Graphics.Rendering.Chart.Axis.Types:invLinMap from Chart-1.5.3

Percentage Accurate: 67.6% → 71.1%

Time: 1.9s

Alternatives: 9

Speedup: 0.7×

Specification

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 67.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 71.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(t - x\right)\\ \mathbf{if}\;z \leq -3 \cdot 10^{+190}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+211}:\\ \;\;\;\;x + \frac{\left(y - z\right) \cdot \left(t - x\right)}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (- t x))))
   (if (<= z -3e+190)
     t_1
     (if (<= z 3.8e+211) (+ x (/ (* (- y z) (- t x)) (- a z))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (t - x);
	double tmp;
	if (z <= -3e+190) {
		tmp = t_1;
	} else if (z <= 3.8e+211) {
		tmp = x + (((y - z) * (t - x)) / (a - z));
	} 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 - x)
    if (z <= (-3d+190)) then
        tmp = t_1
    else if (z <= 3.8d+211) then
        tmp = x + (((y - z) * (t - x)) / (a - z))
    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 - x);
	double tmp;
	if (z <= -3e+190) {
		tmp = t_1;
	} else if (z <= 3.8e+211) {
		tmp = x + (((y - z) * (t - x)) / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (t - x)
	tmp = 0
	if z <= -3e+190:
		tmp = t_1
	elif z <= 3.8e+211:
		tmp = x + (((y - z) * (t - x)) / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(t - x))
	tmp = 0.0
	if (z <= -3e+190)
		tmp = t_1;
	elseif (z <= 3.8e+211)
		tmp = Float64(x + Float64(Float64(Float64(y - z) * Float64(t - x)) / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (t - x);
	tmp = 0.0;
	if (z <= -3e+190)
		tmp = t_1;
	elseif (z <= 3.8e+211)
		tmp = x + (((y - z) * (t - x)) / (a - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(t - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3e+190], t$95$1, If[LessEqual[z, 3.8e+211], N[(x + N[(N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.99999999999999982e190 or 3.80000000000000016e211 < z

   1. Initial program 19.8%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 2.9%

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

   6. Taylor expanded in x around 0

      \[\leadsto x + \left(y - z\right) \cdot t \]

   8. Applied rewrites 4.3%

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

   9. Taylor expanded in x around -inf

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

   11. Applied rewrites 50.8%

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

   if -2.99999999999999982e190 < z < 3.80000000000000016e211

   1. Initial program 74.5%

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

Alternative 2: 33.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y - z}{a - z}\\ \mathbf{if}\;a \leq -9.5 \cdot 10^{+72}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 5.2 \cdot 10^{-43}:\\ \;\;\;\;x + \left(t - x\right)\\ \mathbf{elif}\;a \leq 5 \cdot 10^{+94}:\\ \;\;\;\;x + \left(y - z\right) \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (- y z) (- a z)))))
   (if (<= a -9.5e+72)
     t_1
     (if (<= a 5.2e-43)
       (+ x (- t x))
       (if (<= a 5e+94) (+ x (* (- y z) (- t x))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) / (a - z));
	double tmp;
	if (a <= -9.5e+72) {
		tmp = t_1;
	} else if (a <= 5.2e-43) {
		tmp = x + (t - x);
	} else if (a <= 5e+94) {
		tmp = x + ((y - 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) / (a - z))
    if (a <= (-9.5d+72)) then
        tmp = t_1
    else if (a <= 5.2d-43) then
        tmp = x + (t - x)
    else if (a <= 5d+94) then
        tmp = x + ((y - 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) / (a - z));
	double tmp;
	if (a <= -9.5e+72) {
		tmp = t_1;
	} else if (a <= 5.2e-43) {
		tmp = x + (t - x);
	} else if (a <= 5e+94) {
		tmp = x + ((y - z) * (t - x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) / (a - z))
	tmp = 0
	if a <= -9.5e+72:
		tmp = t_1
	elif a <= 5.2e-43:
		tmp = x + (t - x)
	elif a <= 5e+94:
		tmp = x + ((y - z) * (t - x))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (a <= -9.5e+72)
		tmp = t_1;
	elseif (a <= 5.2e-43)
		tmp = Float64(x + Float64(t - x));
	elseif (a <= 5e+94)
		tmp = Float64(x + Float64(Float64(y - z) * Float64(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) / (a - z));
	tmp = 0.0;
	if (a <= -9.5e+72)
		tmp = t_1;
	elseif (a <= 5.2e-43)
		tmp = x + (t - x);
	elseif (a <= 5e+94)
		tmp = x + ((y - 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[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -9.5e+72], t$95$1, If[LessEqual[a, 5.2e-43], N[(x + N[(t - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5e+94], N[(x + N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if a < -9.50000000000000054e72 or 5.0000000000000001e94 < a

   1. Initial program 68.3%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 41.6%

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

   if -9.50000000000000054e72 < a < 5.2e-43

   1. Initial program 62.1%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 9.6%

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

   6. Taylor expanded in x around 0

      \[\leadsto x + \left(y - z\right) \cdot t \]

   8. Applied rewrites 13.7%

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

   9. Taylor expanded in x around -inf

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

   11. Applied rewrites 28.7%

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

   if 5.2e-43 < a < 5.0000000000000001e94

   1. Initial program 67.8%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 37.6%

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

Alternative 3: 28.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.8 \cdot 10^{+133}:\\ \;\;\;\;x + \left(y - z\right)\\ \mathbf{elif}\;a \leq 5.2 \cdot 10^{-43}:\\ \;\;\;\;x + \left(t - x\right)\\ \mathbf{elif}\;a \leq 2.7 \cdot 10^{+113}:\\ \;\;\;\;x + \left(y - z\right) \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{a - z}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.8e+133)
   (+ x (- y z))
   (if (<= a 5.2e-43)
     (+ x (- t x))
     (if (<= a 2.7e+113)
       (+ x (* (- y z) (- t x)))
       (+ x (/ (- a z) (- a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.8e+133) {
		tmp = x + (y - z);
	} else if (a <= 5.2e-43) {
		tmp = x + (t - x);
	} else if (a <= 2.7e+113) {
		tmp = x + ((y - z) * (t - x));
	} else {
		tmp = x + ((a - z) / (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 (a <= (-1.8d+133)) then
        tmp = x + (y - z)
    else if (a <= 5.2d-43) then
        tmp = x + (t - x)
    else if (a <= 2.7d+113) then
        tmp = x + ((y - z) * (t - x))
    else
        tmp = x + ((a - z) / (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 (a <= -1.8e+133) {
		tmp = x + (y - z);
	} else if (a <= 5.2e-43) {
		tmp = x + (t - x);
	} else if (a <= 2.7e+113) {
		tmp = x + ((y - z) * (t - x));
	} else {
		tmp = x + ((a - z) / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.8e+133:
		tmp = x + (y - z)
	elif a <= 5.2e-43:
		tmp = x + (t - x)
	elif a <= 2.7e+113:
		tmp = x + ((y - z) * (t - x))
	else:
		tmp = x + ((a - z) / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.8e+133)
		tmp = Float64(x + Float64(y - z));
	elseif (a <= 5.2e-43)
		tmp = Float64(x + Float64(t - x));
	elseif (a <= 2.7e+113)
		tmp = Float64(x + Float64(Float64(y - z) * Float64(t - x)));
	else
		tmp = Float64(x + Float64(Float64(a - z) / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.8e+133)
		tmp = x + (y - z);
	elseif (a <= 5.2e-43)
		tmp = x + (t - x);
	elseif (a <= 2.7e+113)
		tmp = x + ((y - z) * (t - x));
	else
		tmp = x + ((a - z) / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.8e+133], N[(x + N[(y - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.2e-43], N[(x + N[(t - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.7e+113], N[(x + N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(a - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if a < -1.79999999999999989e133

   1. Initial program 75.1%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 22.8%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 48.4%

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

   if -1.79999999999999989e133 < a < 5.2e-43

   1. Initial program 61.2%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 10.5%

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

   6. Taylor expanded in x around 0

      \[\leadsto x + \left(y - z\right) \cdot t \]

   8. Applied rewrites 14.6%

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

   9. Taylor expanded in x around -inf

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

   11. Applied rewrites 28.6%

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

   if 5.2e-43 < a < 2.70000000000000011e113

   1. Initial program 68.7%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 36.1%

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

   if 2.70000000000000011e113 < a

   1. Initial program 67.6%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 26.0%

      \[\leadsto x + \frac{\color{blue}{a - z}}{a - z} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 50.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(t - x\right)\\ \mathbf{if}\;z \leq -1.15 \cdot 10^{+184}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+211}:\\ \;\;\;\;\frac{\left(y - z\right) \cdot \left(t - x\right)}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (- t x))))
   (if (<= z -1.15e+184)
     t_1
     (if (<= z 3.8e+211) (/ (* (- y z) (- t x)) (- a z)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (t - x);
	double tmp;
	if (z <= -1.15e+184) {
		tmp = t_1;
	} else if (z <= 3.8e+211) {
		tmp = ((y - z) * (t - x)) / (a - z);
	} 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 - x)
    if (z <= (-1.15d+184)) then
        tmp = t_1
    else if (z <= 3.8d+211) then
        tmp = ((y - z) * (t - x)) / (a - z)
    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 - x);
	double tmp;
	if (z <= -1.15e+184) {
		tmp = t_1;
	} else if (z <= 3.8e+211) {
		tmp = ((y - z) * (t - x)) / (a - z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (t - x)
	tmp = 0
	if z <= -1.15e+184:
		tmp = t_1
	elif z <= 3.8e+211:
		tmp = ((y - z) * (t - x)) / (a - z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(t - x))
	tmp = 0.0
	if (z <= -1.15e+184)
		tmp = t_1;
	elseif (z <= 3.8e+211)
		tmp = Float64(Float64(Float64(y - z) * Float64(t - x)) / Float64(a - z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (t - x);
	tmp = 0.0;
	if (z <= -1.15e+184)
		tmp = t_1;
	elseif (z <= 3.8e+211)
		tmp = ((y - z) * (t - x)) / (a - z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(t - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.15e+184], t$95$1, If[LessEqual[z, 3.8e+211], N[(N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.15e184 or 3.80000000000000016e211 < z

   1. Initial program 21.6%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 2.9%

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

   6. Taylor expanded in x around 0

      \[\leadsto x + \left(y - z\right) \cdot t \]

   8. Applied rewrites 4.3%

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

   9. Taylor expanded in x around -inf

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

   11. Applied rewrites 51.9%

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

   if -1.15e184 < z < 3.80000000000000016e211

   1. Initial program 74.4%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 51.9%

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

Alternative 5: 27.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right)\\ \mathbf{if}\;a \leq -1.8 \cdot 10^{+133}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 5.2 \cdot 10^{-43}:\\ \;\;\;\;x + \left(t - x\right)\\ \mathbf{elif}\;a \leq 7 \cdot 10^{+94}:\\ \;\;\;\;x + \left(y - z\right) \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (- y z))))
   (if (<= a -1.8e+133)
     t_1
     (if (<= a 5.2e-43)
       (+ x (- t x))
       (if (<= a 7e+94) (+ x (* (- y z) (- t x))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y - z);
	double tmp;
	if (a <= -1.8e+133) {
		tmp = t_1;
	} else if (a <= 5.2e-43) {
		tmp = x + (t - x);
	} else if (a <= 7e+94) {
		tmp = x + ((y - 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)
    if (a <= (-1.8d+133)) then
        tmp = t_1
    else if (a <= 5.2d-43) then
        tmp = x + (t - x)
    else if (a <= 7d+94) then
        tmp = x + ((y - 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);
	double tmp;
	if (a <= -1.8e+133) {
		tmp = t_1;
	} else if (a <= 5.2e-43) {
		tmp = x + (t - x);
	} else if (a <= 7e+94) {
		tmp = x + ((y - z) * (t - x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y - z)
	tmp = 0
	if a <= -1.8e+133:
		tmp = t_1
	elif a <= 5.2e-43:
		tmp = x + (t - x)
	elif a <= 7e+94:
		tmp = x + ((y - z) * (t - x))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y - z))
	tmp = 0.0
	if (a <= -1.8e+133)
		tmp = t_1;
	elseif (a <= 5.2e-43)
		tmp = Float64(x + Float64(t - x));
	elseif (a <= 7e+94)
		tmp = Float64(x + Float64(Float64(y - z) * Float64(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);
	tmp = 0.0;
	if (a <= -1.8e+133)
		tmp = t_1;
	elseif (a <= 5.2e-43)
		tmp = x + (t - x);
	elseif (a <= 7e+94)
		tmp = x + ((y - 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 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.8e+133], t$95$1, If[LessEqual[a, 5.2e-43], N[(x + N[(t - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7e+94], N[(x + N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if a < -1.79999999999999989e133 or 6.9999999999999994e94 < a

   1. Initial program 71.4%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 17.5%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 35.8%

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

   if -1.79999999999999989e133 < a < 5.2e-43

   1. Initial program 61.2%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 10.5%

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

   6. Taylor expanded in x around 0

      \[\leadsto x + \left(y - z\right) \cdot t \]

   8. Applied rewrites 14.6%

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

   9. Taylor expanded in x around -inf

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

   11. Applied rewrites 28.6%

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

   if 5.2e-43 < a < 6.9999999999999994e94

   1. Initial program 67.8%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 37.6%

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

Alternative 6: 26.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right)\\ \mathbf{if}\;a \leq -1.8 \cdot 10^{+133}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 9.5 \cdot 10^{-36}:\\ \;\;\;\;x + \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (- y z))))
   (if (<= a -1.8e+133) t_1 (if (<= a 9.5e-36) (+ x (- t x)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y - z);
	double tmp;
	if (a <= -1.8e+133) {
		tmp = t_1;
	} else if (a <= 9.5e-36) {
		tmp = x + (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)
    if (a <= (-1.8d+133)) then
        tmp = t_1
    else if (a <= 9.5d-36) then
        tmp = x + (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);
	double tmp;
	if (a <= -1.8e+133) {
		tmp = t_1;
	} else if (a <= 9.5e-36) {
		tmp = x + (t - x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y - z)
	tmp = 0
	if a <= -1.8e+133:
		tmp = t_1
	elif a <= 9.5e-36:
		tmp = x + (t - x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y - z))
	tmp = 0.0
	if (a <= -1.8e+133)
		tmp = t_1;
	elseif (a <= 9.5e-36)
		tmp = Float64(x + Float64(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);
	tmp = 0.0;
	if (a <= -1.8e+133)
		tmp = t_1;
	elseif (a <= 9.5e-36)
		tmp = x + (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 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.8e+133], t$95$1, If[LessEqual[a, 9.5e-36], N[(x + N[(t - x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -1.79999999999999989e133 or 9.5000000000000003e-36 < a

   1. Initial program 70.3%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 23.9%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 29.4%

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

   if -1.79999999999999989e133 < a < 9.5000000000000003e-36

   1. Initial program 61.2%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 10.5%

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

   6. Taylor expanded in x around 0

      \[\leadsto x + \left(y - z\right) \cdot t \]

   8. Applied rewrites 14.6%

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

   9. Taylor expanded in x around -inf

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

   11. Applied rewrites 28.6%

       \[\leadsto x + \color{blue}{\left(t - x\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 17.4% accurate, 4.1× speedup

Math

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

FPCore

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

C

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

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)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 65.1%

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

3. Taylor expanded in x around 0

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

5. Applied rewrites 16.3%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 16.7%

   \[\leadsto x + \left(y - \color{blue}{z}\right) \]
9. Add Preprocessing

Alternative 8: 9.7% accurate, 4.1× speedup

Math

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

FPCore

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

C

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

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)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 65.1%

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

3. Taylor expanded in x around 0

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

5. Applied rewrites 16.3%

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

6. Taylor expanded in x around 0

   \[\leadsto x + \left(y - z\right) \cdot t \]

8. Applied rewrites 18.2%

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

9. Taylor expanded in x around 0

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

11. Applied rewrites 8.4%

    \[\leadsto x + \left(a - \color{blue}{z}\right) \]
12. Add Preprocessing

Alternative 9: 3.0% accurate, 7.3× speedup

Math

\[\begin{array}{l} \\ a - z \end{array} \]

FPCore

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

C

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

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 = a - z
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(a - z), $MachinePrecision]

Derivation

1. Initial program 65.1%

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

3. Taylor expanded in x around 0

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

5. Applied rewrites 45.7%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 3.2%

   \[\leadsto a - \color{blue}{z} \]
9. Add Preprocessing

Developer Target 1: 84.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - \frac{y}{z} \cdot \left(t - x\right)\\ \mathbf{if}\;z < -1.2536131056095036 \cdot 10^{+188}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z < 4.446702369113811 \cdot 10^{+64}:\\ \;\;\;\;x + \frac{y - z}{\frac{a - z}{t - x}}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (* (/ y z) (- t x)))))
   (if (< z -1.2536131056095036e+188)
     t_1
     (if (< z 4.446702369113811e+64)
       (+ x (/ (- y z) (/ (- a z) (- t x))))
       t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - ((y / z) * (t - x));
	double tmp;
	if (z < -1.2536131056095036e+188) {
		tmp = t_1;
	} else if (z < 4.446702369113811e+64) {
		tmp = x + ((y - z) / ((a - 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 = t - ((y / z) * (t - x))
    if (z < (-1.2536131056095036d+188)) then
        tmp = t_1
    else if (z < 4.446702369113811d+64) then
        tmp = x + ((y - z) / ((a - 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 = t - ((y / z) * (t - x));
	double tmp;
	if (z < -1.2536131056095036e+188) {
		tmp = t_1;
	} else if (z < 4.446702369113811e+64) {
		tmp = x + ((y - z) / ((a - z) / (t - x)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - ((y / z) * (t - x))
	tmp = 0
	if z < -1.2536131056095036e+188:
		tmp = t_1
	elif z < 4.446702369113811e+64:
		tmp = x + ((y - z) / ((a - z) / (t - x)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(Float64(y / z) * Float64(t - x)))
	tmp = 0.0
	if (z < -1.2536131056095036e+188)
		tmp = t_1;
	elseif (z < 4.446702369113811e+64)
		tmp = Float64(x + Float64(Float64(y - z) / Float64(Float64(a - z) / Float64(t - x))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - ((y / z) * (t - x));
	tmp = 0.0;
	if (z < -1.2536131056095036e+188)
		tmp = t_1;
	elseif (z < 4.446702369113811e+64)
		tmp = x + ((y - z) / ((a - z) / (t - x)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(N[(y / z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -1.2536131056095036e+188], t$95$1, If[Less[z, 4.446702369113811e+64], N[(x + N[(N[(y - z), $MachinePrecision] / N[(N[(a - z), $MachinePrecision] / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2024313 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Chart.Axis.Types:invLinMap from Chart-1.5.3"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< z -125361310560950360000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (- t (* (/ y z) (- t x))) (if (< z 44467023691138110000000000000000000000000000000000000000000000000) (+ x (/ (- y z) (/ (- a z) (- t x)))) (- t (* (/ y z) (- t x))))))

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