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

Percentage Accurate: 97.1% → 97.1%

Time: 10.8s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 97.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 65.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{x - y}{z}\\ \mathbf{if}\;y \leq -1.4 \cdot 10^{+181}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -2.2 \cdot 10^{+84}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -3.8 \cdot 10^{+30}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -4.4 \cdot 10^{-150}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{-193}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-39}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{+136}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* t (/ (- x y) z))))
   (if (<= y -1.4e+181)
     t
     (if (<= y -2.2e+84)
       t_1
       (if (<= y -3.8e+30)
         t
         (if (<= y -4.4e-150)
           (* t (/ x (- z y)))
           (if (<= y -1.35e-193)
             t_1
             (if (<= y 8e-39)
               (* x (/ t (- z y)))
               (if (<= y 1.05e+136) t_1 t)))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t * ((x - y) / z);
	double tmp;
	if (y <= -1.4e+181) {
		tmp = t;
	} else if (y <= -2.2e+84) {
		tmp = t_1;
	} else if (y <= -3.8e+30) {
		tmp = t;
	} else if (y <= -4.4e-150) {
		tmp = t * (x / (z - y));
	} else if (y <= -1.35e-193) {
		tmp = t_1;
	} else if (y <= 8e-39) {
		tmp = x * (t / (z - y));
	} else if (y <= 1.05e+136) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = t * ((x - y) / z)
    if (y <= (-1.4d+181)) then
        tmp = t
    else if (y <= (-2.2d+84)) then
        tmp = t_1
    else if (y <= (-3.8d+30)) then
        tmp = t
    else if (y <= (-4.4d-150)) then
        tmp = t * (x / (z - y))
    else if (y <= (-1.35d-193)) then
        tmp = t_1
    else if (y <= 8d-39) then
        tmp = x * (t / (z - y))
    else if (y <= 1.05d+136) 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 t_1 = t * ((x - y) / z);
	double tmp;
	if (y <= -1.4e+181) {
		tmp = t;
	} else if (y <= -2.2e+84) {
		tmp = t_1;
	} else if (y <= -3.8e+30) {
		tmp = t;
	} else if (y <= -4.4e-150) {
		tmp = t * (x / (z - y));
	} else if (y <= -1.35e-193) {
		tmp = t_1;
	} else if (y <= 8e-39) {
		tmp = x * (t / (z - y));
	} else if (y <= 1.05e+136) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t * ((x - y) / z)
	tmp = 0
	if y <= -1.4e+181:
		tmp = t
	elif y <= -2.2e+84:
		tmp = t_1
	elif y <= -3.8e+30:
		tmp = t
	elif y <= -4.4e-150:
		tmp = t * (x / (z - y))
	elif y <= -1.35e-193:
		tmp = t_1
	elif y <= 8e-39:
		tmp = x * (t / (z - y))
	elif y <= 1.05e+136:
		tmp = t_1
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t * Float64(Float64(x - y) / z))
	tmp = 0.0
	if (y <= -1.4e+181)
		tmp = t;
	elseif (y <= -2.2e+84)
		tmp = t_1;
	elseif (y <= -3.8e+30)
		tmp = t;
	elseif (y <= -4.4e-150)
		tmp = Float64(t * Float64(x / Float64(z - y)));
	elseif (y <= -1.35e-193)
		tmp = t_1;
	elseif (y <= 8e-39)
		tmp = Float64(x * Float64(t / Float64(z - y)));
	elseif (y <= 1.05e+136)
		tmp = t_1;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t * ((x - y) / z);
	tmp = 0.0;
	if (y <= -1.4e+181)
		tmp = t;
	elseif (y <= -2.2e+84)
		tmp = t_1;
	elseif (y <= -3.8e+30)
		tmp = t;
	elseif (y <= -4.4e-150)
		tmp = t * (x / (z - y));
	elseif (y <= -1.35e-193)
		tmp = t_1;
	elseif (y <= 8e-39)
		tmp = x * (t / (z - y));
	elseif (y <= 1.05e+136)
		tmp = t_1;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.4e+181], t, If[LessEqual[y, -2.2e+84], t$95$1, If[LessEqual[y, -3.8e+30], t, If[LessEqual[y, -4.4e-150], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.35e-193], t$95$1, If[LessEqual[y, 8e-39], N[(x * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.05e+136], t$95$1, t]]]]]]]]

Derivation

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

Alternative 3: 64.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.2 \cdot 10^{+179}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -1.9 \cdot 10^{+86}:\\ \;\;\;\;\frac{-t}{\frac{z}{y}}\\ \mathbf{elif}\;y \leq -1.5 \cdot 10^{+17}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 60000:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{+62}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.18 \cdot 10^{+122}:\\ \;\;\;\;-t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.2e+179)
   t
   (if (<= y -1.9e+86)
     (/ (- t) (/ z y))
     (if (<= y -1.5e+17)
       t
       (if (<= y 60000.0)
         (* x (/ t (- z y)))
         (if (<= y 1.65e+62) t (if (<= y 1.18e+122) (- (* t (/ y z))) t)))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -1.9e+86) {
		tmp = -t / (z / y);
	} else if (y <= -1.5e+17) {
		tmp = t;
	} else if (y <= 60000.0) {
		tmp = x * (t / (z - y));
	} else if (y <= 1.65e+62) {
		tmp = t;
	} else if (y <= 1.18e+122) {
		tmp = -(t * (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 (y <= (-4.2d+179)) then
        tmp = t
    else if (y <= (-1.9d+86)) then
        tmp = -t / (z / y)
    else if (y <= (-1.5d+17)) then
        tmp = t
    else if (y <= 60000.0d0) then
        tmp = x * (t / (z - y))
    else if (y <= 1.65d+62) then
        tmp = t
    else if (y <= 1.18d+122) then
        tmp = -(t * (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 (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -1.9e+86) {
		tmp = -t / (z / y);
	} else if (y <= -1.5e+17) {
		tmp = t;
	} else if (y <= 60000.0) {
		tmp = x * (t / (z - y));
	} else if (y <= 1.65e+62) {
		tmp = t;
	} else if (y <= 1.18e+122) {
		tmp = -(t * (y / z));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.2e+179:
		tmp = t
	elif y <= -1.9e+86:
		tmp = -t / (z / y)
	elif y <= -1.5e+17:
		tmp = t
	elif y <= 60000.0:
		tmp = x * (t / (z - y))
	elif y <= 1.65e+62:
		tmp = t
	elif y <= 1.18e+122:
		tmp = -(t * (y / z))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -1.9e+86)
		tmp = Float64(Float64(-t) / Float64(z / y));
	elseif (y <= -1.5e+17)
		tmp = t;
	elseif (y <= 60000.0)
		tmp = Float64(x * Float64(t / Float64(z - y)));
	elseif (y <= 1.65e+62)
		tmp = t;
	elseif (y <= 1.18e+122)
		tmp = Float64(-Float64(t * Float64(y / z)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -1.9e+86)
		tmp = -t / (z / y);
	elseif (y <= -1.5e+17)
		tmp = t;
	elseif (y <= 60000.0)
		tmp = x * (t / (z - y));
	elseif (y <= 1.65e+62)
		tmp = t;
	elseif (y <= 1.18e+122)
		tmp = -(t * (y / z));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.2e+179], t, If[LessEqual[y, -1.9e+86], N[((-t) / N[(z / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.5e+17], t, If[LessEqual[y, 60000.0], N[(x * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.65e+62], t, If[LessEqual[y, 1.18e+122], (-N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), t]]]]]]

Derivation

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

Alternative 4: 75.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{+17}:\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{elif}\;y \leq -4.6 \cdot 10^{-151}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;y \leq 3.8 \cdot 10^{-87}:\\ \;\;\;\;\frac{x - y}{\frac{z}{t}}\\ \mathbf{elif}\;y \leq 75000:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t \cdot \frac{-y}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -5e+17)
   (/ t (- 1.0 (/ z y)))
   (if (<= y -4.6e-151)
     (* t (/ x (- z y)))
     (if (<= y 3.8e-87)
       (/ (- x y) (/ z t))
       (if (<= y 75000.0) (/ (* x t) (- z y)) (* t (/ (- y) (- z y))))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -5e+17) {
		tmp = t / (1.0 - (z / y));
	} else if (y <= -4.6e-151) {
		tmp = t * (x / (z - y));
	} else if (y <= 3.8e-87) {
		tmp = (x - y) / (z / t);
	} else if (y <= 75000.0) {
		tmp = (x * t) / (z - y);
	} else {
		tmp = t * (-y / (z - 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 (y <= (-5d+17)) then
        tmp = t / (1.0d0 - (z / y))
    else if (y <= (-4.6d-151)) then
        tmp = t * (x / (z - y))
    else if (y <= 3.8d-87) then
        tmp = (x - y) / (z / t)
    else if (y <= 75000.0d0) then
        tmp = (x * t) / (z - y)
    else
        tmp = t * (-y / (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -5e+17) {
		tmp = t / (1.0 - (z / y));
	} else if (y <= -4.6e-151) {
		tmp = t * (x / (z - y));
	} else if (y <= 3.8e-87) {
		tmp = (x - y) / (z / t);
	} else if (y <= 75000.0) {
		tmp = (x * t) / (z - y);
	} else {
		tmp = t * (-y / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -5e+17:
		tmp = t / (1.0 - (z / y))
	elif y <= -4.6e-151:
		tmp = t * (x / (z - y))
	elif y <= 3.8e-87:
		tmp = (x - y) / (z / t)
	elif y <= 75000.0:
		tmp = (x * t) / (z - y)
	else:
		tmp = t * (-y / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -5e+17)
		tmp = Float64(t / Float64(1.0 - Float64(z / y)));
	elseif (y <= -4.6e-151)
		tmp = Float64(t * Float64(x / Float64(z - y)));
	elseif (y <= 3.8e-87)
		tmp = Float64(Float64(x - y) / Float64(z / t));
	elseif (y <= 75000.0)
		tmp = Float64(Float64(x * t) / Float64(z - y));
	else
		tmp = Float64(t * Float64(Float64(-y) / Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -5e+17)
		tmp = t / (1.0 - (z / y));
	elseif (y <= -4.6e-151)
		tmp = t * (x / (z - y));
	elseif (y <= 3.8e-87)
		tmp = (x - y) / (z / t);
	elseif (y <= 75000.0)
		tmp = (x * t) / (z - y);
	else
		tmp = t * (-y / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -5e+17], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -4.6e-151], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.8e-87], N[(N[(x - y), $MachinePrecision] / N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 75000.0], N[(N[(x * t), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision], N[(t * N[((-y) / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 5: 66.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.4 \cdot 10^{+181}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -3.8 \cdot 10^{+84} \lor \neg \left(y \leq -2.6 \cdot 10^{+17}\right) \land y \leq 1.65 \cdot 10^{+134}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.4e+181)
   t
   (if (or (<= y -3.8e+84) (and (not (<= y -2.6e+17)) (<= y 1.65e+134)))
     (* t (/ x (- z y)))
     t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.4e+181) {
		tmp = t;
	} else if ((y <= -3.8e+84) || (!(y <= -2.6e+17) && (y <= 1.65e+134))) {
		tmp = t * (x / (z - y));
	} 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 (y <= (-1.4d+181)) then
        tmp = t
    else if ((y <= (-3.8d+84)) .or. (.not. (y <= (-2.6d+17))) .and. (y <= 1.65d+134)) then
        tmp = t * (x / (z - y))
    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 (y <= -1.4e+181) {
		tmp = t;
	} else if ((y <= -3.8e+84) || (!(y <= -2.6e+17) && (y <= 1.65e+134))) {
		tmp = t * (x / (z - y));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -1.4e+181:
		tmp = t
	elif (y <= -3.8e+84) or (not (y <= -2.6e+17) and (y <= 1.65e+134)):
		tmp = t * (x / (z - y))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.4e+181)
		tmp = t;
	elseif ((y <= -3.8e+84) || (!(y <= -2.6e+17) && (y <= 1.65e+134)))
		tmp = Float64(t * Float64(x / Float64(z - y)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -1.4e+181)
		tmp = t;
	elseif ((y <= -3.8e+84) || (~((y <= -2.6e+17)) && (y <= 1.65e+134)))
		tmp = t * (x / (z - y));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.4e+181], t, If[Or[LessEqual[y, -3.8e+84], And[N[Not[LessEqual[y, -2.6e+17]], $MachinePrecision], LessEqual[y, 1.65e+134]]], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t]]

Derivation

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

Alternative 6: 75.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{1 - \frac{z}{y}}\\ \mathbf{if}\;y \leq -1.9 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -4.8 \cdot 10^{-151}:\\ \;\;\;\;t \cdot \frac{x}{z - y}\\ \mathbf{elif}\;y \leq 2 \cdot 10^{-91}:\\ \;\;\;\;\frac{x - y}{\frac{z}{t}}\\ \mathbf{elif}\;y \leq 30500:\\ \;\;\;\;\frac{x \cdot t}{z - y}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ t (- 1.0 (/ z y)))))
   (if (<= y -1.9e+16)
     t_1
     (if (<= y -4.8e-151)
       (* t (/ x (- z y)))
       (if (<= y 2e-91)
         (/ (- x y) (/ z t))
         (if (<= y 30500.0) (/ (* x t) (- z y)) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t / (1.0 - (z / y));
	double tmp;
	if (y <= -1.9e+16) {
		tmp = t_1;
	} else if (y <= -4.8e-151) {
		tmp = t * (x / (z - y));
	} else if (y <= 2e-91) {
		tmp = (x - y) / (z / t);
	} else if (y <= 30500.0) {
		tmp = (x * t) / (z - 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 = t / (1.0d0 - (z / y))
    if (y <= (-1.9d+16)) then
        tmp = t_1
    else if (y <= (-4.8d-151)) then
        tmp = t * (x / (z - y))
    else if (y <= 2d-91) then
        tmp = (x - y) / (z / t)
    else if (y <= 30500.0d0) then
        tmp = (x * t) / (z - 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 = t / (1.0 - (z / y));
	double tmp;
	if (y <= -1.9e+16) {
		tmp = t_1;
	} else if (y <= -4.8e-151) {
		tmp = t * (x / (z - y));
	} else if (y <= 2e-91) {
		tmp = (x - y) / (z / t);
	} else if (y <= 30500.0) {
		tmp = (x * t) / (z - y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t / (1.0 - (z / y))
	tmp = 0
	if y <= -1.9e+16:
		tmp = t_1
	elif y <= -4.8e-151:
		tmp = t * (x / (z - y))
	elif y <= 2e-91:
		tmp = (x - y) / (z / t)
	elif y <= 30500.0:
		tmp = (x * t) / (z - y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t / Float64(1.0 - Float64(z / y)))
	tmp = 0.0
	if (y <= -1.9e+16)
		tmp = t_1;
	elseif (y <= -4.8e-151)
		tmp = Float64(t * Float64(x / Float64(z - y)));
	elseif (y <= 2e-91)
		tmp = Float64(Float64(x - y) / Float64(z / t));
	elseif (y <= 30500.0)
		tmp = Float64(Float64(x * t) / Float64(z - y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t / (1.0 - (z / y));
	tmp = 0.0;
	if (y <= -1.9e+16)
		tmp = t_1;
	elseif (y <= -4.8e-151)
		tmp = t * (x / (z - y));
	elseif (y <= 2e-91)
		tmp = (x - y) / (z / t);
	elseif (y <= 30500.0)
		tmp = (x * t) / (z - y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.9e+16], t$95$1, If[LessEqual[y, -4.8e-151], N[(t * N[(x / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2e-91], N[(N[(x - y), $MachinePrecision] / N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 30500.0], N[(N[(x * t), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 7: 60.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.2 \cdot 10^{+179}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -7.8 \cdot 10^{+154}:\\ \;\;\;\;\frac{-t}{\frac{y}{x}}\\ \mathbf{elif}\;y \leq -1.42 \cdot 10^{+28}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 116000:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.2e+179)
   t
   (if (<= y -7.8e+154)
     (/ (- t) (/ y x))
     (if (<= y -1.42e+28) t (if (<= y 116000.0) (* x (/ t z)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -7.8e+154) {
		tmp = -t / (y / x);
	} else if (y <= -1.42e+28) {
		tmp = t;
	} else if (y <= 116000.0) {
		tmp = x * (t / 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 (y <= (-4.2d+179)) then
        tmp = t
    else if (y <= (-7.8d+154)) then
        tmp = -t / (y / x)
    else if (y <= (-1.42d+28)) then
        tmp = t
    else if (y <= 116000.0d0) then
        tmp = x * (t / 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 (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -7.8e+154) {
		tmp = -t / (y / x);
	} else if (y <= -1.42e+28) {
		tmp = t;
	} else if (y <= 116000.0) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.2e+179:
		tmp = t
	elif y <= -7.8e+154:
		tmp = -t / (y / x)
	elif y <= -1.42e+28:
		tmp = t
	elif y <= 116000.0:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -7.8e+154)
		tmp = Float64(Float64(-t) / Float64(y / x));
	elseif (y <= -1.42e+28)
		tmp = t;
	elseif (y <= 116000.0)
		tmp = Float64(x * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -7.8e+154)
		tmp = -t / (y / x);
	elseif (y <= -1.42e+28)
		tmp = t;
	elseif (y <= 116000.0)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.2e+179], t, If[LessEqual[y, -7.8e+154], N[((-t) / N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.42e+28], t, If[LessEqual[y, 116000.0], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]]]

Derivation

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

Alternative 8: 58.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.2 \cdot 10^{+179}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -5 \cdot 10^{+84}:\\ \;\;\;\;-t \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq -3.55 \cdot 10^{+28}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 58000:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.2e+179)
   t
   (if (<= y -5e+84)
     (- (* t (/ y z)))
     (if (<= y -3.55e+28) t (if (<= y 58000.0) (* x (/ t z)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -5e+84) {
		tmp = -(t * (y / z));
	} else if (y <= -3.55e+28) {
		tmp = t;
	} else if (y <= 58000.0) {
		tmp = x * (t / 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 (y <= (-4.2d+179)) then
        tmp = t
    else if (y <= (-5d+84)) then
        tmp = -(t * (y / z))
    else if (y <= (-3.55d+28)) then
        tmp = t
    else if (y <= 58000.0d0) then
        tmp = x * (t / 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 (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -5e+84) {
		tmp = -(t * (y / z));
	} else if (y <= -3.55e+28) {
		tmp = t;
	} else if (y <= 58000.0) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.2e+179:
		tmp = t
	elif y <= -5e+84:
		tmp = -(t * (y / z))
	elif y <= -3.55e+28:
		tmp = t
	elif y <= 58000.0:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -5e+84)
		tmp = Float64(-Float64(t * Float64(y / z)));
	elseif (y <= -3.55e+28)
		tmp = t;
	elseif (y <= 58000.0)
		tmp = Float64(x * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -5e+84)
		tmp = -(t * (y / z));
	elseif (y <= -3.55e+28)
		tmp = t;
	elseif (y <= 58000.0)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.2e+179], t, If[LessEqual[y, -5e+84], (-N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), If[LessEqual[y, -3.55e+28], t, If[LessEqual[y, 58000.0], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]]]

Derivation

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

Alternative 9: 58.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.2 \cdot 10^{+179}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{+85}:\\ \;\;\;\;\frac{-t}{\frac{z}{y}}\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{+28}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 92000:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -4.2e+179)
   t
   (if (<= y -1.75e+85)
     (/ (- t) (/ z y))
     (if (<= y -3.5e+28) t (if (<= y 92000.0) (* x (/ t z)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -1.75e+85) {
		tmp = -t / (z / y);
	} else if (y <= -3.5e+28) {
		tmp = t;
	} else if (y <= 92000.0) {
		tmp = x * (t / 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 (y <= (-4.2d+179)) then
        tmp = t
    else if (y <= (-1.75d+85)) then
        tmp = -t / (z / y)
    else if (y <= (-3.5d+28)) then
        tmp = t
    else if (y <= 92000.0d0) then
        tmp = x * (t / 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 (y <= -4.2e+179) {
		tmp = t;
	} else if (y <= -1.75e+85) {
		tmp = -t / (z / y);
	} else if (y <= -3.5e+28) {
		tmp = t;
	} else if (y <= 92000.0) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -4.2e+179:
		tmp = t
	elif y <= -1.75e+85:
		tmp = -t / (z / y)
	elif y <= -3.5e+28:
		tmp = t
	elif y <= 92000.0:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -1.75e+85)
		tmp = Float64(Float64(-t) / Float64(z / y));
	elseif (y <= -3.5e+28)
		tmp = t;
	elseif (y <= 92000.0)
		tmp = Float64(x * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -4.2e+179)
		tmp = t;
	elseif (y <= -1.75e+85)
		tmp = -t / (z / y);
	elseif (y <= -3.5e+28)
		tmp = t;
	elseif (y <= 92000.0)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -4.2e+179], t, If[LessEqual[y, -1.75e+85], N[((-t) / N[(z / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -3.5e+28], t, If[LessEqual[y, 92000.0], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]]]

Derivation

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

Alternative 10: 75.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+16} \lor \neg \left(y \leq 64000\right):\\ \;\;\;\;\frac{t}{1 - \frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{t}{z - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -2.15e+16) (not (<= y 64000.0)))
   (/ t (- 1.0 (/ z y)))
   (* x (/ t (- z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -2.15e+16) || !(y <= 64000.0)) {
		tmp = t / (1.0 - (z / y));
	} else {
		tmp = x * (t / (z - 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 ((y <= (-2.15d+16)) .or. (.not. (y <= 64000.0d0))) then
        tmp = t / (1.0d0 - (z / y))
    else
        tmp = x * (t / (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -2.15e+16) || !(y <= 64000.0)) {
		tmp = t / (1.0 - (z / y));
	} else {
		tmp = x * (t / (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -2.15e+16) or not (y <= 64000.0):
		tmp = t / (1.0 - (z / y))
	else:
		tmp = x * (t / (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -2.15e+16) || !(y <= 64000.0))
		tmp = Float64(t / Float64(1.0 - Float64(z / y)));
	else
		tmp = Float64(x * Float64(t / Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -2.15e+16) || ~((y <= 64000.0)))
		tmp = t / (1.0 - (z / y));
	else
		tmp = x * (t / (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -2.15e+16], N[Not[LessEqual[y, 64000.0]], $MachinePrecision]], N[(t / N[(1.0 - N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x * N[(t / N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 60.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.45 \cdot 10^{+28}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 54000:\\ \;\;\;\;x \cdot \frac{t}{z}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -2.45e+28) t (if (<= y 54000.0) (* x (/ t z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -2.45e+28) {
		tmp = t;
	} else if (y <= 54000.0) {
		tmp = x * (t / 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 (y <= (-2.45d+28)) then
        tmp = t
    else if (y <= 54000.0d0) then
        tmp = x * (t / 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 (y <= -2.45e+28) {
		tmp = t;
	} else if (y <= 54000.0) {
		tmp = x * (t / z);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -2.45e+28:
		tmp = t
	elif y <= 54000.0:
		tmp = x * (t / z)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -2.45e+28)
		tmp = t;
	elseif (y <= 54000.0)
		tmp = Float64(x * Float64(t / z));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -2.45e+28)
		tmp = t;
	elseif (y <= 54000.0)
		tmp = x * (t / z);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -2.45e+28], t, If[LessEqual[y, 54000.0], N[(x * N[(t / z), $MachinePrecision]), $MachinePrecision], t]]

Derivation

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

Alternative 12: 35.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq 1.5 \cdot 10^{+195}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{t}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (if (<= z 1.5e+195) t (* x (/ t y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= 1.5e+195) {
		tmp = t;
	} else {
		tmp = x * (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 <= 1.5d+195) then
        tmp = t
    else
        tmp = x * (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 <= 1.5e+195) {
		tmp = t;
	} else {
		tmp = x * (t / y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= 1.5e+195:
		tmp = t
	else:
		tmp = x * (t / y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= 1.5e+195)
		tmp = t;
	else
		tmp = Float64(x * Float64(t / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= 1.5e+195)
		tmp = t;
	else
		tmp = x * (t / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, 1.5e+195], t, N[(x * N[(t / y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 35.5% 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: 97.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023347 
(FPCore (x y z t)
  :name "Numeric.Signal.Multichannel:$cput from hsignal-0.2.7.1"
  :precision binary64

  :herbie-target
  (/ t (/ (- z y) (- x y)))

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