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

Percentage Accurate: 97.2% → 99.7%

Time: 14.8s

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: 97.2% 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

1. Initial program 95.3%

   \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
2. Step-by-step derivation

   1. associate-/r/ 99.1%

      \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
4. Add Preprocessing

5. Final simplification 99.1%

   \[\leadsto x + a \cdot \frac{z - y}{\left(t - z\right) + 1} \]
6. Add Preprocessing

Alternative 2: 61.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \frac{y}{-1 - t}\\ \mathbf{if}\;y \leq -9 \cdot 10^{+154}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.05 \cdot 10^{-212}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-256}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.75 \cdot 10^{+225}:\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* a (/ y (- -1.0 t)))))
   (if (<= y -9e+154)
     t_1
     (if (<= y -1.05e-212)
       (- x a)
       (if (<= y 1.4e-256) x (if (<= y 2.75e+225) (- x a) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = a * (y / (-1.0 - t));
	double tmp;
	if (y <= -9e+154) {
		tmp = t_1;
	} else if (y <= -1.05e-212) {
		tmp = x - a;
	} else if (y <= 1.4e-256) {
		tmp = x;
	} else if (y <= 2.75e+225) {
		tmp = x - a;
	} 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 = a * (y / ((-1.0d0) - t))
    if (y <= (-9d+154)) then
        tmp = t_1
    else if (y <= (-1.05d-212)) then
        tmp = x - a
    else if (y <= 1.4d-256) then
        tmp = x
    else if (y <= 2.75d+225) then
        tmp = x - a
    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 = a * (y / (-1.0 - t));
	double tmp;
	if (y <= -9e+154) {
		tmp = t_1;
	} else if (y <= -1.05e-212) {
		tmp = x - a;
	} else if (y <= 1.4e-256) {
		tmp = x;
	} else if (y <= 2.75e+225) {
		tmp = x - a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = a * (y / (-1.0 - t))
	tmp = 0
	if y <= -9e+154:
		tmp = t_1
	elif y <= -1.05e-212:
		tmp = x - a
	elif y <= 1.4e-256:
		tmp = x
	elif y <= 2.75e+225:
		tmp = x - a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(a * Float64(y / Float64(-1.0 - t)))
	tmp = 0.0
	if (y <= -9e+154)
		tmp = t_1;
	elseif (y <= -1.05e-212)
		tmp = Float64(x - a);
	elseif (y <= 1.4e-256)
		tmp = x;
	elseif (y <= 2.75e+225)
		tmp = Float64(x - a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = a * (y / (-1.0 - t));
	tmp = 0.0;
	if (y <= -9e+154)
		tmp = t_1;
	elseif (y <= -1.05e-212)
		tmp = x - a;
	elseif (y <= 1.4e-256)
		tmp = x;
	elseif (y <= 2.75e+225)
		tmp = x - a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(a * N[(y / N[(-1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -9e+154], t$95$1, If[LessEqual[y, -1.05e-212], N[(x - a), $MachinePrecision], If[LessEqual[y, 1.4e-256], x, If[LessEqual[y, 2.75e+225], N[(x - a), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -9.00000000000000018e154 or 2.74999999999999993e225 < y

   1. Initial program 86.7%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.7%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 81.3%

      \[\leadsto \color{blue}{a \cdot \left(\frac{z}{\left(1 + t\right) - z} - \frac{y}{\left(1 + t\right) - z}\right)} \]
   6. Step-by-step derivation

      1. associate--l+ 81.3%

         \[\leadsto a \cdot \left(\frac{z}{\color{blue}{1 + \left(t - z\right)}} - \frac{y}{\left(1 + t\right) - z}\right) \]

      2. +-commutative 81.3%

         \[\leadsto a \cdot \left(\frac{z}{\color{blue}{\left(t - z\right) + 1}} - \frac{y}{\left(1 + t\right) - z}\right) \]

      3. associate--l+ 81.3%

         \[\leadsto a \cdot \left(\frac{z}{\left(t - z\right) + 1} - \frac{y}{\color{blue}{1 + \left(t - z\right)}}\right) \]

      4. +-commutative 81.3%

         \[\leadsto a \cdot \left(\frac{z}{\left(t - z\right) + 1} - \frac{y}{\color{blue}{\left(t - z\right) + 1}}\right) \]

      5. div-sub 81.3%

         \[\leadsto a \cdot \color{blue}{\frac{z - y}{\left(t - z\right) + 1}} \]

      6. +-commutative 81.3%

         \[\leadsto a \cdot \frac{z - y}{\color{blue}{1 + \left(t - z\right)}} \]

   7. Simplified 81.3%

      \[\leadsto \color{blue}{a \cdot \frac{z - y}{1 + \left(t - z\right)}} \]

   8. Taylor expanded in z around 0 56.3%

      \[\leadsto a \cdot \color{blue}{\left(-1 \cdot \frac{y}{1 + t}\right)} \]
   9. Step-by-step derivation

      1. mul-1-neg 56.3%

         \[\leadsto a \cdot \color{blue}{\left(-\frac{y}{1 + t}\right)} \]

      2. distribute-neg-frac 56.3%

         \[\leadsto a \cdot \color{blue}{\frac{-y}{1 + t}} \]

   10. Simplified 56.3%

       \[\leadsto a \cdot \color{blue}{\frac{-y}{1 + t}} \]
   11. Step-by-step derivation

       1. frac-2neg 56.3%

          \[\leadsto a \cdot \color{blue}{\frac{-\left(-y\right)}{-\left(1 + t\right)}} \]

       2. div-inv 56.1%

          \[\leadsto a \cdot \color{blue}{\left(\left(-\left(-y\right)\right) \cdot \frac{1}{-\left(1 + t\right)}\right)} \]

       3. remove-double-neg 56.1%

          \[\leadsto a \cdot \left(\color{blue}{y} \cdot \frac{1}{-\left(1 + t\right)}\right) \]

       4. +-commutative 56.1%

          \[\leadsto a \cdot \left(y \cdot \frac{1}{-\color{blue}{\left(t + 1\right)}}\right) \]

       5. distribute-neg-in 56.1%

          \[\leadsto a \cdot \left(y \cdot \frac{1}{\color{blue}{\left(-t\right) + \left(-1\right)}}\right) \]

       6. metadata-eval 56.1%

          \[\leadsto a \cdot \left(y \cdot \frac{1}{\left(-t\right) + \color{blue}{-1}}\right) \]

   12. Applied egg-rr 56.1%

       \[\leadsto a \cdot \color{blue}{\left(y \cdot \frac{1}{\left(-t\right) + -1}\right)} \]
   13. Step-by-step derivation

       1. associate-*r/ 56.3%

          \[\leadsto a \cdot \color{blue}{\frac{y \cdot 1}{\left(-t\right) + -1}} \]

       2. *-rgt-identity 56.3%

          \[\leadsto a \cdot \frac{\color{blue}{y}}{\left(-t\right) + -1} \]

       3. +-commutative 56.3%

          \[\leadsto a \cdot \frac{y}{\color{blue}{-1 + \left(-t\right)}} \]

       4. unsub-neg 56.3%

          \[\leadsto a \cdot \frac{y}{\color{blue}{-1 - t}} \]

   14. Simplified 56.3%

       \[\leadsto a \cdot \color{blue}{\frac{y}{-1 - t}} \]

   if -9.00000000000000018e154 < y < -1.05e-212 or 1.40000000000000012e-256 < y < 2.74999999999999993e225

   1. Initial program 97.1%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.4%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.4%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 71.5%

      \[\leadsto x - \color{blue}{a} \]

   if -1.05e-212 < y < 1.40000000000000012e-256

   1. Initial program 99.8%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 97.1%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 97.1%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 67.9%

      \[\leadsto \color{blue}{x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 67.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{+154}:\\ \;\;\;\;a \cdot \frac{y}{-1 - t}\\ \mathbf{elif}\;y \leq -1.05 \cdot 10^{-212}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-256}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.75 \cdot 10^{+225}:\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;a \cdot \frac{y}{-1 - t}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 70.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \frac{a}{\frac{t}{y}}\\ \mathbf{if}\;z \leq -1.9 \cdot 10^{+21}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{-19}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.46 \cdot 10^{+65}:\\ \;\;\;\;x + a \cdot \frac{y}{z}\\ \mathbf{elif}\;z \leq 2.15 \cdot 10^{+117}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (/ a (/ t y)))))
   (if (<= z -1.9e+21)
     (- x a)
     (if (<= z 3.5e-19)
       t_1
       (if (<= z 1.46e+65)
         (+ x (* a (/ y z)))
         (if (<= z 2.15e+117) t_1 (- x a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (a / (t / y));
	double tmp;
	if (z <= -1.9e+21) {
		tmp = x - a;
	} else if (z <= 3.5e-19) {
		tmp = t_1;
	} else if (z <= 1.46e+65) {
		tmp = x + (a * (y / z));
	} else if (z <= 2.15e+117) {
		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))
    if (z <= (-1.9d+21)) then
        tmp = x - a
    else if (z <= 3.5d-19) then
        tmp = t_1
    else if (z <= 1.46d+65) then
        tmp = x + (a * (y / z))
    else if (z <= 2.15d+117) 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));
	double tmp;
	if (z <= -1.9e+21) {
		tmp = x - a;
	} else if (z <= 3.5e-19) {
		tmp = t_1;
	} else if (z <= 1.46e+65) {
		tmp = x + (a * (y / z));
	} else if (z <= 2.15e+117) {
		tmp = t_1;
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - (a / (t / y))
	tmp = 0
	if z <= -1.9e+21:
		tmp = x - a
	elif z <= 3.5e-19:
		tmp = t_1
	elif z <= 1.46e+65:
		tmp = x + (a * (y / z))
	elif z <= 2.15e+117:
		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 / y)))
	tmp = 0.0
	if (z <= -1.9e+21)
		tmp = Float64(x - a);
	elseif (z <= 3.5e-19)
		tmp = t_1;
	elseif (z <= 1.46e+65)
		tmp = Float64(x + Float64(a * Float64(y / z)));
	elseif (z <= 2.15e+117)
		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));
	tmp = 0.0;
	if (z <= -1.9e+21)
		tmp = x - a;
	elseif (z <= 3.5e-19)
		tmp = t_1;
	elseif (z <= 1.46e+65)
		tmp = x + (a * (y / z));
	elseif (z <= 2.15e+117)
		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 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.9e+21], N[(x - a), $MachinePrecision], If[LessEqual[z, 3.5e-19], t$95$1, If[LessEqual[z, 1.46e+65], N[(x + N[(a * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.15e+117], t$95$1, N[(x - a), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.9e21 or 2.14999999999999999e117 < z

   1. Initial program 93.4%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 78.2%

      \[\leadsto x - \color{blue}{a} \]

   if -1.9e21 < z < 3.50000000000000015e-19 or 1.45999999999999999e65 < z < 2.14999999999999999e117

   1. Initial program 96.9%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 98.5%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 98.5%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 68.9%

      \[\leadsto x - \color{blue}{\frac{a \cdot \left(y - z\right)}{t}} \]
   6. Step-by-step derivation

      1. associate-/l* 70.9%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   7. Simplified 70.9%

      \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   8. Taylor expanded in y around inf 66.6%

      \[\leadsto x - \color{blue}{\frac{a \cdot y}{t}} \]
   9. Step-by-step derivation

      1. associate-/l* 68.4%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y}}} \]

   10. Simplified 68.4%

       \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y}}} \]

   if 3.50000000000000015e-19 < z < 1.45999999999999999e65

   1. Initial program 94.8%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 79.1%

      \[\leadsto x - \frac{y - z}{\color{blue}{-1 \cdot z}} \cdot a \]
   6. Step-by-step derivation

      1. mul-1-neg 79.1%

         \[\leadsto x - \frac{y - z}{\color{blue}{-z}} \cdot a \]

   7. Simplified 79.1%

      \[\leadsto x - \frac{y - z}{\color{blue}{-z}} \cdot a \]

   8. Taylor expanded in y around inf 77.0%

      \[\leadsto x - \color{blue}{\left(-1 \cdot \frac{y}{z}\right)} \cdot a \]
   9. Step-by-step derivation

      1. mul-1-neg 77.0%

         \[\leadsto x - \color{blue}{\left(-\frac{y}{z}\right)} \cdot a \]

      2. distribute-neg-frac 77.0%

         \[\leadsto x - \color{blue}{\frac{-y}{z}} \cdot a \]

   10. Simplified 77.0%

       \[\leadsto x - \color{blue}{\frac{-y}{z}} \cdot a \]
3. Recombined 3 regimes into one program.

4. Final simplification 73.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{+21}:\\ \;\;\;\;x - a\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{-19}:\\ \;\;\;\;x - \frac{a}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq 1.46 \cdot 10^{+65}:\\ \;\;\;\;x + a \cdot \frac{y}{z}\\ \mathbf{elif}\;z \leq 2.15 \cdot 10^{+117}:\\ \;\;\;\;x - \frac{a}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 91.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8.4 \cdot 10^{+74}:\\ \;\;\;\;x + \frac{z - y}{\frac{t}{a}}\\ \mathbf{elif}\;t \leq 2.1 \cdot 10^{+78}:\\ \;\;\;\;x + a \cdot \frac{z - y}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \frac{z - y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -8.4e+74)
   (+ x (/ (- z y) (/ t a)))
   (if (<= t 2.1e+78)
     (+ x (* a (/ (- z y) (- 1.0 z))))
     (+ x (* a (/ (- z y) t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -8.4e+74) {
		tmp = x + ((z - y) / (t / a));
	} else if (t <= 2.1e+78) {
		tmp = x + (a * ((z - y) / (1.0 - z)));
	} else {
		tmp = x + (a * ((z - y) / t));
	}
	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 <= (-8.4d+74)) then
        tmp = x + ((z - y) / (t / a))
    else if (t <= 2.1d+78) then
        tmp = x + (a * ((z - y) / (1.0d0 - z)))
    else
        tmp = x + (a * ((z - y) / t))
    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 <= -8.4e+74) {
		tmp = x + ((z - y) / (t / a));
	} else if (t <= 2.1e+78) {
		tmp = x + (a * ((z - y) / (1.0 - z)));
	} else {
		tmp = x + (a * ((z - y) / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -8.4e+74:
		tmp = x + ((z - y) / (t / a))
	elif t <= 2.1e+78:
		tmp = x + (a * ((z - y) / (1.0 - z)))
	else:
		tmp = x + (a * ((z - y) / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -8.4e+74)
		tmp = Float64(x + Float64(Float64(z - y) / Float64(t / a)));
	elseif (t <= 2.1e+78)
		tmp = Float64(x + Float64(a * Float64(Float64(z - y) / Float64(1.0 - z))));
	else
		tmp = Float64(x + Float64(a * Float64(Float64(z - y) / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -8.4e+74)
		tmp = x + ((z - y) / (t / a));
	elseif (t <= 2.1e+78)
		tmp = x + (a * ((z - y) / (1.0 - z)));
	else
		tmp = x + (a * ((z - y) / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -8.4e+74], N[(x + N[(N[(z - y), $MachinePrecision] / N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.1e+78], N[(x + N[(a * N[(N[(z - y), $MachinePrecision] / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a * N[(N[(z - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -8.3999999999999995e74

   1. Initial program 97.9%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 89.8%

      \[\leadsto x - \frac{y - z}{\frac{\color{blue}{t}}{a}} \]

   if -8.3999999999999995e74 < t < 2.1000000000000001e78

   1. Initial program 96.8%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 95.9%

      \[\leadsto x - \color{blue}{\frac{y - z}{1 - z}} \cdot a \]

   if 2.1000000000000001e78 < t

   1. Initial program 87.8%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.8%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 84.9%

      \[\leadsto x - \color{blue}{\frac{y - z}{t}} \cdot a \]
3. Recombined 3 regimes into one program.

4. Final simplification 92.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -8.4 \cdot 10^{+74}:\\ \;\;\;\;x + \frac{z - y}{\frac{t}{a}}\\ \mathbf{elif}\;t \leq 2.1 \cdot 10^{+78}:\\ \;\;\;\;x + a \cdot \frac{z - y}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \frac{z - y}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 88.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.9 \cdot 10^{+30} \lor \neg \left(z \leq 2 \cdot 10^{+122}\right):\\ \;\;\;\;x + a \cdot \frac{z - y}{-z}\\ \mathbf{else}:\\ \;\;\;\;x - a \cdot \frac{y}{t + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.9e+30) (not (<= z 2e+122)))
   (+ x (* a (/ (- z y) (- z))))
   (- x (* a (/ y (+ t 1.0))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.9e+30) || !(z <= 2e+122)) {
		tmp = x + (a * ((z - y) / -z));
	} 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 <= (-4.9d+30)) .or. (.not. (z <= 2d+122))) then
        tmp = x + (a * ((z - y) / -z))
    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 <= -4.9e+30) || !(z <= 2e+122)) {
		tmp = x + (a * ((z - y) / -z));
	} else {
		tmp = x - (a * (y / (t + 1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.9e+30) or not (z <= 2e+122):
		tmp = x + (a * ((z - y) / -z))
	else:
		tmp = x - (a * (y / (t + 1.0)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.9e+30) || !(z <= 2e+122))
		tmp = Float64(x + Float64(a * Float64(Float64(z - y) / Float64(-z))));
	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 <= -4.9e+30) || ~((z <= 2e+122)))
		tmp = x + (a * ((z - y) / -z));
	else
		tmp = x - (a * (y / (t + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.9e+30], N[Not[LessEqual[z, 2e+122]], $MachinePrecision]], N[(x + N[(a * N[(N[(z - y), $MachinePrecision] / (-z)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(a * N[(y / N[(t + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.89999999999999984e30 or 2.00000000000000003e122 < z

   1. Initial program 93.0%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 87.7%

      \[\leadsto x - \frac{y - z}{\color{blue}{-1 \cdot z}} \cdot a \]
   6. Step-by-step derivation

      1. mul-1-neg 87.7%

         \[\leadsto x - \frac{y - z}{\color{blue}{-z}} \cdot a \]

   7. Simplified 87.7%

      \[\leadsto x - \frac{y - z}{\color{blue}{-z}} \cdot a \]

   if -4.89999999999999984e30 < z < 2.00000000000000003e122

   1. Initial program 96.8%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 98.7%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 98.7%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around 0 85.7%

      \[\leadsto x - \color{blue}{\frac{y}{1 + t}} \cdot a \]
3. Recombined 2 regimes into one program.

4. Final simplification 86.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.9 \cdot 10^{+30} \lor \neg \left(z \leq 2 \cdot 10^{+122}\right):\\ \;\;\;\;x + a \cdot \frac{z - y}{-z}\\ \mathbf{else}:\\ \;\;\;\;x - a \cdot \frac{y}{t + 1}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 72.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.15 \cdot 10^{+67} \lor \neg \left(t \leq 8.5 \cdot 10^{+113}\right):\\ \;\;\;\;x - \frac{a}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \frac{z}{1 - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -2.15e+67) (not (<= t 8.5e+113)))
   (- x (/ a (/ t y)))
   (+ x (* a (/ z (- 1.0 z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -2.15e+67) || !(t <= 8.5e+113)) {
		tmp = x - (a / (t / y));
	} else {
		tmp = x + (a * (z / (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 <= (-2.15d+67)) .or. (.not. (t <= 8.5d+113))) then
        tmp = x - (a / (t / y))
    else
        tmp = x + (a * (z / (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 <= -2.15e+67) || !(t <= 8.5e+113)) {
		tmp = x - (a / (t / y));
	} else {
		tmp = x + (a * (z / (1.0 - z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -2.15e+67) or not (t <= 8.5e+113):
		tmp = x - (a / (t / y))
	else:
		tmp = x + (a * (z / (1.0 - z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -2.15e+67) || !(t <= 8.5e+113))
		tmp = Float64(x - Float64(a / Float64(t / y)));
	else
		tmp = Float64(x + Float64(a * Float64(z / Float64(1.0 - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -2.15e+67) || ~((t <= 8.5e+113)))
		tmp = x - (a / (t / y));
	else
		tmp = x + (a * (z / (1.0 - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -2.15e+67], N[Not[LessEqual[t, 8.5e+113]], $MachinePrecision]], N[(x - N[(a / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(a * N[(z / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -2.1500000000000001e67 or 8.5000000000000001e113 < t

   1. Initial program 92.6%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 97.7%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 97.7%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 82.1%

      \[\leadsto x - \color{blue}{\frac{a \cdot \left(y - z\right)}{t}} \]
   6. Step-by-step derivation

      1. associate-/l* 87.5%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   7. Simplified 87.5%

      \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   8. Taylor expanded in y around inf 74.7%

      \[\leadsto x - \color{blue}{\frac{a \cdot y}{t}} \]
   9. Step-by-step derivation

      1. associate-/l* 78.5%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y}}} \]

   10. Simplified 78.5%

       \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y}}} \]

   if -2.1500000000000001e67 < t < 8.5000000000000001e113

   1. Initial program 96.9%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate-/r/ 96.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\frac{\left(t - z\right) + 1}{a}}} \]

      2. div-inv 96.8%

         \[\leadsto x - \frac{y - z}{\color{blue}{\left(\left(t - z\right) + 1\right) \cdot \frac{1}{a}}} \]

      3. associate-/r* 99.7%

         \[\leadsto x - \color{blue}{\frac{\frac{y - z}{\left(t - z\right) + 1}}{\frac{1}{a}}} \]

   6. Applied egg-rr 99.7%

      \[\leadsto x - \color{blue}{\frac{\frac{y - z}{\left(t - z\right) + 1}}{\frac{1}{a}}} \]

   7. Taylor expanded in t around 0 75.0%

      \[\leadsto x - \color{blue}{\frac{a \cdot \left(y - z\right)}{1 - z}} \]

   8. Taylor expanded in y around 0 58.4%

      \[\leadsto \color{blue}{x - -1 \cdot \frac{a \cdot z}{1 - z}} \]
   9. Step-by-step derivation

      1. cancel-sign-sub-inv 58.4%

         \[\leadsto \color{blue}{x + \left(--1\right) \cdot \frac{a \cdot z}{1 - z}} \]

      2. metadata-eval 58.4%

         \[\leadsto x + \color{blue}{1} \cdot \frac{a \cdot z}{1 - z} \]

      3. associate-*r/ 74.8%

         \[\leadsto x + 1 \cdot \color{blue}{\left(a \cdot \frac{z}{1 - z}\right)} \]

      4. *-lft-identity 74.8%

         \[\leadsto x + \color{blue}{a \cdot \frac{z}{1 - z}} \]

   10. Simplified 74.8%

       \[\leadsto \color{blue}{x + a \cdot \frac{z}{1 - z}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 76.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.15 \cdot 10^{+67} \lor \neg \left(t \leq 8.5 \cdot 10^{+113}\right):\\ \;\;\;\;x - \frac{a}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + a \cdot \frac{z}{1 - z}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 85.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+54} \lor \neg \left(z \leq 1.85 \cdot 10^{+122}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - a \cdot \frac{y}{t + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -2.8e+54) (not (<= z 1.85e+122)))
   (- x a)
   (- x (* a (/ y (+ t 1.0))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.8e+54) || !(z <= 1.85e+122)) {
		tmp = x - 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 <= (-2.8d+54)) .or. (.not. (z <= 1.85d+122))) then
        tmp = x - 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 <= -2.8e+54) || !(z <= 1.85e+122)) {
		tmp = x - a;
	} else {
		tmp = x - (a * (y / (t + 1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -2.8e+54) or not (z <= 1.85e+122):
		tmp = x - a
	else:
		tmp = x - (a * (y / (t + 1.0)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -2.8e+54) || !(z <= 1.85e+122))
		tmp = Float64(x - 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 <= -2.8e+54) || ~((z <= 1.85e+122)))
		tmp = x - 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, -2.8e+54], N[Not[LessEqual[z, 1.85e+122]], $MachinePrecision]], N[(x - a), $MachinePrecision], N[(x - N[(a * N[(y / N[(t + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.80000000000000015e54 or 1.8499999999999998e122 < z

   1. Initial program 92.7%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 79.3%

      \[\leadsto x - \color{blue}{a} \]

   if -2.80000000000000015e54 < z < 1.8499999999999998e122

   1. Initial program 96.9%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 98.7%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 98.7%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around 0 85.0%

      \[\leadsto x - \color{blue}{\frac{y}{1 + t}} \cdot a \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+54} \lor \neg \left(z \leq 1.85 \cdot 10^{+122}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - a \cdot \frac{y}{t + 1}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 64.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.8 \cdot 10^{+141} \lor \neg \left(t \leq 1.1 \cdot 10^{+175}\right):\\ \;\;\;\;x + \frac{a}{\frac{t}{z}}\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -4.8e+141) (not (<= t 1.1e+175))) (+ x (/ a (/ t z))) (- x a)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -4.8e+141) || !(t <= 1.1e+175)) {
		tmp = x + (a / (t / z));
	} 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 ((t <= (-4.8d+141)) .or. (.not. (t <= 1.1d+175))) then
        tmp = x + (a / (t / z))
    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 ((t <= -4.8e+141) || !(t <= 1.1e+175)) {
		tmp = x + (a / (t / z));
	} else {
		tmp = x - a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -4.8e+141) or not (t <= 1.1e+175):
		tmp = x + (a / (t / z))
	else:
		tmp = x - a
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -4.8e+141) || !(t <= 1.1e+175))
		tmp = Float64(x + Float64(a / Float64(t / z)));
	else
		tmp = Float64(x - a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -4.8e+141) || ~((t <= 1.1e+175)))
		tmp = x + (a / (t / z));
	else
		tmp = x - a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -4.8e+141], N[Not[LessEqual[t, 1.1e+175]], $MachinePrecision]], N[(x + N[(a / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - a), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -4.79999999999999995e141 or 1.1e175 < t

   1. Initial program 93.0%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 97.1%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 97.1%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 88.5%

      \[\leadsto x - \color{blue}{\frac{a \cdot \left(y - z\right)}{t}} \]
   6. Step-by-step derivation

      1. associate-/l* 91.5%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   7. Simplified 91.5%

      \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   8. Taylor expanded in y around 0 76.0%

      \[\leadsto \color{blue}{x - -1 \cdot \frac{a \cdot z}{t}} \]
   9. Step-by-step derivation

      1. sub-neg 76.0%

         \[\leadsto \color{blue}{x + \left(--1 \cdot \frac{a \cdot z}{t}\right)} \]

      2. mul-1-neg 76.0%

         \[\leadsto x + \left(-\color{blue}{\left(-\frac{a \cdot z}{t}\right)}\right) \]

      3. remove-double-neg 76.0%

         \[\leadsto x + \color{blue}{\frac{a \cdot z}{t}} \]

      4. associate-/l* 76.1%

         \[\leadsto x + \color{blue}{\frac{a}{\frac{t}{z}}} \]

   10. Simplified 76.1%

       \[\leadsto \color{blue}{x + \frac{a}{\frac{t}{z}}} \]

   if -4.79999999999999995e141 < t < 1.1e175

   1. Initial program 96.2%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 64.2%

      \[\leadsto x - \color{blue}{a} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -4.8 \cdot 10^{+141} \lor \neg \left(t \leq 1.1 \cdot 10^{+175}\right):\\ \;\;\;\;x + \frac{a}{\frac{t}{z}}\\ \mathbf{else}:\\ \;\;\;\;x - a\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 70.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.32 \cdot 10^{+19} \lor \neg \left(z \leq 2.15 \cdot 10^{+117}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - \frac{a}{\frac{t}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.32e+19) (not (<= z 2.15e+117))) (- x a) (- x (/ a (/ t y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.32e+19) || !(z <= 2.15e+117)) {
		tmp = x - a;
	} else {
		tmp = x - (a / (t / 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 <= (-1.32d+19)) .or. (.not. (z <= 2.15d+117))) then
        tmp = x - a
    else
        tmp = x - (a / (t / 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 <= -1.32e+19) || !(z <= 2.15e+117)) {
		tmp = x - a;
	} else {
		tmp = x - (a / (t / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.32e+19) or not (z <= 2.15e+117):
		tmp = x - a
	else:
		tmp = x - (a / (t / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.32e+19) || !(z <= 2.15e+117))
		tmp = Float64(x - a);
	else
		tmp = Float64(x - Float64(a / Float64(t / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.32e+19) || ~((z <= 2.15e+117)))
		tmp = x - a;
	else
		tmp = x - (a / (t / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.32e+19], N[Not[LessEqual[z, 2.15e+117]], $MachinePrecision]], N[(x - a), $MachinePrecision], N[(x - N[(a / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.32e19 or 2.14999999999999999e117 < z

   1. Initial program 93.4%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 78.2%

      \[\leadsto x - \color{blue}{a} \]

   if -1.32e19 < z < 2.14999999999999999e117

   1. Initial program 96.7%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 98.6%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 98.6%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 67.1%

      \[\leadsto x - \color{blue}{\frac{a \cdot \left(y - z\right)}{t}} \]
   6. Step-by-step derivation

      1. associate-/l* 68.9%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   7. Simplified 68.9%

      \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y - z}}} \]

   8. Taylor expanded in y around inf 65.1%

      \[\leadsto x - \color{blue}{\frac{a \cdot y}{t}} \]
   9. Step-by-step derivation

      1. associate-/l* 66.7%

         \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y}}} \]

   10. Simplified 66.7%

       \[\leadsto x - \color{blue}{\frac{a}{\frac{t}{y}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 71.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.32 \cdot 10^{+19} \lor \neg \left(z \leq 2.15 \cdot 10^{+117}\right):\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x - \frac{a}{\frac{t}{y}}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 62.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.6 \cdot 10^{+177}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 1.12 \cdot 10^{+183}:\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.6e+177) x (if (<= t 1.12e+183) (- x a) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.6e+177) {
		tmp = x;
	} else if (t <= 1.12e+183) {
		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 (t <= (-1.6d+177)) then
        tmp = x
    else if (t <= 1.12d+183) 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 (t <= -1.6e+177) {
		tmp = x;
	} else if (t <= 1.12e+183) {
		tmp = x - a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.6e+177:
		tmp = x
	elif t <= 1.12e+183:
		tmp = x - a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.6e+177)
		tmp = x;
	elseif (t <= 1.12e+183)
		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 (t <= -1.6e+177)
		tmp = x;
	elseif (t <= 1.12e+183)
		tmp = x - a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.6e+177], x, If[LessEqual[t, 1.12e+183], N[(x - a), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if t < -1.6e177 or 1.12000000000000003e183 < t

   1. Initial program 93.1%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 96.4%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 96.4%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 66.8%

      \[\leadsto \color{blue}{x} \]

   if -1.6e177 < t < 1.12000000000000003e183

   1. Initial program 96.0%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 62.8%

      \[\leadsto x - \color{blue}{a} \]
3. Recombined 2 regimes into one program.

4. Final simplification 63.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.6 \cdot 10^{+177}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 1.12 \cdot 10^{+183}:\\ \;\;\;\;x - a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 55.5% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.9 \cdot 10^{+220} \lor \neg \left(a \leq 1.8 \cdot 10^{+139}\right):\\ \;\;\;\;-a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -2.9e+220) (not (<= a 1.8e+139))) (- a) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -2.9e+220) || !(a <= 1.8e+139)) {
		tmp = -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 ((a <= (-2.9d+220)) .or. (.not. (a <= 1.8d+139))) then
        tmp = -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 ((a <= -2.9e+220) || !(a <= 1.8e+139)) {
		tmp = -a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -2.9e+220) or not (a <= 1.8e+139):
		tmp = -a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -2.9e+220) || !(a <= 1.8e+139))
		tmp = Float64(-a);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -2.9e+220) || ~((a <= 1.8e+139)))
		tmp = -a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -2.9e+220], N[Not[LessEqual[a, 1.8e+139]], $MachinePrecision]], (-a), x]

Derivation

1. Split input into 2 regimes

2. if a < -2.89999999999999991e220 or 1.79999999999999993e139 < a

   1. Initial program 99.6%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 96.6%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 96.6%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 86.0%

      \[\leadsto \color{blue}{a \cdot \left(\frac{z}{\left(1 + t\right) - z} - \frac{y}{\left(1 + t\right) - z}\right)} \]
   6. Step-by-step derivation

      1. associate--l+ 86.0%

         \[\leadsto a \cdot \left(\frac{z}{\color{blue}{1 + \left(t - z\right)}} - \frac{y}{\left(1 + t\right) - z}\right) \]

      2. +-commutative 86.0%

         \[\leadsto a \cdot \left(\frac{z}{\color{blue}{\left(t - z\right) + 1}} - \frac{y}{\left(1 + t\right) - z}\right) \]

      3. associate--l+ 86.0%

         \[\leadsto a \cdot \left(\frac{z}{\left(t - z\right) + 1} - \frac{y}{\color{blue}{1 + \left(t - z\right)}}\right) \]

      4. +-commutative 86.0%

         \[\leadsto a \cdot \left(\frac{z}{\left(t - z\right) + 1} - \frac{y}{\color{blue}{\left(t - z\right) + 1}}\right) \]

      5. div-sub 85.9%

         \[\leadsto a \cdot \color{blue}{\frac{z - y}{\left(t - z\right) + 1}} \]

      6. +-commutative 85.9%

         \[\leadsto a \cdot \frac{z - y}{\color{blue}{1 + \left(t - z\right)}} \]

   7. Simplified 85.9%

      \[\leadsto \color{blue}{a \cdot \frac{z - y}{1 + \left(t - z\right)}} \]

   8. Taylor expanded in z around inf 47.2%

      \[\leadsto \color{blue}{-1 \cdot a} \]
   9. Step-by-step derivation

      1. neg-mul-1 47.2%

         \[\leadsto \color{blue}{-a} \]

   10. Simplified 47.2%

       \[\leadsto \color{blue}{-a} \]

   if -2.89999999999999991e220 < a < 1.79999999999999993e139

   1. Initial program 94.1%

      \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
   2. Step-by-step derivation

      1. associate-/r/ 99.9%

         \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 62.1%

      \[\leadsto \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 58.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -2.9 \cdot 10^{+220} \lor \neg \left(a \leq 1.8 \cdot 10^{+139}\right):\\ \;\;\;\;-a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 54.2% 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

1. Initial program 95.3%

   \[x - \frac{y - z}{\frac{\left(t - z\right) + 1}{a}} \]
2. Step-by-step derivation

   1. associate-/r/ 99.1%

      \[\leadsto x - \color{blue}{\frac{y - z}{\left(t - z\right) + 1} \cdot a} \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{x - \frac{y - z}{\left(t - z\right) + 1} \cdot a} \]
4. Add Preprocessing

5. Taylor expanded in x around inf 50.3%

   \[\leadsto \color{blue}{x} \]

6. Final simplification 50.3%

   \[\leadsto x \]
7. 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 2024010 
(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))))