Linear.Quaternion:$csinh from linear-1.19.1.3

Percentage Accurate: 99.9% → 99.9%

Time: 18.4s

Alternatives: 7

Speedup: 1.0×

• 49.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \cosh x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (cosh x) (/ (sin y) y)))

C

double code(double x, double y) {
	return cosh(x) * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = cosh(x) * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return Math.cosh(x) * (Math.sin(y) / y);
}

Python

def code(x, y):
	return math.cosh(x) * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(cosh(x) * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = cosh(x) * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(N[Cosh[x], $MachinePrecision] * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cosh x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (cosh x) (/ (sin y) y)))

C

double code(double x, double y) {
	return cosh(x) * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = cosh(x) * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return Math.cosh(x) * (Math.sin(y) / y);
}

Python

def code(x, y):
	return math.cosh(x) * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(cosh(x) * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = cosh(x) * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(N[Cosh[x], $MachinePrecision] * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\cosh x \cdot \sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* (cosh x) (sin y)) y))

C

double code(double x, double y) {
	return (cosh(x) * sin(y)) / y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (cosh(x) * sin(y)) / y
end function

Java

public static double code(double x, double y) {
	return (Math.cosh(x) * Math.sin(y)) / y;
}

Python

def code(x, y):
	return (math.cosh(x) * math.sin(y)) / y

Julia

function code(x, y)
	return Float64(Float64(cosh(x) * sin(y)) / y)
end

MATLAB

function tmp = code(x, y)
	tmp = (cosh(x) * sin(y)) / y;
end

Wolfram

code[x_, y_] := N[(N[(N[Cosh[x], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\cosh x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. associate-*r/ 99.9%

      \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

4. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]
5. Add Preprocessing

Alternative 2: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cosh x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (cosh x) (/ (sin y) y)))

C

double code(double x, double y) {
	return cosh(x) * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = cosh(x) * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return Math.cosh(x) * (Math.sin(y) / y);
}

Python

def code(x, y):
	return math.cosh(x) * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(cosh(x) * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = cosh(x) * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(N[Cosh[x], $MachinePrecision] * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\cosh x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 3: 86.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.036 \lor \neg \left(x \leq 1.25 \cdot 10^{+154}\right):\\ \;\;\;\;\sin y \cdot \left(0.5 \cdot \frac{x \cdot x}{y} + \frac{1}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;\cosh x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x 0.036) (not (<= x 1.25e+154)))
   (* (sin y) (+ (* 0.5 (/ (* x x) y)) (/ 1.0 y)))
   (cosh x)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= 0.036) || !(x <= 1.25e+154)) {
		tmp = sin(y) * ((0.5 * ((x * x) / y)) + (1.0 / y));
	} else {
		tmp = cosh(x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= 0.036d0) .or. (.not. (x <= 1.25d+154))) then
        tmp = sin(y) * ((0.5d0 * ((x * x) / y)) + (1.0d0 / y))
    else
        tmp = cosh(x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= 0.036) || !(x <= 1.25e+154)) {
		tmp = Math.sin(y) * ((0.5 * ((x * x) / y)) + (1.0 / y));
	} else {
		tmp = Math.cosh(x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= 0.036) or not (x <= 1.25e+154):
		tmp = math.sin(y) * ((0.5 * ((x * x) / y)) + (1.0 / y))
	else:
		tmp = math.cosh(x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= 0.036) || !(x <= 1.25e+154))
		tmp = Float64(sin(y) * Float64(Float64(0.5 * Float64(Float64(x * x) / y)) + Float64(1.0 / y)));
	else
		tmp = cosh(x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= 0.036) || ~((x <= 1.25e+154)))
		tmp = sin(y) * ((0.5 * ((x * x) / y)) + (1.0 / y));
	else
		tmp = cosh(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, 0.036], N[Not[LessEqual[x, 1.25e+154]], $MachinePrecision]], N[(N[Sin[y], $MachinePrecision] * N[(N[(0.5 * N[(N[(x * x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(1.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[Cosh[x], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.0359999999999999973 or 1.25000000000000001e154 < x

   1. Initial program 99.9%

      \[\cosh x \cdot \frac{\sin y}{y} \]
   2. Step-by-step derivation

      1. *-commutative 99.9%

         \[\leadsto \color{blue}{\frac{\sin y}{y} \cdot \cosh x} \]

      2. associate-*l/ 99.9%

         \[\leadsto \color{blue}{\frac{\sin y \cdot \cosh x}{y}} \]

      3. associate-/l* 99.7%

         \[\leadsto \color{blue}{\sin y \cdot \frac{\cosh x}{y}} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\sin y \cdot \frac{\cosh x}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 91.0%

      \[\leadsto \sin y \cdot \color{blue}{\left(0.5 \cdot \frac{{x}^{2}}{y} + \frac{1}{y}\right)} \]
   6. Step-by-step derivation

      1. unpow2 91.0%

         \[\leadsto \sin y \cdot \left(0.5 \cdot \frac{\color{blue}{x \cdot x}}{y} + \frac{1}{y}\right) \]

   7. Applied egg-rr 91.0%

      \[\leadsto \sin y \cdot \left(0.5 \cdot \frac{\color{blue}{x \cdot x}}{y} + \frac{1}{y}\right) \]

   if 0.0359999999999999973 < x < 1.25000000000000001e154

   1. Initial program 100.0%

      \[\cosh x \cdot \frac{\sin y}{y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

   5. Taylor expanded in y around 0 78.8%

      \[\leadsto \frac{\cosh x \cdot \color{blue}{y}}{y} \]
   6. Step-by-step derivation

      1. *-commutative 78.8%

         \[\leadsto \frac{\color{blue}{y \cdot \cosh x}}{y} \]

      2. associate-/l* 78.7%

         \[\leadsto \color{blue}{y \cdot \frac{\cosh x}{y}} \]

   7. Applied egg-rr 78.7%

      \[\leadsto \color{blue}{y \cdot \frac{\cosh x}{y}} \]
   8. Step-by-step derivation

      1. associate-*r/ 78.8%

         \[\leadsto \color{blue}{\frac{y \cdot \cosh x}{y}} \]

      2. *-lft-identity 78.8%

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

      3. associate-*l/ 78.8%

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

      4. associate-*r* 78.8%

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

      5. lft-mult-inverse 78.8%

         \[\leadsto \color{blue}{1} \cdot \cosh x \]

      6. *-lft-identity 78.8%

         \[\leadsto \color{blue}{\cosh x} \]

   9. Simplified 78.8%

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

4. Final simplification 89.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.036 \lor \neg \left(x \leq 1.25 \cdot 10^{+154}\right):\\ \;\;\;\;\sin y \cdot \left(0.5 \cdot \frac{x \cdot x}{y} + \frac{1}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;\cosh x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 68.1% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.11:\\ \;\;\;\;\frac{\sin y}{y}\\ \mathbf{else}:\\ \;\;\;\;\cosh x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= x 0.11) (/ (sin y) y) (cosh x)))

C

double code(double x, double y) {
	double tmp;
	if (x <= 0.11) {
		tmp = sin(y) / y;
	} else {
		tmp = cosh(x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= 0.11d0) then
        tmp = sin(y) / y
    else
        tmp = cosh(x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= 0.11) {
		tmp = Math.sin(y) / y;
	} else {
		tmp = Math.cosh(x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= 0.11:
		tmp = math.sin(y) / y
	else:
		tmp = math.cosh(x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 0.11)
		tmp = Float64(sin(y) / y);
	else
		tmp = cosh(x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= 0.11)
		tmp = sin(y) / y;
	else
		tmp = cosh(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, 0.11], N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision], N[Cosh[x], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.110000000000000001

   1. Initial program 99.8%

      \[\cosh x \cdot \frac{\sin y}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 74.1%

      \[\leadsto \color{blue}{\frac{\sin y}{y}} \]

   if 0.110000000000000001 < x

   1. Initial program 100.0%

      \[\cosh x \cdot \frac{\sin y}{y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

   5. Taylor expanded in y around 0 66.7%

      \[\leadsto \frac{\cosh x \cdot \color{blue}{y}}{y} \]
   6. Step-by-step derivation

      1. *-commutative 66.7%

         \[\leadsto \frac{\color{blue}{y \cdot \cosh x}}{y} \]

      2. associate-/l* 66.6%

         \[\leadsto \color{blue}{y \cdot \frac{\cosh x}{y}} \]

   7. Applied egg-rr 66.6%

      \[\leadsto \color{blue}{y \cdot \frac{\cosh x}{y}} \]
   8. Step-by-step derivation

      1. associate-*r/ 66.7%

         \[\leadsto \color{blue}{\frac{y \cdot \cosh x}{y}} \]

      2. *-lft-identity 66.7%

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

      3. associate-*l/ 66.7%

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

      4. associate-*r* 66.7%

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

      5. lft-mult-inverse 66.7%

         \[\leadsto \color{blue}{1} \cdot \cosh x \]

      6. *-lft-identity 66.7%

         \[\leadsto \color{blue}{\cosh x} \]

   9. Simplified 66.7%

      \[\leadsto \color{blue}{\cosh x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 62.8% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \cosh x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (cosh x))

C

double code(double x, double y) {
	return cosh(x);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = cosh(x)
end function

Java

public static double code(double x, double y) {
	return Math.cosh(x);
}

Python

def code(x, y):
	return math.cosh(x)

Julia

function code(x, y)
	return cosh(x)
end

MATLAB

function tmp = code(x, y)
	tmp = cosh(x);
end

Wolfram

code[x_, y_] := N[Cosh[x], $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\cosh x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. associate-*r/ 99.9%

      \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

4. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\frac{\cosh x \cdot \sin y}{y}} \]

5. Taylor expanded in y around 0 54.4%

   \[\leadsto \frac{\cosh x \cdot \color{blue}{y}}{y} \]
6. Step-by-step derivation

   1. *-commutative 54.4%

      \[\leadsto \frac{\color{blue}{y \cdot \cosh x}}{y} \]

   2. associate-/l* 54.3%

      \[\leadsto \color{blue}{y \cdot \frac{\cosh x}{y}} \]

7. Applied egg-rr 54.3%

   \[\leadsto \color{blue}{y \cdot \frac{\cosh x}{y}} \]
8. Step-by-step derivation

   1. associate-*r/ 54.4%

      \[\leadsto \color{blue}{\frac{y \cdot \cosh x}{y}} \]

   2. *-lft-identity 54.4%

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

   3. associate-*l/ 54.3%

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

   4. associate-*r* 54.3%

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

   5. lft-mult-inverse 54.4%

      \[\leadsto \color{blue}{1} \cdot \cosh x \]

   6. *-lft-identity 54.4%

      \[\leadsto \color{blue}{\cosh x} \]

9. Simplified 54.4%

   \[\leadsto \color{blue}{\cosh x} \]
10. Add Preprocessing

Alternative 6: 51.0% accurate, 15.8× speedup

Math

\[\begin{array}{l} \\ y \cdot \left(0.5 \cdot \frac{x \cdot x}{y} + \frac{1}{y}\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* y (+ (* 0.5 (/ (* x x) y)) (/ 1.0 y))))

C

double code(double x, double y) {
	return y * ((0.5 * ((x * x) / y)) + (1.0 / y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y * ((0.5d0 * ((x * x) / y)) + (1.0d0 / y))
end function

Java

public static double code(double x, double y) {
	return y * ((0.5 * ((x * x) / y)) + (1.0 / y));
}

Python

def code(x, y):
	return y * ((0.5 * ((x * x) / y)) + (1.0 / y))

Julia

function code(x, y)
	return Float64(y * Float64(Float64(0.5 * Float64(Float64(x * x) / y)) + Float64(1.0 / y)))
end

MATLAB

function tmp = code(x, y)
	tmp = y * ((0.5 * ((x * x) / y)) + (1.0 / y));
end

Wolfram

code[x_, y_] := N[(y * N[(N[(0.5 * N[(N[(x * x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(1.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\cosh x \cdot \frac{\sin y}{y} \]
2. Step-by-step derivation

   1. *-commutative 99.9%

      \[\leadsto \color{blue}{\frac{\sin y}{y} \cdot \cosh x} \]

   2. associate-*l/ 99.9%

      \[\leadsto \color{blue}{\frac{\sin y \cdot \cosh x}{y}} \]

   3. associate-/l* 99.8%

      \[\leadsto \color{blue}{\sin y \cdot \frac{\cosh x}{y}} \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{\sin y \cdot \frac{\cosh x}{y}} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 81.7%

   \[\leadsto \sin y \cdot \color{blue}{\left(0.5 \cdot \frac{{x}^{2}}{y} + \frac{1}{y}\right)} \]
6. Step-by-step derivation

   1. unpow2 81.7%

      \[\leadsto \sin y \cdot \left(0.5 \cdot \frac{\color{blue}{x \cdot x}}{y} + \frac{1}{y}\right) \]

7. Applied egg-rr 81.7%

   \[\leadsto \sin y \cdot \left(0.5 \cdot \frac{\color{blue}{x \cdot x}}{y} + \frac{1}{y}\right) \]

8. Taylor expanded in y around 0 42.1%

   \[\leadsto \color{blue}{y} \cdot \left(0.5 \cdot \frac{x \cdot x}{y} + \frac{1}{y}\right) \]
9. Add Preprocessing

Alternative 7: 26.3% accurate, 205.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 1.0)

C

double code(double x, double y) {
	return 1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0
end function

Java

public static double code(double x, double y) {
	return 1.0;
}

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0;
end

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 99.9%

   \[\cosh x \cdot \frac{\sin y}{y} \]
2. Step-by-step derivation

   1. *-commutative 99.9%

      \[\leadsto \color{blue}{\frac{\sin y}{y} \cdot \cosh x} \]

   2. associate-*l/ 99.9%

      \[\leadsto \color{blue}{\frac{\sin y \cdot \cosh x}{y}} \]

   3. associate-/l* 99.8%

      \[\leadsto \color{blue}{\sin y \cdot \frac{\cosh x}{y}} \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{\sin y \cdot \frac{\cosh x}{y}} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 56.5%

   \[\leadsto \sin y \cdot \frac{\color{blue}{1}}{y} \]

6. Taylor expanded in y around 0 26.0%

   \[\leadsto \color{blue}{1} \]
7. Add Preprocessing

Developer Target 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\cosh x \cdot \sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* (cosh x) (sin y)) y))

C

double code(double x, double y) {
	return (cosh(x) * sin(y)) / y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (cosh(x) * sin(y)) / y
end function

Java

public static double code(double x, double y) {
	return (Math.cosh(x) * Math.sin(y)) / y;
}

Python

def code(x, y):
	return (math.cosh(x) * math.sin(y)) / y

Julia

function code(x, y)
	return Float64(Float64(cosh(x) * sin(y)) / y)
end

MATLAB

function tmp = code(x, y)
	tmp = (cosh(x) * sin(y)) / y;
end

Wolfram

code[x_, y_] := N[(N[(N[Cosh[x], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]

Reproduce

herbie shell --seed 2024143 
(FPCore (x y)
  :name "Linear.Quaternion:$csinh from linear-1.19.1.3"
  :precision binary64

  :alt
  (! :herbie-platform default (/ (* (cosh x) (sin y)) y))

  (* (cosh x) (/ (sin y) y)))