Numeric.Signal:interpolate from hsignal-0.2.7.1

Percentage Accurate: 80.4% → 90.7%

Time: 18.5s

Alternatives: 17

Speedup: 0.3×

Specification

Math

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

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(y - z) * Float64(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[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 80.4% accurate, 1.0× speedup

Math

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

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(y - z) * Float64(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[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 90.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t_1 \leq -1 \cdot 10^{-266} \lor \neg \left(t_1 \leq 0\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (or (<= t_1 -1e-266) (not (<= t_1 0.0)))
     t_1
     (+ t (/ (- x t) (/ z (- y a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-266) || !(t_1 <= 0.0)) {
		tmp = t_1;
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + ((y - z) * ((t - x) / (a - z)))
    if ((t_1 <= (-1d-266)) .or. (.not. (t_1 <= 0.0d0))) then
        tmp = t_1
    else
        tmp = t + ((x - t) / (z / (y - a)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-266) || !(t_1 <= 0.0)) {
		tmp = t_1;
	} else {
		tmp = t + ((x - t) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if (t_1 <= -1e-266) or not (t_1 <= 0.0):
		tmp = t_1
	else:
		tmp = t + ((x - t) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if ((t_1 <= -1e-266) || !(t_1 <= 0.0))
		tmp = t_1;
	else
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / Float64(y - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if ((t_1 <= -1e-266) || ~((t_1 <= 0.0)))
		tmp = t_1;
	else
		tmp = t + ((x - t) / (z / (y - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e-266], N[Not[LessEqual[t$95$1, 0.0]], $MachinePrecision]], t$95$1, N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 46.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -2.1 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-262}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{-303}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 3.3 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+139}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 1.06 \cdot 10^{+185}:\\ \;\;\;\;\frac{-x}{\frac{z}{a - y}}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= z -2.1e+20)
     t
     (if (<= z -2.1e-262)
       t_1
       (if (<= z 4.1e-303)
         (/ t (/ a y))
         (if (<= z 3.3e+23)
           t_1
           (if (<= z 5.2e+139)
             t
             (if (<= z 1.06e+185) (/ (- x) (/ z (- a y))) t))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -2.1e-262) {
		tmp = t_1;
	} else if (z <= 4.1e-303) {
		tmp = t / (a / y);
	} else if (z <= 3.3e+23) {
		tmp = t_1;
	} else if (z <= 5.2e+139) {
		tmp = t;
	} else if (z <= 1.06e+185) {
		tmp = -x / (z / (a - y));
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (z <= (-2.1d+20)) then
        tmp = t
    else if (z <= (-2.1d-262)) then
        tmp = t_1
    else if (z <= 4.1d-303) then
        tmp = t / (a / y)
    else if (z <= 3.3d+23) then
        tmp = t_1
    else if (z <= 5.2d+139) then
        tmp = t
    else if (z <= 1.06d+185) then
        tmp = -x / (z / (a - y))
    else
        tmp = t
    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 * (1.0 - (y / a));
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -2.1e-262) {
		tmp = t_1;
	} else if (z <= 4.1e-303) {
		tmp = t / (a / y);
	} else if (z <= 3.3e+23) {
		tmp = t_1;
	} else if (z <= 5.2e+139) {
		tmp = t;
	} else if (z <= 1.06e+185) {
		tmp = -x / (z / (a - y));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -2.1e+20:
		tmp = t
	elif z <= -2.1e-262:
		tmp = t_1
	elif z <= 4.1e-303:
		tmp = t / (a / y)
	elif z <= 3.3e+23:
		tmp = t_1
	elif z <= 5.2e+139:
		tmp = t
	elif z <= 1.06e+185:
		tmp = -x / (z / (a - y))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -2.1e-262)
		tmp = t_1;
	elseif (z <= 4.1e-303)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 3.3e+23)
		tmp = t_1;
	elseif (z <= 5.2e+139)
		tmp = t;
	elseif (z <= 1.06e+185)
		tmp = Float64(Float64(-x) / Float64(z / Float64(a - y)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -2.1e-262)
		tmp = t_1;
	elseif (z <= 4.1e-303)
		tmp = t / (a / y);
	elseif (z <= 3.3e+23)
		tmp = t_1;
	elseif (z <= 5.2e+139)
		tmp = t;
	elseif (z <= 1.06e+185)
		tmp = -x / (z / (a - y));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.1e+20], t, If[LessEqual[z, -2.1e-262], t$95$1, If[LessEqual[z, 4.1e-303], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.3e+23], t$95$1, If[LessEqual[z, 5.2e+139], t, If[LessEqual[z, 1.06e+185], N[((-x) / N[(z / N[(a - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t]]]]]]]

Derivation

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

Alternative 3: 38.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.32 \cdot 10^{+19}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -9 \cdot 10^{-260}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.85 \cdot 10^{-302}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 2.5 \cdot 10^{-107}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{-76}:\\ \;\;\;\;t \cdot \frac{y - z}{a}\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{+23}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.32e+19)
   t
   (if (<= z -9e-260)
     x
     (if (<= z 2.85e-302)
       (/ t (/ a y))
       (if (<= z 2.5e-107)
         x
         (if (<= z 2.1e-76) (* t (/ (- y z) a)) (if (<= z 3.7e+23) x t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.32e+19) {
		tmp = t;
	} else if (z <= -9e-260) {
		tmp = x;
	} else if (z <= 2.85e-302) {
		tmp = t / (a / y);
	} else if (z <= 2.5e-107) {
		tmp = x;
	} else if (z <= 2.1e-76) {
		tmp = t * ((y - z) / a);
	} else if (z <= 3.7e+23) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-1.32d+19)) then
        tmp = t
    else if (z <= (-9d-260)) then
        tmp = x
    else if (z <= 2.85d-302) then
        tmp = t / (a / y)
    else if (z <= 2.5d-107) then
        tmp = x
    else if (z <= 2.1d-76) then
        tmp = t * ((y - z) / a)
    else if (z <= 3.7d+23) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.32e+19) {
		tmp = t;
	} else if (z <= -9e-260) {
		tmp = x;
	} else if (z <= 2.85e-302) {
		tmp = t / (a / y);
	} else if (z <= 2.5e-107) {
		tmp = x;
	} else if (z <= 2.1e-76) {
		tmp = t * ((y - z) / a);
	} else if (z <= 3.7e+23) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.32e+19:
		tmp = t
	elif z <= -9e-260:
		tmp = x
	elif z <= 2.85e-302:
		tmp = t / (a / y)
	elif z <= 2.5e-107:
		tmp = x
	elif z <= 2.1e-76:
		tmp = t * ((y - z) / a)
	elif z <= 3.7e+23:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.32e+19)
		tmp = t;
	elseif (z <= -9e-260)
		tmp = x;
	elseif (z <= 2.85e-302)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 2.5e-107)
		tmp = x;
	elseif (z <= 2.1e-76)
		tmp = Float64(t * Float64(Float64(y - z) / a));
	elseif (z <= 3.7e+23)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.32e+19)
		tmp = t;
	elseif (z <= -9e-260)
		tmp = x;
	elseif (z <= 2.85e-302)
		tmp = t / (a / y);
	elseif (z <= 2.5e-107)
		tmp = x;
	elseif (z <= 2.1e-76)
		tmp = t * ((y - z) / a);
	elseif (z <= 3.7e+23)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.32e+19], t, If[LessEqual[z, -9e-260], x, If[LessEqual[z, 2.85e-302], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.5e-107], x, If[LessEqual[z, 2.1e-76], N[(t * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.7e+23], x, t]]]]]]

Derivation

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

Alternative 4: 55.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ t_2 := t \cdot \frac{y - z}{a - z}\\ \mathbf{if}\;z \leq -2.75 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{-263}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-303}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+14}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))) (t_2 (* t (/ (- y z) (- a z)))))
   (if (<= z -2.75e+17)
     t_2
     (if (<= z -8.5e-263)
       t_1
       (if (<= z 4e-303) (/ t (/ a y)) (if (<= z 2.7e+14) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double t_2 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -2.75e+17) {
		tmp = t_2;
	} else if (z <= -8.5e-263) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 2.7e+14) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    t_2 = t * ((y - z) / (a - z))
    if (z <= (-2.75d+17)) then
        tmp = t_2
    else if (z <= (-8.5d-263)) then
        tmp = t_1
    else if (z <= 4d-303) then
        tmp = t / (a / y)
    else if (z <= 2.7d+14) then
        tmp = t_1
    else
        tmp = t_2
    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 * (1.0 - (y / a));
	double t_2 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -2.75e+17) {
		tmp = t_2;
	} else if (z <= -8.5e-263) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 2.7e+14) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	t_2 = t * ((y - z) / (a - z))
	tmp = 0
	if z <= -2.75e+17:
		tmp = t_2
	elif z <= -8.5e-263:
		tmp = t_1
	elif z <= 4e-303:
		tmp = t / (a / y)
	elif z <= 2.7e+14:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	t_2 = Float64(t * Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (z <= -2.75e+17)
		tmp = t_2;
	elseif (z <= -8.5e-263)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 2.7e+14)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	t_2 = t * ((y - z) / (a - z));
	tmp = 0.0;
	if (z <= -2.75e+17)
		tmp = t_2;
	elseif (z <= -8.5e-263)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = t / (a / y);
	elseif (z <= 2.7e+14)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.75e+17], t$95$2, If[LessEqual[z, -8.5e-263], t$95$1, If[LessEqual[z, 4e-303], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.7e+14], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 5: 50.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ t_2 := t \cdot \left(-\frac{z}{a - z}\right)\\ \mathbf{if}\;z \leq -1.85 \cdot 10^{+20}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-262}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-303}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))) (t_2 (* t (- (/ z (- a z))))))
   (if (<= z -1.85e+20)
     t_2
     (if (<= z -1.3e-262)
       t_1
       (if (<= z 4e-303) (/ t (/ a y)) (if (<= z 1.85e+23) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double t_2 = t * -(z / (a - z));
	double tmp;
	if (z <= -1.85e+20) {
		tmp = t_2;
	} else if (z <= -1.3e-262) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 1.85e+23) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    t_2 = t * -(z / (a - z))
    if (z <= (-1.85d+20)) then
        tmp = t_2
    else if (z <= (-1.3d-262)) then
        tmp = t_1
    else if (z <= 4d-303) then
        tmp = t / (a / y)
    else if (z <= 1.85d+23) then
        tmp = t_1
    else
        tmp = t_2
    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 * (1.0 - (y / a));
	double t_2 = t * -(z / (a - z));
	double tmp;
	if (z <= -1.85e+20) {
		tmp = t_2;
	} else if (z <= -1.3e-262) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 1.85e+23) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	t_2 = t * -(z / (a - z))
	tmp = 0
	if z <= -1.85e+20:
		tmp = t_2
	elif z <= -1.3e-262:
		tmp = t_1
	elif z <= 4e-303:
		tmp = t / (a / y)
	elif z <= 1.85e+23:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	t_2 = Float64(t * Float64(-Float64(z / Float64(a - z))))
	tmp = 0.0
	if (z <= -1.85e+20)
		tmp = t_2;
	elseif (z <= -1.3e-262)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 1.85e+23)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	t_2 = t * -(z / (a - z));
	tmp = 0.0;
	if (z <= -1.85e+20)
		tmp = t_2;
	elseif (z <= -1.3e-262)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = t / (a / y);
	elseif (z <= 1.85e+23)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t * (-N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision])), $MachinePrecision]}, If[LessEqual[z, -1.85e+20], t$95$2, If[LessEqual[z, -1.3e-262], t$95$1, If[LessEqual[z, 4e-303], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.85e+23], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 6: 50.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -9.5 \cdot 10^{+17}:\\ \;\;\;\;\frac{t}{\frac{-z}{y - z}}\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{-263}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.8 \cdot 10^{-303}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 7.3 \cdot 10^{+22}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(-\frac{z}{a - z}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= z -9.5e+17)
     (/ t (/ (- z) (- y z)))
     (if (<= z -8.5e-263)
       t_1
       (if (<= z 4.8e-303)
         (/ t (/ a y))
         (if (<= z 7.3e+22) t_1 (* t (- (/ z (- a z))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -9.5e+17) {
		tmp = t / (-z / (y - z));
	} else if (z <= -8.5e-263) {
		tmp = t_1;
	} else if (z <= 4.8e-303) {
		tmp = t / (a / y);
	} else if (z <= 7.3e+22) {
		tmp = t_1;
	} else {
		tmp = t * -(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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (z <= (-9.5d+17)) then
        tmp = t / (-z / (y - z))
    else if (z <= (-8.5d-263)) then
        tmp = t_1
    else if (z <= 4.8d-303) then
        tmp = t / (a / y)
    else if (z <= 7.3d+22) then
        tmp = t_1
    else
        tmp = t * -(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 t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -9.5e+17) {
		tmp = t / (-z / (y - z));
	} else if (z <= -8.5e-263) {
		tmp = t_1;
	} else if (z <= 4.8e-303) {
		tmp = t / (a / y);
	} else if (z <= 7.3e+22) {
		tmp = t_1;
	} else {
		tmp = t * -(z / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -9.5e+17:
		tmp = t / (-z / (y - z))
	elif z <= -8.5e-263:
		tmp = t_1
	elif z <= 4.8e-303:
		tmp = t / (a / y)
	elif z <= 7.3e+22:
		tmp = t_1
	else:
		tmp = t * -(z / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -9.5e+17)
		tmp = Float64(t / Float64(Float64(-z) / Float64(y - z)));
	elseif (z <= -8.5e-263)
		tmp = t_1;
	elseif (z <= 4.8e-303)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 7.3e+22)
		tmp = t_1;
	else
		tmp = Float64(t * Float64(-Float64(z / Float64(a - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -9.5e+17)
		tmp = t / (-z / (y - z));
	elseif (z <= -8.5e-263)
		tmp = t_1;
	elseif (z <= 4.8e-303)
		tmp = t / (a / y);
	elseif (z <= 7.3e+22)
		tmp = t_1;
	else
		tmp = t * -(z / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -9.5e+17], N[(t / N[((-z) / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -8.5e-263], t$95$1, If[LessEqual[z, 4.8e-303], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.3e+22], t$95$1, N[(t * (-N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision])), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 47.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -2.1 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{-263}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-303}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= z -2.1e+20)
     t
     (if (<= z -8.5e-263)
       t_1
       (if (<= z 4e-303) (/ t (/ a y)) (if (<= z 3.5e+23) t_1 t))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -8.5e-263) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 3.5e+23) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (z <= (-2.1d+20)) then
        tmp = t
    else if (z <= (-8.5d-263)) then
        tmp = t_1
    else if (z <= 4d-303) then
        tmp = t / (a / y)
    else if (z <= 3.5d+23) then
        tmp = t_1
    else
        tmp = t
    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 * (1.0 - (y / a));
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -8.5e-263) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 3.5e+23) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -2.1e+20:
		tmp = t
	elif z <= -8.5e-263:
		tmp = t_1
	elif z <= 4e-303:
		tmp = t / (a / y)
	elif z <= 3.5e+23:
		tmp = t_1
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -8.5e-263)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 3.5e+23)
		tmp = t_1;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -8.5e-263)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = t / (a / y);
	elseif (z <= 3.5e+23)
		tmp = t_1;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.1e+20], t, If[LessEqual[z, -8.5e-263], t$95$1, If[LessEqual[z, 4e-303], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.5e+23], t$95$1, t]]]]]

Derivation

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

Alternative 8: 50.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -1.22 \cdot 10^{+18}:\\ \;\;\;\;\frac{t}{\frac{-z}{y - z}}\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-262}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-303}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{\frac{z - a}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= z -1.22e+18)
     (/ t (/ (- z) (- y z)))
     (if (<= z -2.1e-262)
       t_1
       (if (<= z 4e-303)
         (/ t (/ a y))
         (if (<= z 1.95e+23) t_1 (/ t (/ (- z a) z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -1.22e+18) {
		tmp = t / (-z / (y - z));
	} else if (z <= -2.1e-262) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 1.95e+23) {
		tmp = t_1;
	} else {
		tmp = t / ((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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (z <= (-1.22d+18)) then
        tmp = t / (-z / (y - z))
    else if (z <= (-2.1d-262)) then
        tmp = t_1
    else if (z <= 4d-303) then
        tmp = t / (a / y)
    else if (z <= 1.95d+23) then
        tmp = t_1
    else
        tmp = t / ((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 t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -1.22e+18) {
		tmp = t / (-z / (y - z));
	} else if (z <= -2.1e-262) {
		tmp = t_1;
	} else if (z <= 4e-303) {
		tmp = t / (a / y);
	} else if (z <= 1.95e+23) {
		tmp = t_1;
	} else {
		tmp = t / ((z - a) / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -1.22e+18:
		tmp = t / (-z / (y - z))
	elif z <= -2.1e-262:
		tmp = t_1
	elif z <= 4e-303:
		tmp = t / (a / y)
	elif z <= 1.95e+23:
		tmp = t_1
	else:
		tmp = t / ((z - a) / z)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -1.22e+18)
		tmp = Float64(t / Float64(Float64(-z) / Float64(y - z)));
	elseif (z <= -2.1e-262)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 1.95e+23)
		tmp = t_1;
	else
		tmp = Float64(t / Float64(Float64(z - a) / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -1.22e+18)
		tmp = t / (-z / (y - z));
	elseif (z <= -2.1e-262)
		tmp = t_1;
	elseif (z <= 4e-303)
		tmp = t / (a / y);
	elseif (z <= 1.95e+23)
		tmp = t_1;
	else
		tmp = t / ((z - a) / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.22e+18], N[(t / N[((-z) / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.1e-262], t$95$1, If[LessEqual[z, 4e-303], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.95e+23], t$95$1, N[(t / N[(N[(z - a), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 9: 74.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.62 \cdot 10^{-56} \lor \neg \left(a \leq 6.8 \cdot 10^{-24}\right):\\ \;\;\;\;x + \left(y - z\right) \cdot \frac{t - x}{a}\\ \mathbf{else}:\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -1.62e-56) (not (<= a 6.8e-24)))
   (+ x (* (- y z) (/ (- t x) a)))
   (+ t (/ (- x t) (/ z y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -1.62e-56) || !(a <= 6.8e-24)) {
		tmp = x + ((y - z) * ((t - x) / a));
	} else {
		tmp = t + ((x - t) / (z / 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 ((a <= (-1.62d-56)) .or. (.not. (a <= 6.8d-24))) then
        tmp = x + ((y - z) * ((t - x) / a))
    else
        tmp = t + ((x - t) / (z / 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 ((a <= -1.62e-56) || !(a <= 6.8e-24)) {
		tmp = x + ((y - z) * ((t - x) / a));
	} else {
		tmp = t + ((x - t) / (z / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -1.62e-56) or not (a <= 6.8e-24):
		tmp = x + ((y - z) * ((t - x) / a))
	else:
		tmp = t + ((x - t) / (z / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -1.62e-56) || !(a <= 6.8e-24))
		tmp = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / a)));
	else
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -1.62e-56) || ~((a <= 6.8e-24)))
		tmp = x + ((y - z) * ((t - x) / a));
	else
		tmp = t + ((x - t) / (z / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -1.62e-56], N[Not[LessEqual[a, 6.8e-24]], $MachinePrecision]], N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 75.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.15 \cdot 10^{+18} \lor \neg \left(z \leq 2.9 \cdot 10^{+23}\right):\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y - a}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - z\right) \cdot \frac{t - x}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.15e+18) (not (<= z 2.9e+23)))
   (+ t (/ (- x t) (/ z (- y a))))
   (+ x (* (- y z) (/ (- t x) a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.15e+18) || !(z <= 2.9e+23)) {
		tmp = t + ((x - t) / (z / (y - a)));
	} else {
		tmp = x + ((y - z) * ((t - x) / a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-1.15d+18)) .or. (.not. (z <= 2.9d+23))) then
        tmp = t + ((x - t) / (z / (y - a)))
    else
        tmp = x + ((y - z) * ((t - x) / a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.15e+18) || !(z <= 2.9e+23)) {
		tmp = t + ((x - t) / (z / (y - a)));
	} else {
		tmp = x + ((y - z) * ((t - x) / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.15e+18) or not (z <= 2.9e+23):
		tmp = t + ((x - t) / (z / (y - a)))
	else:
		tmp = x + ((y - z) * ((t - x) / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.15e+18) || !(z <= 2.9e+23))
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / Float64(y - a))));
	else
		tmp = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.15e+18) || ~((z <= 2.9e+23)))
		tmp = t + ((x - t) / (z / (y - a)));
	else
		tmp = x + ((y - z) * ((t - x) / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.15e+18], N[Not[LessEqual[z, 2.9e+23]], $MachinePrecision]], N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 66.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.4 \cdot 10^{+19} \lor \neg \left(z \leq 2.75 \cdot 10^{+20}\right):\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -2.4e+19) (not (<= z 2.75e+20)))
   (* t (/ (- y z) (- a z)))
   (+ x (/ y (/ a (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.4e+19) || !(z <= 2.75e+20)) {
		tmp = t * ((y - z) / (a - z));
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-2.4d+19)) .or. (.not. (z <= 2.75d+20))) then
        tmp = t * ((y - z) / (a - z))
    else
        tmp = x + (y / (a / (t - x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.4e+19) || !(z <= 2.75e+20)) {
		tmp = t * ((y - z) / (a - z));
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -2.4e+19) or not (z <= 2.75e+20):
		tmp = t * ((y - z) / (a - z))
	else:
		tmp = x + (y / (a / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -2.4e+19) || !(z <= 2.75e+20))
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	else
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -2.4e+19) || ~((z <= 2.75e+20)))
		tmp = t * ((y - z) / (a - z));
	else
		tmp = x + (y / (a / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -2.4e+19], N[Not[LessEqual[z, 2.75e+20]], $MachinePrecision]], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 69.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.08 \cdot 10^{+18} \lor \neg \left(z \leq 1.42 \cdot 10^{+22}\right):\\ \;\;\;\;t + \frac{x - t}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.08e+18) (not (<= z 1.42e+22)))
   (+ t (/ (- x t) (/ z y)))
   (+ x (/ y (/ a (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.08e+18) || !(z <= 1.42e+22)) {
		tmp = t + ((x - t) / (z / y));
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-1.08d+18)) .or. (.not. (z <= 1.42d+22))) then
        tmp = t + ((x - t) / (z / y))
    else
        tmp = x + (y / (a / (t - x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.08e+18) || !(z <= 1.42e+22)) {
		tmp = t + ((x - t) / (z / y));
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.08e+18) or not (z <= 1.42e+22):
		tmp = t + ((x - t) / (z / y))
	else:
		tmp = x + (y / (a / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.08e+18) || !(z <= 1.42e+22))
		tmp = Float64(t + Float64(Float64(x - t) / Float64(z / y)));
	else
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.08e+18) || ~((z <= 1.42e+22)))
		tmp = t + ((x - t) / (z / y));
	else
		tmp = x + (y / (a / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.08e+18], N[Not[LessEqual[z, 1.42e+22]], $MachinePrecision]], N[(t + N[(N[(x - t), $MachinePrecision] / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 38.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.6 \cdot 10^{-262}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{-303}:\\ \;\;\;\;y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{+23}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.1e+20)
   t
   (if (<= z -1.6e-262)
     x
     (if (<= z 4.1e-303) (* y (/ t a)) (if (<= z 3.7e+23) x t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -1.6e-262) {
		tmp = x;
	} else if (z <= 4.1e-303) {
		tmp = y * (t / a);
	} else if (z <= 3.7e+23) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-2.1d+20)) then
        tmp = t
    else if (z <= (-1.6d-262)) then
        tmp = x
    else if (z <= 4.1d-303) then
        tmp = y * (t / a)
    else if (z <= 3.7d+23) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -1.6e-262) {
		tmp = x;
	} else if (z <= 4.1e-303) {
		tmp = y * (t / a);
	} else if (z <= 3.7e+23) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.1e+20:
		tmp = t
	elif z <= -1.6e-262:
		tmp = x
	elif z <= 4.1e-303:
		tmp = y * (t / a)
	elif z <= 3.7e+23:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -1.6e-262)
		tmp = x;
	elseif (z <= 4.1e-303)
		tmp = Float64(y * Float64(t / a));
	elseif (z <= 3.7e+23)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -1.6e-262)
		tmp = x;
	elseif (z <= 4.1e-303)
		tmp = y * (t / a);
	elseif (z <= 3.7e+23)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.1e+20], t, If[LessEqual[z, -1.6e-262], x, If[LessEqual[z, 4.1e-303], N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.7e+23], x, t]]]]

Derivation

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

Alternative 14: 38.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.05 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -5.2 \cdot 10^{-262}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 7 \cdot 10^{-295}:\\ \;\;\;\;\frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{+20}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.05e+20)
   t
   (if (<= z -5.2e-262)
     x
     (if (<= z 7e-295) (/ t (/ a y)) (if (<= z 4.1e+20) x t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.05e+20) {
		tmp = t;
	} else if (z <= -5.2e-262) {
		tmp = x;
	} else if (z <= 7e-295) {
		tmp = t / (a / y);
	} else if (z <= 4.1e+20) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-2.05d+20)) then
        tmp = t
    else if (z <= (-5.2d-262)) then
        tmp = x
    else if (z <= 7d-295) then
        tmp = t / (a / y)
    else if (z <= 4.1d+20) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.05e+20) {
		tmp = t;
	} else if (z <= -5.2e-262) {
		tmp = x;
	} else if (z <= 7e-295) {
		tmp = t / (a / y);
	} else if (z <= 4.1e+20) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.05e+20:
		tmp = t
	elif z <= -5.2e-262:
		tmp = x
	elif z <= 7e-295:
		tmp = t / (a / y)
	elif z <= 4.1e+20:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.05e+20)
		tmp = t;
	elseif (z <= -5.2e-262)
		tmp = x;
	elseif (z <= 7e-295)
		tmp = Float64(t / Float64(a / y));
	elseif (z <= 4.1e+20)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.05e+20)
		tmp = t;
	elseif (z <= -5.2e-262)
		tmp = x;
	elseif (z <= 7e-295)
		tmp = t / (a / y);
	elseif (z <= 4.1e+20)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.05e+20], t, If[LessEqual[z, -5.2e-262], x, If[LessEqual[z, 7e-295], N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.1e+20], x, t]]]]

Derivation

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

Alternative 15: 37.9% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.85 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 5.7 \cdot 10^{+20}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.85e+20) t (if (<= z 5.7e+20) x t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.85e+20) {
		tmp = t;
	} else if (z <= 5.7e+20) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-1.85d+20)) then
        tmp = t
    else if (z <= 5.7d+20) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.85e+20) {
		tmp = t;
	} else if (z <= 5.7e+20) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.85e+20:
		tmp = t
	elif z <= 5.7e+20:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.85e+20)
		tmp = t;
	elseif (z <= 5.7e+20)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.85e+20)
		tmp = t;
	elseif (z <= 5.7e+20)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.85e+20], t, If[LessEqual[z, 5.7e+20], x, t]]

Derivation

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

Alternative 16: 2.8% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 0.0)

C

double code(double x, double y, double z, double t, double a) {
	return 0.0;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = 0.0d0
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return 0.0;
}

Python

def code(x, y, z, t, a):
	return 0.0

Julia

function code(x, y, z, t, a)
	return 0.0
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = 0.0;
end

Wolfram

code[x_, y_, z_, t_, a_] := 0.0

Derivation

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

Alternative 17: 24.7% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z t a)
  :name "Numeric.Signal:interpolate   from hsignal-0.2.7.1"
  :precision binary64
  (+ x (* (- y z) (/ (- t x) (- a z)))))