Data.Metrics.Snapshot:quantile from metrics-0.3.0.2

Percentage Accurate: 100.0% → 100.0%

Time: 7.4s

Alternatives: 13

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: 62.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + x \cdot z\\ t_2 := y \cdot \left(t - x\right)\\ t_3 := z \cdot \left(-t\right)\\ \mathbf{if}\;y \leq -1.55 \cdot 10^{-30}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.08 \cdot 10^{-250}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{-239}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 9.1 \cdot 10^{-187}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+36}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ x (* x z))) (t_2 (* y (- t x))) (t_3 (* z (- t))))
   (if (<= y -1.55e-30)
     t_2
     (if (<= y -1.08e-250)
       t_3
       (if (<= y 1.6e-239)
         t_1
         (if (<= y 9.1e-187) t_3 (if (<= y 5.6e+36) t_1 t_2)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + (x * z);
	double t_2 = y * (t - x);
	double t_3 = z * -t;
	double tmp;
	if (y <= -1.55e-30) {
		tmp = t_2;
	} else if (y <= -1.08e-250) {
		tmp = t_3;
	} else if (y <= 1.6e-239) {
		tmp = t_1;
	} else if (y <= 9.1e-187) {
		tmp = t_3;
	} else if (y <= 5.6e+36) {
		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) :: t_3
    real(8) :: tmp
    t_1 = x + (x * z)
    t_2 = y * (t - x)
    t_3 = z * -t
    if (y <= (-1.55d-30)) then
        tmp = t_2
    else if (y <= (-1.08d-250)) then
        tmp = t_3
    else if (y <= 1.6d-239) then
        tmp = t_1
    else if (y <= 9.1d-187) then
        tmp = t_3
    else if (y <= 5.6d+36) 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 = x + (x * z);
	double t_2 = y * (t - x);
	double t_3 = z * -t;
	double tmp;
	if (y <= -1.55e-30) {
		tmp = t_2;
	} else if (y <= -1.08e-250) {
		tmp = t_3;
	} else if (y <= 1.6e-239) {
		tmp = t_1;
	} else if (y <= 9.1e-187) {
		tmp = t_3;
	} else if (y <= 5.6e+36) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + (x * z)
	t_2 = y * (t - x)
	t_3 = z * -t
	tmp = 0
	if y <= -1.55e-30:
		tmp = t_2
	elif y <= -1.08e-250:
		tmp = t_3
	elif y <= 1.6e-239:
		tmp = t_1
	elif y <= 9.1e-187:
		tmp = t_3
	elif y <= 5.6e+36:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(x * z))
	t_2 = Float64(y * Float64(t - x))
	t_3 = Float64(z * Float64(-t))
	tmp = 0.0
	if (y <= -1.55e-30)
		tmp = t_2;
	elseif (y <= -1.08e-250)
		tmp = t_3;
	elseif (y <= 1.6e-239)
		tmp = t_1;
	elseif (y <= 9.1e-187)
		tmp = t_3;
	elseif (y <= 5.6e+36)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + (x * z);
	t_2 = y * (t - x);
	t_3 = z * -t;
	tmp = 0.0;
	if (y <= -1.55e-30)
		tmp = t_2;
	elseif (y <= -1.08e-250)
		tmp = t_3;
	elseif (y <= 1.6e-239)
		tmp = t_1;
	elseif (y <= 9.1e-187)
		tmp = t_3;
	elseif (y <= 5.6e+36)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(x * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z * (-t)), $MachinePrecision]}, If[LessEqual[y, -1.55e-30], t$95$2, If[LessEqual[y, -1.08e-250], t$95$3, If[LessEqual[y, 1.6e-239], t$95$1, If[LessEqual[y, 9.1e-187], t$95$3, If[LessEqual[y, 5.6e+36], t$95$1, t$95$2]]]]]]]]

Derivation

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

Alternative 3: 77.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(t - x\right)\\ t_2 := x + t \cdot \left(y - z\right)\\ \mathbf{if}\;y \leq -3.1 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 3.7 \cdot 10^{-186}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{-170}:\\ \;\;\;\;x + x \cdot z\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+57}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- t x))) (t_2 (+ x (* t (- y z)))))
   (if (<= y -3.1e+23)
     t_1
     (if (<= y 3.7e-186)
       t_2
       (if (<= y 4.2e-170) (+ x (* x z)) (if (<= y 1.35e+57) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double t_2 = x + (t * (y - z));
	double tmp;
	if (y <= -3.1e+23) {
		tmp = t_1;
	} else if (y <= 3.7e-186) {
		tmp = t_2;
	} else if (y <= 4.2e-170) {
		tmp = x + (x * z);
	} else if (y <= 1.35e+57) {
		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 = y * (t - x)
    t_2 = x + (t * (y - z))
    if (y <= (-3.1d+23)) then
        tmp = t_1
    else if (y <= 3.7d-186) then
        tmp = t_2
    else if (y <= 4.2d-170) then
        tmp = x + (x * z)
    else if (y <= 1.35d+57) 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 = y * (t - x);
	double t_2 = x + (t * (y - z));
	double tmp;
	if (y <= -3.1e+23) {
		tmp = t_1;
	} else if (y <= 3.7e-186) {
		tmp = t_2;
	} else if (y <= 4.2e-170) {
		tmp = x + (x * z);
	} else if (y <= 1.35e+57) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (t - x)
	t_2 = x + (t * (y - z))
	tmp = 0
	if y <= -3.1e+23:
		tmp = t_1
	elif y <= 3.7e-186:
		tmp = t_2
	elif y <= 4.2e-170:
		tmp = x + (x * z)
	elif y <= 1.35e+57:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(t - x))
	t_2 = Float64(x + Float64(t * Float64(y - z)))
	tmp = 0.0
	if (y <= -3.1e+23)
		tmp = t_1;
	elseif (y <= 3.7e-186)
		tmp = t_2;
	elseif (y <= 4.2e-170)
		tmp = Float64(x + Float64(x * z));
	elseif (y <= 1.35e+57)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (t - x);
	t_2 = x + (t * (y - z));
	tmp = 0.0;
	if (y <= -3.1e+23)
		tmp = t_1;
	elseif (y <= 3.7e-186)
		tmp = t_2;
	elseif (y <= 4.2e-170)
		tmp = x + (x * z);
	elseif (y <= 1.35e+57)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.1e+23], t$95$1, If[LessEqual[y, 3.7e-186], t$95$2, If[LessEqual[y, 4.2e-170], N[(x + N[(x * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.35e+57], t$95$2, t$95$1]]]]]]

Derivation

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

Alternative 4: 49.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(-t\right)\\ t_2 := x \cdot \left(1 - y\right)\\ \mathbf{if}\;z \leq -2.2 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 9.2 \cdot 10^{-147}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-88}:\\ \;\;\;\;y \cdot t\\ \mathbf{elif}\;z \leq 500:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- t))) (t_2 (* x (- 1.0 y))))
   (if (<= z -2.2e+17)
     t_1
     (if (<= z 9.2e-147)
       t_2
       (if (<= z 3.4e-88) (* y t) (if (<= z 500.0) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * -t;
	double t_2 = x * (1.0 - y);
	double tmp;
	if (z <= -2.2e+17) {
		tmp = t_1;
	} else if (z <= 9.2e-147) {
		tmp = t_2;
	} else if (z <= 3.4e-88) {
		tmp = y * t;
	} else if (z <= 500.0) {
		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 * -t
    t_2 = x * (1.0d0 - y)
    if (z <= (-2.2d+17)) then
        tmp = t_1
    else if (z <= 9.2d-147) then
        tmp = t_2
    else if (z <= 3.4d-88) then
        tmp = y * t
    else if (z <= 500.0d0) 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 * -t;
	double t_2 = x * (1.0 - y);
	double tmp;
	if (z <= -2.2e+17) {
		tmp = t_1;
	} else if (z <= 9.2e-147) {
		tmp = t_2;
	} else if (z <= 3.4e-88) {
		tmp = y * t;
	} else if (z <= 500.0) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * -t
	t_2 = x * (1.0 - y)
	tmp = 0
	if z <= -2.2e+17:
		tmp = t_1
	elif z <= 9.2e-147:
		tmp = t_2
	elif z <= 3.4e-88:
		tmp = y * t
	elif z <= 500.0:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(-t))
	t_2 = Float64(x * Float64(1.0 - y))
	tmp = 0.0
	if (z <= -2.2e+17)
		tmp = t_1;
	elseif (z <= 9.2e-147)
		tmp = t_2;
	elseif (z <= 3.4e-88)
		tmp = Float64(y * t);
	elseif (z <= 500.0)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * -t;
	t_2 = x * (1.0 - y);
	tmp = 0.0;
	if (z <= -2.2e+17)
		tmp = t_1;
	elseif (z <= 9.2e-147)
		tmp = t_2;
	elseif (z <= 3.4e-88)
		tmp = y * t;
	elseif (z <= 500.0)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * (-t)), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.2e+17], t$95$1, If[LessEqual[z, 9.2e-147], t$95$2, If[LessEqual[z, 3.4e-88], N[(y * t), $MachinePrecision], If[LessEqual[z, 500.0], t$95$2, t$95$1]]]]]]

Derivation

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

Alternative 5: 57.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(t - x\right)\\ t_2 := z \cdot \left(-t\right)\\ \mathbf{if}\;y \leq -3.8 \cdot 10^{-30}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{-160}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{-29}:\\ \;\;\;\;x + y \cdot t\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{+36}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- t x))) (t_2 (* z (- t))))
   (if (<= y -3.8e-30)
     t_1
     (if (<= y 1.15e-160)
       t_2
       (if (<= y 1.9e-29) (+ x (* y t)) (if (<= y 3.6e+36) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double t_2 = z * -t;
	double tmp;
	if (y <= -3.8e-30) {
		tmp = t_1;
	} else if (y <= 1.15e-160) {
		tmp = t_2;
	} else if (y <= 1.9e-29) {
		tmp = x + (y * t);
	} else if (y <= 3.6e+36) {
		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 = y * (t - x)
    t_2 = z * -t
    if (y <= (-3.8d-30)) then
        tmp = t_1
    else if (y <= 1.15d-160) then
        tmp = t_2
    else if (y <= 1.9d-29) then
        tmp = x + (y * t)
    else if (y <= 3.6d+36) 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 = y * (t - x);
	double t_2 = z * -t;
	double tmp;
	if (y <= -3.8e-30) {
		tmp = t_1;
	} else if (y <= 1.15e-160) {
		tmp = t_2;
	} else if (y <= 1.9e-29) {
		tmp = x + (y * t);
	} else if (y <= 3.6e+36) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (t - x)
	t_2 = z * -t
	tmp = 0
	if y <= -3.8e-30:
		tmp = t_1
	elif y <= 1.15e-160:
		tmp = t_2
	elif y <= 1.9e-29:
		tmp = x + (y * t)
	elif y <= 3.6e+36:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(t - x))
	t_2 = Float64(z * Float64(-t))
	tmp = 0.0
	if (y <= -3.8e-30)
		tmp = t_1;
	elseif (y <= 1.15e-160)
		tmp = t_2;
	elseif (y <= 1.9e-29)
		tmp = Float64(x + Float64(y * t));
	elseif (y <= 3.6e+36)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (t - x);
	t_2 = z * -t;
	tmp = 0.0;
	if (y <= -3.8e-30)
		tmp = t_1;
	elseif (y <= 1.15e-160)
		tmp = t_2;
	elseif (y <= 1.9e-29)
		tmp = x + (y * t);
	elseif (y <= 3.6e+36)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(z * (-t)), $MachinePrecision]}, If[LessEqual[y, -3.8e-30], t$95$1, If[LessEqual[y, 1.15e-160], t$95$2, If[LessEqual[y, 1.9e-29], N[(x + N[(y * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.6e+36], t$95$2, t$95$1]]]]]]

Derivation

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

Alternative 6: 38.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(-x\right)\\ \mathbf{if}\;y \leq -3.4 \cdot 10^{+161}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -3.9 \cdot 10^{+70}:\\ \;\;\;\;y \cdot t\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+36}:\\ \;\;\;\;z \cdot \left(-t\right)\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+150}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;y \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- x))))
   (if (<= y -3.4e+161)
     t_1
     (if (<= y -3.9e+70)
       (* y t)
       (if (<= y 7e+36) (* z (- t)) (if (<= y 4.4e+150) t_1 (* y t)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * -x;
	double tmp;
	if (y <= -3.4e+161) {
		tmp = t_1;
	} else if (y <= -3.9e+70) {
		tmp = y * t;
	} else if (y <= 7e+36) {
		tmp = z * -t;
	} else if (y <= 4.4e+150) {
		tmp = t_1;
	} else {
		tmp = y * 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 = y * -x
    if (y <= (-3.4d+161)) then
        tmp = t_1
    else if (y <= (-3.9d+70)) then
        tmp = y * t
    else if (y <= 7d+36) then
        tmp = z * -t
    else if (y <= 4.4d+150) then
        tmp = t_1
    else
        tmp = y * t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * -x;
	double tmp;
	if (y <= -3.4e+161) {
		tmp = t_1;
	} else if (y <= -3.9e+70) {
		tmp = y * t;
	} else if (y <= 7e+36) {
		tmp = z * -t;
	} else if (y <= 4.4e+150) {
		tmp = t_1;
	} else {
		tmp = y * t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * -x
	tmp = 0
	if y <= -3.4e+161:
		tmp = t_1
	elif y <= -3.9e+70:
		tmp = y * t
	elif y <= 7e+36:
		tmp = z * -t
	elif y <= 4.4e+150:
		tmp = t_1
	else:
		tmp = y * t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(-x))
	tmp = 0.0
	if (y <= -3.4e+161)
		tmp = t_1;
	elseif (y <= -3.9e+70)
		tmp = Float64(y * t);
	elseif (y <= 7e+36)
		tmp = Float64(z * Float64(-t));
	elseif (y <= 4.4e+150)
		tmp = t_1;
	else
		tmp = Float64(y * t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * -x;
	tmp = 0.0;
	if (y <= -3.4e+161)
		tmp = t_1;
	elseif (y <= -3.9e+70)
		tmp = y * t;
	elseif (y <= 7e+36)
		tmp = z * -t;
	elseif (y <= 4.4e+150)
		tmp = t_1;
	else
		tmp = y * t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * (-x)), $MachinePrecision]}, If[LessEqual[y, -3.4e+161], t$95$1, If[LessEqual[y, -3.9e+70], N[(y * t), $MachinePrecision], If[LessEqual[y, 7e+36], N[(z * (-t)), $MachinePrecision], If[LessEqual[y, 4.4e+150], t$95$1, N[(y * t), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 71.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(t - x\right)\\ \mathbf{if}\;y \leq -2.6 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{-186}:\\ \;\;\;\;x - z \cdot t\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{+36}:\\ \;\;\;\;x + x \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- t x))))
   (if (<= y -2.6e+17)
     t_1
     (if (<= y 3.3e-186)
       (- x (* z t))
       (if (<= y 3.6e+36) (+ x (* x z)) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double tmp;
	if (y <= -2.6e+17) {
		tmp = t_1;
	} else if (y <= 3.3e-186) {
		tmp = x - (z * t);
	} else if (y <= 3.6e+36) {
		tmp = x + (x * 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 = y * (t - x)
    if (y <= (-2.6d+17)) then
        tmp = t_1
    else if (y <= 3.3d-186) then
        tmp = x - (z * t)
    else if (y <= 3.6d+36) then
        tmp = x + (x * 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 = y * (t - x);
	double tmp;
	if (y <= -2.6e+17) {
		tmp = t_1;
	} else if (y <= 3.3e-186) {
		tmp = x - (z * t);
	} else if (y <= 3.6e+36) {
		tmp = x + (x * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (t - x)
	tmp = 0
	if y <= -2.6e+17:
		tmp = t_1
	elif y <= 3.3e-186:
		tmp = x - (z * t)
	elif y <= 3.6e+36:
		tmp = x + (x * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(t - x))
	tmp = 0.0
	if (y <= -2.6e+17)
		tmp = t_1;
	elseif (y <= 3.3e-186)
		tmp = Float64(x - Float64(z * t));
	elseif (y <= 3.6e+36)
		tmp = Float64(x + Float64(x * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (t - x);
	tmp = 0.0;
	if (y <= -2.6e+17)
		tmp = t_1;
	elseif (y <= 3.3e-186)
		tmp = x - (z * t);
	elseif (y <= 3.6e+36)
		tmp = x + (x * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.6e+17], t$95$1, If[LessEqual[y, 3.3e-186], N[(x - N[(z * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.6e+36], N[(x + N[(x * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 8: 80.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.9 \cdot 10^{-75} \lor \neg \left(t \leq 1.65 \cdot 10^{+31}\right):\\ \;\;\;\;x + t \cdot \left(y - z\right)\\ \mathbf{else}:\\ \;\;\;\;x + x \cdot \left(z - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -3.9e-75) (not (<= t 1.65e+31)))
   (+ x (* t (- y z)))
   (+ x (* x (- z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -3.9e-75) || !(t <= 1.65e+31)) {
		tmp = x + (t * (y - z));
	} else {
		tmp = x + (x * (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 ((t <= (-3.9d-75)) .or. (.not. (t <= 1.65d+31))) then
        tmp = x + (t * (y - z))
    else
        tmp = x + (x * (z - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -3.9e-75) || !(t <= 1.65e+31)) {
		tmp = x + (t * (y - z));
	} else {
		tmp = x + (x * (z - y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -3.9e-75) or not (t <= 1.65e+31):
		tmp = x + (t * (y - z))
	else:
		tmp = x + (x * (z - y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -3.9e-75) || !(t <= 1.65e+31))
		tmp = Float64(x + Float64(t * Float64(y - z)));
	else
		tmp = Float64(x + Float64(x * Float64(z - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -3.9e-75) || ~((t <= 1.65e+31)))
		tmp = x + (t * (y - z));
	else
		tmp = x + (x * (z - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -3.9e-75], N[Not[LessEqual[t, 1.65e+31]], $MachinePrecision]], N[(x + N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 83.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(t - x\right)\\ \mathbf{if}\;y \leq -4.4 \cdot 10^{+73}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.6 \cdot 10^{+37}:\\ \;\;\;\;x - z \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;x + t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- t x))))
   (if (<= y -4.4e+73) t_1 (if (<= y 2.6e+37) (- x (* z (- t x))) (+ x t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double tmp;
	if (y <= -4.4e+73) {
		tmp = t_1;
	} else if (y <= 2.6e+37) {
		tmp = x - (z * (t - x));
	} 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 = y * (t - x)
    if (y <= (-4.4d+73)) then
        tmp = t_1
    else if (y <= 2.6d+37) then
        tmp = x - (z * (t - x))
    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 = y * (t - x);
	double tmp;
	if (y <= -4.4e+73) {
		tmp = t_1;
	} else if (y <= 2.6e+37) {
		tmp = x - (z * (t - x));
	} else {
		tmp = x + t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (t - x)
	tmp = 0
	if y <= -4.4e+73:
		tmp = t_1
	elif y <= 2.6e+37:
		tmp = x - (z * (t - x))
	else:
		tmp = x + t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(t - x))
	tmp = 0.0
	if (y <= -4.4e+73)
		tmp = t_1;
	elseif (y <= 2.6e+37)
		tmp = Float64(x - Float64(z * Float64(t - x)));
	else
		tmp = Float64(x + t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (t - x);
	tmp = 0.0;
	if (y <= -4.4e+73)
		tmp = t_1;
	elseif (y <= 2.6e+37)
		tmp = x - (z * (t - x));
	else
		tmp = x + t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -4.4e+73], t$95$1, If[LessEqual[y, 2.6e+37], N[(x - N[(z * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t$95$1), $MachinePrecision]]]]

Derivation

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

Alternative 10: 56.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.8 \cdot 10^{-31} \lor \neg \left(y \leq 4.2 \cdot 10^{+36}\right):\\ \;\;\;\;y \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -5.8e-31) (not (<= y 4.2e+36))) (* y (- t x)) (* z (- t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -5.8e-31) || !(y <= 4.2e+36)) {
		tmp = y * (t - x);
	} else {
		tmp = z * -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 <= (-5.8d-31)) .or. (.not. (y <= 4.2d+36))) then
        tmp = y * (t - x)
    else
        tmp = z * -t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -5.8e-31) || !(y <= 4.2e+36)) {
		tmp = y * (t - x);
	} else {
		tmp = z * -t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -5.8e-31) or not (y <= 4.2e+36):
		tmp = y * (t - x)
	else:
		tmp = z * -t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -5.8e-31) || !(y <= 4.2e+36))
		tmp = Float64(y * Float64(t - x));
	else
		tmp = Float64(z * Float64(-t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -5.8e-31) || ~((y <= 4.2e+36)))
		tmp = y * (t - x);
	else
		tmp = z * -t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -5.8e-31], N[Not[LessEqual[y, 4.2e+36]], $MachinePrecision]], N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision], N[(z * (-t)), $MachinePrecision]]

Derivation

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

Alternative 11: 34.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.52 \cdot 10^{-64} \lor \neg \left(t \leq 6.6 \cdot 10^{+125}\right):\\ \;\;\;\;y \cdot t\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -1.52e-64) (not (<= t 6.6e+125))) (* y t) (* y (- x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -1.52e-64) || !(t <= 6.6e+125)) {
		tmp = y * t;
	} else {
		tmp = y * -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 ((t <= (-1.52d-64)) .or. (.not. (t <= 6.6d+125))) then
        tmp = y * t
    else
        tmp = y * -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -1.52e-64) || !(t <= 6.6e+125)) {
		tmp = y * t;
	} else {
		tmp = y * -x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -1.52e-64) or not (t <= 6.6e+125):
		tmp = y * t
	else:
		tmp = y * -x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -1.52e-64) || !(t <= 6.6e+125))
		tmp = Float64(y * t);
	else
		tmp = Float64(y * Float64(-x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -1.52e-64) || ~((t <= 6.6e+125)))
		tmp = y * t;
	else
		tmp = y * -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -1.52e-64], N[Not[LessEqual[t, 6.6e+125]], $MachinePrecision]], N[(y * t), $MachinePrecision], N[(y * (-x)), $MachinePrecision]]

Derivation

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

Alternative 12: 38.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.6 \cdot 10^{-33} \lor \neg \left(y \leq 7.2 \cdot 10^{-64}\right):\\ \;\;\;\;y \cdot t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -8.6e-33) (not (<= y 7.2e-64))) (* y t) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -8.6e-33) || !(y <= 7.2e-64)) {
		tmp = y * 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 ((y <= (-8.6d-33)) .or. (.not. (y <= 7.2d-64))) then
        tmp = y * 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 ((y <= -8.6e-33) || !(y <= 7.2e-64)) {
		tmp = y * t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -8.6e-33) or not (y <= 7.2e-64):
		tmp = y * t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -8.6e-33) || !(y <= 7.2e-64))
		tmp = Float64(y * t);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -8.6e-33) || ~((y <= 7.2e-64)))
		tmp = y * t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -8.6e-33], N[Not[LessEqual[y, 7.2e-64]], $MachinePrecision]], N[(y * t), $MachinePrecision], x]

Derivation

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

Alternative 13: 19.0% 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: 96.2% 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 2024003 
(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))))