Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTicks from plot-0.2.3.4, B

Percentage Accurate: 85.3% → 99.7%

Time: 12.5s

Alternatives: 18

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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)) / (a - t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 85.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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)) / (a - t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;y \cdot \left(\frac{x}{y} + \frac{z - t}{a - t}\right)\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+301}:\\ \;\;\;\;x + t\_1\\ \mathbf{else}:\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{y}{t - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (* y (- z t)) (- a t))))
   (if (<= t_1 (- INFINITY))
     (* y (+ (/ x y) (/ (- z t) (- a t))))
     (if (<= t_1 5e+301) (+ x t_1) (+ x (* (- t z) (/ y (- t a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y * (z - t)) / (a - t);
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = y * ((x / y) + ((z - t) / (a - t)));
	} else if (t_1 <= 5e+301) {
		tmp = x + t_1;
	} else {
		tmp = x + ((t - z) * (y / (t - a)));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (y * (z - t)) / (a - t);
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = y * ((x / y) + ((z - t) / (a - t)));
	} else if (t_1 <= 5e+301) {
		tmp = x + t_1;
	} else {
		tmp = x + ((t - z) * (y / (t - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y * (z - t)) / (a - t)
	tmp = 0
	if t_1 <= -math.inf:
		tmp = y * ((x / y) + ((z - t) / (a - t)))
	elif t_1 <= 5e+301:
		tmp = x + t_1
	else:
		tmp = x + ((t - z) * (y / (t - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y * Float64(z - t)) / Float64(a - t))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(y * Float64(Float64(x / y) + Float64(Float64(z - t) / Float64(a - t))));
	elseif (t_1 <= 5e+301)
		tmp = Float64(x + t_1);
	else
		tmp = Float64(x + Float64(Float64(t - z) * Float64(y / Float64(t - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y * (z - t)) / (a - t);
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = y * ((x / y) + ((z - t) / (a - t)));
	elseif (t_1 <= 5e+301)
		tmp = x + t_1;
	else
		tmp = x + ((t - z) * (y / (t - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(y * N[(N[(x / y), $MachinePrecision] + N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 5e+301], N[(x + t$95$1), $MachinePrecision], N[(x + N[(N[(t - z), $MachinePrecision] * N[(y / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t)) < -inf.0

   1. Initial program 44.7%

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

   3. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -inf.0 < (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t)) < 5.0000000000000004e301

   1. Initial program 99.4%

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

   if 5.0000000000000004e301 < (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t))

   1. Initial program 56.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+264}:\\ \;\;\;\;\frac{t - z}{\frac{t - a}{y}} + x\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+301}:\\ \;\;\;\;x + t\_1\\ \mathbf{else}:\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{y}{t - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (* y (- z t)) (- a t))))
   (if (<= t_1 -2e+264)
     (+ (/ (- t z) (/ (- t a) y)) x)
     (if (<= t_1 5e+301) (+ x t_1) (+ x (* (- t z) (/ y (- t a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y * (z - t)) / (a - t);
	double tmp;
	if (t_1 <= -2e+264) {
		tmp = ((t - z) / ((t - a) / y)) + x;
	} else if (t_1 <= 5e+301) {
		tmp = x + t_1;
	} else {
		tmp = x + ((t - z) * (y / (t - 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 = (y * (z - t)) / (a - t)
    if (t_1 <= (-2d+264)) then
        tmp = ((t - z) / ((t - a) / y)) + x
    else if (t_1 <= 5d+301) then
        tmp = x + t_1
    else
        tmp = x + ((t - z) * (y / (t - 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 = (y * (z - t)) / (a - t);
	double tmp;
	if (t_1 <= -2e+264) {
		tmp = ((t - z) / ((t - a) / y)) + x;
	} else if (t_1 <= 5e+301) {
		tmp = x + t_1;
	} else {
		tmp = x + ((t - z) * (y / (t - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y * (z - t)) / (a - t)
	tmp = 0
	if t_1 <= -2e+264:
		tmp = ((t - z) / ((t - a) / y)) + x
	elif t_1 <= 5e+301:
		tmp = x + t_1
	else:
		tmp = x + ((t - z) * (y / (t - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y * Float64(z - t)) / Float64(a - t))
	tmp = 0.0
	if (t_1 <= -2e+264)
		tmp = Float64(Float64(Float64(t - z) / Float64(Float64(t - a) / y)) + x);
	elseif (t_1 <= 5e+301)
		tmp = Float64(x + t_1);
	else
		tmp = Float64(x + Float64(Float64(t - z) * Float64(y / Float64(t - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y * (z - t)) / (a - t);
	tmp = 0.0;
	if (t_1 <= -2e+264)
		tmp = ((t - z) / ((t - a) / y)) + x;
	elseif (t_1 <= 5e+301)
		tmp = x + t_1;
	else
		tmp = x + ((t - z) * (y / (t - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+264], N[(N[(N[(t - z), $MachinePrecision] / N[(N[(t - a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t$95$1, 5e+301], N[(x + t$95$1), $MachinePrecision], N[(x + N[(N[(t - z), $MachinePrecision] * N[(y / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t)) < -2.00000000000000009e264

   1. Initial program 49.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   6. Applied egg-rr 0

      \[\leadsto expr\]

   if -2.00000000000000009e264 < (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t)) < 5.0000000000000004e301

   1. Initial program 99.4%

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

   if 5.0000000000000004e301 < (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t))

   1. Initial program 56.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 99.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(t - z\right) \cdot \frac{y}{t - a}\\ t_2 := \frac{y \cdot \left(z - t\right)}{a - t}\\ \mathbf{if}\;t\_2 \leq -\infty:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 5 \cdot 10^{+301}:\\ \;\;\;\;x + t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- t z) (/ y (- t a))))) (t_2 (/ (* y (- z t)) (- a t))))
   (if (<= t_2 (- INFINITY)) t_1 (if (<= t_2 5e+301) (+ x t_2) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * (y / (t - a)));
	double t_2 = (y * (z - t)) / (a - t);
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_1;
	} else if (t_2 <= 5e+301) {
		tmp = x + t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * (y / (t - a)));
	double t_2 = (y * (z - t)) / (a - t);
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else if (t_2 <= 5e+301) {
		tmp = x + t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((t - z) * (y / (t - a)))
	t_2 = (y * (z - t)) / (a - t)
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_1
	elif t_2 <= 5e+301:
		tmp = x + t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(t - z) * Float64(y / Float64(t - a))))
	t_2 = Float64(Float64(y * Float64(z - t)) / Float64(a - t))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_1;
	elseif (t_2 <= 5e+301)
		tmp = Float64(x + t_2);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((t - z) * (y / (t - a)));
	t_2 = (y * (z - t)) / (a - t);
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_1;
	elseif (t_2 <= 5e+301)
		tmp = x + t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(t - z), $MachinePrecision] * N[(y / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$1, If[LessEqual[t$95$2, 5e+301], N[(x + t$95$2), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t)) < -inf.0 or 5.0000000000000004e301 < (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t))

   1. Initial program 50.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   if -inf.0 < (/.f64 (*.f64 y (-.f64 z t)) (-.f64 a t)) < 5.0000000000000004e301

   1. Initial program 99.4%

      \[x + \frac{y \cdot \left(z - t\right)}{a - t} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 74.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -5.7 \cdot 10^{-42}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq -1.2 \cdot 10^{-156}:\\ \;\;\;\;\frac{y}{\frac{a}{z}} + x\\ \mathbf{elif}\;t \leq -2.15 \cdot 10^{-239}:\\ \;\;\;\;\frac{y \cdot z}{a - t}\\ \mathbf{elif}\;t \leq 1.05 \cdot 10^{-46}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{elif}\;t \leq 1.9 \cdot 10^{+68}:\\ \;\;\;\;y \cdot \frac{-z}{t} + x\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -5.7e-42)
   (+ y x)
   (if (<= t -1.2e-156)
     (+ (/ y (/ a z)) x)
     (if (<= t -2.15e-239)
       (/ (* y z) (- a t))
       (if (<= t 1.05e-46)
         (+ x (* y (/ (- z t) a)))
         (if (<= t 1.9e+68) (+ (* y (/ (- z) t)) x) (+ y x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -5.7e-42) {
		tmp = y + x;
	} else if (t <= -1.2e-156) {
		tmp = (y / (a / z)) + x;
	} else if (t <= -2.15e-239) {
		tmp = (y * z) / (a - t);
	} else if (t <= 1.05e-46) {
		tmp = x + (y * ((z - t) / a));
	} else if (t <= 1.9e+68) {
		tmp = (y * (-z / t)) + x;
	} else {
		tmp = y + 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 <= (-5.7d-42)) then
        tmp = y + x
    else if (t <= (-1.2d-156)) then
        tmp = (y / (a / z)) + x
    else if (t <= (-2.15d-239)) then
        tmp = (y * z) / (a - t)
    else if (t <= 1.05d-46) then
        tmp = x + (y * ((z - t) / a))
    else if (t <= 1.9d+68) then
        tmp = (y * (-z / t)) + x
    else
        tmp = y + 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 <= -5.7e-42) {
		tmp = y + x;
	} else if (t <= -1.2e-156) {
		tmp = (y / (a / z)) + x;
	} else if (t <= -2.15e-239) {
		tmp = (y * z) / (a - t);
	} else if (t <= 1.05e-46) {
		tmp = x + (y * ((z - t) / a));
	} else if (t <= 1.9e+68) {
		tmp = (y * (-z / t)) + x;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -5.7e-42:
		tmp = y + x
	elif t <= -1.2e-156:
		tmp = (y / (a / z)) + x
	elif t <= -2.15e-239:
		tmp = (y * z) / (a - t)
	elif t <= 1.05e-46:
		tmp = x + (y * ((z - t) / a))
	elif t <= 1.9e+68:
		tmp = (y * (-z / t)) + x
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -5.7e-42)
		tmp = Float64(y + x);
	elseif (t <= -1.2e-156)
		tmp = Float64(Float64(y / Float64(a / z)) + x);
	elseif (t <= -2.15e-239)
		tmp = Float64(Float64(y * z) / Float64(a - t));
	elseif (t <= 1.05e-46)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	elseif (t <= 1.9e+68)
		tmp = Float64(Float64(y * Float64(Float64(-z) / t)) + x);
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -5.7e-42)
		tmp = y + x;
	elseif (t <= -1.2e-156)
		tmp = (y / (a / z)) + x;
	elseif (t <= -2.15e-239)
		tmp = (y * z) / (a - t);
	elseif (t <= 1.05e-46)
		tmp = x + (y * ((z - t) / a));
	elseif (t <= 1.9e+68)
		tmp = (y * (-z / t)) + x;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -5.7e-42], N[(y + x), $MachinePrecision], If[LessEqual[t, -1.2e-156], N[(N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, -2.15e-239], N[(N[(y * z), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.05e-46], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.9e+68], N[(N[(y * N[((-z) / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

2. if t < -5.6999999999999999e-42 or 1.9e68 < t

   1. Initial program 78.7%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -5.6999999999999999e-42 < t < -1.2e-156

   1. Initial program 99.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -1.2e-156 < t < -2.15e-239

   1. Initial program 95.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.15e-239 < t < 1.04999999999999994e-46

   1. Initial program 98.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 1.04999999999999994e-46 < t < 1.9e68

   1. Initial program 91.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 5: 79.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.25 \cdot 10^{-62}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right) + x\\ \mathbf{elif}\;t \leq -2.2 \cdot 10^{-156}:\\ \;\;\;\;\frac{y}{\frac{a}{z}} + x\\ \mathbf{elif}\;t \leq -2.15 \cdot 10^{-239}:\\ \;\;\;\;\frac{y \cdot \left(z - t\right)}{a - t}\\ \mathbf{elif}\;t \leq 1.55 \cdot 10^{-38}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{t - z}{\frac{t}{y}} + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.25e-62)
   (+ (* y (- 1.0 (/ z t))) x)
   (if (<= t -2.2e-156)
     (+ (/ y (/ a z)) x)
     (if (<= t -2.15e-239)
       (/ (* y (- z t)) (- a t))
       (if (<= t 1.55e-38)
         (+ x (* y (/ (- z t) a)))
         (+ (/ (- t z) (/ t y)) x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.25e-62) {
		tmp = (y * (1.0 - (z / t))) + x;
	} else if (t <= -2.2e-156) {
		tmp = (y / (a / z)) + x;
	} else if (t <= -2.15e-239) {
		tmp = (y * (z - t)) / (a - t);
	} else if (t <= 1.55e-38) {
		tmp = x + (y * ((z - t) / a));
	} else {
		tmp = ((t - z) / (t / y)) + 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 <= (-1.25d-62)) then
        tmp = (y * (1.0d0 - (z / t))) + x
    else if (t <= (-2.2d-156)) then
        tmp = (y / (a / z)) + x
    else if (t <= (-2.15d-239)) then
        tmp = (y * (z - t)) / (a - t)
    else if (t <= 1.55d-38) then
        tmp = x + (y * ((z - t) / a))
    else
        tmp = ((t - z) / (t / y)) + 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 <= -1.25e-62) {
		tmp = (y * (1.0 - (z / t))) + x;
	} else if (t <= -2.2e-156) {
		tmp = (y / (a / z)) + x;
	} else if (t <= -2.15e-239) {
		tmp = (y * (z - t)) / (a - t);
	} else if (t <= 1.55e-38) {
		tmp = x + (y * ((z - t) / a));
	} else {
		tmp = ((t - z) / (t / y)) + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.25e-62:
		tmp = (y * (1.0 - (z / t))) + x
	elif t <= -2.2e-156:
		tmp = (y / (a / z)) + x
	elif t <= -2.15e-239:
		tmp = (y * (z - t)) / (a - t)
	elif t <= 1.55e-38:
		tmp = x + (y * ((z - t) / a))
	else:
		tmp = ((t - z) / (t / y)) + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.25e-62)
		tmp = Float64(Float64(y * Float64(1.0 - Float64(z / t))) + x);
	elseif (t <= -2.2e-156)
		tmp = Float64(Float64(y / Float64(a / z)) + x);
	elseif (t <= -2.15e-239)
		tmp = Float64(Float64(y * Float64(z - t)) / Float64(a - t));
	elseif (t <= 1.55e-38)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	else
		tmp = Float64(Float64(Float64(t - z) / Float64(t / y)) + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.25e-62)
		tmp = (y * (1.0 - (z / t))) + x;
	elseif (t <= -2.2e-156)
		tmp = (y / (a / z)) + x;
	elseif (t <= -2.15e-239)
		tmp = (y * (z - t)) / (a - t);
	elseif (t <= 1.55e-38)
		tmp = x + (y * ((z - t) / a));
	else
		tmp = ((t - z) / (t / y)) + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.25e-62], N[(N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, -2.2e-156], N[(N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, -2.15e-239], N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.55e-38], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(t - z), $MachinePrecision] / N[(t / y), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if t < -1.25e-62

   1. Initial program 79.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.25e-62 < t < -2.1999999999999999e-156

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -2.1999999999999999e-156 < t < -2.15e-239

   1. Initial program 95.1%

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

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.15e-239 < t < 1.54999999999999991e-38

   1. Initial program 98.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 1.54999999999999991e-38 < t

   1. Initial program 83.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   6. Applied egg-rr 0

      \[\leadsto expr\]

   7. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   9. Simplified 0

      \[\leadsto expr\]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 6: 78.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(t - z\right) \cdot \frac{y}{t}\\ \mathbf{if}\;t \leq -9.2 \cdot 10^{-85}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -1.2 \cdot 10^{-156}:\\ \;\;\;\;y \cdot \frac{z}{a} + x\\ \mathbf{elif}\;t \leq -2.15 \cdot 10^{-239}:\\ \;\;\;\;\frac{y \cdot z}{a - t}\\ \mathbf{elif}\;t \leq 4.2 \cdot 10^{-42}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- t z) (/ y t)))))
   (if (<= t -9.2e-85)
     t_1
     (if (<= t -1.2e-156)
       (+ (* y (/ z a)) x)
       (if (<= t -2.15e-239)
         (/ (* y z) (- a t))
         (if (<= t 4.2e-42) (+ x (* y (/ (- z t) a))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * (y / t));
	double tmp;
	if (t <= -9.2e-85) {
		tmp = t_1;
	} else if (t <= -1.2e-156) {
		tmp = (y * (z / a)) + x;
	} else if (t <= -2.15e-239) {
		tmp = (y * z) / (a - t);
	} else if (t <= 4.2e-42) {
		tmp = x + (y * ((z - t) / 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 = x + ((t - z) * (y / t))
    if (t <= (-9.2d-85)) then
        tmp = t_1
    else if (t <= (-1.2d-156)) then
        tmp = (y * (z / a)) + x
    else if (t <= (-2.15d-239)) then
        tmp = (y * z) / (a - t)
    else if (t <= 4.2d-42) then
        tmp = x + (y * ((z - t) / 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 = x + ((t - z) * (y / t));
	double tmp;
	if (t <= -9.2e-85) {
		tmp = t_1;
	} else if (t <= -1.2e-156) {
		tmp = (y * (z / a)) + x;
	} else if (t <= -2.15e-239) {
		tmp = (y * z) / (a - t);
	} else if (t <= 4.2e-42) {
		tmp = x + (y * ((z - t) / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((t - z) * (y / t))
	tmp = 0
	if t <= -9.2e-85:
		tmp = t_1
	elif t <= -1.2e-156:
		tmp = (y * (z / a)) + x
	elif t <= -2.15e-239:
		tmp = (y * z) / (a - t)
	elif t <= 4.2e-42:
		tmp = x + (y * ((z - t) / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(t - z) * Float64(y / t)))
	tmp = 0.0
	if (t <= -9.2e-85)
		tmp = t_1;
	elseif (t <= -1.2e-156)
		tmp = Float64(Float64(y * Float64(z / a)) + x);
	elseif (t <= -2.15e-239)
		tmp = Float64(Float64(y * z) / Float64(a - t));
	elseif (t <= 4.2e-42)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((t - z) * (y / t));
	tmp = 0.0;
	if (t <= -9.2e-85)
		tmp = t_1;
	elseif (t <= -1.2e-156)
		tmp = (y * (z / a)) + x;
	elseif (t <= -2.15e-239)
		tmp = (y * z) / (a - t);
	elseif (t <= 4.2e-42)
		tmp = x + (y * ((z - t) / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(t - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -9.2e-85], t$95$1, If[LessEqual[t, -1.2e-156], N[(N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, -2.15e-239], N[(N[(y * z), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4.2e-42], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -9.2000000000000001e-85 or 4.20000000000000013e-42 < t

   1. Initial program 82.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -9.2000000000000001e-85 < t < -1.2e-156

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.2e-156 < t < -2.15e-239

   1. Initial program 95.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.15e-239 < t < 4.20000000000000013e-42

   1. Initial program 98.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 7: 73.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{\frac{a}{z}} + x\\ \mathbf{if}\;t \leq -4.4 \cdot 10^{-41}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq -1.2 \cdot 10^{-156}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -2.15 \cdot 10^{-239}:\\ \;\;\;\;\frac{y \cdot z}{a - t}\\ \mathbf{elif}\;t \leq 4.8 \cdot 10^{+36}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (/ y (/ a z)) x)))
   (if (<= t -4.4e-41)
     (+ y x)
     (if (<= t -1.2e-156)
       t_1
       (if (<= t -2.15e-239)
         (/ (* y z) (- a t))
         (if (<= t 4.8e+36) t_1 (+ y x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y / (a / z)) + x;
	double tmp;
	if (t <= -4.4e-41) {
		tmp = y + x;
	} else if (t <= -1.2e-156) {
		tmp = t_1;
	} else if (t <= -2.15e-239) {
		tmp = (y * z) / (a - t);
	} else if (t <= 4.8e+36) {
		tmp = t_1;
	} else {
		tmp = y + 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) :: t_1
    real(8) :: tmp
    t_1 = (y / (a / z)) + x
    if (t <= (-4.4d-41)) then
        tmp = y + x
    else if (t <= (-1.2d-156)) then
        tmp = t_1
    else if (t <= (-2.15d-239)) then
        tmp = (y * z) / (a - t)
    else if (t <= 4.8d+36) then
        tmp = t_1
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (y / (a / z)) + x;
	double tmp;
	if (t <= -4.4e-41) {
		tmp = y + x;
	} else if (t <= -1.2e-156) {
		tmp = t_1;
	} else if (t <= -2.15e-239) {
		tmp = (y * z) / (a - t);
	} else if (t <= 4.8e+36) {
		tmp = t_1;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y / (a / z)) + x
	tmp = 0
	if t <= -4.4e-41:
		tmp = y + x
	elif t <= -1.2e-156:
		tmp = t_1
	elif t <= -2.15e-239:
		tmp = (y * z) / (a - t)
	elif t <= 4.8e+36:
		tmp = t_1
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y / Float64(a / z)) + x)
	tmp = 0.0
	if (t <= -4.4e-41)
		tmp = Float64(y + x);
	elseif (t <= -1.2e-156)
		tmp = t_1;
	elseif (t <= -2.15e-239)
		tmp = Float64(Float64(y * z) / Float64(a - t));
	elseif (t <= 4.8e+36)
		tmp = t_1;
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y / (a / z)) + x;
	tmp = 0.0;
	if (t <= -4.4e-41)
		tmp = y + x;
	elseif (t <= -1.2e-156)
		tmp = t_1;
	elseif (t <= -2.15e-239)
		tmp = (y * z) / (a - t);
	elseif (t <= 4.8e+36)
		tmp = t_1;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]}, If[LessEqual[t, -4.4e-41], N[(y + x), $MachinePrecision], If[LessEqual[t, -1.2e-156], t$95$1, If[LessEqual[t, -2.15e-239], N[(N[(y * z), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4.8e+36], t$95$1, N[(y + x), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -4.4e-41 or 4.79999999999999985e36 < t

   1. Initial program 79.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -4.4e-41 < t < -1.2e-156 or -2.15e-239 < t < 4.79999999999999985e36

   1. Initial program 97.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -1.2e-156 < t < -2.15e-239

   1. Initial program 95.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 60.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.4 \cdot 10^{+135}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq -1.9 \cdot 10^{-160}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 1.65 \cdot 10^{-254}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;a \leq 1.2 \cdot 10^{+158}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -4.4e+135)
   x
   (if (<= a -1.9e-160)
     (+ y x)
     (if (<= a 1.65e-254)
       (* y (- 1.0 (/ z t)))
       (if (<= a 1.2e+158) (+ y x) x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.4e+135) {
		tmp = x;
	} else if (a <= -1.9e-160) {
		tmp = y + x;
	} else if (a <= 1.65e-254) {
		tmp = y * (1.0 - (z / t));
	} else if (a <= 1.2e+158) {
		tmp = y + x;
	} 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 <= (-4.4d+135)) then
        tmp = x
    else if (a <= (-1.9d-160)) then
        tmp = y + x
    else if (a <= 1.65d-254) then
        tmp = y * (1.0d0 - (z / t))
    else if (a <= 1.2d+158) then
        tmp = y + x
    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 <= -4.4e+135) {
		tmp = x;
	} else if (a <= -1.9e-160) {
		tmp = y + x;
	} else if (a <= 1.65e-254) {
		tmp = y * (1.0 - (z / t));
	} else if (a <= 1.2e+158) {
		tmp = y + x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -4.4e+135:
		tmp = x
	elif a <= -1.9e-160:
		tmp = y + x
	elif a <= 1.65e-254:
		tmp = y * (1.0 - (z / t))
	elif a <= 1.2e+158:
		tmp = y + x
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -4.4e+135)
		tmp = x;
	elseif (a <= -1.9e-160)
		tmp = Float64(y + x);
	elseif (a <= 1.65e-254)
		tmp = Float64(y * Float64(1.0 - Float64(z / t)));
	elseif (a <= 1.2e+158)
		tmp = Float64(y + x);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -4.4e+135)
		tmp = x;
	elseif (a <= -1.9e-160)
		tmp = y + x;
	elseif (a <= 1.65e-254)
		tmp = y * (1.0 - (z / t));
	elseif (a <= 1.2e+158)
		tmp = y + x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -4.4e+135], x, If[LessEqual[a, -1.9e-160], N[(y + x), $MachinePrecision], If[LessEqual[a, 1.65e-254], N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.2e+158], N[(y + x), $MachinePrecision], x]]]]

Derivation

1. Split input into 3 regimes

2. if a < -4.3999999999999999e135 or 1.20000000000000004e158 < a

   1. Initial program 90.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -4.3999999999999999e135 < a < -1.8999999999999999e-160 or 1.65000000000000008e-254 < a < 1.20000000000000004e158

   1. Initial program 87.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.8999999999999999e-160 < a < 1.65000000000000008e-254

   1. Initial program 90.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 58.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.3 \cdot 10^{-83}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 1.85 \cdot 10^{-244}:\\ \;\;\;\;x\\ \mathbf{elif}\;t \leq 6 \cdot 10^{-15}:\\ \;\;\;\;\frac{y \cdot z}{a}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.3e-83)
   (+ y x)
   (if (<= t 1.85e-244) x (if (<= t 6e-15) (/ (* y z) a) (+ y x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.3e-83) {
		tmp = y + x;
	} else if (t <= 1.85e-244) {
		tmp = x;
	} else if (t <= 6e-15) {
		tmp = (y * z) / a;
	} else {
		tmp = y + 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 <= (-1.3d-83)) then
        tmp = y + x
    else if (t <= 1.85d-244) then
        tmp = x
    else if (t <= 6d-15) then
        tmp = (y * z) / a
    else
        tmp = y + 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 <= -1.3e-83) {
		tmp = y + x;
	} else if (t <= 1.85e-244) {
		tmp = x;
	} else if (t <= 6e-15) {
		tmp = (y * z) / a;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.3e-83:
		tmp = y + x
	elif t <= 1.85e-244:
		tmp = x
	elif t <= 6e-15:
		tmp = (y * z) / a
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.3e-83)
		tmp = Float64(y + x);
	elseif (t <= 1.85e-244)
		tmp = x;
	elseif (t <= 6e-15)
		tmp = Float64(Float64(y * z) / a);
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.3e-83)
		tmp = y + x;
	elseif (t <= 1.85e-244)
		tmp = x;
	elseif (t <= 6e-15)
		tmp = (y * z) / a;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.3e-83], N[(y + x), $MachinePrecision], If[LessEqual[t, 1.85e-244], x, If[LessEqual[t, 6e-15], N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision], N[(y + x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -1.30000000000000004e-83 or 6e-15 < t

   1. Initial program 81.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.30000000000000004e-83 < t < 1.8500000000000001e-244

   1. Initial program 98.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 1.8500000000000001e-244 < t < 6e-15

   1. Initial program 98.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 80.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z - t}{a}\\ \mathbf{if}\;a \leq -0.3:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 5.8 \cdot 10^{+153}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right) + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ (- z t) a)))))
   (if (<= a -0.3) t_1 (if (<= a 5.8e+153) (+ (* y (- 1.0 (/ z t))) x) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * ((z - t) / a));
	double tmp;
	if (a <= -0.3) {
		tmp = t_1;
	} else if (a <= 5.8e+153) {
		tmp = (y * (1.0 - (z / t))) + x;
	} 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 + (y * ((z - t) / a))
    if (a <= (-0.3d0)) then
        tmp = t_1
    else if (a <= 5.8d+153) then
        tmp = (y * (1.0d0 - (z / t))) + x
    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 + (y * ((z - t) / a));
	double tmp;
	if (a <= -0.3) {
		tmp = t_1;
	} else if (a <= 5.8e+153) {
		tmp = (y * (1.0 - (z / t))) + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * ((z - t) / a))
	tmp = 0
	if a <= -0.3:
		tmp = t_1
	elif a <= 5.8e+153:
		tmp = (y * (1.0 - (z / t))) + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(Float64(z - t) / a)))
	tmp = 0.0
	if (a <= -0.3)
		tmp = t_1;
	elseif (a <= 5.8e+153)
		tmp = Float64(Float64(y * Float64(1.0 - Float64(z / t))) + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * ((z - t) / a));
	tmp = 0.0;
	if (a <= -0.3)
		tmp = t_1;
	elseif (a <= 5.8e+153)
		tmp = (y * (1.0 - (z / t))) + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -0.3], t$95$1, If[LessEqual[a, 5.8e+153], N[(N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -0.299999999999999989 or 5.80000000000000004e153 < a

   1. Initial program 88.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -0.299999999999999989 < a < 5.80000000000000004e153

   1. Initial program 89.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 76.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.2 \cdot 10^{-40}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 3.8 \cdot 10^{+34}:\\ \;\;\;\;\frac{y}{\frac{a}{z}} + x\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.2e-40) (+ y x) (if (<= t 3.8e+34) (+ (/ y (/ a z)) x) (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.2e-40) {
		tmp = y + x;
	} else if (t <= 3.8e+34) {
		tmp = (y / (a / z)) + x;
	} else {
		tmp = y + 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 <= (-1.2d-40)) then
        tmp = y + x
    else if (t <= 3.8d+34) then
        tmp = (y / (a / z)) + x
    else
        tmp = y + 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 <= -1.2e-40) {
		tmp = y + x;
	} else if (t <= 3.8e+34) {
		tmp = (y / (a / z)) + x;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.2e-40:
		tmp = y + x
	elif t <= 3.8e+34:
		tmp = (y / (a / z)) + x
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.2e-40)
		tmp = Float64(y + x);
	elseif (t <= 3.8e+34)
		tmp = Float64(Float64(y / Float64(a / z)) + x);
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.2e-40)
		tmp = y + x;
	elseif (t <= 3.8e+34)
		tmp = (y / (a / z)) + x;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.2e-40], N[(y + x), $MachinePrecision], If[LessEqual[t, 3.8e+34], N[(N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < -1.19999999999999996e-40 or 3.8000000000000001e34 < t

   1. Initial program 79.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.19999999999999996e-40 < t < 3.8000000000000001e34

   1. Initial program 97.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 76.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.8 \cdot 10^{-41}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 6 \cdot 10^{+30}:\\ \;\;\;\;y \cdot \frac{z}{a} + x\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -4.8e-41) (+ y x) (if (<= t 6e+30) (+ (* y (/ z a)) x) (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -4.8e-41) {
		tmp = y + x;
	} else if (t <= 6e+30) {
		tmp = (y * (z / a)) + x;
	} else {
		tmp = y + 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 <= (-4.8d-41)) then
        tmp = y + x
    else if (t <= 6d+30) then
        tmp = (y * (z / a)) + x
    else
        tmp = y + 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 <= -4.8e-41) {
		tmp = y + x;
	} else if (t <= 6e+30) {
		tmp = (y * (z / a)) + x;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -4.8e-41:
		tmp = y + x
	elif t <= 6e+30:
		tmp = (y * (z / a)) + x
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -4.8e-41)
		tmp = Float64(y + x);
	elseif (t <= 6e+30)
		tmp = Float64(Float64(y * Float64(z / a)) + x);
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -4.8e-41)
		tmp = y + x;
	elseif (t <= 6e+30)
		tmp = (y * (z / a)) + x;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -4.8e-41], N[(y + x), $MachinePrecision], If[LessEqual[t, 6e+30], N[(N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < -4.80000000000000044e-41 or 5.99999999999999956e30 < t

   1. Initial program 79.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -4.80000000000000044e-41 < t < 5.99999999999999956e30

   1. Initial program 97.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 62.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.75 \cdot 10^{-84}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 10^{-130}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.75e-84) (+ y x) (if (<= t 1e-130) x (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.75e-84) {
		tmp = y + x;
	} else if (t <= 1e-130) {
		tmp = x;
	} else {
		tmp = y + 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 <= (-1.75d-84)) then
        tmp = y + x
    else if (t <= 1d-130) then
        tmp = x
    else
        tmp = y + 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 <= -1.75e-84) {
		tmp = y + x;
	} else if (t <= 1e-130) {
		tmp = x;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.75e-84:
		tmp = y + x
	elif t <= 1e-130:
		tmp = x
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.75e-84)
		tmp = Float64(y + x);
	elseif (t <= 1e-130)
		tmp = x;
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.75e-84)
		tmp = y + x;
	elseif (t <= 1e-130)
		tmp = x;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.75e-84], N[(y + x), $MachinePrecision], If[LessEqual[t, 1e-130], x, N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < -1.7500000000000001e-84 or 1.0000000000000001e-130 < t

   1. Initial program 84.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.7500000000000001e-84 < t < 1.0000000000000001e-130

   1. Initial program 97.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 52.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.5 \cdot 10^{+63}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{+183}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -1.5e+63) y (if (<= y 7.2e+183) x y)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.5e+63) {
		tmp = y;
	} else if (y <= 7.2e+183) {
		tmp = x;
	} else {
		tmp = 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 (y <= (-1.5d+63)) then
        tmp = y
    else if (y <= 7.2d+183) then
        tmp = x
    else
        tmp = 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 (y <= -1.5e+63) {
		tmp = y;
	} else if (y <= 7.2e+183) {
		tmp = x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -1.5e+63:
		tmp = y
	elif y <= 7.2e+183:
		tmp = x
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -1.5e+63)
		tmp = y;
	elseif (y <= 7.2e+183)
		tmp = x;
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -1.5e+63)
		tmp = y;
	elseif (y <= 7.2e+183)
		tmp = x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -1.5e+63], y, If[LessEqual[y, 7.2e+183], x, y]]

Derivation

1. Split input into 2 regimes

2. if y < -1.5e63 or 7.20000000000000046e183 < y

   1. Initial program 72.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.5e63 < y < 7.20000000000000046e183

   1. Initial program 97.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 95.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.8%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing
5. Add Preprocessing

Alternative 16: 59.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq 2.3 \cdot 10^{+176}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z 2.3e+176) (+ y x) (/ y (/ a z))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= 2.3e+176) {
		tmp = y + x;
	} else {
		tmp = y / (a / 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 <= 2.3d+176) then
        tmp = y + x
    else
        tmp = y / (a / 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 <= 2.3e+176) {
		tmp = y + x;
	} else {
		tmp = y / (a / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= 2.3e+176:
		tmp = y + x
	else:
		tmp = y / (a / z)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= 2.3e+176)
		tmp = Float64(y + x);
	else
		tmp = Float64(y / Float64(a / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= 2.3e+176)
		tmp = y + x;
	else
		tmp = y / (a / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, 2.3e+176], N[(y + x), $MachinePrecision], N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 2.29999999999999996e176

   1. Initial program 88.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 2.29999999999999996e176 < z

   1. Initial program 96.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 17: 59.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq 1.12 \cdot 10^{+176}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z 1.12e+176) (+ y x) (* y (/ z a))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= 1.12e+176) {
		tmp = y + x;
	} else {
		tmp = y * (z / 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 <= 1.12d+176) then
        tmp = y + x
    else
        tmp = y * (z / 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 <= 1.12e+176) {
		tmp = y + x;
	} else {
		tmp = y * (z / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= 1.12e+176:
		tmp = y + x
	else:
		tmp = y * (z / a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= 1.12e+176)
		tmp = Float64(y + x);
	else
		tmp = Float64(y * Float64(z / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= 1.12e+176)
		tmp = y + x;
	else
		tmp = y * (z / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, 1.12e+176], N[(y + x), $MachinePrecision], N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 1.12e176

   1. Initial program 88.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 1.12e176 < z

   1. Initial program 96.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in z around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 18: 50.1% accurate, 11.0× 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 88.8%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in x around inf 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

Developer target: 98.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y}{\frac{a - t}{z - t}} \end{array} \]

FPCore

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

C

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

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 / ((a - t) / (z - t)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024110 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTicks from plot-0.2.3.4, B"
  :precision binary64

  :alt
  (+ x (/ y (/ (- a t) (- z t))))

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