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

Percentage Accurate: 97.1% → 99.7%

Time: 11.3s

Alternatives: 12

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, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 84.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{z - y}{\frac{-z}{a}}\\ \mathbf{if}\;z \leq -3.9 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.45 \cdot 10^{-35}:\\ \;\;\;\;x - \frac{a}{\frac{t}{y - z}}\\ \mathbf{elif}\;z \leq -1.2 \cdot 10^{-78}:\\ \;\;\;\;a \cdot \frac{z - y}{\left(t - z\right) + 1}\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+77}:\\ \;\;\;\;x - \frac{a}{\left(t + 1\right) \cdot \frac{1}{y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (- z y) (/ (- z) a)))))
   (if (<= z -3.9e+16)
     t_1
     (if (<= z -1.45e-35)
       (- x (/ a (/ t (- y z))))
       (if (<= z -1.2e-78)
         (* a (/ (- z y) (+ (- t z) 1.0)))
         (if (<= z 7.5e+77) (- x (/ a (* (+ t 1.0) (/ 1.0 y)))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((z - y) / (-z / a));
	double tmp;
	if (z <= -3.9e+16) {
		tmp = t_1;
	} else if (z <= -1.45e-35) {
		tmp = x - (a / (t / (y - z)));
	} else if (z <= -1.2e-78) {
		tmp = a * ((z - y) / ((t - z) + 1.0));
	} else if (z <= 7.5e+77) {
		tmp = x - (a / ((t + 1.0) * (1.0 / y)));
	} else {
		tmp = 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 = x + ((z - y) / (-z / a))
    if (z <= (-3.9d+16)) then
        tmp = t_1
    else if (z <= (-1.45d-35)) then
        tmp = x - (a / (t / (y - z)))
    else if (z <= (-1.2d-78)) then
        tmp = a * ((z - y) / ((t - z) + 1.0d0))
    else if (z <= 7.5d+77) then
        tmp = x - (a / ((t + 1.0d0) * (1.0d0 / y)))
    else
        tmp = 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 = x + ((z - y) / (-z / a));
	double tmp;
	if (z <= -3.9e+16) {
		tmp = t_1;
	} else if (z <= -1.45e-35) {
		tmp = x - (a / (t / (y - z)));
	} else if (z <= -1.2e-78) {
		tmp = a * ((z - y) / ((t - z) + 1.0));
	} else if (z <= 7.5e+77) {
		tmp = x - (a / ((t + 1.0) * (1.0 / y)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((z - y) / (-z / a))
	tmp = 0
	if z <= -3.9e+16:
		tmp = t_1
	elif z <= -1.45e-35:
		tmp = x - (a / (t / (y - z)))
	elif z <= -1.2e-78:
		tmp = a * ((z - y) / ((t - z) + 1.0))
	elif z <= 7.5e+77:
		tmp = x - (a / ((t + 1.0) * (1.0 / y)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(z - y) / Float64(Float64(-z) / a)))
	tmp = 0.0
	if (z <= -3.9e+16)
		tmp = t_1;
	elseif (z <= -1.45e-35)
		tmp = Float64(x - Float64(a / Float64(t / Float64(y - z))));
	elseif (z <= -1.2e-78)
		tmp = Float64(a * Float64(Float64(z - y) / Float64(Float64(t - z) + 1.0)));
	elseif (z <= 7.5e+77)
		tmp = Float64(x - Float64(a / Float64(Float64(t + 1.0) * Float64(1.0 / y))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((z - y) / (-z / a));
	tmp = 0.0;
	if (z <= -3.9e+16)
		tmp = t_1;
	elseif (z <= -1.45e-35)
		tmp = x - (a / (t / (y - z)));
	elseif (z <= -1.2e-78)
		tmp = a * ((z - y) / ((t - z) + 1.0));
	elseif (z <= 7.5e+77)
		tmp = x - (a / ((t + 1.0) * (1.0 / y)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(z - y), $MachinePrecision] / N[((-z) / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.9e+16], t$95$1, If[LessEqual[z, -1.45e-35], N[(x - N[(a / N[(t / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.2e-78], N[(a * N[(N[(z - y), $MachinePrecision] / N[(N[(t - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.5e+77], N[(x - N[(a / N[(N[(t + 1.0), $MachinePrecision] * N[(1.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 3: 84.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{z - y}{\frac{-z}{a}}\\ \mathbf{if}\;z \leq -2.4 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.2 \cdot 10^{-35}:\\ \;\;\;\;x - \frac{a}{\frac{t}{y - z}}\\ \mathbf{elif}\;z \leq -1.2 \cdot 10^{-78}:\\ \;\;\;\;a \cdot \frac{z - y}{\left(t - z\right) + 1}\\ \mathbf{elif}\;z \leq 7 \cdot 10^{+77}:\\ \;\;\;\;x - \frac{a}{\frac{t + 1}{y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (- z y) (/ (- z) a)))))
   (if (<= z -2.4e+16)
     t_1
     (if (<= z -2.2e-35)
       (- x (/ a (/ t (- y z))))
       (if (<= z -1.2e-78)
         (* a (/ (- z y) (+ (- t z) 1.0)))
         (if (<= z 7e+77) (- x (/ a (/ (+ t 1.0) y))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((z - y) / (-z / a));
	double tmp;
	if (z <= -2.4e+16) {
		tmp = t_1;
	} else if (z <= -2.2e-35) {
		tmp = x - (a / (t / (y - z)));
	} else if (z <= -1.2e-78) {
		tmp = a * ((z - y) / ((t - z) + 1.0));
	} else if (z <= 7e+77) {
		tmp = x - (a / ((t + 1.0) / y));
	} else {
		tmp = 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 = x + ((z - y) / (-z / a))
    if (z <= (-2.4d+16)) then
        tmp = t_1
    else if (z <= (-2.2d-35)) then
        tmp = x - (a / (t / (y - z)))
    else if (z <= (-1.2d-78)) then
        tmp = a * ((z - y) / ((t - z) + 1.0d0))
    else if (z <= 7d+77) then
        tmp = x - (a / ((t + 1.0d0) / y))
    else
        tmp = 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 = x + ((z - y) / (-z / a));
	double tmp;
	if (z <= -2.4e+16) {
		tmp = t_1;
	} else if (z <= -2.2e-35) {
		tmp = x - (a / (t / (y - z)));
	} else if (z <= -1.2e-78) {
		tmp = a * ((z - y) / ((t - z) + 1.0));
	} else if (z <= 7e+77) {
		tmp = x - (a / ((t + 1.0) / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((z - y) / (-z / a))
	tmp = 0
	if z <= -2.4e+16:
		tmp = t_1
	elif z <= -2.2e-35:
		tmp = x - (a / (t / (y - z)))
	elif z <= -1.2e-78:
		tmp = a * ((z - y) / ((t - z) + 1.0))
	elif z <= 7e+77:
		tmp = x - (a / ((t + 1.0) / y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(z - y) / Float64(Float64(-z) / a)))
	tmp = 0.0
	if (z <= -2.4e+16)
		tmp = t_1;
	elseif (z <= -2.2e-35)
		tmp = Float64(x - Float64(a / Float64(t / Float64(y - z))));
	elseif (z <= -1.2e-78)
		tmp = Float64(a * Float64(Float64(z - y) / Float64(Float64(t - z) + 1.0)));
	elseif (z <= 7e+77)
		tmp = Float64(x - Float64(a / Float64(Float64(t + 1.0) / y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((z - y) / (-z / a));
	tmp = 0.0;
	if (z <= -2.4e+16)
		tmp = t_1;
	elseif (z <= -2.2e-35)
		tmp = x - (a / (t / (y - z)));
	elseif (z <= -1.2e-78)
		tmp = a * ((z - y) / ((t - z) + 1.0));
	elseif (z <= 7e+77)
		tmp = x - (a / ((t + 1.0) / y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(z - y), $MachinePrecision] / N[((-z) / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.4e+16], t$95$1, If[LessEqual[z, -2.2e-35], N[(x - N[(a / N[(t / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.2e-78], N[(a * N[(N[(z - y), $MachinePrecision] / N[(N[(t - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7e+77], N[(x - N[(a / N[(N[(t + 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 4: 72.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \frac{a}{\frac{t}{y - z}}\\ \mathbf{if}\;z \leq -2 \cdot 10^{+15}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq -1.2 \cdot 10^{-36}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-50}:\\ \;\;\;\;x - y \cdot a\\ \mathbf{elif}\;z \leq 9 \cdot 10^{+80}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (/ a (/ t (- y z))))))
   (if (<= z -2e+15)
     (- x a)
     (if (<= z -1.2e-36)
       t_1
       (if (<= z 4.5e-50) (- x (* y a)) (if (<= z 9e+80) t_1 (- x a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (a / (t / (y - z)));
	double tmp;
	if (z <= -2e+15) {
		tmp = x - a;
	} else if (z <= -1.2e-36) {
		tmp = t_1;
	} else if (z <= 4.5e-50) {
		tmp = x - (y * a);
	} else if (z <= 9e+80) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = x - (a / (t / (y - z)))
    if (z <= (-2d+15)) then
        tmp = x - a
    else if (z <= (-1.2d-36)) then
        tmp = t_1
    else if (z <= 4.5d-50) then
        tmp = x - (y * a)
    else if (z <= 9d+80) then
        tmp = t_1
    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 t_1 = x - (a / (t / (y - z)));
	double tmp;
	if (z <= -2e+15) {
		tmp = x - a;
	} else if (z <= -1.2e-36) {
		tmp = t_1;
	} else if (z <= 4.5e-50) {
		tmp = x - (y * a);
	} else if (z <= 9e+80) {
		tmp = t_1;
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - (a / (t / (y - z)))
	tmp = 0
	if z <= -2e+15:
		tmp = x - a
	elif z <= -1.2e-36:
		tmp = t_1
	elif z <= 4.5e-50:
		tmp = x - (y * a)
	elif z <= 9e+80:
		tmp = t_1
	else:
		tmp = x - a
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(a / Float64(t / Float64(y - z))))
	tmp = 0.0
	if (z <= -2e+15)
		tmp = Float64(x - a);
	elseif (z <= -1.2e-36)
		tmp = t_1;
	elseif (z <= 4.5e-50)
		tmp = Float64(x - Float64(y * a));
	elseif (z <= 9e+80)
		tmp = t_1;
	else
		tmp = Float64(x - a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - (a / (t / (y - z)));
	tmp = 0.0;
	if (z <= -2e+15)
		tmp = x - a;
	elseif (z <= -1.2e-36)
		tmp = t_1;
	elseif (z <= 4.5e-50)
		tmp = x - (y * a);
	elseif (z <= 9e+80)
		tmp = t_1;
	else
		tmp = x - a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(a / N[(t / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2e+15], N[(x - a), $MachinePrecision], If[LessEqual[z, -1.2e-36], t$95$1, If[LessEqual[z, 4.5e-50], N[(x - N[(y * a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 9e+80], t$95$1, N[(x - a), $MachinePrecision]]]]]]

Derivation

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

Alternative 5: 86.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{-6} \lor \neg \left(z \leq 1.4 \cdot 10^{+78}\right):\\ \;\;\;\;x + \frac{z - y}{\frac{-z}{a}}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{a}{\frac{t + 1}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.4e-6) (not (<= z 1.4e+78)))
   (+ x (/ (- z y) (/ (- z) a)))
   (- x (/ a (/ (+ t 1.0) y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.4e-6) || !(z <= 1.4e+78)) {
		tmp = x + ((z - y) / (-z / a));
	} else {
		tmp = x - (a / ((t + 1.0) / y));
	}
	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 <= (-4.4d-6)) .or. (.not. (z <= 1.4d+78))) then
        tmp = x + ((z - y) / (-z / a))
    else
        tmp = x - (a / ((t + 1.0d0) / y))
    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 <= -4.4e-6) || !(z <= 1.4e+78)) {
		tmp = x + ((z - y) / (-z / a));
	} else {
		tmp = x - (a / ((t + 1.0) / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.4e-6) or not (z <= 1.4e+78):
		tmp = x + ((z - y) / (-z / a))
	else:
		tmp = x - (a / ((t + 1.0) / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.4e-6) || !(z <= 1.4e+78))
		tmp = Float64(x + Float64(Float64(z - y) / Float64(Float64(-z) / a)));
	else
		tmp = Float64(x - Float64(a / Float64(Float64(t + 1.0) / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -4.4e-6) || ~((z <= 1.4e+78)))
		tmp = x + ((z - y) / (-z / a));
	else
		tmp = x - (a / ((t + 1.0) / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.4e-6], N[Not[LessEqual[z, 1.4e+78]], $MachinePrecision]], N[(x + N[(N[(z - y), $MachinePrecision] / N[((-z) / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(a / N[(N[(t + 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 83.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{-6} \lor \neg \left(z \leq 9.5 \cdot 10^{+77}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - \frac{a}{\frac{t + 1}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.4e-6) (not (<= z 9.5e+77)))
   (- x a)
   (- x (/ a (/ (+ t 1.0) y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.4e-6) || !(z <= 9.5e+77)) {
		tmp = x - a;
	} else {
		tmp = x - (a / ((t + 1.0) / y));
	}
	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 <= (-4.4d-6)) .or. (.not. (z <= 9.5d+77))) then
        tmp = x - a
    else
        tmp = x - (a / ((t + 1.0d0) / y))
    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 <= -4.4e-6) || !(z <= 9.5e+77)) {
		tmp = x - a;
	} else {
		tmp = x - (a / ((t + 1.0) / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.4e-6) or not (z <= 9.5e+77):
		tmp = x - a
	else:
		tmp = x - (a / ((t + 1.0) / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.4e-6) || !(z <= 9.5e+77))
		tmp = Float64(x - a);
	else
		tmp = Float64(x - Float64(a / Float64(Float64(t + 1.0) / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -4.4e-6) || ~((z <= 9.5e+77)))
		tmp = x - a;
	else
		tmp = x - (a / ((t + 1.0) / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.4e-6], N[Not[LessEqual[z, 9.5e+77]], $MachinePrecision]], N[(x - a), $MachinePrecision], N[(x - N[(a / N[(N[(t + 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 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 8: 64.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{-6}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq 1.02 \cdot 10^{-254}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{-210}:\\ \;\;\;\;y \cdot \left(-a\right)\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+79}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -4.4e-6)
   (- x a)
   (if (<= z 1.02e-254)
     x
     (if (<= z 1.3e-210) (* y (- a)) (if (<= z 1.05e+79) x (- x a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4.4e-6) {
		tmp = x - a;
	} else if (z <= 1.02e-254) {
		tmp = x;
	} else if (z <= 1.3e-210) {
		tmp = y * -a;
	} else if (z <= 1.05e+79) {
		tmp = x;
	} 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 <= (-4.4d-6)) then
        tmp = x - a
    else if (z <= 1.02d-254) then
        tmp = x
    else if (z <= 1.3d-210) then
        tmp = y * -a
    else if (z <= 1.05d+79) then
        tmp = x
    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 <= -4.4e-6) {
		tmp = x - a;
	} else if (z <= 1.02e-254) {
		tmp = x;
	} else if (z <= 1.3e-210) {
		tmp = y * -a;
	} else if (z <= 1.05e+79) {
		tmp = x;
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -4.4e-6:
		tmp = x - a
	elif z <= 1.02e-254:
		tmp = x
	elif z <= 1.3e-210:
		tmp = y * -a
	elif z <= 1.05e+79:
		tmp = x
	else:
		tmp = x - a
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -4.4e-6)
		tmp = Float64(x - a);
	elseif (z <= 1.02e-254)
		tmp = x;
	elseif (z <= 1.3e-210)
		tmp = Float64(y * Float64(-a));
	elseif (z <= 1.05e+79)
		tmp = x;
	else
		tmp = Float64(x - a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -4.4e-6)
		tmp = x - a;
	elseif (z <= 1.02e-254)
		tmp = x;
	elseif (z <= 1.3e-210)
		tmp = y * -a;
	elseif (z <= 1.05e+79)
		tmp = x;
	else
		tmp = x - a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -4.4e-6], N[(x - a), $MachinePrecision], If[LessEqual[z, 1.02e-254], x, If[LessEqual[z, 1.3e-210], N[(y * (-a)), $MachinePrecision], If[LessEqual[z, 1.05e+79], x, N[(x - a), $MachinePrecision]]]]]

Derivation

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

Alternative 9: 71.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{+14}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-29}:\\ \;\;\;\;x + z \cdot \frac{a}{t}\\ \mathbf{elif}\;z \leq 1.86 \cdot 10^{+71}:\\ \;\;\;\;x - y \cdot a\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -6.5e+14)
   (- x a)
   (if (<= z -3.8e-29)
     (+ x (* z (/ a t)))
     (if (<= z 1.86e+71) (- x (* y a)) (- x a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -6.5e+14) {
		tmp = x - a;
	} else if (z <= -3.8e-29) {
		tmp = x + (z * (a / t));
	} else if (z <= 1.86e+71) {
		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 <= (-6.5d+14)) then
        tmp = x - a
    else if (z <= (-3.8d-29)) then
        tmp = x + (z * (a / t))
    else if (z <= 1.86d+71) 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 <= -6.5e+14) {
		tmp = x - a;
	} else if (z <= -3.8e-29) {
		tmp = x + (z * (a / t));
	} else if (z <= 1.86e+71) {
		tmp = x - (y * a);
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -6.5e+14:
		tmp = x - a
	elif z <= -3.8e-29:
		tmp = x + (z * (a / t))
	elif z <= 1.86e+71:
		tmp = x - (y * a)
	else:
		tmp = x - a
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -6.5e+14)
		tmp = Float64(x - a);
	elseif (z <= -3.8e-29)
		tmp = Float64(x + Float64(z * Float64(a / t)));
	elseif (z <= 1.86e+71)
		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 <= -6.5e+14)
		tmp = x - a;
	elseif (z <= -3.8e-29)
		tmp = x + (z * (a / t));
	elseif (z <= 1.86e+71)
		tmp = x - (y * a);
	else
		tmp = x - a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -6.5e+14], N[(x - a), $MachinePrecision], If[LessEqual[z, -3.8e-29], N[(x + N[(z * N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.86e+71], N[(x - N[(y * a), $MachinePrecision]), $MachinePrecision], N[(x - a), $MachinePrecision]]]]

Derivation

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

Alternative 10: 71.3% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.6 \cdot 10^{-28} \lor \neg \left(z \leq 2.9 \cdot 10^{+71}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.6e-28) (not (<= z 2.9e+71))) (- x a) (- x (* y a))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.6e-28) || !(z <= 2.9e+71)) {
		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 <= (-1.6d-28)) .or. (.not. (z <= 2.9d+71))) 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 <= -1.6e-28) || !(z <= 2.9e+71)) {
		tmp = x - a;
	} else {
		tmp = x - (y * a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.6e-28) or not (z <= 2.9e+71):
		tmp = x - a
	else:
		tmp = x - (y * a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.6e-28) || !(z <= 2.9e+71))
		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 <= -1.6e-28) || ~((z <= 2.9e+71)))
		tmp = x - a;
	else
		tmp = x - (y * a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.6e-28], N[Not[LessEqual[z, 2.9e+71]], $MachinePrecision]], N[(x - a), $MachinePrecision], N[(x - N[(y * a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 65.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{-6} \lor \neg \left(z \leq 1.05 \cdot 10^{+79}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.4e-6) (not (<= z 1.05e+79))) (- x a) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.4e-6) || !(z <= 1.05e+79)) {
		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 <= (-4.4d-6)) .or. (.not. (z <= 1.05d+79))) 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 <= -4.4e-6) || !(z <= 1.05e+79)) {
		tmp = x - a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.4e-6) or not (z <= 1.05e+79):
		tmp = x - a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.4e-6) || !(z <= 1.05e+79))
		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 <= -4.4e-6) || ~((z <= 1.05e+79)))
		tmp = x - a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.4e-6], N[Not[LessEqual[z, 1.05e+79]], $MachinePrecision]], N[(x - a), $MachinePrecision], x]

Derivation

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

Alternative 12: 53.2% 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 2023350 
(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))))