Graphics.Rendering.Chart.Axis.Types:hBufferRect from Chart-1.5.3

Percentage Accurate: 99.9% → 100.0%

Time: 4.2s

Alternatives: 5

Speedup: 1.8×

Specification

Math

\[\begin{array}{l} \\ x + \frac{x - y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (- x y) 2.0)))

C

double code(double x, double y) {
	return x + ((x - y) / 2.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + ((x - y) / 2.0d0)
end function

Java

public static double code(double x, double y) {
	return x + ((x - y) / 2.0);
}

Python

def code(x, y):
	return x + ((x - y) / 2.0)

Julia

function code(x, y)
	return Float64(x + Float64(Float64(x - y) / 2.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + ((x - y) / 2.0);
end

Wolfram

code[x_, y_] := N[(x + N[(N[(x - y), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{x - y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (- x y) 2.0)))

C

double code(double x, double y) {
	return x + ((x - y) / 2.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + ((x - y) / 2.0d0)
end function

Java

public static double code(double x, double y) {
	return x + ((x - y) / 2.0);
}

Python

def code(x, y):
	return x + ((x - y) / 2.0)

Julia

function code(x, y)
	return Float64(x + Float64(Float64(x - y) / 2.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + ((x - y) / 2.0);
end

Wolfram

code[x_, y_] := N[(x + N[(N[(x - y), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, 1.5, y \cdot -0.5\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma x 1.5 (* y -0.5)))

C

double code(double x, double y) {
	return fma(x, 1.5, (y * -0.5));
}

Julia

function code(x, y)
	return fma(x, 1.5, Float64(y * -0.5))
end

Wolfram

code[x_, y_] := N[(x * 1.5 + N[(y * -0.5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{x + \frac{x - y}{2}} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{\frac{x - y}{2} + x} \]

   3. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x - y}{2}} + x \]

   4. lift--.f64 N/A

      \[\leadsto \frac{\color{blue}{x - y}}{2} + x \]

   5. div-sub N/A

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

   6. sub-neg N/A

      \[\leadsto \color{blue}{\left(\frac{x}{2} + \left(\mathsf{neg}\left(\frac{y}{2}\right)\right)\right)} + x \]

   7. associate-+l+ N/A

      \[\leadsto \color{blue}{\frac{x}{2} + \left(\left(\mathsf{neg}\left(\frac{y}{2}\right)\right) + x\right)} \]

   8. div-inv N/A

      \[\leadsto \color{blue}{x \cdot \frac{1}{2}} + \left(\left(\mathsf{neg}\left(\frac{y}{2}\right)\right) + x\right) \]

   9. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, \frac{1}{2}, \left(\mathsf{neg}\left(\frac{y}{2}\right)\right) + x\right)} \]

   10. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(x, \color{blue}{\frac{1}{2}}, \left(\mathsf{neg}\left(\frac{y}{2}\right)\right) + x\right) \]

   11. div-inv N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, \left(\mathsf{neg}\left(\color{blue}{y \cdot \frac{1}{2}}\right)\right) + x\right) \]

   12. distribute-rgt-neg-in N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, \color{blue}{y \cdot \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)} + x\right) \]

   13. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, y \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{1}{2}}\right)\right) + x\right) \]

   14. metadata-eval N/A

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

   15. metadata-eval N/A

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

   16. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, y \cdot \frac{1}{\color{blue}{\mathsf{neg}\left(2\right)}} + x\right) \]

   17. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, \color{blue}{\mathsf{fma}\left(y, \frac{1}{\mathsf{neg}\left(2\right)}, x\right)}\right) \]

   18. metadata-eval N/A

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

   19. metadata-eval 99.9

       \[\leadsto \mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(y, \color{blue}{-0.5}, x\right)\right) \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(y, -0.5, x\right)\right)} \]
5. Step-by-step derivation

   1. lift-fma.f64 N/A

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

   2. lift-fma.f64 N/A

      \[\leadsto x \cdot \frac{1}{2} + \color{blue}{\left(y \cdot \frac{-1}{2} + x\right)} \]

   3. lift-*.f64 N/A

      \[\leadsto x \cdot \frac{1}{2} + \left(\color{blue}{y \cdot \frac{-1}{2}} + x\right) \]

   4. +-commutative N/A

      \[\leadsto x \cdot \frac{1}{2} + \color{blue}{\left(x + y \cdot \frac{-1}{2}\right)} \]

   5. associate-+r+ N/A

      \[\leadsto \color{blue}{\left(x \cdot \frac{1}{2} + x\right) + y \cdot \frac{-1}{2}} \]

   6. *-commutative N/A

      \[\leadsto \left(\color{blue}{\frac{1}{2} \cdot x} + x\right) + y \cdot \frac{-1}{2} \]

   7. distribute-lft1-in N/A

      \[\leadsto \color{blue}{\left(\frac{1}{2} + 1\right) \cdot x} + y \cdot \frac{-1}{2} \]

   8. metadata-eval N/A

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

   9. *-commutative N/A

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

   10. lower-fma.f64 100.0

       \[\leadsto \color{blue}{\mathsf{fma}\left(x, 1.5, y \cdot -0.5\right)} \]

   11. lift-*.f64 N/A

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

   12. *-commutative N/A

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

   13. lower-*.f64 100.0

       \[\leadsto \mathsf{fma}\left(x, 1.5, \color{blue}{-0.5 \cdot y}\right) \]

6. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 1.5, -0.5 \cdot y\right)} \]

7. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(x, 1.5, y \cdot -0.5\right) \]
8. Add Preprocessing

Alternative 2: 76.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.105:\\ \;\;\;\;1.5 \cdot x\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+113}:\\ \;\;\;\;y \cdot -0.5 + x\\ \mathbf{else}:\\ \;\;\;\;1.5 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.105) (* 1.5 x) (if (<= x 2.3e+113) (+ (* y -0.5) x) (* 1.5 x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.105) {
		tmp = 1.5 * x;
	} else if (x <= 2.3e+113) {
		tmp = (y * -0.5) + x;
	} else {
		tmp = 1.5 * 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 <= (-0.105d0)) then
        tmp = 1.5d0 * x
    else if (x <= 2.3d+113) then
        tmp = (y * (-0.5d0)) + x
    else
        tmp = 1.5d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.105) {
		tmp = 1.5 * x;
	} else if (x <= 2.3e+113) {
		tmp = (y * -0.5) + x;
	} else {
		tmp = 1.5 * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.105:
		tmp = 1.5 * x
	elif x <= 2.3e+113:
		tmp = (y * -0.5) + x
	else:
		tmp = 1.5 * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.105)
		tmp = Float64(1.5 * x);
	elseif (x <= 2.3e+113)
		tmp = Float64(Float64(y * -0.5) + x);
	else
		tmp = Float64(1.5 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.105)
		tmp = 1.5 * x;
	elseif (x <= 2.3e+113)
		tmp = (y * -0.5) + x;
	else
		tmp = 1.5 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.105], N[(1.5 * x), $MachinePrecision], If[LessEqual[x, 2.3e+113], N[(N[(y * -0.5), $MachinePrecision] + x), $MachinePrecision], N[(1.5 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.104999999999999996 or 2.29999999999999997e113 < x

   1. Initial program 99.8%

      \[x + \frac{x - y}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + \frac{1}{2} \cdot x} \]
   4. Step-by-step derivation

      1. distribute-rgt1-in N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} + 1\right) \cdot x} \]

      2. metadata-eval N/A

         \[\leadsto \color{blue}{\frac{3}{2}} \cdot x \]

      3. lower-*.f64 87.6

         \[\leadsto \color{blue}{1.5 \cdot x} \]

   5. Applied rewrites 87.6%

      \[\leadsto \color{blue}{1.5 \cdot x} \]

   if -0.104999999999999996 < x < 2.29999999999999997e113

   1. Initial program 99.9%

      \[x + \frac{x - y}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto x + \color{blue}{\frac{-1}{2} \cdot y} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x + \color{blue}{y \cdot \frac{-1}{2}} \]

      2. lower-*.f64 78.3

         \[\leadsto x + \color{blue}{y \cdot -0.5} \]

   5. Applied rewrites 78.3%

      \[\leadsto x + \color{blue}{y \cdot -0.5} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.105:\\ \;\;\;\;1.5 \cdot x\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+113}:\\ \;\;\;\;y \cdot -0.5 + x\\ \mathbf{else}:\\ \;\;\;\;1.5 \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 74.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.105:\\ \;\;\;\;1.5 \cdot x\\ \mathbf{elif}\;x \leq 1.25 \cdot 10^{+98}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;1.5 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.105) (* 1.5 x) (if (<= x 1.25e+98) (* y -0.5) (* 1.5 x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.105) {
		tmp = 1.5 * x;
	} else if (x <= 1.25e+98) {
		tmp = y * -0.5;
	} else {
		tmp = 1.5 * 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 <= (-0.105d0)) then
        tmp = 1.5d0 * x
    else if (x <= 1.25d+98) then
        tmp = y * (-0.5d0)
    else
        tmp = 1.5d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.105) {
		tmp = 1.5 * x;
	} else if (x <= 1.25e+98) {
		tmp = y * -0.5;
	} else {
		tmp = 1.5 * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.105:
		tmp = 1.5 * x
	elif x <= 1.25e+98:
		tmp = y * -0.5
	else:
		tmp = 1.5 * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.105)
		tmp = Float64(1.5 * x);
	elseif (x <= 1.25e+98)
		tmp = Float64(y * -0.5);
	else
		tmp = Float64(1.5 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.105)
		tmp = 1.5 * x;
	elseif (x <= 1.25e+98)
		tmp = y * -0.5;
	else
		tmp = 1.5 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.105], N[(1.5 * x), $MachinePrecision], If[LessEqual[x, 1.25e+98], N[(y * -0.5), $MachinePrecision], N[(1.5 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.104999999999999996 or 1.25e98 < x

   1. Initial program 99.8%

      \[x + \frac{x - y}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + \frac{1}{2} \cdot x} \]
   4. Step-by-step derivation

      1. distribute-rgt1-in N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} + 1\right) \cdot x} \]

      2. metadata-eval N/A

         \[\leadsto \color{blue}{\frac{3}{2}} \cdot x \]

      3. lower-*.f64 87.0

         \[\leadsto \color{blue}{1.5 \cdot x} \]

   5. Applied rewrites 87.0%

      \[\leadsto \color{blue}{1.5 \cdot x} \]

   if -0.104999999999999996 < x < 1.25e98

   1. Initial program 99.9%

      \[x + \frac{x - y}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot y} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{y \cdot \frac{-1}{2}} \]

      2. lower-*.f64 74.8

         \[\leadsto \color{blue}{y \cdot -0.5} \]

   5. Applied rewrites 74.8%

      \[\leadsto \color{blue}{y \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 99.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y - x, -0.5, x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma (- y x) -0.5 x))

C

double code(double x, double y) {
	return fma((y - x), -0.5, x);
}

Julia

function code(x, y)
	return fma(Float64(y - x), -0.5, x)
end

Wolfram

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

Derivation

1. Initial program 99.9%

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

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{x + \frac{x - y}{2}} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{\frac{x - y}{2} + x} \]

   3. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x - y}{2}} + x \]

   4. frac-2neg N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{\mathsf{neg}\left(2\right)}} + x \]

   5. div-inv N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\left(x - y\right)\right)\right) \cdot \frac{1}{\mathsf{neg}\left(2\right)}} + x \]

   6. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{neg}\left(\left(x - y\right)\right), \frac{1}{\mathsf{neg}\left(2\right)}, x\right)} \]

   7. neg-sub0 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{0 - \left(x - y\right)}, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   8. lift--.f64 N/A

      \[\leadsto \mathsf{fma}\left(0 - \color{blue}{\left(x - y\right)}, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   9. sub-neg N/A

      \[\leadsto \mathsf{fma}\left(0 - \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)}, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   10. +-commutative N/A

       \[\leadsto \mathsf{fma}\left(0 - \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)}, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   11. associate--r+ N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\left(0 - \left(\mathsf{neg}\left(y\right)\right)\right) - x}, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   12. neg-sub0 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(y\right)\right)\right)\right)} - x, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   13. remove-double-neg N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{y} - x, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   14. lower--.f64 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{y - x}, \frac{1}{\mathsf{neg}\left(2\right)}, x\right) \]

   15. metadata-eval N/A

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

   16. metadata-eval 99.9

       \[\leadsto \mathsf{fma}\left(y - x, \color{blue}{-0.5}, x\right) \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y - x, -0.5, x\right)} \]
5. Add Preprocessing

Alternative 5: 49.4% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ 1.5 \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 1.5 x))

C

double code(double x, double y) {
	return 1.5 * x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.5d0 * x
end function

Java

public static double code(double x, double y) {
	return 1.5 * x;
}

Python

def code(x, y):
	return 1.5 * x

Julia

function code(x, y)
	return Float64(1.5 * x)
end

MATLAB

function tmp = code(x, y)
	tmp = 1.5 * x;
end

Wolfram

code[x_, y_] := N[(1.5 * x), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x + \frac{x - y}{2} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{x + \frac{1}{2} \cdot x} \]
4. Step-by-step derivation

   1. distribute-rgt1-in N/A

      \[\leadsto \color{blue}{\left(\frac{1}{2} + 1\right) \cdot x} \]

   2. metadata-eval N/A

      \[\leadsto \color{blue}{\frac{3}{2}} \cdot x \]

   3. lower-*.f64 54.7

      \[\leadsto \color{blue}{1.5 \cdot x} \]

5. Applied rewrites 54.7%

   \[\leadsto \color{blue}{1.5 \cdot x} \]
6. Add Preprocessing

Developer Target 1: 99.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ 1.5 \cdot x - 0.5 \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (* 1.5 x) (* 0.5 y)))

C

double code(double x, double y) {
	return (1.5 * x) - (0.5 * y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.5d0 * x) - (0.5d0 * y)
end function

Java

public static double code(double x, double y) {
	return (1.5 * x) - (0.5 * y);
}

Python

def code(x, y):
	return (1.5 * x) - (0.5 * y)

Julia

function code(x, y)
	return Float64(Float64(1.5 * x) - Float64(0.5 * y))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.5 * x) - (0.5 * y);
end

Wolfram

code[x_, y_] := N[(N[(1.5 * x), $MachinePrecision] - N[(0.5 * y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024235 
(FPCore (x y)
  :name "Graphics.Rendering.Chart.Axis.Types:hBufferRect from Chart-1.5.3"
  :precision binary64

  :alt
  (! :herbie-platform default (- (* 3/2 x) (* 1/2 y)))

  (+ x (/ (- x y) 2.0)))