)) <= 0.5) {
		tmp = t_0 + 1.0;
	} else {
		tmp = 3.0 * t_0;
	}
	return tmp;
}

Python

def code(x):
	t_0 = (x * 3.0) * x
	tmp = 0
	if (t_0 - (x * 4.0)) <= 0.5:
		tmp = t_0 + 1.0
	else:
		tmp = 3.0 * t_0
	return tmp

Julia

function code(x)
	t_0 = Float64(Float64(x * 3.0) * x)
	tmp = 0.0
	if (Float64(t_0 - Float64(x * 4.0)) <= 0.5)
		tmp = Float64(t_0 + 1.0);
	else
		tmp = Float64(3.0 * t_0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = (x * 3.0) * x;
	tmp = 0.0;
	if ((t_0 - (x * 4.0)) <= 0.5)
		tmp = t_0 + 1.0;
	else
		tmp = 3.0 * t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[N[(t$95$0 - N[(x * 4.0), $MachinePrecision]), $MachinePrecision], 0.5], N[(t$95$0 + 1.0), $MachinePrecision], N[(3.0 * t$95$0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (*.f64 (*.f64 x #s(literal 3 binary64)) x) (*.f64 x #s(literal 4 binary64))) < 0.5

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 17.8%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 17.7%

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

   if 0.5 < (-.f64 (*.f64 (*.f64 x #s(literal 3 binary64)) x) (*.f64 x #s(literal 4 binary64)))

   1. Initial program 99.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto 3 \cdot \color{blue}{1} \]

   5. Applied rewrites 98.1%

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

Alternative 4: 59.1% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[(N[(3.0 * N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] + 1.0), $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. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 38.3%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 38.3%

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

9. Taylor expanded in x around 0

   \[\leadsto 3 \cdot {x}^{2} + 1 \]

11. Applied rewrites 61.2%

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

Alternative 5: 57.9% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[(3.0 * N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] + 1.0), $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. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 38.3%

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

6. Taylor expanded in x around 0

   \[\leadsto -4 \cdot x + 1 \]

8. Applied rewrites 60.1%

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

Alternative 6: 38.5% accurate, 1.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision] + 1.0), $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. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 38.3%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 38.3%

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

Alternative 7: 31.7% accurate, 2.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

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

5. Applied rewrites 31.7%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 31.8%

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

Alternative 8: 3.8% accurate, 4.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return x * 3.0

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(x * 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. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 38.3%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 3.6%

   \[\leadsto x \cdot \color{blue}{3} \]
9. 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 2024321 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D"
  :precision binary64
  :pre (TRUE)

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

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