Numeric.Signal.Multichannel:$cget from hsignal-0.2.7.1

Percentage Accurate: 97.8% → 98.4%

Time: 10.1s

Alternatives: 15

Speedup: 0.5×

• 25.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x / y) * (z - t)) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x / y) * (z - t)) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.4% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t + \frac{x}{y} \cdot \left(z - t\right) \leq -2 \cdot 10^{+306}:\\ \;\;\;\;\mathsf{fma}\left(x \cdot \left(z - t\right), \frac{1}{y}, t\right)\\ \mathbf{else}:\\ \;\;\;\;t + \frac{z - t}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (+ t (* (/ x y) (- z t))) -2e+306)
   (fma (* x (- z t)) (/ 1.0 y) t)
   (+ t (/ (- z t) (/ y x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t + ((x / y) * (z - t))) <= -2e+306) {
		tmp = fma((x * (z - t)), (1.0 / y), t);
	} else {
		tmp = t + ((z - t) / (y / x));
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(t + Float64(Float64(x / y) * Float64(z - t))) <= -2e+306)
		tmp = fma(Float64(x * Float64(z - t)), Float64(1.0 / y), t);
	else
		tmp = Float64(t + Float64(Float64(z - t) / Float64(y / x)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(t + N[(N[(x / y), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -2e+306], N[(N[(x * N[(z - t), $MachinePrecision]), $MachinePrecision] * N[(1.0 / y), $MachinePrecision] + t), $MachinePrecision], N[(t + N[(N[(z - t), $MachinePrecision] / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 2: 91.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(1 - \frac{x}{y}\right)\\ t_2 := x \cdot \frac{z - t}{y}\\ \mathbf{if}\;\frac{x}{y} \leq -5 \cdot 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;\frac{x}{y} \leq -5000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{x}{y} \leq 10^{-21}:\\ \;\;\;\;t + x \cdot \frac{z}{y}\\ \mathbf{elif}\;\frac{x}{y} \leq 4000000000000:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* t (- 1.0 (/ x y)))) (t_2 (* x (/ (- z t) y))))
   (if (<= (/ x y) -5e+60)
     t_2
     (if (<= (/ x y) -5000000.0)
       t_1
       (if (<= (/ x y) 1e-21)
         (+ t (* x (/ z y)))
         (if (<= (/ x y) 4000000000000.0) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t * (1.0 - (x / y));
	double t_2 = x * ((z - t) / y);
	double tmp;
	if ((x / y) <= -5e+60) {
		tmp = t_2;
	} else if ((x / y) <= -5000000.0) {
		tmp = t_1;
	} else if ((x / y) <= 1e-21) {
		tmp = t + (x * (z / y));
	} else if ((x / y) <= 4000000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t * (1.0d0 - (x / y))
    t_2 = x * ((z - t) / y)
    if ((x / y) <= (-5d+60)) then
        tmp = t_2
    else if ((x / y) <= (-5000000.0d0)) then
        tmp = t_1
    else if ((x / y) <= 1d-21) then
        tmp = t + (x * (z / y))
    else if ((x / y) <= 4000000000000.0d0) 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 t_1 = t * (1.0 - (x / y));
	double t_2 = x * ((z - t) / y);
	double tmp;
	if ((x / y) <= -5e+60) {
		tmp = t_2;
	} else if ((x / y) <= -5000000.0) {
		tmp = t_1;
	} else if ((x / y) <= 1e-21) {
		tmp = t + (x * (z / y));
	} else if ((x / y) <= 4000000000000.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t * (1.0 - (x / y))
	t_2 = x * ((z - t) / y)
	tmp = 0
	if (x / y) <= -5e+60:
		tmp = t_2
	elif (x / y) <= -5000000.0:
		tmp = t_1
	elif (x / y) <= 1e-21:
		tmp = t + (x * (z / y))
	elif (x / y) <= 4000000000000.0:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t * Float64(1.0 - Float64(x / y)))
	t_2 = Float64(x * Float64(Float64(z - t) / y))
	tmp = 0.0
	if (Float64(x / y) <= -5e+60)
		tmp = t_2;
	elseif (Float64(x / y) <= -5000000.0)
		tmp = t_1;
	elseif (Float64(x / y) <= 1e-21)
		tmp = Float64(t + Float64(x * Float64(z / y)));
	elseif (Float64(x / y) <= 4000000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t * (1.0 - (x / y));
	t_2 = x * ((z - t) / y);
	tmp = 0.0;
	if ((x / y) <= -5e+60)
		tmp = t_2;
	elseif ((x / y) <= -5000000.0)
		tmp = t_1;
	elseif ((x / y) <= 1e-21)
		tmp = t + (x * (z / y));
	elseif ((x / y) <= 4000000000000.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -5e+60], t$95$2, If[LessEqual[N[(x / y), $MachinePrecision], -5000000.0], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], 1e-21], N[(t + N[(x * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 4000000000000.0], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 3: 94.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x \cdot \left(z - t\right)}{y}\\ \mathbf{if}\;\frac{x}{y} \leq -1 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{x}{y} \leq 10^{-8}:\\ \;\;\;\;t + \frac{x}{y} \cdot z\\ \mathbf{elif}\;\frac{x}{y} \leq 4000000000000:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;\frac{x}{y} \leq 2 \cdot 10^{+19}:\\ \;\;\;\;x \cdot \frac{z - t}{y}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (* x (- z t)) y)))
   (if (<= (/ x y) -1e+16)
     t_1
     (if (<= (/ x y) 1e-8)
       (+ t (* (/ x y) z))
       (if (<= (/ x y) 4000000000000.0)
         (* t (- 1.0 (/ x y)))
         (if (<= (/ x y) 2e+19) (* x (/ (- z t) y)) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x * (z - t)) / y;
	double tmp;
	if ((x / y) <= -1e+16) {
		tmp = t_1;
	} else if ((x / y) <= 1e-8) {
		tmp = t + ((x / y) * z);
	} else if ((x / y) <= 4000000000000.0) {
		tmp = t * (1.0 - (x / y));
	} else if ((x / y) <= 2e+19) {
		tmp = x * ((z - t) / y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x * (z - t)) / y
    if ((x / y) <= (-1d+16)) then
        tmp = t_1
    else if ((x / y) <= 1d-8) then
        tmp = t + ((x / y) * z)
    else if ((x / y) <= 4000000000000.0d0) then
        tmp = t * (1.0d0 - (x / y))
    else if ((x / y) <= 2d+19) then
        tmp = x * ((z - t) / y)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x * (z - t)) / y;
	double tmp;
	if ((x / y) <= -1e+16) {
		tmp = t_1;
	} else if ((x / y) <= 1e-8) {
		tmp = t + ((x / y) * z);
	} else if ((x / y) <= 4000000000000.0) {
		tmp = t * (1.0 - (x / y));
	} else if ((x / y) <= 2e+19) {
		tmp = x * ((z - t) / y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x * (z - t)) / y
	tmp = 0
	if (x / y) <= -1e+16:
		tmp = t_1
	elif (x / y) <= 1e-8:
		tmp = t + ((x / y) * z)
	elif (x / y) <= 4000000000000.0:
		tmp = t * (1.0 - (x / y))
	elif (x / y) <= 2e+19:
		tmp = x * ((z - t) / y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x * Float64(z - t)) / y)
	tmp = 0.0
	if (Float64(x / y) <= -1e+16)
		tmp = t_1;
	elseif (Float64(x / y) <= 1e-8)
		tmp = Float64(t + Float64(Float64(x / y) * z));
	elseif (Float64(x / y) <= 4000000000000.0)
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	elseif (Float64(x / y) <= 2e+19)
		tmp = Float64(x * Float64(Float64(z - t) / y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x * (z - t)) / y;
	tmp = 0.0;
	if ((x / y) <= -1e+16)
		tmp = t_1;
	elseif ((x / y) <= 1e-8)
		tmp = t + ((x / y) * z);
	elseif ((x / y) <= 4000000000000.0)
		tmp = t * (1.0 - (x / y));
	elseif ((x / y) <= 2e+19)
		tmp = x * ((z - t) / y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x * N[(z - t), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -1e+16], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], 1e-8], N[(t + N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 4000000000000.0], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 2e+19], N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 4: 65.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} \cdot \left(-t\right)\\ \mathbf{if}\;\frac{x}{y} \leq -2 \cdot 10^{+63}:\\ \;\;\;\;\frac{z}{\frac{y}{x}}\\ \mathbf{elif}\;\frac{x}{y} \leq -0.5:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{x}{y} \leq 0.02:\\ \;\;\;\;t\\ \mathbf{elif}\;\frac{x}{y} \leq 10^{+241}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{y}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (/ x y) (- t))))
   (if (<= (/ x y) -2e+63)
     (/ z (/ y x))
     (if (<= (/ x y) -0.5)
       t_1
       (if (<= (/ x y) 0.02) t (if (<= (/ x y) 1e+241) t_1 (/ x (/ y z))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) * -t;
	double tmp;
	if ((x / y) <= -2e+63) {
		tmp = z / (y / x);
	} else if ((x / y) <= -0.5) {
		tmp = t_1;
	} else if ((x / y) <= 0.02) {
		tmp = t;
	} else if ((x / y) <= 1e+241) {
		tmp = t_1;
	} else {
		tmp = x / (y / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x / y) * -t
    if ((x / y) <= (-2d+63)) then
        tmp = z / (y / x)
    else if ((x / y) <= (-0.5d0)) then
        tmp = t_1
    else if ((x / y) <= 0.02d0) then
        tmp = t
    else if ((x / y) <= 1d+241) then
        tmp = t_1
    else
        tmp = x / (y / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) * -t;
	double tmp;
	if ((x / y) <= -2e+63) {
		tmp = z / (y / x);
	} else if ((x / y) <= -0.5) {
		tmp = t_1;
	} else if ((x / y) <= 0.02) {
		tmp = t;
	} else if ((x / y) <= 1e+241) {
		tmp = t_1;
	} else {
		tmp = x / (y / z);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) * -t
	tmp = 0
	if (x / y) <= -2e+63:
		tmp = z / (y / x)
	elif (x / y) <= -0.5:
		tmp = t_1
	elif (x / y) <= 0.02:
		tmp = t
	elif (x / y) <= 1e+241:
		tmp = t_1
	else:
		tmp = x / (y / z)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) * Float64(-t))
	tmp = 0.0
	if (Float64(x / y) <= -2e+63)
		tmp = Float64(z / Float64(y / x));
	elseif (Float64(x / y) <= -0.5)
		tmp = t_1;
	elseif (Float64(x / y) <= 0.02)
		tmp = t;
	elseif (Float64(x / y) <= 1e+241)
		tmp = t_1;
	else
		tmp = Float64(x / Float64(y / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) * -t;
	tmp = 0.0;
	if ((x / y) <= -2e+63)
		tmp = z / (y / x);
	elseif ((x / y) <= -0.5)
		tmp = t_1;
	elseif ((x / y) <= 0.02)
		tmp = t;
	elseif ((x / y) <= 1e+241)
		tmp = t_1;
	else
		tmp = x / (y / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] * (-t)), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -2e+63], N[(z / N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], -0.5], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], 0.02], t, If[LessEqual[N[(x / y), $MachinePrecision], 1e+241], t$95$1, N[(x / N[(y / z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 5: 65.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-t}{\frac{y}{x}}\\ \mathbf{if}\;\frac{x}{y} \leq -2 \cdot 10^{+63}:\\ \;\;\;\;\frac{z}{\frac{y}{x}}\\ \mathbf{elif}\;\frac{x}{y} \leq -0.5:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{x}{y} \leq 0.02:\\ \;\;\;\;t\\ \mathbf{elif}\;\frac{x}{y} \leq 10^{+241}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{y}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- t) (/ y x))))
   (if (<= (/ x y) -2e+63)
     (/ z (/ y x))
     (if (<= (/ x y) -0.5)
       t_1
       (if (<= (/ x y) 0.02) t (if (<= (/ x y) 1e+241) t_1 (/ x (/ y z))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = -t / (y / x);
	double tmp;
	if ((x / y) <= -2e+63) {
		tmp = z / (y / x);
	} else if ((x / y) <= -0.5) {
		tmp = t_1;
	} else if ((x / y) <= 0.02) {
		tmp = t;
	} else if ((x / y) <= 1e+241) {
		tmp = t_1;
	} else {
		tmp = x / (y / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = -t / (y / x)
    if ((x / y) <= (-2d+63)) then
        tmp = z / (y / x)
    else if ((x / y) <= (-0.5d0)) then
        tmp = t_1
    else if ((x / y) <= 0.02d0) then
        tmp = t
    else if ((x / y) <= 1d+241) then
        tmp = t_1
    else
        tmp = x / (y / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = -t / (y / x);
	double tmp;
	if ((x / y) <= -2e+63) {
		tmp = z / (y / x);
	} else if ((x / y) <= -0.5) {
		tmp = t_1;
	} else if ((x / y) <= 0.02) {
		tmp = t;
	} else if ((x / y) <= 1e+241) {
		tmp = t_1;
	} else {
		tmp = x / (y / z);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = -t / (y / x)
	tmp = 0
	if (x / y) <= -2e+63:
		tmp = z / (y / x)
	elif (x / y) <= -0.5:
		tmp = t_1
	elif (x / y) <= 0.02:
		tmp = t
	elif (x / y) <= 1e+241:
		tmp = t_1
	else:
		tmp = x / (y / z)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(-t) / Float64(y / x))
	tmp = 0.0
	if (Float64(x / y) <= -2e+63)
		tmp = Float64(z / Float64(y / x));
	elseif (Float64(x / y) <= -0.5)
		tmp = t_1;
	elseif (Float64(x / y) <= 0.02)
		tmp = t;
	elseif (Float64(x / y) <= 1e+241)
		tmp = t_1;
	else
		tmp = Float64(x / Float64(y / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = -t / (y / x);
	tmp = 0.0;
	if ((x / y) <= -2e+63)
		tmp = z / (y / x);
	elseif ((x / y) <= -0.5)
		tmp = t_1;
	elseif ((x / y) <= 0.02)
		tmp = t;
	elseif ((x / y) <= 1e+241)
		tmp = t_1;
	else
		tmp = x / (y / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[((-t) / N[(y / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -2e+63], N[(z / N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], -0.5], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], 0.02], t, If[LessEqual[N[(x / y), $MachinePrecision], 1e+241], t$95$1, N[(x / N[(y / z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 93.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{z - t}{y}\\ \mathbf{if}\;\frac{x}{y} \leq -5 \cdot 10^{+60}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{x}{y} \leq -5000000:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;\frac{x}{y} \leq 0.02:\\ \;\;\;\;t + \frac{x}{y} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (/ (- z t) y))))
   (if (<= (/ x y) -5e+60)
     t_1
     (if (<= (/ x y) -5000000.0)
       (* t (- 1.0 (/ x y)))
       (if (<= (/ x y) 0.02) (+ t (* (/ x y) z)) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * ((z - t) / y);
	double tmp;
	if ((x / y) <= -5e+60) {
		tmp = t_1;
	} else if ((x / y) <= -5000000.0) {
		tmp = t * (1.0 - (x / y));
	} else if ((x / y) <= 0.02) {
		tmp = t + ((x / y) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * ((z - t) / y)
    if ((x / y) <= (-5d+60)) then
        tmp = t_1
    else if ((x / y) <= (-5000000.0d0)) then
        tmp = t * (1.0d0 - (x / y))
    else if ((x / y) <= 0.02d0) then
        tmp = t + ((x / y) * 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 t_1 = x * ((z - t) / y);
	double tmp;
	if ((x / y) <= -5e+60) {
		tmp = t_1;
	} else if ((x / y) <= -5000000.0) {
		tmp = t * (1.0 - (x / y));
	} else if ((x / y) <= 0.02) {
		tmp = t + ((x / y) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * ((z - t) / y)
	tmp = 0
	if (x / y) <= -5e+60:
		tmp = t_1
	elif (x / y) <= -5000000.0:
		tmp = t * (1.0 - (x / y))
	elif (x / y) <= 0.02:
		tmp = t + ((x / y) * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(Float64(z - t) / y))
	tmp = 0.0
	if (Float64(x / y) <= -5e+60)
		tmp = t_1;
	elseif (Float64(x / y) <= -5000000.0)
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	elseif (Float64(x / y) <= 0.02)
		tmp = Float64(t + Float64(Float64(x / y) * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * ((z - t) / y);
	tmp = 0.0;
	if ((x / y) <= -5e+60)
		tmp = t_1;
	elseif ((x / y) <= -5000000.0)
		tmp = t * (1.0 - (x / y));
	elseif ((x / y) <= 0.02)
		tmp = t + ((x / y) * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -5e+60], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], -5000000.0], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 0.02], N[(t + N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 7: 93.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{z - t}{y}\\ \mathbf{if}\;\frac{x}{y} \leq -5 \cdot 10^{+60}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{x}{y} \leq -5000000:\\ \;\;\;\;t - \frac{t}{\frac{y}{x}}\\ \mathbf{elif}\;\frac{x}{y} \leq 0.02:\\ \;\;\;\;t + \frac{x}{y} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (/ (- z t) y))))
   (if (<= (/ x y) -5e+60)
     t_1
     (if (<= (/ x y) -5000000.0)
       (- t (/ t (/ y x)))
       (if (<= (/ x y) 0.02) (+ t (* (/ x y) z)) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * ((z - t) / y);
	double tmp;
	if ((x / y) <= -5e+60) {
		tmp = t_1;
	} else if ((x / y) <= -5000000.0) {
		tmp = t - (t / (y / x));
	} else if ((x / y) <= 0.02) {
		tmp = t + ((x / y) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * ((z - t) / y)
    if ((x / y) <= (-5d+60)) then
        tmp = t_1
    else if ((x / y) <= (-5000000.0d0)) then
        tmp = t - (t / (y / x))
    else if ((x / y) <= 0.02d0) then
        tmp = t + ((x / y) * 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 t_1 = x * ((z - t) / y);
	double tmp;
	if ((x / y) <= -5e+60) {
		tmp = t_1;
	} else if ((x / y) <= -5000000.0) {
		tmp = t - (t / (y / x));
	} else if ((x / y) <= 0.02) {
		tmp = t + ((x / y) * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * ((z - t) / y)
	tmp = 0
	if (x / y) <= -5e+60:
		tmp = t_1
	elif (x / y) <= -5000000.0:
		tmp = t - (t / (y / x))
	elif (x / y) <= 0.02:
		tmp = t + ((x / y) * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(Float64(z - t) / y))
	tmp = 0.0
	if (Float64(x / y) <= -5e+60)
		tmp = t_1;
	elseif (Float64(x / y) <= -5000000.0)
		tmp = Float64(t - Float64(t / Float64(y / x)));
	elseif (Float64(x / y) <= 0.02)
		tmp = Float64(t + Float64(Float64(x / y) * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * ((z - t) / y);
	tmp = 0.0;
	if ((x / y) <= -5e+60)
		tmp = t_1;
	elseif ((x / y) <= -5000000.0)
		tmp = t - (t / (y / x));
	elseif ((x / y) <= 0.02)
		tmp = t + ((x / y) * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -5e+60], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], -5000000.0], N[(t - N[(t / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 0.02], N[(t + N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 8: 98.4% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 9: 98.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t + \frac{x}{y} \cdot \left(z - t\right) \leq -\infty:\\ \;\;\;\;\frac{x \cdot \left(z - t\right)}{y}\\ \mathbf{else}:\\ \;\;\;\;t + \frac{z - t}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (+ t (* (/ x y) (- z t))) (- INFINITY))
   (/ (* x (- z t)) y)
   (+ t (/ (- z t) (/ y x)))))

C

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

Java

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

Python

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

Julia

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

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t + ((x / y) * (z - t))) <= -Inf)
		tmp = (x * (z - t)) / y;
	else
		tmp = t + ((z - t) / (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

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

Derivation

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

Alternative 10: 82.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -5 \cdot 10^{+60} \lor \neg \left(\frac{x}{y} \leq 4000000000000\right):\\ \;\;\;\;x \cdot \frac{z - t}{y}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ x y) -5e+60) (not (<= (/ x y) 4000000000000.0)))
   (* x (/ (- z t) y))
   (* t (- 1.0 (/ x y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -5e+60) || !((x / y) <= 4000000000000.0)) {
		tmp = x * ((z - t) / y);
	} else {
		tmp = t * (1.0 - (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (((x / y) <= (-5d+60)) .or. (.not. ((x / y) <= 4000000000000.0d0))) then
        tmp = x * ((z - t) / y)
    else
        tmp = t * (1.0d0 - (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -5e+60) || !((x / y) <= 4000000000000.0)) {
		tmp = x * ((z - t) / y);
	} else {
		tmp = t * (1.0 - (x / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((x / y) <= -5e+60) or not ((x / y) <= 4000000000000.0):
		tmp = x * ((z - t) / y)
	else:
		tmp = t * (1.0 - (x / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(x / y) <= -5e+60) || !(Float64(x / y) <= 4000000000000.0))
		tmp = Float64(x * Float64(Float64(z - t) / y));
	else
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((x / y) <= -5e+60) || ~(((x / y) <= 4000000000000.0)))
		tmp = x * ((z - t) / y);
	else
		tmp = t * (1.0 - (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(x / y), $MachinePrecision], -5e+60], N[Not[LessEqual[N[(x / y), $MachinePrecision], 4000000000000.0]], $MachinePrecision]], N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 65.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -5 \cdot 10^{-26} \lor \neg \left(\frac{x}{y} \leq 10^{-11}\right):\\ \;\;\;\;\frac{x}{y} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ x y) -5e-26) (not (<= (/ x y) 1e-11))) (* (/ x y) z) t))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -5e-26) || !((x / y) <= 1e-11)) {
		tmp = (x / y) * z;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (((x / y) <= (-5d-26)) .or. (.not. ((x / y) <= 1d-11))) then
        tmp = (x / y) * z
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -5e-26) || !((x / y) <= 1e-11)) {
		tmp = (x / y) * z;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((x / y) <= -5e-26) or not ((x / y) <= 1e-11):
		tmp = (x / y) * z
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(x / y) <= -5e-26) || !(Float64(x / y) <= 1e-11))
		tmp = Float64(Float64(x / y) * z);
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((x / y) <= -5e-26) || ~(((x / y) <= 1e-11)))
		tmp = (x / y) * z;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(x / y), $MachinePrecision], -5e-26], N[Not[LessEqual[N[(x / y), $MachinePrecision], 1e-11]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision], t]

Derivation

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

Alternative 12: 93.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.45 \cdot 10^{+178} \lor \neg \left(z \leq 1.35 \cdot 10^{+85}\right):\\ \;\;\;\;t + \frac{x}{y} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t + x \cdot \frac{z - t}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -2.45e+178) (not (<= z 1.35e+85)))
   (+ t (* (/ x y) z))
   (+ t (* x (/ (- z t) y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.45e+178) || !(z <= 1.35e+85)) {
		tmp = t + ((x / y) * z);
	} else {
		tmp = t + (x * ((z - t) / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-2.45d+178)) .or. (.not. (z <= 1.35d+85))) then
        tmp = t + ((x / y) * z)
    else
        tmp = t + (x * ((z - t) / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.45e+178) || !(z <= 1.35e+85)) {
		tmp = t + ((x / y) * z);
	} else {
		tmp = t + (x * ((z - t) / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -2.45e+178) or not (z <= 1.35e+85):
		tmp = t + ((x / y) * z)
	else:
		tmp = t + (x * ((z - t) / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -2.45e+178) || !(z <= 1.35e+85))
		tmp = Float64(t + Float64(Float64(x / y) * z));
	else
		tmp = Float64(t + Float64(x * Float64(Float64(z - t) / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -2.45e+178) || ~((z <= 1.35e+85)))
		tmp = t + ((x / y) * z);
	else
		tmp = t + (x * ((z - t) / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -2.45e+178], N[Not[LessEqual[z, 1.35e+85]], $MachinePrecision]], N[(t + N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], N[(t + N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 65.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -5 \cdot 10^{-26}:\\ \;\;\;\;\frac{z}{\frac{y}{x}}\\ \mathbf{elif}\;\frac{x}{y} \leq 10^{-11}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ x y) -5e-26)
   (/ z (/ y x))
   (if (<= (/ x y) 1e-11) t (* (/ x y) z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x / y) <= -5e-26) {
		tmp = z / (y / x);
	} else if ((x / y) <= 1e-11) {
		tmp = t;
	} else {
		tmp = (x / y) * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((x / y) <= (-5d-26)) then
        tmp = z / (y / x)
    else if ((x / y) <= 1d-11) then
        tmp = t
    else
        tmp = (x / y) * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x / y) <= -5e-26) {
		tmp = z / (y / x);
	} else if ((x / y) <= 1e-11) {
		tmp = t;
	} else {
		tmp = (x / y) * z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x / y) <= -5e-26:
		tmp = z / (y / x)
	elif (x / y) <= 1e-11:
		tmp = t
	else:
		tmp = (x / y) * z
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(x / y) <= -5e-26)
		tmp = Float64(z / Float64(y / x));
	elseif (Float64(x / y) <= 1e-11)
		tmp = t;
	else
		tmp = Float64(Float64(x / y) * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x / y) <= -5e-26)
		tmp = z / (y / x);
	elseif ((x / y) <= 1e-11)
		tmp = t;
	else
		tmp = (x / y) * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(x / y), $MachinePrecision], -5e-26], N[(z / N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 1e-11], t, N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]]]

Derivation

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

Alternative 14: 74.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.32 \cdot 10^{+105} \lor \neg \left(z \leq 5 \cdot 10^{+118}\right):\\ \;\;\;\;\frac{x}{y} \cdot z\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.32e+105) (not (<= z 5e+118)))
   (* (/ x y) z)
   (* t (- 1.0 (/ x y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.32e+105) || !(z <= 5e+118)) {
		tmp = (x / y) * z;
	} else {
		tmp = t * (1.0 - (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-1.32d+105)) .or. (.not. (z <= 5d+118))) then
        tmp = (x / y) * z
    else
        tmp = t * (1.0d0 - (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.32e+105) || !(z <= 5e+118)) {
		tmp = (x / y) * z;
	} else {
		tmp = t * (1.0 - (x / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.32e+105) or not (z <= 5e+118):
		tmp = (x / y) * z
	else:
		tmp = t * (1.0 - (x / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.32e+105) || !(z <= 5e+118))
		tmp = Float64(Float64(x / y) * z);
	else
		tmp = Float64(t * Float64(1.0 - Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.32e+105) || ~((z <= 5e+118)))
		tmp = (x / y) * z;
	else
		tmp = t * (1.0 - (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.32e+105], N[Not[LessEqual[z, 5e+118]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision], N[(t * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 38.8% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 97.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} \cdot \left(z - t\right) + t\\ \mathbf{if}\;z < 2.759456554562692 \cdot 10^{-282}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z < 2.326994450874436 \cdot 10^{-110}:\\ \;\;\;\;x \cdot \frac{z - t}{y} + t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ (* (/ x y) (- z t)) t)))
   (if (< z 2.759456554562692e-282)
     t_1
     (if (< z 2.326994450874436e-110) (+ (* x (/ (- z t) y)) t) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((x / y) * (z - t)) + t;
	double tmp;
	if (z < 2.759456554562692e-282) {
		tmp = t_1;
	} else if (z < 2.326994450874436e-110) {
		tmp = (x * ((z - t) / y)) + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((x / y) * (z - t)) + t
    if (z < 2.759456554562692d-282) then
        tmp = t_1
    else if (z < 2.326994450874436d-110) then
        tmp = (x * ((z - t) / y)) + t
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = ((x / y) * (z - t)) + t;
	double tmp;
	if (z < 2.759456554562692e-282) {
		tmp = t_1;
	} else if (z < 2.326994450874436e-110) {
		tmp = (x * ((z - t) / y)) + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = ((x / y) * (z - t)) + t
	tmp = 0
	if z < 2.759456554562692e-282:
		tmp = t_1
	elif z < 2.326994450874436e-110:
		tmp = (x * ((z - t) / y)) + t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(x / y) * Float64(z - t)) + t)
	tmp = 0.0
	if (z < 2.759456554562692e-282)
		tmp = t_1;
	elseif (z < 2.326994450874436e-110)
		tmp = Float64(Float64(x * Float64(Float64(z - t) / y)) + t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = ((x / y) * (z - t)) + t;
	tmp = 0.0;
	if (z < 2.759456554562692e-282)
		tmp = t_1;
	elseif (z < 2.326994450874436e-110)
		tmp = (x * ((z - t) / y)) + t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(x / y), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]}, If[Less[z, 2.759456554562692e-282], t$95$1, If[Less[z, 2.326994450874436e-110], N[(N[(x * N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t)
  :name "Numeric.Signal.Multichannel:$cget from hsignal-0.2.7.1"
  :precision binary64

  :herbie-target
  (if (< z 2.759456554562692e-282) (+ (* (/ x y) (- z t)) t) (if (< z 2.326994450874436e-110) (+ (* x (/ (- z t) y)) t) (+ (* (/ x y) (- z t)) t)))

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