Data.Metrics.Snapshot:quantile from metrics-0.3.0.2

Percentage Accurate: 100.0% → 100.0%

Time: 6.8s

Alternatives: 12

Speedup: 1.0×

• 34.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

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 - x))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 - x))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 + ((t - x) * (y - z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 80.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1650000 \lor \neg \left(t \leq 6.5 \cdot 10^{+33} \lor \neg \left(t \leq 4.8 \cdot 10^{+83}\right) \land t \leq 3.5 \cdot 10^{+126}\right):\\ \;\;\;\;x + t \cdot \left(y - z\right)\\ \mathbf{else}:\\ \;\;\;\;x - x \cdot \left(y - z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -1650000.0)
         (not (or (<= t 6.5e+33) (and (not (<= t 4.8e+83)) (<= t 3.5e+126)))))
   (+ x (* t (- y z)))
   (- x (* x (- y z)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -1650000.0) || !((t <= 6.5e+33) || (!(t <= 4.8e+83) && (t <= 3.5e+126)))) {
		tmp = x + (t * (y - z));
	} else {
		tmp = x - (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 ((t <= (-1650000.0d0)) .or. (.not. (t <= 6.5d+33) .or. (.not. (t <= 4.8d+83)) .and. (t <= 3.5d+126))) then
        tmp = x + (t * (y - z))
    else
        tmp = x - (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 ((t <= -1650000.0) || !((t <= 6.5e+33) || (!(t <= 4.8e+83) && (t <= 3.5e+126)))) {
		tmp = x + (t * (y - z));
	} else {
		tmp = x - (x * (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -1650000.0) or not ((t <= 6.5e+33) or (not (t <= 4.8e+83) and (t <= 3.5e+126))):
		tmp = x + (t * (y - z))
	else:
		tmp = x - (x * (y - z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -1650000.0) || !((t <= 6.5e+33) || (!(t <= 4.8e+83) && (t <= 3.5e+126))))
		tmp = Float64(x + Float64(t * Float64(y - z)));
	else
		tmp = Float64(x - Float64(x * Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -1650000.0) || ~(((t <= 6.5e+33) || (~((t <= 4.8e+83)) && (t <= 3.5e+126)))))
		tmp = x + (t * (y - z));
	else
		tmp = x - (x * (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -1650000.0], N[Not[Or[LessEqual[t, 6.5e+33], And[N[Not[LessEqual[t, 4.8e+83]], $MachinePrecision], LessEqual[t, 3.5e+126]]]], $MachinePrecision]], N[(x + N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(x * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 3: 37.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(-y\right)\\ t_2 := z \cdot \left(-t\right)\\ \mathbf{if}\;y \leq -1.5 \cdot 10^{+166}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{+132}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.66 \cdot 10^{-251}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{+61}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (- y))) (t_2 (* z (- t))))
   (if (<= y -1.5e+166)
     t_1
     (if (<= y -3.5e+132)
       t_2
       (if (<= y -1.0)
         t_1
         (if (<= y 1.66e-251) x (if (<= y 1.6e+61) t_2 t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * -y;
	double t_2 = z * -t;
	double tmp;
	if (y <= -1.5e+166) {
		tmp = t_1;
	} else if (y <= -3.5e+132) {
		tmp = t_2;
	} else if (y <= -1.0) {
		tmp = t_1;
	} else if (y <= 1.66e-251) {
		tmp = x;
	} else if (y <= 1.6e+61) {
		tmp = t_2;
	} 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) :: t_2
    real(8) :: tmp
    t_1 = x * -y
    t_2 = z * -t
    if (y <= (-1.5d+166)) then
        tmp = t_1
    else if (y <= (-3.5d+132)) then
        tmp = t_2
    else if (y <= (-1.0d0)) then
        tmp = t_1
    else if (y <= 1.66d-251) then
        tmp = x
    else if (y <= 1.6d+61) then
        tmp = t_2
    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;
	double t_2 = z * -t;
	double tmp;
	if (y <= -1.5e+166) {
		tmp = t_1;
	} else if (y <= -3.5e+132) {
		tmp = t_2;
	} else if (y <= -1.0) {
		tmp = t_1;
	} else if (y <= 1.66e-251) {
		tmp = x;
	} else if (y <= 1.6e+61) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * -y
	t_2 = z * -t
	tmp = 0
	if y <= -1.5e+166:
		tmp = t_1
	elif y <= -3.5e+132:
		tmp = t_2
	elif y <= -1.0:
		tmp = t_1
	elif y <= 1.66e-251:
		tmp = x
	elif y <= 1.6e+61:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(-y))
	t_2 = Float64(z * Float64(-t))
	tmp = 0.0
	if (y <= -1.5e+166)
		tmp = t_1;
	elseif (y <= -3.5e+132)
		tmp = t_2;
	elseif (y <= -1.0)
		tmp = t_1;
	elseif (y <= 1.66e-251)
		tmp = x;
	elseif (y <= 1.6e+61)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * -y;
	t_2 = z * -t;
	tmp = 0.0;
	if (y <= -1.5e+166)
		tmp = t_1;
	elseif (y <= -3.5e+132)
		tmp = t_2;
	elseif (y <= -1.0)
		tmp = t_1;
	elseif (y <= 1.66e-251)
		tmp = x;
	elseif (y <= 1.6e+61)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * (-y)), $MachinePrecision]}, Block[{t$95$2 = N[(z * (-t)), $MachinePrecision]}, If[LessEqual[y, -1.5e+166], t$95$1, If[LessEqual[y, -3.5e+132], t$95$2, If[LessEqual[y, -1.0], t$95$1, If[LessEqual[y, 1.66e-251], x, If[LessEqual[y, 1.6e+61], t$95$2, t$95$1]]]]]]]

Derivation

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

Alternative 4: 64.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(x - t\right)\\ t_2 := x \cdot \left(1 - y\right)\\ \mathbf{if}\;z \leq -4.4 \cdot 10^{+134}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.7 \cdot 10^{-164}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.85 \cdot 10^{-271}:\\ \;\;\;\;x + y \cdot t\\ \mathbf{elif}\;z \leq 80000000000:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- x t))) (t_2 (* x (- 1.0 y))))
   (if (<= z -4.4e+134)
     t_1
     (if (<= z -2.7e-164)
       t_2
       (if (<= z -1.85e-271)
         (+ x (* y t))
         (if (<= z 80000000000.0) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * (x - t);
	double t_2 = x * (1.0 - y);
	double tmp;
	if (z <= -4.4e+134) {
		tmp = t_1;
	} else if (z <= -2.7e-164) {
		tmp = t_2;
	} else if (z <= -1.85e-271) {
		tmp = x + (y * t);
	} else if (z <= 80000000000.0) {
		tmp = t_2;
	} 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) :: t_2
    real(8) :: tmp
    t_1 = z * (x - t)
    t_2 = x * (1.0d0 - y)
    if (z <= (-4.4d+134)) then
        tmp = t_1
    else if (z <= (-2.7d-164)) then
        tmp = t_2
    else if (z <= (-1.85d-271)) then
        tmp = x + (y * t)
    else if (z <= 80000000000.0d0) then
        tmp = t_2
    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 = z * (x - t);
	double t_2 = x * (1.0 - y);
	double tmp;
	if (z <= -4.4e+134) {
		tmp = t_1;
	} else if (z <= -2.7e-164) {
		tmp = t_2;
	} else if (z <= -1.85e-271) {
		tmp = x + (y * t);
	} else if (z <= 80000000000.0) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * (x - t)
	t_2 = x * (1.0 - y)
	tmp = 0
	if z <= -4.4e+134:
		tmp = t_1
	elif z <= -2.7e-164:
		tmp = t_2
	elif z <= -1.85e-271:
		tmp = x + (y * t)
	elif z <= 80000000000.0:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(x - t))
	t_2 = Float64(x * Float64(1.0 - y))
	tmp = 0.0
	if (z <= -4.4e+134)
		tmp = t_1;
	elseif (z <= -2.7e-164)
		tmp = t_2;
	elseif (z <= -1.85e-271)
		tmp = Float64(x + Float64(y * t));
	elseif (z <= 80000000000.0)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * (x - t);
	t_2 = x * (1.0 - y);
	tmp = 0.0;
	if (z <= -4.4e+134)
		tmp = t_1;
	elseif (z <= -2.7e-164)
		tmp = t_2;
	elseif (z <= -1.85e-271)
		tmp = x + (y * t);
	elseif (z <= 80000000000.0)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.4e+134], t$95$1, If[LessEqual[z, -2.7e-164], t$95$2, If[LessEqual[z, -1.85e-271], N[(x + N[(y * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 80000000000.0], t$95$2, t$95$1]]]]]]

Derivation

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

Alternative 5: 64.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(x - t\right)\\ \mathbf{if}\;z \leq -4.4 \cdot 10^{+134}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.4 \cdot 10^{-161}:\\ \;\;\;\;x - x \cdot y\\ \mathbf{elif}\;z \leq -4.3 \cdot 10^{-271}:\\ \;\;\;\;x + y \cdot t\\ \mathbf{elif}\;z \leq 86000000000000:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- x t))))
   (if (<= z -4.4e+134)
     t_1
     (if (<= z -2.4e-161)
       (- x (* x y))
       (if (<= z -4.3e-271)
         (+ x (* y t))
         (if (<= z 86000000000000.0) (* x (- 1.0 y)) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * (x - t);
	double tmp;
	if (z <= -4.4e+134) {
		tmp = t_1;
	} else if (z <= -2.4e-161) {
		tmp = x - (x * y);
	} else if (z <= -4.3e-271) {
		tmp = x + (y * t);
	} else if (z <= 86000000000000.0) {
		tmp = x * (1.0 - 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 = z * (x - t)
    if (z <= (-4.4d+134)) then
        tmp = t_1
    else if (z <= (-2.4d-161)) then
        tmp = x - (x * y)
    else if (z <= (-4.3d-271)) then
        tmp = x + (y * t)
    else if (z <= 86000000000000.0d0) then
        tmp = x * (1.0d0 - 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 = z * (x - t);
	double tmp;
	if (z <= -4.4e+134) {
		tmp = t_1;
	} else if (z <= -2.4e-161) {
		tmp = x - (x * y);
	} else if (z <= -4.3e-271) {
		tmp = x + (y * t);
	} else if (z <= 86000000000000.0) {
		tmp = x * (1.0 - y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * (x - t)
	tmp = 0
	if z <= -4.4e+134:
		tmp = t_1
	elif z <= -2.4e-161:
		tmp = x - (x * y)
	elif z <= -4.3e-271:
		tmp = x + (y * t)
	elif z <= 86000000000000.0:
		tmp = x * (1.0 - y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(x - t))
	tmp = 0.0
	if (z <= -4.4e+134)
		tmp = t_1;
	elseif (z <= -2.4e-161)
		tmp = Float64(x - Float64(x * y));
	elseif (z <= -4.3e-271)
		tmp = Float64(x + Float64(y * t));
	elseif (z <= 86000000000000.0)
		tmp = Float64(x * Float64(1.0 - y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * (x - t);
	tmp = 0.0;
	if (z <= -4.4e+134)
		tmp = t_1;
	elseif (z <= -2.4e-161)
		tmp = x - (x * y);
	elseif (z <= -4.3e-271)
		tmp = x + (y * t);
	elseif (z <= 86000000000000.0)
		tmp = x * (1.0 - y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.4e+134], t$95$1, If[LessEqual[z, -2.4e-161], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -4.3e-271], N[(x + N[(y * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 86000000000000.0], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 6: 82.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{+134} \lor \neg \left(z \leq 2.05 \cdot 10^{+34}\right):\\ \;\;\;\;z \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \left(x - t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -4.4e+134) (not (<= z 2.05e+34)))
   (* z (- x t))
   (- x (* y (- x t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -4.4e+134) || !(z <= 2.05e+34)) {
		tmp = z * (x - t);
	} else {
		tmp = x - (y * (x - 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 ((z <= (-4.4d+134)) .or. (.not. (z <= 2.05d+34))) then
        tmp = z * (x - t)
    else
        tmp = x - (y * (x - t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -4.4e+134) || !(z <= 2.05e+34)) {
		tmp = z * (x - t);
	} else {
		tmp = x - (y * (x - t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -4.4e+134) or not (z <= 2.05e+34):
		tmp = z * (x - t)
	else:
		tmp = x - (y * (x - t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -4.4e+134) || !(z <= 2.05e+34))
		tmp = Float64(z * Float64(x - t));
	else
		tmp = Float64(x - Float64(y * Float64(x - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -4.4e+134) || ~((z <= 2.05e+34)))
		tmp = z * (x - t);
	else
		tmp = x - (y * (x - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -4.4e+134], N[Not[LessEqual[z, 2.05e+34]], $MachinePrecision]], N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(x - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 67.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.75 \cdot 10^{+111}:\\ \;\;\;\;x + x \cdot z\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{-46}:\\ \;\;\;\;x + \left(y - z\right) \cdot t\\ \mathbf{else}:\\ \;\;\;\;x - x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.75e+111)
   (+ x (* x z))
   (if (<= x 1.8e-46) (+ x (* (- y z) t)) (- x (* x y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.75e+111) {
		tmp = x + (x * z);
	} else if (x <= 1.8e-46) {
		tmp = x + ((y - z) * t);
	} else {
		tmp = x - (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 <= (-1.75d+111)) then
        tmp = x + (x * z)
    else if (x <= 1.8d-46) then
        tmp = x + ((y - z) * t)
    else
        tmp = x - (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 <= -1.75e+111) {
		tmp = x + (x * z);
	} else if (x <= 1.8e-46) {
		tmp = x + ((y - z) * t);
	} else {
		tmp = x - (x * y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.75e+111:
		tmp = x + (x * z)
	elif x <= 1.8e-46:
		tmp = x + ((y - z) * t)
	else:
		tmp = x - (x * y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.75e+111)
		tmp = Float64(x + Float64(x * z));
	elseif (x <= 1.8e-46)
		tmp = Float64(x + Float64(Float64(y - z) * t));
	else
		tmp = Float64(x - Float64(x * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.75e+111)
		tmp = x + (x * z);
	elseif (x <= 1.8e-46)
		tmp = x + ((y - z) * t);
	else
		tmp = x - (x * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.75e+111], N[(x + N[(x * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.8e-46], N[(x + N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 8: 82.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(x - t\right)\\ \mathbf{if}\;z \leq -4.4 \cdot 10^{+134}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.02 \cdot 10^{+34}:\\ \;\;\;\;x - y \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;x + t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- x t))))
   (if (<= z -4.4e+134)
     t_1
     (if (<= z 1.02e+34) (- x (* y (- x t))) (+ x t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * (x - t);
	double tmp;
	if (z <= -4.4e+134) {
		tmp = t_1;
	} else if (z <= 1.02e+34) {
		tmp = x - (y * (x - t));
	} else {
		tmp = x + 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 = z * (x - t)
    if (z <= (-4.4d+134)) then
        tmp = t_1
    else if (z <= 1.02d+34) then
        tmp = x - (y * (x - t))
    else
        tmp = x + t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = z * (x - t);
	double tmp;
	if (z <= -4.4e+134) {
		tmp = t_1;
	} else if (z <= 1.02e+34) {
		tmp = x - (y * (x - t));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * (x - t)
	tmp = 0
	if z <= -4.4e+134:
		tmp = t_1
	elif z <= 1.02e+34:
		tmp = x - (y * (x - t))
	else:
		tmp = x + t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(x - t))
	tmp = 0.0
	if (z <= -4.4e+134)
		tmp = t_1;
	elseif (z <= 1.02e+34)
		tmp = Float64(x - Float64(y * Float64(x - t)));
	else
		tmp = Float64(x + t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * (x - t);
	tmp = 0.0;
	if (z <= -4.4e+134)
		tmp = t_1;
	elseif (z <= 1.02e+34)
		tmp = x - (y * (x - t));
	else
		tmp = x + t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.4e+134], t$95$1, If[LessEqual[z, 1.02e+34], N[(x - N[(y * N[(x - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t$95$1), $MachinePrecision]]]]

Derivation

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

Alternative 9: 49.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+134} \lor \neg \left(z \leq 8 \cdot 10^{+19}\right):\\ \;\;\;\;z \cdot \left(-t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -5e+134) (not (<= z 8e+19))) (* z (- t)) (* x (- 1.0 y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -5e+134) || !(z <= 8e+19)) {
		tmp = z * -t;
	} else {
		tmp = x * (1.0 - 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 <= (-5d+134)) .or. (.not. (z <= 8d+19))) then
        tmp = z * -t
    else
        tmp = x * (1.0d0 - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -5e+134) || !(z <= 8e+19)) {
		tmp = z * -t;
	} else {
		tmp = x * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -5e+134) or not (z <= 8e+19):
		tmp = z * -t
	else:
		tmp = x * (1.0 - y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -5e+134) || !(z <= 8e+19))
		tmp = Float64(z * Float64(-t));
	else
		tmp = Float64(x * Float64(1.0 - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -5e+134) || ~((z <= 8e+19)))
		tmp = z * -t;
	else
		tmp = x * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -5e+134], N[Not[LessEqual[z, 8e+19]], $MachinePrecision]], N[(z * (-t)), $MachinePrecision], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 63.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{+134} \lor \neg \left(z \leq 1550000000000\right):\\ \;\;\;\;z \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -4.4e+134) (not (<= z 1550000000000.0)))
   (* z (- x t))
   (* x (- 1.0 y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -4.4e+134) || !(z <= 1550000000000.0)) {
		tmp = z * (x - t);
	} else {
		tmp = x * (1.0 - 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 <= (-4.4d+134)) .or. (.not. (z <= 1550000000000.0d0))) then
        tmp = z * (x - t)
    else
        tmp = x * (1.0d0 - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -4.4e+134) || !(z <= 1550000000000.0)) {
		tmp = z * (x - t);
	} else {
		tmp = x * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -4.4e+134) or not (z <= 1550000000000.0):
		tmp = z * (x - t)
	else:
		tmp = x * (1.0 - y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -4.4e+134) || !(z <= 1550000000000.0))
		tmp = Float64(z * Float64(x - t));
	else
		tmp = Float64(x * Float64(1.0 - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -4.4e+134) || ~((z <= 1550000000000.0)))
		tmp = z * (x - t);
	else
		tmp = x * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -4.4e+134], N[Not[LessEqual[z, 1550000000000.0]], $MachinePrecision]], N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 36.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.36 \cdot 10^{-24} \lor \neg \left(z \leq 3.1 \cdot 10^{-97}\right):\\ \;\;\;\;z \cdot \left(-t\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.36e-24) (not (<= z 3.1e-97))) (* z (- t)) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.36e-24) || !(z <= 3.1e-97)) {
		tmp = z * -t;
	} else {
		tmp = x;
	}
	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 <= (-1.36d-24)) .or. (.not. (z <= 3.1d-97))) then
        tmp = z * -t
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.36e-24) || !(z <= 3.1e-97)) {
		tmp = z * -t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.36e-24) or not (z <= 3.1e-97):
		tmp = z * -t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.36e-24) || !(z <= 3.1e-97))
		tmp = Float64(z * Float64(-t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.36e-24) || ~((z <= 3.1e-97)))
		tmp = z * -t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.36e-24], N[Not[LessEqual[z, 3.1e-97]], $MachinePrecision]], N[(z * (-t)), $MachinePrecision], x]

Derivation

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

Alternative 12: 18.1% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 96.4% accurate, 0.6× speedup

Math

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

FPCore

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

C

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

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 + ((t * (y - z)) + (-x * (y - z)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t)
  :name "Data.Metrics.Snapshot:quantile from metrics-0.3.0.2"
  :precision binary64

  :herbie-target
  (+ x (+ (* t (- y z)) (* (- x) (- y z))))

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