Graphics.Rendering.Chart.SparkLine:renderSparkLine from Chart-1.5.3

Percentage Accurate: 97.1% → 99.7%

Time: 8.6s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y z t a)
 :precision binary64
 (- x (/ (- y z) (/ (+ (- t z) 1.0) a))))

C

double code(double x, double y, double z, double t, double a) {
	return x - ((y - z) / (((t - z) + 1.0) / a));
}

Fortran

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

Java

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

Python

def code(x, y, z, t, a):
	return x - ((y - z) / (((t - z) + 1.0) / a))

Julia

function code(x, y, z, t, a)
	return Float64(x - Float64(Float64(y - z) / Float64(Float64(Float64(t - z) + 1.0) / a)))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = x - ((y - z) / (((t - z) + 1.0) / a));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(x - N[(N[(y - z), $MachinePrecision] / N[(N[(N[(t - z), $MachinePrecision] + 1.0), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 97.1% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t a)
 :precision binary64
 (- x (/ (- y z) (/ (+ (- t z) 1.0) a))))

C

double code(double x, double y, double z, double t, double a) {
	return x - ((y - z) / (((t - z) + 1.0) / a));
}

Fortran

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

Java

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

Python

def code(x, y, z, t, a):
	return x - ((y - z) / (((t - z) + 1.0) / a))

Julia

function code(x, y, z, t, a)
	return Float64(x - Float64(Float64(y - z) / Float64(Float64(Float64(t - z) + 1.0) / a)))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = x - ((y - z) / (((t - z) + 1.0) / a));
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(x - N[(N[(y - z), $MachinePrecision] / N[(N[(N[(t - z), $MachinePrecision] + 1.0), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 89.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.8 \cdot 10^{+25} \lor \neg \left(z \leq 3.7 \cdot 10^{+40}\right):\\ \;\;\;\;x + \left(\frac{a}{\frac{z}{y}} - a\right)\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{a}{t + \left(1 - z\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.8e+25) (not (<= z 3.7e+40)))
   (+ x (- (/ a (/ z y)) a))
   (- x (* y (/ a (+ t (- 1.0 z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.8e+25) || !(z <= 3.7e+40)) {
		tmp = x + ((a / (z / y)) - a);
	} else {
		tmp = x - (y * (a / (t + (1.0 - z))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-4.8d+25)) .or. (.not. (z <= 3.7d+40))) then
        tmp = x + ((a / (z / y)) - a)
    else
        tmp = x - (y * (a / (t + (1.0d0 - z))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.8e+25) || !(z <= 3.7e+40)) {
		tmp = x + ((a / (z / y)) - a);
	} else {
		tmp = x - (y * (a / (t + (1.0 - z))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.8e+25) or not (z <= 3.7e+40):
		tmp = x + ((a / (z / y)) - a)
	else:
		tmp = x - (y * (a / (t + (1.0 - z))))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.8e+25) || !(z <= 3.7e+40))
		tmp = Float64(x + Float64(Float64(a / Float64(z / y)) - a));
	else
		tmp = Float64(x - Float64(y * Float64(a / Float64(t + Float64(1.0 - z)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -4.8e+25) || ~((z <= 3.7e+40)))
		tmp = x + ((a / (z / y)) - a);
	else
		tmp = x - (y * (a / (t + (1.0 - z))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.8e+25], N[Not[LessEqual[z, 3.7e+40]], $MachinePrecision]], N[(x + N[(N[(a / N[(z / y), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(a / N[(t + N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 3: 89.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.6 \cdot 10^{+25}:\\ \;\;\;\;x + a \cdot \left(-1 + \frac{y + -1}{z}\right)\\ \mathbf{elif}\;z \leq 10^{+42}:\\ \;\;\;\;x - y \cdot \frac{a}{t + \left(1 - z\right)}\\ \mathbf{else}:\\ \;\;\;\;x + \left(\frac{a}{\frac{z}{y}} - a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -5.6e+25)
   (+ x (* a (+ -1.0 (/ (+ y -1.0) z))))
   (if (<= z 1e+42)
     (- x (* y (/ a (+ t (- 1.0 z)))))
     (+ x (- (/ a (/ z y)) a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5.6e+25) {
		tmp = x + (a * (-1.0 + ((y + -1.0) / z)));
	} else if (z <= 1e+42) {
		tmp = x - (y * (a / (t + (1.0 - z))));
	} else {
		tmp = x + ((a / (z / y)) - a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-5.6d+25)) then
        tmp = x + (a * ((-1.0d0) + ((y + (-1.0d0)) / z)))
    else if (z <= 1d+42) then
        tmp = x - (y * (a / (t + (1.0d0 - z))))
    else
        tmp = x + ((a / (z / y)) - a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5.6e+25) {
		tmp = x + (a * (-1.0 + ((y + -1.0) / z)));
	} else if (z <= 1e+42) {
		tmp = x - (y * (a / (t + (1.0 - z))));
	} else {
		tmp = x + ((a / (z / y)) - a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -5.6e+25:
		tmp = x + (a * (-1.0 + ((y + -1.0) / z)))
	elif z <= 1e+42:
		tmp = x - (y * (a / (t + (1.0 - z))))
	else:
		tmp = x + ((a / (z / y)) - a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -5.6e+25)
		tmp = Float64(x + Float64(a * Float64(-1.0 + Float64(Float64(y + -1.0) / z))));
	elseif (z <= 1e+42)
		tmp = Float64(x - Float64(y * Float64(a / Float64(t + Float64(1.0 - z)))));
	else
		tmp = Float64(x + Float64(Float64(a / Float64(z / y)) - a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -5.6e+25)
		tmp = x + (a * (-1.0 + ((y + -1.0) / z)));
	elseif (z <= 1e+42)
		tmp = x - (y * (a / (t + (1.0 - z))));
	else
		tmp = x + ((a / (z / y)) - a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -5.6e+25], N[(x + N[(a * N[(-1.0 + N[(N[(y + -1.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1e+42], N[(x - N[(y * N[(a / N[(t + N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(a / N[(z / y), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 4: 90.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.9 \cdot 10^{+83}:\\ \;\;\;\;x + a \cdot \frac{z - y}{t}\\ \mathbf{elif}\;t \leq 1.62 \cdot 10^{-7}:\\ \;\;\;\;x + a \cdot \frac{z - y}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{a}{t + \left(1 - z\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -3.9e+83)
   (+ x (* a (/ (- z y) t)))
   (if (<= t 1.62e-7)
     (+ x (* a (/ (- z y) (- 1.0 z))))
     (- x (* y (/ a (+ t (- 1.0 z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -3.9e+83) {
		tmp = x + (a * ((z - y) / t));
	} else if (t <= 1.62e-7) {
		tmp = x + (a * ((z - y) / (1.0 - z)));
	} else {
		tmp = x - (y * (a / (t + (1.0 - z))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-3.9d+83)) then
        tmp = x + (a * ((z - y) / t))
    else if (t <= 1.62d-7) then
        tmp = x + (a * ((z - y) / (1.0d0 - z)))
    else
        tmp = x - (y * (a / (t + (1.0d0 - z))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -3.9e+83) {
		tmp = x + (a * ((z - y) / t));
	} else if (t <= 1.62e-7) {
		tmp = x + (a * ((z - y) / (1.0 - z)));
	} else {
		tmp = x - (y * (a / (t + (1.0 - z))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -3.9e+83:
		tmp = x + (a * ((z - y) / t))
	elif t <= 1.62e-7:
		tmp = x + (a * ((z - y) / (1.0 - z)))
	else:
		tmp = x - (y * (a / (t + (1.0 - z))))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -3.9e+83)
		tmp = Float64(x + Float64(a * Float64(Float64(z - y) / t)));
	elseif (t <= 1.62e-7)
		tmp = Float64(x + Float64(a * Float64(Float64(z - y) / Float64(1.0 - z))));
	else
		tmp = Float64(x - Float64(y * Float64(a / Float64(t + Float64(1.0 - z)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -3.9e+83)
		tmp = x + (a * ((z - y) / t));
	elseif (t <= 1.62e-7)
		tmp = x + (a * ((z - y) / (1.0 - z)));
	else
		tmp = x - (y * (a / (t + (1.0 - z))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -3.9e+83], N[(x + N[(a * N[(N[(z - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.62e-7], N[(x + N[(a * N[(N[(z - y), $MachinePrecision] / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(a / N[(t + N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 5: 89.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t + \left(1 - z\right)\\ \mathbf{if}\;t \leq -3.3 \cdot 10^{+40}:\\ \;\;\;\;x + a \cdot \frac{z}{t_1}\\ \mathbf{elif}\;t \leq 1.62 \cdot 10^{-7}:\\ \;\;\;\;x + a \cdot \frac{z - y}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{a}{t_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ t (- 1.0 z))))
   (if (<= t -3.3e+40)
     (+ x (* a (/ z t_1)))
     (if (<= t 1.62e-7)
       (+ x (* a (/ (- z y) (- 1.0 z))))
       (- x (* y (/ a t_1)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t + (1.0 - z);
	double tmp;
	if (t <= -3.3e+40) {
		tmp = x + (a * (z / t_1));
	} else if (t <= 1.62e-7) {
		tmp = x + (a * ((z - y) / (1.0 - z)));
	} else {
		tmp = x - (y * (a / t_1));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t + (1.0d0 - z)
    if (t <= (-3.3d+40)) then
        tmp = x + (a * (z / t_1))
    else if (t <= 1.62d-7) then
        tmp = x + (a * ((z - y) / (1.0d0 - z)))
    else
        tmp = x - (y * (a / t_1))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t + (1.0 - z);
	double tmp;
	if (t <= -3.3e+40) {
		tmp = x + (a * (z / t_1));
	} else if (t <= 1.62e-7) {
		tmp = x + (a * ((z - y) / (1.0 - z)));
	} else {
		tmp = x - (y * (a / t_1));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t + (1.0 - z)
	tmp = 0
	if t <= -3.3e+40:
		tmp = x + (a * (z / t_1))
	elif t <= 1.62e-7:
		tmp = x + (a * ((z - y) / (1.0 - z)))
	else:
		tmp = x - (y * (a / t_1))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t + Float64(1.0 - z))
	tmp = 0.0
	if (t <= -3.3e+40)
		tmp = Float64(x + Float64(a * Float64(z / t_1)));
	elseif (t <= 1.62e-7)
		tmp = Float64(x + Float64(a * Float64(Float64(z - y) / Float64(1.0 - z))));
	else
		tmp = Float64(x - Float64(y * Float64(a / t_1)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t + (1.0 - z);
	tmp = 0.0;
	if (t <= -3.3e+40)
		tmp = x + (a * (z / t_1));
	elseif (t <= 1.62e-7)
		tmp = x + (a * ((z - y) / (1.0 - z)));
	else
		tmp = x - (y * (a / t_1));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t + N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.3e+40], N[(x + N[(a * N[(z / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.62e-7], N[(x + N[(a * N[(N[(z - y), $MachinePrecision] / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(a / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 6: 72.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.2 \cdot 10^{+157} \lor \neg \left(t \leq 1.62 \cdot 10^{-7}\right):\\ \;\;\;\;x - a \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{a}{-1 + \frac{1}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -3.2e+157) (not (<= t 1.62e-7)))
   (- x (* a (/ y t)))
   (+ x (/ a (+ -1.0 (/ 1.0 z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -3.2e+157) || !(t <= 1.62e-7)) {
		tmp = x - (a * (y / t));
	} else {
		tmp = x + (a / (-1.0 + (1.0 / z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((t <= (-3.2d+157)) .or. (.not. (t <= 1.62d-7))) then
        tmp = x - (a * (y / t))
    else
        tmp = x + (a / ((-1.0d0) + (1.0d0 / z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -3.2e+157) || !(t <= 1.62e-7)) {
		tmp = x - (a * (y / t));
	} else {
		tmp = x + (a / (-1.0 + (1.0 / z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -3.2e+157) or not (t <= 1.62e-7):
		tmp = x - (a * (y / t))
	else:
		tmp = x + (a / (-1.0 + (1.0 / z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -3.2e+157) || !(t <= 1.62e-7))
		tmp = Float64(x - Float64(a * Float64(y / t)));
	else
		tmp = Float64(x + Float64(a / Float64(-1.0 + Float64(1.0 / z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -3.2e+157) || ~((t <= 1.62e-7)))
		tmp = x - (a * (y / t));
	else
		tmp = x + (a / (-1.0 + (1.0 / z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -3.2e+157], N[Not[LessEqual[t, 1.62e-7]], $MachinePrecision]], N[(x - N[(a * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a / N[(-1.0 + N[(1.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 89.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.6 \cdot 10^{+21} \lor \neg \left(z \leq 3.3 \cdot 10^{+40}\right):\\ \;\;\;\;x + \left(\frac{a}{\frac{z}{y}} - a\right)\\ \mathbf{else}:\\ \;\;\;\;x - a \cdot \frac{y}{t + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -2.6e+21) (not (<= z 3.3e+40)))
   (+ x (- (/ a (/ z y)) a))
   (- x (* a (/ y (+ t 1.0))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.6e+21) || !(z <= 3.3e+40)) {
		tmp = x + ((a / (z / y)) - a);
	} else {
		tmp = x - (a * (y / (t + 1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-2.6d+21)) .or. (.not. (z <= 3.3d+40))) then
        tmp = x + ((a / (z / y)) - a)
    else
        tmp = x - (a * (y / (t + 1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.6e+21) || !(z <= 3.3e+40)) {
		tmp = x + ((a / (z / y)) - a);
	} else {
		tmp = x - (a * (y / (t + 1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -2.6e+21) or not (z <= 3.3e+40):
		tmp = x + ((a / (z / y)) - a)
	else:
		tmp = x - (a * (y / (t + 1.0)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -2.6e+21) || !(z <= 3.3e+40))
		tmp = Float64(x + Float64(Float64(a / Float64(z / y)) - a));
	else
		tmp = Float64(x - Float64(a * Float64(y / Float64(t + 1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -2.6e+21) || ~((z <= 3.3e+40)))
		tmp = x + ((a / (z / y)) - a);
	else
		tmp = x - (a * (y / (t + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -2.6e+21], N[Not[LessEqual[z, 3.3e+40]], $MachinePrecision]], N[(x + N[(N[(a / N[(z / y), $MachinePrecision]), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision], N[(x - N[(a * N[(y / N[(t + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 84.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.95 \cdot 10^{+66}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq 0.00065:\\ \;\;\;\;x - a \cdot \frac{y}{t + 1}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{a}{-1 + \frac{1}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.95e+66)
   (- x a)
   (if (<= z 0.00065)
     (- x (* a (/ y (+ t 1.0))))
     (+ x (/ a (+ -1.0 (/ 1.0 z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.95e+66) {
		tmp = x - a;
	} else if (z <= 0.00065) {
		tmp = x - (a * (y / (t + 1.0)));
	} else {
		tmp = x + (a / (-1.0 + (1.0 / z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-1.95d+66)) then
        tmp = x - a
    else if (z <= 0.00065d0) then
        tmp = x - (a * (y / (t + 1.0d0)))
    else
        tmp = x + (a / ((-1.0d0) + (1.0d0 / z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.95e+66) {
		tmp = x - a;
	} else if (z <= 0.00065) {
		tmp = x - (a * (y / (t + 1.0)));
	} else {
		tmp = x + (a / (-1.0 + (1.0 / z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.95e+66:
		tmp = x - a
	elif z <= 0.00065:
		tmp = x - (a * (y / (t + 1.0)))
	else:
		tmp = x + (a / (-1.0 + (1.0 / z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.95e+66)
		tmp = Float64(x - a);
	elseif (z <= 0.00065)
		tmp = Float64(x - Float64(a * Float64(y / Float64(t + 1.0))));
	else
		tmp = Float64(x + Float64(a / Float64(-1.0 + Float64(1.0 / z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.95e+66)
		tmp = x - a;
	elseif (z <= 0.00065)
		tmp = x - (a * (y / (t + 1.0)));
	else
		tmp = x + (a / (-1.0 + (1.0 / z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.95e+66], N[(x - a), $MachinePrecision], If[LessEqual[z, 0.00065], N[(x - N[(a * N[(y / N[(t + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a / N[(-1.0 + N[(1.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 9: 71.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+21}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{-147}:\\ \;\;\;\;x - y \cdot \frac{a}{t}\\ \mathbf{elif}\;z \leq 3.5:\\ \;\;\;\;x - y \cdot a\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.3e+21)
   (- x a)
   (if (<= z 1.9e-147)
     (- x (* y (/ a t)))
     (if (<= z 3.5) (- x (* y a)) (- x a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.3e+21) {
		tmp = x - a;
	} else if (z <= 1.9e-147) {
		tmp = x - (y * (a / t));
	} else if (z <= 3.5) {
		tmp = x - (y * a);
	} else {
		tmp = x - a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-2.3d+21)) then
        tmp = x - a
    else if (z <= 1.9d-147) then
        tmp = x - (y * (a / t))
    else if (z <= 3.5d0) then
        tmp = x - (y * a)
    else
        tmp = x - a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.3e+21) {
		tmp = x - a;
	} else if (z <= 1.9e-147) {
		tmp = x - (y * (a / t));
	} else if (z <= 3.5) {
		tmp = x - (y * a);
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.3e+21:
		tmp = x - a
	elif z <= 1.9e-147:
		tmp = x - (y * (a / t))
	elif z <= 3.5:
		tmp = x - (y * a)
	else:
		tmp = x - a
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.3e+21)
		tmp = Float64(x - a);
	elseif (z <= 1.9e-147)
		tmp = Float64(x - Float64(y * Float64(a / t)));
	elseif (z <= 3.5)
		tmp = Float64(x - Float64(y * a));
	else
		tmp = Float64(x - a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.3e+21)
		tmp = x - a;
	elseif (z <= 1.9e-147)
		tmp = x - (y * (a / t));
	elseif (z <= 3.5)
		tmp = x - (y * a);
	else
		tmp = x - a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.3e+21], N[(x - a), $MachinePrecision], If[LessEqual[z, 1.9e-147], N[(x - N[(y * N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.5], N[(x - N[(y * a), $MachinePrecision]), $MachinePrecision], N[(x - a), $MachinePrecision]]]]

Derivation

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

Alternative 10: 71.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.6 \cdot 10^{+21}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq 8.5 \cdot 10^{-148}:\\ \;\;\;\;x - a \cdot \frac{y}{t}\\ \mathbf{elif}\;z \leq 1.1:\\ \;\;\;\;x - y \cdot a\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -6.6e+21)
   (- x a)
   (if (<= z 8.5e-148)
     (- x (* a (/ y t)))
     (if (<= z 1.1) (- x (* y a)) (- x a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -6.6e+21) {
		tmp = x - a;
	} else if (z <= 8.5e-148) {
		tmp = x - (a * (y / t));
	} else if (z <= 1.1) {
		tmp = x - (y * a);
	} else {
		tmp = x - a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-6.6d+21)) then
        tmp = x - a
    else if (z <= 8.5d-148) then
        tmp = x - (a * (y / t))
    else if (z <= 1.1d0) then
        tmp = x - (y * a)
    else
        tmp = x - a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -6.6e+21) {
		tmp = x - a;
	} else if (z <= 8.5e-148) {
		tmp = x - (a * (y / t));
	} else if (z <= 1.1) {
		tmp = x - (y * a);
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -6.6e+21:
		tmp = x - a
	elif z <= 8.5e-148:
		tmp = x - (a * (y / t))
	elif z <= 1.1:
		tmp = x - (y * a)
	else:
		tmp = x - a
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -6.6e+21)
		tmp = Float64(x - a);
	elseif (z <= 8.5e-148)
		tmp = Float64(x - Float64(a * Float64(y / t)));
	elseif (z <= 1.1)
		tmp = Float64(x - Float64(y * a));
	else
		tmp = Float64(x - a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -6.6e+21)
		tmp = x - a;
	elseif (z <= 8.5e-148)
		tmp = x - (a * (y / t));
	elseif (z <= 1.1)
		tmp = x - (y * a);
	else
		tmp = x - a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -6.6e+21], N[(x - a), $MachinePrecision], If[LessEqual[z, 8.5e-148], N[(x - N[(a * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.1], N[(x - N[(y * a), $MachinePrecision]), $MachinePrecision], N[(x - a), $MachinePrecision]]]]

Derivation

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

Alternative 11: 72.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.45 \cdot 10^{+66} \lor \neg \left(z \leq 14000\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.45e+66) (not (<= z 14000.0))) (- x a) (- x (* y a))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.45e+66) || !(z <= 14000.0)) {
		tmp = x - a;
	} else {
		tmp = x - (y * a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-1.45d+66)) .or. (.not. (z <= 14000.0d0))) then
        tmp = x - a
    else
        tmp = x - (y * a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.45e+66) || !(z <= 14000.0)) {
		tmp = x - a;
	} else {
		tmp = x - (y * a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.45e+66) or not (z <= 14000.0):
		tmp = x - a
	else:
		tmp = x - (y * a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.45e+66) || !(z <= 14000.0))
		tmp = Float64(x - a);
	else
		tmp = Float64(x - Float64(y * a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.45e+66) || ~((z <= 14000.0)))
		tmp = x - a;
	else
		tmp = x - (y * a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.45e+66], N[Not[LessEqual[z, 14000.0]], $MachinePrecision]], N[(x - a), $MachinePrecision], N[(x - N[(y * a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 66.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -22.5 \lor \neg \left(z \leq 7200\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -22.5) (not (<= z 7200.0))) (- x a) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -22.5) || !(z <= 7200.0)) {
		tmp = x - a;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-22.5d0)) .or. (.not. (z <= 7200.0d0))) then
        tmp = x - a
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -22.5) || !(z <= 7200.0)) {
		tmp = x - a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -22.5) or not (z <= 7200.0):
		tmp = x - a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -22.5) || !(z <= 7200.0))
		tmp = Float64(x - a);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -22.5) || ~((z <= 7200.0)))
		tmp = x - a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -22.5], N[Not[LessEqual[z, 7200.0]], $MachinePrecision]], N[(x - a), $MachinePrecision], x]

Derivation

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

Alternative 13: 53.4% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := x

Derivation

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

Developer target: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Chart.SparkLine:renderSparkLine from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (- x (* (/ (- y z) (+ (- t z) 1.0)) a))

  (- x (/ (- y z) (/ (+ (- t z) 1.0) a))))