Data.Metrics.Snapshot:quantile from metrics-0.3.0.2

Percentage Accurate: 100.0% → 100.0%

Time: 9.7s

Alternatives: 13

Speedup: 1.0×

• 34.5% 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, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y - z, t - x, x\right) \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 38.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(-t\right)\\ t_2 := y \cdot \left(-x\right)\\ \mathbf{if}\;y \leq -2.95 \cdot 10^{+18}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.18 \cdot 10^{-85}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{-188}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -6.5 \cdot 10^{-265}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-139}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{-44}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+40}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- t))) (t_2 (* y (- x))))
   (if (<= y -2.95e+18)
     t_2
     (if (<= y -1.18e-85)
       (* z x)
       (if (<= y -8.5e-188)
         t_1
         (if (<= y -6.5e-265)
           x
           (if (<= y 1.3e-139)
             t_1
             (if (<= y 1.45e-44) x (if (<= y 2.2e+40) t_1 t_2)))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * -t;
	double t_2 = y * -x;
	double tmp;
	if (y <= -2.95e+18) {
		tmp = t_2;
	} else if (y <= -1.18e-85) {
		tmp = z * x;
	} else if (y <= -8.5e-188) {
		tmp = t_1;
	} else if (y <= -6.5e-265) {
		tmp = x;
	} else if (y <= 1.3e-139) {
		tmp = t_1;
	} else if (y <= 1.45e-44) {
		tmp = x;
	} else if (y <= 2.2e+40) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = z * -t
    t_2 = y * -x
    if (y <= (-2.95d+18)) then
        tmp = t_2
    else if (y <= (-1.18d-85)) then
        tmp = z * x
    else if (y <= (-8.5d-188)) then
        tmp = t_1
    else if (y <= (-6.5d-265)) then
        tmp = x
    else if (y <= 1.3d-139) then
        tmp = t_1
    else if (y <= 1.45d-44) then
        tmp = x
    else if (y <= 2.2d+40) 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 = z * -t;
	double t_2 = y * -x;
	double tmp;
	if (y <= -2.95e+18) {
		tmp = t_2;
	} else if (y <= -1.18e-85) {
		tmp = z * x;
	} else if (y <= -8.5e-188) {
		tmp = t_1;
	} else if (y <= -6.5e-265) {
		tmp = x;
	} else if (y <= 1.3e-139) {
		tmp = t_1;
	} else if (y <= 1.45e-44) {
		tmp = x;
	} else if (y <= 2.2e+40) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * -t
	t_2 = y * -x
	tmp = 0
	if y <= -2.95e+18:
		tmp = t_2
	elif y <= -1.18e-85:
		tmp = z * x
	elif y <= -8.5e-188:
		tmp = t_1
	elif y <= -6.5e-265:
		tmp = x
	elif y <= 1.3e-139:
		tmp = t_1
	elif y <= 1.45e-44:
		tmp = x
	elif y <= 2.2e+40:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(-t))
	t_2 = Float64(y * Float64(-x))
	tmp = 0.0
	if (y <= -2.95e+18)
		tmp = t_2;
	elseif (y <= -1.18e-85)
		tmp = Float64(z * x);
	elseif (y <= -8.5e-188)
		tmp = t_1;
	elseif (y <= -6.5e-265)
		tmp = x;
	elseif (y <= 1.3e-139)
		tmp = t_1;
	elseif (y <= 1.45e-44)
		tmp = x;
	elseif (y <= 2.2e+40)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * -t;
	t_2 = y * -x;
	tmp = 0.0;
	if (y <= -2.95e+18)
		tmp = t_2;
	elseif (y <= -1.18e-85)
		tmp = z * x;
	elseif (y <= -8.5e-188)
		tmp = t_1;
	elseif (y <= -6.5e-265)
		tmp = x;
	elseif (y <= 1.3e-139)
		tmp = t_1;
	elseif (y <= 1.45e-44)
		tmp = x;
	elseif (y <= 2.2e+40)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * (-t)), $MachinePrecision]}, Block[{t$95$2 = N[(y * (-x)), $MachinePrecision]}, If[LessEqual[y, -2.95e+18], t$95$2, If[LessEqual[y, -1.18e-85], N[(z * x), $MachinePrecision], If[LessEqual[y, -8.5e-188], t$95$1, If[LessEqual[y, -6.5e-265], x, If[LessEqual[y, 1.3e-139], t$95$1, If[LessEqual[y, 1.45e-44], x, If[LessEqual[y, 2.2e+40], t$95$1, t$95$2]]]]]]]]]

Derivation

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

Alternative 3: 65.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(-t\right)\\ t_2 := y \cdot \left(t - x\right)\\ t_3 := x \cdot \left(z + 1\right)\\ \mathbf{if}\;y \leq -90000000000000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{-85}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -4.8 \cdot 10^{-177}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{-43}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{+38}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- t))) (t_2 (* y (- t x))) (t_3 (* x (+ z 1.0))))
   (if (<= y -90000000000000.0)
     t_2
     (if (<= y -6.8e-85)
       t_3
       (if (<= y -4.8e-177)
         t_1
         (if (<= y 5.2e-43) t_3 (if (<= y 1.65e+38) t_1 t_2)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * -t;
	double t_2 = y * (t - x);
	double t_3 = x * (z + 1.0);
	double tmp;
	if (y <= -90000000000000.0) {
		tmp = t_2;
	} else if (y <= -6.8e-85) {
		tmp = t_3;
	} else if (y <= -4.8e-177) {
		tmp = t_1;
	} else if (y <= 5.2e-43) {
		tmp = t_3;
	} else if (y <= 1.65e+38) {
		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 = z * -t
    t_2 = y * (t - x)
    t_3 = x * (z + 1.0d0)
    if (y <= (-90000000000000.0d0)) then
        tmp = t_2
    else if (y <= (-6.8d-85)) then
        tmp = t_3
    else if (y <= (-4.8d-177)) then
        tmp = t_1
    else if (y <= 5.2d-43) then
        tmp = t_3
    else if (y <= 1.65d+38) 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 = z * -t;
	double t_2 = y * (t - x);
	double t_3 = x * (z + 1.0);
	double tmp;
	if (y <= -90000000000000.0) {
		tmp = t_2;
	} else if (y <= -6.8e-85) {
		tmp = t_3;
	} else if (y <= -4.8e-177) {
		tmp = t_1;
	} else if (y <= 5.2e-43) {
		tmp = t_3;
	} else if (y <= 1.65e+38) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * -t
	t_2 = y * (t - x)
	t_3 = x * (z + 1.0)
	tmp = 0
	if y <= -90000000000000.0:
		tmp = t_2
	elif y <= -6.8e-85:
		tmp = t_3
	elif y <= -4.8e-177:
		tmp = t_1
	elif y <= 5.2e-43:
		tmp = t_3
	elif y <= 1.65e+38:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(-t))
	t_2 = Float64(y * Float64(t - x))
	t_3 = Float64(x * Float64(z + 1.0))
	tmp = 0.0
	if (y <= -90000000000000.0)
		tmp = t_2;
	elseif (y <= -6.8e-85)
		tmp = t_3;
	elseif (y <= -4.8e-177)
		tmp = t_1;
	elseif (y <= 5.2e-43)
		tmp = t_3;
	elseif (y <= 1.65e+38)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * -t;
	t_2 = y * (t - x);
	t_3 = x * (z + 1.0);
	tmp = 0.0;
	if (y <= -90000000000000.0)
		tmp = t_2;
	elseif (y <= -6.8e-85)
		tmp = t_3;
	elseif (y <= -4.8e-177)
		tmp = t_1;
	elseif (y <= 5.2e-43)
		tmp = t_3;
	elseif (y <= 1.65e+38)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * (-t)), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x * N[(z + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -90000000000000.0], t$95$2, If[LessEqual[y, -6.8e-85], t$95$3, If[LessEqual[y, -4.8e-177], t$95$1, If[LessEqual[y, 5.2e-43], t$95$3, If[LessEqual[y, 1.65e+38], t$95$1, t$95$2]]]]]]]]

Derivation

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

Alternative 4: 37.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(-x\right)\\ \mathbf{if}\;y \leq -6.6 \cdot 10^{+18}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.65 \cdot 10^{-188}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{-176}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{-136}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;y \leq 9.2 \cdot 10^{-9}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- x))))
   (if (<= y -6.6e+18)
     t_1
     (if (<= y -2.65e-188)
       (* z x)
       (if (<= y 3.1e-176)
         x
         (if (<= y 3.3e-136) (* z x) (if (<= y 9.2e-9) x t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * -x;
	double tmp;
	if (y <= -6.6e+18) {
		tmp = t_1;
	} else if (y <= -2.65e-188) {
		tmp = z * x;
	} else if (y <= 3.1e-176) {
		tmp = x;
	} else if (y <= 3.3e-136) {
		tmp = z * x;
	} else if (y <= 9.2e-9) {
		tmp = x;
	} 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 * -x
    if (y <= (-6.6d+18)) then
        tmp = t_1
    else if (y <= (-2.65d-188)) then
        tmp = z * x
    else if (y <= 3.1d-176) then
        tmp = x
    else if (y <= 3.3d-136) then
        tmp = z * x
    else if (y <= 9.2d-9) then
        tmp = x
    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 * -x;
	double tmp;
	if (y <= -6.6e+18) {
		tmp = t_1;
	} else if (y <= -2.65e-188) {
		tmp = z * x;
	} else if (y <= 3.1e-176) {
		tmp = x;
	} else if (y <= 3.3e-136) {
		tmp = z * x;
	} else if (y <= 9.2e-9) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * -x
	tmp = 0
	if y <= -6.6e+18:
		tmp = t_1
	elif y <= -2.65e-188:
		tmp = z * x
	elif y <= 3.1e-176:
		tmp = x
	elif y <= 3.3e-136:
		tmp = z * x
	elif y <= 9.2e-9:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(-x))
	tmp = 0.0
	if (y <= -6.6e+18)
		tmp = t_1;
	elseif (y <= -2.65e-188)
		tmp = Float64(z * x);
	elseif (y <= 3.1e-176)
		tmp = x;
	elseif (y <= 3.3e-136)
		tmp = Float64(z * x);
	elseif (y <= 9.2e-9)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * -x;
	tmp = 0.0;
	if (y <= -6.6e+18)
		tmp = t_1;
	elseif (y <= -2.65e-188)
		tmp = z * x;
	elseif (y <= 3.1e-176)
		tmp = x;
	elseif (y <= 3.3e-136)
		tmp = z * x;
	elseif (y <= 9.2e-9)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * (-x)), $MachinePrecision]}, If[LessEqual[y, -6.6e+18], t$95$1, If[LessEqual[y, -2.65e-188], N[(z * x), $MachinePrecision], If[LessEqual[y, 3.1e-176], x, If[LessEqual[y, 3.3e-136], N[(z * x), $MachinePrecision], If[LessEqual[y, 9.2e-9], x, t$95$1]]]]]]

Derivation

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

Alternative 5: 48.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(-t\right)\\ t_2 := y \cdot \left(-x\right)\\ t_3 := x \cdot \left(z + 1\right)\\ \mathbf{if}\;y \leq -4.6 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -5.5 \cdot 10^{-86}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -3.8 \cdot 10^{-177}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-44}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq 3.4 \cdot 10^{+41}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- t))) (t_2 (* y (- x))) (t_3 (* x (+ z 1.0))))
   (if (<= y -4.6e+17)
     t_2
     (if (<= y -5.5e-86)
       t_3
       (if (<= y -3.8e-177)
         t_1
         (if (<= y 7e-44) t_3 (if (<= y 3.4e+41) t_1 t_2)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * -t;
	double t_2 = y * -x;
	double t_3 = x * (z + 1.0);
	double tmp;
	if (y <= -4.6e+17) {
		tmp = t_2;
	} else if (y <= -5.5e-86) {
		tmp = t_3;
	} else if (y <= -3.8e-177) {
		tmp = t_1;
	} else if (y <= 7e-44) {
		tmp = t_3;
	} else if (y <= 3.4e+41) {
		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 = z * -t
    t_2 = y * -x
    t_3 = x * (z + 1.0d0)
    if (y <= (-4.6d+17)) then
        tmp = t_2
    else if (y <= (-5.5d-86)) then
        tmp = t_3
    else if (y <= (-3.8d-177)) then
        tmp = t_1
    else if (y <= 7d-44) then
        tmp = t_3
    else if (y <= 3.4d+41) 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 = z * -t;
	double t_2 = y * -x;
	double t_3 = x * (z + 1.0);
	double tmp;
	if (y <= -4.6e+17) {
		tmp = t_2;
	} else if (y <= -5.5e-86) {
		tmp = t_3;
	} else if (y <= -3.8e-177) {
		tmp = t_1;
	} else if (y <= 7e-44) {
		tmp = t_3;
	} else if (y <= 3.4e+41) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * -t
	t_2 = y * -x
	t_3 = x * (z + 1.0)
	tmp = 0
	if y <= -4.6e+17:
		tmp = t_2
	elif y <= -5.5e-86:
		tmp = t_3
	elif y <= -3.8e-177:
		tmp = t_1
	elif y <= 7e-44:
		tmp = t_3
	elif y <= 3.4e+41:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(-t))
	t_2 = Float64(y * Float64(-x))
	t_3 = Float64(x * Float64(z + 1.0))
	tmp = 0.0
	if (y <= -4.6e+17)
		tmp = t_2;
	elseif (y <= -5.5e-86)
		tmp = t_3;
	elseif (y <= -3.8e-177)
		tmp = t_1;
	elseif (y <= 7e-44)
		tmp = t_3;
	elseif (y <= 3.4e+41)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * -t;
	t_2 = y * -x;
	t_3 = x * (z + 1.0);
	tmp = 0.0;
	if (y <= -4.6e+17)
		tmp = t_2;
	elseif (y <= -5.5e-86)
		tmp = t_3;
	elseif (y <= -3.8e-177)
		tmp = t_1;
	elseif (y <= 7e-44)
		tmp = t_3;
	elseif (y <= 3.4e+41)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * (-t)), $MachinePrecision]}, Block[{t$95$2 = N[(y * (-x)), $MachinePrecision]}, Block[{t$95$3 = N[(x * N[(z + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -4.6e+17], t$95$2, If[LessEqual[y, -5.5e-86], t$95$3, If[LessEqual[y, -3.8e-177], t$95$1, If[LessEqual[y, 7e-44], t$95$3, If[LessEqual[y, 3.4e+41], t$95$1, t$95$2]]]]]]]]

Derivation

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

Alternative 6: 71.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(t - x\right)\\ \mathbf{if}\;y \leq -15000000000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{-84}:\\ \;\;\;\;x \cdot \left(z + 1\right)\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+38}:\\ \;\;\;\;x - z \cdot t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- t x))))
   (if (<= y -15000000000000.0)
     t_1
     (if (<= y -1.35e-84)
       (* x (+ z 1.0))
       (if (<= y 4.4e+38) (- x (* z t)) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double tmp;
	if (y <= -15000000000000.0) {
		tmp = t_1;
	} else if (y <= -1.35e-84) {
		tmp = x * (z + 1.0);
	} else if (y <= 4.4e+38) {
		tmp = x - (z * t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (t - x)
    if (y <= (-15000000000000.0d0)) then
        tmp = t_1
    else if (y <= (-1.35d-84)) then
        tmp = x * (z + 1.0d0)
    else if (y <= 4.4d+38) then
        tmp = x - (z * t)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double tmp;
	if (y <= -15000000000000.0) {
		tmp = t_1;
	} else if (y <= -1.35e-84) {
		tmp = x * (z + 1.0);
	} else if (y <= 4.4e+38) {
		tmp = x - (z * t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (t - x)
	tmp = 0
	if y <= -15000000000000.0:
		tmp = t_1
	elif y <= -1.35e-84:
		tmp = x * (z + 1.0)
	elif y <= 4.4e+38:
		tmp = x - (z * t)
	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 <= -15000000000000.0)
		tmp = t_1;
	elseif (y <= -1.35e-84)
		tmp = Float64(x * Float64(z + 1.0));
	elseif (y <= 4.4e+38)
		tmp = Float64(x - Float64(z * t));
	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 <= -15000000000000.0)
		tmp = t_1;
	elseif (y <= -1.35e-84)
		tmp = x * (z + 1.0);
	elseif (y <= 4.4e+38)
		tmp = x - (z * t);
	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, -15000000000000.0], t$95$1, If[LessEqual[y, -1.35e-84], N[(x * N[(z + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4.4e+38], N[(x - N[(z * t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 7: 71.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(t - x\right)\\ \mathbf{if}\;y \leq -3.8 \cdot 10^{+68}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -6.4 \cdot 10^{-85}:\\ \;\;\;\;x \cdot \left(\left(z - y\right) + 1\right)\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{+38}:\\ \;\;\;\;x - z \cdot t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (- t x))))
   (if (<= y -3.8e+68)
     t_1
     (if (<= y -6.4e-85)
       (* x (+ (- z y) 1.0))
       (if (<= y 1.65e+38) (- x (* z t)) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double tmp;
	if (y <= -3.8e+68) {
		tmp = t_1;
	} else if (y <= -6.4e-85) {
		tmp = x * ((z - y) + 1.0);
	} else if (y <= 1.65e+38) {
		tmp = x - (z * t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (t - x)
    if (y <= (-3.8d+68)) then
        tmp = t_1
    else if (y <= (-6.4d-85)) then
        tmp = x * ((z - y) + 1.0d0)
    else if (y <= 1.65d+38) then
        tmp = x - (z * t)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (t - x);
	double tmp;
	if (y <= -3.8e+68) {
		tmp = t_1;
	} else if (y <= -6.4e-85) {
		tmp = x * ((z - y) + 1.0);
	} else if (y <= 1.65e+38) {
		tmp = x - (z * t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (t - x)
	tmp = 0
	if y <= -3.8e+68:
		tmp = t_1
	elif y <= -6.4e-85:
		tmp = x * ((z - y) + 1.0)
	elif y <= 1.65e+38:
		tmp = x - (z * t)
	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 <= -3.8e+68)
		tmp = t_1;
	elseif (y <= -6.4e-85)
		tmp = Float64(x * Float64(Float64(z - y) + 1.0));
	elseif (y <= 1.65e+38)
		tmp = Float64(x - Float64(z * t));
	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 <= -3.8e+68)
		tmp = t_1;
	elseif (y <= -6.4e-85)
		tmp = x * ((z - y) + 1.0);
	elseif (y <= 1.65e+38)
		tmp = x - (z * t);
	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, -3.8e+68], t$95$1, If[LessEqual[y, -6.4e-85], N[(x * N[(N[(z - y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.65e+38], N[(x - N[(z * t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 8: 81.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.8 \cdot 10^{+59} \lor \neg \left(x \leq 7 \cdot 10^{+17}\right):\\ \;\;\;\;x \cdot \left(\left(z - y\right) + 1\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - z\right) \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -7.8e+59) (not (<= x 7e+17)))
   (* x (+ (- z y) 1.0))
   (+ x (* (- y z) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -7.8e+59) || !(x <= 7e+17)) {
		tmp = x * ((z - y) + 1.0);
	} else {
		tmp = x + ((y - 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 ((x <= (-7.8d+59)) .or. (.not. (x <= 7d+17))) then
        tmp = x * ((z - y) + 1.0d0)
    else
        tmp = x + ((y - z) * t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -7.8e+59) || !(x <= 7e+17)) {
		tmp = x * ((z - y) + 1.0);
	} else {
		tmp = x + ((y - z) * t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -7.8e+59) or not (x <= 7e+17):
		tmp = x * ((z - y) + 1.0)
	else:
		tmp = x + ((y - z) * t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -7.8e+59) || !(x <= 7e+17))
		tmp = Float64(x * Float64(Float64(z - y) + 1.0));
	else
		tmp = Float64(x + Float64(Float64(y - z) * t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -7.8e+59) || ~((x <= 7e+17)))
		tmp = x * ((z - y) + 1.0);
	else
		tmp = x + ((y - z) * t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -7.8e+59], N[Not[LessEqual[x, 7e+17]], $MachinePrecision]], N[(x * N[(N[(z - y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 84.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{+14} \lor \neg \left(y \leq 4 \cdot 10^{+49}\right):\\ \;\;\;\;y \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \left(x - t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -2.2e+14) (not (<= y 4e+49)))
   (* y (- t x))
   (+ x (* z (- x t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -2.2e+14) || !(y <= 4e+49)) {
		tmp = y * (t - x);
	} else {
		tmp = x + (z * (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 ((y <= (-2.2d+14)) .or. (.not. (y <= 4d+49))) then
        tmp = y * (t - x)
    else
        tmp = x + (z * (x - t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -2.2e+14) || !(y <= 4e+49)) {
		tmp = y * (t - x);
	} else {
		tmp = x + (z * (x - t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -2.2e+14) or not (y <= 4e+49):
		tmp = y * (t - x)
	else:
		tmp = x + (z * (x - t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -2.2e+14) || !(y <= 4e+49))
		tmp = Float64(y * Float64(t - x));
	else
		tmp = Float64(x + Float64(z * Float64(x - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -2.2e+14) || ~((y <= 4e+49)))
		tmp = y * (t - x);
	else
		tmp = x + (z * (x - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -2.2e+14], N[Not[LessEqual[y, 4e+49]], $MachinePrecision]], N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 49.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.2 \cdot 10^{+72} \lor \neg \left(z \leq 2 \cdot 10^{+95}\right):\\ \;\;\;\;z \cdot \left(-t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.2e+72) (not (<= z 2e+95))) (* z (- t)) (* x (- 1.0 y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.2e+72) || !(z <= 2e+95)) {
		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 <= (-1.2d+72)) .or. (.not. (z <= 2d+95))) 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 <= -1.2e+72) || !(z <= 2e+95)) {
		tmp = z * -t;
	} else {
		tmp = x * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.2e+72) or not (z <= 2e+95):
		tmp = z * -t
	else:
		tmp = x * (1.0 - y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.2e+72) || !(z <= 2e+95))
		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 <= -1.2e+72) || ~((z <= 2e+95)))
		tmp = z * -t;
	else
		tmp = x * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.2e+72], N[Not[LessEqual[z, 2e+95]], $MachinePrecision]], N[(z * (-t)), $MachinePrecision], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 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]

Derivation

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

Alternative 12: 37.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -25500 \lor \neg \left(z \leq 14\right):\\ \;\;\;\;z \cdot x\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -25500.0) (not (<= z 14.0))) (* z x) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -25500.0) || !(z <= 14.0)) {
		tmp = z * x;
	} 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 <= (-25500.0d0)) .or. (.not. (z <= 14.0d0))) then
        tmp = z * x
    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 <= -25500.0) || !(z <= 14.0)) {
		tmp = z * x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -25500.0) or not (z <= 14.0):
		tmp = z * x
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -25500.0) || !(z <= 14.0))
		tmp = Float64(z * x);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -25500.0) || ~((z <= 14.0)))
		tmp = z * x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -25500.0], N[Not[LessEqual[z, 14.0]], $MachinePrecision]], N[(z * x), $MachinePrecision], x]

Derivation

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

Alternative 13: 18.4% 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: 96.5% 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 2023350 
(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))))