Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3

Percentage Accurate: 97.9% → 100.0%

Time: 3.6s

Alternatives: 7

Speedup: 1.3×

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

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x * y) + ((x - 1.0) * 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 = (x * y) + ((x - 1.0d0) * z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x * y) + ((x - 1.0) * 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 = (x * y) + ((x - 1.0d0) * z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 57.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{-159}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{-102}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{+52} \lor \neg \left(x \leq 4.3 \cdot 10^{+209}\right) \land x \leq 5.5 \cdot 10^{+284}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;z \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -9e-159)
   (* y x)
   (if (<= x 2.75e-102)
     (- z)
     (if (or (<= x 5.5e+52) (and (not (<= x 4.3e+209)) (<= x 5.5e+284)))
       (* y x)
       (* z x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -9e-159) {
		tmp = y * x;
	} else if (x <= 2.75e-102) {
		tmp = -z;
	} else if ((x <= 5.5e+52) || (!(x <= 4.3e+209) && (x <= 5.5e+284))) {
		tmp = y * x;
	} else {
		tmp = z * 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 (x <= (-9d-159)) then
        tmp = y * x
    else if (x <= 2.75d-102) then
        tmp = -z
    else if ((x <= 5.5d+52) .or. (.not. (x <= 4.3d+209)) .and. (x <= 5.5d+284)) then
        tmp = y * x
    else
        tmp = z * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -9e-159) {
		tmp = y * x;
	} else if (x <= 2.75e-102) {
		tmp = -z;
	} else if ((x <= 5.5e+52) || (!(x <= 4.3e+209) && (x <= 5.5e+284))) {
		tmp = y * x;
	} else {
		tmp = z * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -9e-159:
		tmp = y * x
	elif x <= 2.75e-102:
		tmp = -z
	elif (x <= 5.5e+52) or (not (x <= 4.3e+209) and (x <= 5.5e+284)):
		tmp = y * x
	else:
		tmp = z * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -9e-159)
		tmp = Float64(y * x);
	elseif (x <= 2.75e-102)
		tmp = Float64(-z);
	elseif ((x <= 5.5e+52) || (!(x <= 4.3e+209) && (x <= 5.5e+284)))
		tmp = Float64(y * x);
	else
		tmp = Float64(z * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -9e-159)
		tmp = y * x;
	elseif (x <= 2.75e-102)
		tmp = -z;
	elseif ((x <= 5.5e+52) || (~((x <= 4.3e+209)) && (x <= 5.5e+284)))
		tmp = y * x;
	else
		tmp = z * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -9e-159], N[(y * x), $MachinePrecision], If[LessEqual[x, 2.75e-102], (-z), If[Or[LessEqual[x, 5.5e+52], And[N[Not[LessEqual[x, 4.3e+209]], $MachinePrecision], LessEqual[x, 5.5e+284]]], N[(y * x), $MachinePrecision], N[(z * x), $MachinePrecision]]]]

Derivation

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

Alternative 3: 87.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7.6 \cdot 10^{+43} \lor \neg \left(z \leq 4.8 \cdot 10^{+35}\right):\\ \;\;\;\;z \cdot x - z\\ \mathbf{else}:\\ \;\;\;\;y \cdot x - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -7.6e+43) (not (<= z 4.8e+35))) (- (* z x) z) (- (* y x) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -7.6e+43) || !(z <= 4.8e+35)) {
		tmp = (z * x) - z;
	} else {
		tmp = (y * x) - 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 ((z <= (-7.6d+43)) .or. (.not. (z <= 4.8d+35))) then
        tmp = (z * x) - z
    else
        tmp = (y * x) - z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -7.6e+43) || !(z <= 4.8e+35)) {
		tmp = (z * x) - z;
	} else {
		tmp = (y * x) - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -7.6e+43) or not (z <= 4.8e+35):
		tmp = (z * x) - z
	else:
		tmp = (y * x) - z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -7.6e+43) || !(z <= 4.8e+35))
		tmp = Float64(Float64(z * x) - z);
	else
		tmp = Float64(Float64(y * x) - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -7.6e+43) || ~((z <= 4.8e+35)))
		tmp = (z * x) - z;
	else
		tmp = (y * x) - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -7.6e+43], N[Not[LessEqual[z, 4.8e+35]], $MachinePrecision]], N[(N[(z * x), $MachinePrecision] - z), $MachinePrecision], N[(N[(y * x), $MachinePrecision] - z), $MachinePrecision]]

Derivation

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

Alternative 4: 57.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.4 \cdot 10^{-159} \lor \neg \left(x \leq 2.6 \cdot 10^{-102}\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.4e-159) (not (<= x 2.6e-102))) (* y x) (- z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.4e-159) || !(x <= 2.6e-102)) {
		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 ((x <= (-2.4d-159)) .or. (.not. (x <= 2.6d-102))) 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 ((x <= -2.4e-159) || !(x <= 2.6e-102)) {
		tmp = y * x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.4e-159) or not (x <= 2.6e-102):
		tmp = y * x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.4e-159) || !(x <= 2.6e-102))
		tmp = Float64(y * x);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.4e-159) || ~((x <= 2.6e-102)))
		tmp = y * x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.4e-159], N[Not[LessEqual[x, 2.6e-102]], $MachinePrecision]], N[(y * x), $MachinePrecision], (-z)]

Derivation

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

Alternative 5: 100.0% accurate, 1.3× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return ((y + z) * 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 = ((y + z) * x) - z
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 6: 75.6% accurate, 1.8× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (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 = (y * x) - z
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 7: 36.1% accurate, 4.5× 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 Float64(-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

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z)
  :name "Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3"
  :precision binary64
  (+ (* x y) (* (- x 1.0) z)))