Graphics.Rendering.Chart.Plot.Vectors:renderPlotVectors from Chart-1.5.3

Percentage Accurate: 77.8% → 100.0%

Time: 12.0s

Alternatives: 8

Speedup: 1.3×

Specification

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ x (* (- 1.0 x) (- 1.0 y))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 77.8% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ x (* (- 1.0 x) (- 1.0 y))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x + -1, y, 1\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma (+ x -1.0) y 1.0))

C

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

Julia

function code(x, y)
	return fma(Float64(x + -1.0), y, 1.0)
end

Wolfram

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

Derivation

1. Initial program 77.1%

   \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

6. Applied egg-rr 0

   \[\leadsto expr\]
7. Add Preprocessing

Alternative 2: 61.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.02 \cdot 10^{+290}:\\ \;\;\;\;-y\\ \mathbf{elif}\;y \leq -1.22 \cdot 10^{+195}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -8000:\\ \;\;\;\;-y\\ \mathbf{elif}\;y \leq -3.45 \cdot 10^{-93}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -5.2 \cdot 10^{-147}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{-31}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.02e+290)
   (- y)
   (if (<= y -1.22e+195)
     (* y x)
     (if (<= y -8000.0)
       (- y)
       (if (<= y -3.45e-93)
         1.0
         (if (<= y -5.2e-147) (* y x) (if (<= y 2.9e-31) 1.0 (* y x))))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.02e+290) {
		tmp = -y;
	} else if (y <= -1.22e+195) {
		tmp = y * x;
	} else if (y <= -8000.0) {
		tmp = -y;
	} else if (y <= -3.45e-93) {
		tmp = 1.0;
	} else if (y <= -5.2e-147) {
		tmp = y * x;
	} else if (y <= 2.9e-31) {
		tmp = 1.0;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-1.02d+290)) then
        tmp = -y
    else if (y <= (-1.22d+195)) then
        tmp = y * x
    else if (y <= (-8000.0d0)) then
        tmp = -y
    else if (y <= (-3.45d-93)) then
        tmp = 1.0d0
    else if (y <= (-5.2d-147)) then
        tmp = y * x
    else if (y <= 2.9d-31) then
        tmp = 1.0d0
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.02e+290) {
		tmp = -y;
	} else if (y <= -1.22e+195) {
		tmp = y * x;
	} else if (y <= -8000.0) {
		tmp = -y;
	} else if (y <= -3.45e-93) {
		tmp = 1.0;
	} else if (y <= -5.2e-147) {
		tmp = y * x;
	} else if (y <= 2.9e-31) {
		tmp = 1.0;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.02e+290:
		tmp = -y
	elif y <= -1.22e+195:
		tmp = y * x
	elif y <= -8000.0:
		tmp = -y
	elif y <= -3.45e-93:
		tmp = 1.0
	elif y <= -5.2e-147:
		tmp = y * x
	elif y <= 2.9e-31:
		tmp = 1.0
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.02e+290)
		tmp = Float64(-y);
	elseif (y <= -1.22e+195)
		tmp = Float64(y * x);
	elseif (y <= -8000.0)
		tmp = Float64(-y);
	elseif (y <= -3.45e-93)
		tmp = 1.0;
	elseif (y <= -5.2e-147)
		tmp = Float64(y * x);
	elseif (y <= 2.9e-31)
		tmp = 1.0;
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.02e+290)
		tmp = -y;
	elseif (y <= -1.22e+195)
		tmp = y * x;
	elseif (y <= -8000.0)
		tmp = -y;
	elseif (y <= -3.45e-93)
		tmp = 1.0;
	elseif (y <= -5.2e-147)
		tmp = y * x;
	elseif (y <= 2.9e-31)
		tmp = 1.0;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.02e+290], (-y), If[LessEqual[y, -1.22e+195], N[(y * x), $MachinePrecision], If[LessEqual[y, -8000.0], (-y), If[LessEqual[y, -3.45e-93], 1.0, If[LessEqual[y, -5.2e-147], N[(y * x), $MachinePrecision], If[LessEqual[y, 2.9e-31], 1.0, N[(y * x), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.02e290 or -1.2200000000000001e195 < y < -8e3

   1. Initial program 100.0%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -1.02e290 < y < -1.2200000000000001e195 or -3.45000000000000015e-93 < y < -5.1999999999999997e-147 or 2.9000000000000001e-31 < y

   1. Initial program 86.1%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -8e3 < y < -3.45000000000000015e-93 or -5.1999999999999997e-147 < y < 2.9000000000000001e-31

   1. Initial program 62.1%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 98.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + y \cdot x\\ \mathbf{if}\;x \leq -1:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 5.6 \cdot 10^{-25}:\\ \;\;\;\;1 - y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (+ 1.0 (* y x))))
   (if (<= x -1.0) t_0 (if (<= x 5.6e-25) (- 1.0 y) t_0))))

C

double code(double x, double y) {
	double t_0 = 1.0 + (y * x);
	double tmp;
	if (x <= -1.0) {
		tmp = t_0;
	} else if (x <= 5.6e-25) {
		tmp = 1.0 - y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 + (y * x)
    if (x <= (-1.0d0)) then
        tmp = t_0
    else if (x <= 5.6d-25) then
        tmp = 1.0d0 - y
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 + (y * x);
	double tmp;
	if (x <= -1.0) {
		tmp = t_0;
	} else if (x <= 5.6e-25) {
		tmp = 1.0 - y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 + (y * x)
	tmp = 0
	if x <= -1.0:
		tmp = t_0
	elif x <= 5.6e-25:
		tmp = 1.0 - y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 + Float64(y * x))
	tmp = 0.0
	if (x <= -1.0)
		tmp = t_0;
	elseif (x <= 5.6e-25)
		tmp = Float64(1.0 - y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 + (y * x);
	tmp = 0.0;
	if (x <= -1.0)
		tmp = t_0;
	elseif (x <= 5.6e-25)
		tmp = 1.0 - y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 + N[(y * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.0], t$95$0, If[LessEqual[x, 5.6e-25], N[(1.0 - y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 5.59999999999999976e-25 < x

   1. Initial program 54.3%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1 < x < 5.59999999999999976e-25

   1. Initial program 100.0%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 85.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{+22}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 4.1 \cdot 10^{+102}:\\ \;\;\;\;1 - y\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + -1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -4.5e+22) (* y x) (if (<= x 4.1e+102) (- 1.0 y) (* y (+ x -1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -4.5e+22) {
		tmp = y * x;
	} else if (x <= 4.1e+102) {
		tmp = 1.0 - y;
	} else {
		tmp = y * (x + -1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-4.5d+22)) then
        tmp = y * x
    else if (x <= 4.1d+102) then
        tmp = 1.0d0 - y
    else
        tmp = y * (x + (-1.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -4.5e+22) {
		tmp = y * x;
	} else if (x <= 4.1e+102) {
		tmp = 1.0 - y;
	} else {
		tmp = y * (x + -1.0);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -4.5e+22:
		tmp = y * x
	elif x <= 4.1e+102:
		tmp = 1.0 - y
	else:
		tmp = y * (x + -1.0)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -4.5e+22)
		tmp = Float64(y * x);
	elseif (x <= 4.1e+102)
		tmp = Float64(1.0 - y);
	else
		tmp = Float64(y * Float64(x + -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -4.5e+22)
		tmp = y * x;
	elseif (x <= 4.1e+102)
		tmp = 1.0 - y;
	else
		tmp = y * (x + -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -4.5e+22], N[(y * x), $MachinePrecision], If[LessEqual[x, 4.1e+102], N[(1.0 - y), $MachinePrecision], N[(y * N[(x + -1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.4999999999999998e22

   1. Initial program 53.1%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -4.4999999999999998e22 < x < 4.1e102

   1. Initial program 91.9%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 4.1e102 < x

   1. Initial program 54.2%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 85.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+28}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 3.4 \cdot 10^{+102}:\\ \;\;\;\;1 - y\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -4e+28) (* y x) (if (<= x 3.4e+102) (- 1.0 y) (* y x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -4e+28) {
		tmp = y * x;
	} else if (x <= 3.4e+102) {
		tmp = 1.0 - y;
	} else {
		tmp = y * 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 <= (-4d+28)) then
        tmp = y * x
    else if (x <= 3.4d+102) then
        tmp = 1.0d0 - y
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -4e+28) {
		tmp = y * x;
	} else if (x <= 3.4e+102) {
		tmp = 1.0 - y;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -4e+28:
		tmp = y * x
	elif x <= 3.4e+102:
		tmp = 1.0 - y
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -4e+28)
		tmp = Float64(y * x);
	elseif (x <= 3.4e+102)
		tmp = Float64(1.0 - y);
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -4e+28)
		tmp = y * x;
	elseif (x <= 3.4e+102)
		tmp = 1.0 - y;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -4e+28], N[(y * x), $MachinePrecision], If[LessEqual[x, 3.4e+102], N[(1.0 - y), $MachinePrecision], N[(y * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.99999999999999983e28 or 3.4e102 < x

   1. Initial program 53.7%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.99999999999999983e28 < x < 3.4e102

   1. Initial program 91.9%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 61.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8000:\\ \;\;\;\;-y\\ \mathbf{elif}\;y \leq 0.0009:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -8000.0) (- y) (if (<= y 0.0009) 1.0 (- y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -8000.0) {
		tmp = -y;
	} else if (y <= 0.0009) {
		tmp = 1.0;
	} else {
		tmp = -y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-8000.0d0)) then
        tmp = -y
    else if (y <= 0.0009d0) then
        tmp = 1.0d0
    else
        tmp = -y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -8000.0) {
		tmp = -y;
	} else if (y <= 0.0009) {
		tmp = 1.0;
	} else {
		tmp = -y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -8000.0:
		tmp = -y
	elif y <= 0.0009:
		tmp = 1.0
	else:
		tmp = -y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -8000.0)
		tmp = Float64(-y);
	elseif (y <= 0.0009)
		tmp = 1.0;
	else
		tmp = Float64(-y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -8000.0)
		tmp = -y;
	elseif (y <= 0.0009)
		tmp = 1.0;
	else
		tmp = -y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -8000.0], (-y), If[LessEqual[y, 0.0009], 1.0, (-y)]]

Derivation

1. Split input into 2 regimes

2. if y < -8e3 or 8.9999999999999998e-4 < y

   1. Initial program 99.9%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -8e3 < y < 8.9999999999999998e-4

   1. Initial program 57.1%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 100.0% accurate, 1.3× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ 1.0 (* y (+ x -1.0))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 77.1%

   \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing
5. Add Preprocessing

Alternative 8: 38.0% accurate, 9.0× 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 77.1%

   \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in y around 0 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

Developer target: 100.0% accurate, 1.3× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (- (* y x) (- y 1.0)))

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return (y * x) - (y - 1.0)

Julia

function code(x, y)
	return Float64(Float64(y * x) - Float64(y - 1.0))
end

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024110 
(FPCore (x y)
  :name "Graphics.Rendering.Chart.Plot.Vectors:renderPlotVectors from Chart-1.5.3"
  :precision binary64

  :alt
  (- (* y x) (- y 1.0))

  (+ x (* (- 1.0 x) (- 1.0 y))))