Diagrams.TwoD.Segment:bezierClip from diagrams-lib-1.3.0.3

Percentage Accurate: 97.8% → 100.0%

Time: 4.9s

Alternatives: 8

Speedup: 1.3×

• 21.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 59.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(-z\right)\\ \mathbf{if}\;y \leq -1.1 \cdot 10^{+204}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{+91}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -16500:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-203}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{-169}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 2 \cdot 10^{-35}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 275000000000 \lor \neg \left(y \leq 1.15 \cdot 10^{+175}\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (- z))))
   (if (<= y -1.1e+204)
     (* y x)
     (if (<= y -1.2e+91)
       t_0
       (if (<= y -16500.0)
         (* y x)
         (if (<= y 1.7e-203)
           z
           (if (<= y 2.2e-169)
             (* y x)
             (if (<= y 2e-35)
               z
               (if (or (<= y 275000000000.0) (not (<= y 1.15e+175)))
                 (* y x)
                 t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = y * -z;
	double tmp;
	if (y <= -1.1e+204) {
		tmp = y * x;
	} else if (y <= -1.2e+91) {
		tmp = t_0;
	} else if (y <= -16500.0) {
		tmp = y * x;
	} else if (y <= 1.7e-203) {
		tmp = z;
	} else if (y <= 2.2e-169) {
		tmp = y * x;
	} else if (y <= 2e-35) {
		tmp = z;
	} else if ((y <= 275000000000.0) || !(y <= 1.15e+175)) {
		tmp = y * x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * -z
    if (y <= (-1.1d+204)) then
        tmp = y * x
    else if (y <= (-1.2d+91)) then
        tmp = t_0
    else if (y <= (-16500.0d0)) then
        tmp = y * x
    else if (y <= 1.7d-203) then
        tmp = z
    else if (y <= 2.2d-169) then
        tmp = y * x
    else if (y <= 2d-35) then
        tmp = z
    else if ((y <= 275000000000.0d0) .or. (.not. (y <= 1.15d+175))) then
        tmp = y * x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = y * -z;
	double tmp;
	if (y <= -1.1e+204) {
		tmp = y * x;
	} else if (y <= -1.2e+91) {
		tmp = t_0;
	} else if (y <= -16500.0) {
		tmp = y * x;
	} else if (y <= 1.7e-203) {
		tmp = z;
	} else if (y <= 2.2e-169) {
		tmp = y * x;
	} else if (y <= 2e-35) {
		tmp = z;
	} else if ((y <= 275000000000.0) || !(y <= 1.15e+175)) {
		tmp = y * x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * -z
	tmp = 0
	if y <= -1.1e+204:
		tmp = y * x
	elif y <= -1.2e+91:
		tmp = t_0
	elif y <= -16500.0:
		tmp = y * x
	elif y <= 1.7e-203:
		tmp = z
	elif y <= 2.2e-169:
		tmp = y * x
	elif y <= 2e-35:
		tmp = z
	elif (y <= 275000000000.0) or not (y <= 1.15e+175):
		tmp = y * x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(-z))
	tmp = 0.0
	if (y <= -1.1e+204)
		tmp = Float64(y * x);
	elseif (y <= -1.2e+91)
		tmp = t_0;
	elseif (y <= -16500.0)
		tmp = Float64(y * x);
	elseif (y <= 1.7e-203)
		tmp = z;
	elseif (y <= 2.2e-169)
		tmp = Float64(y * x);
	elseif (y <= 2e-35)
		tmp = z;
	elseif ((y <= 275000000000.0) || !(y <= 1.15e+175))
		tmp = Float64(y * x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * -z;
	tmp = 0.0;
	if (y <= -1.1e+204)
		tmp = y * x;
	elseif (y <= -1.2e+91)
		tmp = t_0;
	elseif (y <= -16500.0)
		tmp = y * x;
	elseif (y <= 1.7e-203)
		tmp = z;
	elseif (y <= 2.2e-169)
		tmp = y * x;
	elseif (y <= 2e-35)
		tmp = z;
	elseif ((y <= 275000000000.0) || ~((y <= 1.15e+175)))
		tmp = y * x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * (-z)), $MachinePrecision]}, If[LessEqual[y, -1.1e+204], N[(y * x), $MachinePrecision], If[LessEqual[y, -1.2e+91], t$95$0, If[LessEqual[y, -16500.0], N[(y * x), $MachinePrecision], If[LessEqual[y, 1.7e-203], z, If[LessEqual[y, 2.2e-169], N[(y * x), $MachinePrecision], If[LessEqual[y, 2e-35], z, If[Or[LessEqual[y, 275000000000.0], N[Not[LessEqual[y, 1.15e+175]], $MachinePrecision]], N[(y * x), $MachinePrecision], t$95$0]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 83.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(x - z\right)\\ \mathbf{if}\;y \leq -1.46 \cdot 10^{-9}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-203}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{-169}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{-27}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (- x z))))
   (if (<= y -1.46e-9)
     t_0
     (if (<= y 1.7e-203)
       z
       (if (<= y 2.2e-169) (* y x) (if (<= y 1.05e-27) z t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = y * (x - z);
	double tmp;
	if (y <= -1.46e-9) {
		tmp = t_0;
	} else if (y <= 1.7e-203) {
		tmp = z;
	} else if (y <= 2.2e-169) {
		tmp = y * x;
	} else if (y <= 1.05e-27) {
		tmp = z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * (x - z)
    if (y <= (-1.46d-9)) then
        tmp = t_0
    else if (y <= 1.7d-203) then
        tmp = z
    else if (y <= 2.2d-169) then
        tmp = y * x
    else if (y <= 1.05d-27) then
        tmp = z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = y * (x - z);
	double tmp;
	if (y <= -1.46e-9) {
		tmp = t_0;
	} else if (y <= 1.7e-203) {
		tmp = z;
	} else if (y <= 2.2e-169) {
		tmp = y * x;
	} else if (y <= 1.05e-27) {
		tmp = z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * (x - z)
	tmp = 0
	if y <= -1.46e-9:
		tmp = t_0
	elif y <= 1.7e-203:
		tmp = z
	elif y <= 2.2e-169:
		tmp = y * x
	elif y <= 1.05e-27:
		tmp = z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(x - z))
	tmp = 0.0
	if (y <= -1.46e-9)
		tmp = t_0;
	elseif (y <= 1.7e-203)
		tmp = z;
	elseif (y <= 2.2e-169)
		tmp = Float64(y * x);
	elseif (y <= 1.05e-27)
		tmp = z;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * (x - z);
	tmp = 0.0;
	if (y <= -1.46e-9)
		tmp = t_0;
	elseif (y <= 1.7e-203)
		tmp = z;
	elseif (y <= 2.2e-169)
		tmp = y * x;
	elseif (y <= 1.05e-27)
		tmp = z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * N[(x - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.46e-9], t$95$0, If[LessEqual[y, 1.7e-203], z, If[LessEqual[y, 2.2e-169], N[(y * x), $MachinePrecision], If[LessEqual[y, 1.05e-27], z, t$95$0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 83.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(x - z\right)\\ \mathbf{if}\;y \leq -16500:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{-203}:\\ \;\;\;\;z \cdot \left(1 - y\right)\\ \mathbf{elif}\;y \leq 2.3 \cdot 10^{-169}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{-26}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (- x z))))
   (if (<= y -16500.0)
     t_0
     (if (<= y 1.7e-203)
       (* z (- 1.0 y))
       (if (<= y 2.3e-169) (* y x) (if (<= y 4.2e-26) z t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = y * (x - z);
	double tmp;
	if (y <= -16500.0) {
		tmp = t_0;
	} else if (y <= 1.7e-203) {
		tmp = z * (1.0 - y);
	} else if (y <= 2.3e-169) {
		tmp = y * x;
	} else if (y <= 4.2e-26) {
		tmp = z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * (x - z)
    if (y <= (-16500.0d0)) then
        tmp = t_0
    else if (y <= 1.7d-203) then
        tmp = z * (1.0d0 - y)
    else if (y <= 2.3d-169) then
        tmp = y * x
    else if (y <= 4.2d-26) then
        tmp = z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = y * (x - z);
	double tmp;
	if (y <= -16500.0) {
		tmp = t_0;
	} else if (y <= 1.7e-203) {
		tmp = z * (1.0 - y);
	} else if (y <= 2.3e-169) {
		tmp = y * x;
	} else if (y <= 4.2e-26) {
		tmp = z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * (x - z)
	tmp = 0
	if y <= -16500.0:
		tmp = t_0
	elif y <= 1.7e-203:
		tmp = z * (1.0 - y)
	elif y <= 2.3e-169:
		tmp = y * x
	elif y <= 4.2e-26:
		tmp = z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(x - z))
	tmp = 0.0
	if (y <= -16500.0)
		tmp = t_0;
	elseif (y <= 1.7e-203)
		tmp = Float64(z * Float64(1.0 - y));
	elseif (y <= 2.3e-169)
		tmp = Float64(y * x);
	elseif (y <= 4.2e-26)
		tmp = z;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * (x - z);
	tmp = 0.0;
	if (y <= -16500.0)
		tmp = t_0;
	elseif (y <= 1.7e-203)
		tmp = z * (1.0 - y);
	elseif (y <= 2.3e-169)
		tmp = y * x;
	elseif (y <= 4.2e-26)
		tmp = z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * N[(x - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -16500.0], t$95$0, If[LessEqual[y, 1.7e-203], N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.3e-169], N[(y * x), $MachinePrecision], If[LessEqual[y, 4.2e-26], z, t$95$0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 59.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -16500 \lor \neg \left(y \leq 1.7 \cdot 10^{-203}\right) \land \left(y \leq 2.2 \cdot 10^{-169} \lor \neg \left(y \leq 1.5 \cdot 10^{-35}\right)\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -16500.0)
         (and (not (<= y 1.7e-203)) (or (<= y 2.2e-169) (not (<= y 1.5e-35)))))
   (* y x)
   z))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -16500.0) || (!(y <= 1.7e-203) && ((y <= 2.2e-169) || !(y <= 1.5e-35)))) {
		tmp = y * x;
	} else {
		tmp = z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-16500.0d0)) .or. (.not. (y <= 1.7d-203)) .and. (y <= 2.2d-169) .or. (.not. (y <= 1.5d-35))) then
        tmp = y * x
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -16500.0) || (!(y <= 1.7e-203) && ((y <= 2.2e-169) || !(y <= 1.5e-35)))) {
		tmp = y * x;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -16500.0) or (not (y <= 1.7e-203) and ((y <= 2.2e-169) or not (y <= 1.5e-35))):
		tmp = y * x
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -16500.0) || (!(y <= 1.7e-203) && ((y <= 2.2e-169) || !(y <= 1.5e-35))))
		tmp = Float64(y * x);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -16500.0) || (~((y <= 1.7e-203)) && ((y <= 2.2e-169) || ~((y <= 1.5e-35)))))
		tmp = y * x;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -16500.0], And[N[Not[LessEqual[y, 1.7e-203]], $MachinePrecision], Or[LessEqual[y, 2.2e-169], N[Not[LessEqual[y, 1.5e-35]], $MachinePrecision]]]], N[(y * x), $MachinePrecision], z]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 98.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.0) (not (<= y 1.0))) (* y (- x z)) (+ z (* y x))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.0) || !(y <= 1.0)) {
		tmp = y * (x - z);
	} else {
		tmp = z + (y * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-1.0d0)) .or. (.not. (y <= 1.0d0))) then
        tmp = y * (x - z)
    else
        tmp = z + (y * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.0) || !(y <= 1.0)) {
		tmp = y * (x - z);
	} else {
		tmp = z + (y * x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.0) or not (y <= 1.0):
		tmp = y * (x - z)
	else:
		tmp = z + (y * x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.0) || !(y <= 1.0))
		tmp = Float64(y * Float64(x - z));
	else
		tmp = Float64(z + Float64(y * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.0) || ~((y <= 1.0)))
		tmp = y * (x - z);
	else
		tmp = z + (y * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.0], N[Not[LessEqual[y, 1.0]], $MachinePrecision]], N[(y * N[(x - z), $MachinePrecision]), $MachinePrecision], N[(z + N[(y * x), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 100.0% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 35.9% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return z

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Developer target: 100.0% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023348 
(FPCore (x y z)
  :name "Diagrams.TwoD.Segment:bezierClip from diagrams-lib-1.3.0.3"
  :precision binary64

  :herbie-target
  (- z (* (- z x) y))

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