Data.Metrics.Snapshot:quantile from metrics-0.3.0.2

Percentage Accurate: 100.0% → 100.0%

Time: 8.3s

Alternatives: 12

Speedup: 1.0×

• 34.6% 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: 37.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -y \cdot x\\ \mathbf{if}\;y \leq -3.1 \cdot 10^{+69}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -8.8 \cdot 10^{-141}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;y \leq 3.95 \cdot 10^{-44}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+75}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{+157} \lor \neg \left(y \leq 1.35 \cdot 10^{+234}\right) \land y \leq 8.6 \cdot 10^{+293}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;y \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* y x))))
   (if (<= y -3.1e+69)
     t_1
     (if (<= y -8.8e-141)
       (* z x)
       (if (<= y 3.95e-44)
         x
         (if (<= y 4.2e+75)
           (* z x)
           (if (or (<= y 3.2e+157)
                   (and (not (<= y 1.35e+234)) (<= y 8.6e+293)))
             t_1
             (* y t))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = -(y * x);
	double tmp;
	if (y <= -3.1e+69) {
		tmp = t_1;
	} else if (y <= -8.8e-141) {
		tmp = z * x;
	} else if (y <= 3.95e-44) {
		tmp = x;
	} else if (y <= 4.2e+75) {
		tmp = z * x;
	} else if ((y <= 3.2e+157) || (!(y <= 1.35e+234) && (y <= 8.6e+293))) {
		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 <= (-3.1d+69)) then
        tmp = t_1
    else if (y <= (-8.8d-141)) then
        tmp = z * x
    else if (y <= 3.95d-44) then
        tmp = x
    else if (y <= 4.2d+75) then
        tmp = z * x
    else if ((y <= 3.2d+157) .or. (.not. (y <= 1.35d+234)) .and. (y <= 8.6d+293)) 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 <= -3.1e+69) {
		tmp = t_1;
	} else if (y <= -8.8e-141) {
		tmp = z * x;
	} else if (y <= 3.95e-44) {
		tmp = x;
	} else if (y <= 4.2e+75) {
		tmp = z * x;
	} else if ((y <= 3.2e+157) || (!(y <= 1.35e+234) && (y <= 8.6e+293))) {
		tmp = t_1;
	} else {
		tmp = y * t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = -(y * x)
	tmp = 0
	if y <= -3.1e+69:
		tmp = t_1
	elif y <= -8.8e-141:
		tmp = z * x
	elif y <= 3.95e-44:
		tmp = x
	elif y <= 4.2e+75:
		tmp = z * x
	elif (y <= 3.2e+157) or (not (y <= 1.35e+234) and (y <= 8.6e+293)):
		tmp = t_1
	else:
		tmp = y * t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(-Float64(y * x))
	tmp = 0.0
	if (y <= -3.1e+69)
		tmp = t_1;
	elseif (y <= -8.8e-141)
		tmp = Float64(z * x);
	elseif (y <= 3.95e-44)
		tmp = x;
	elseif (y <= 4.2e+75)
		tmp = Float64(z * x);
	elseif ((y <= 3.2e+157) || (!(y <= 1.35e+234) && (y <= 8.6e+293)))
		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 <= -3.1e+69)
		tmp = t_1;
	elseif (y <= -8.8e-141)
		tmp = z * x;
	elseif (y <= 3.95e-44)
		tmp = x;
	elseif (y <= 4.2e+75)
		tmp = z * x;
	elseif ((y <= 3.2e+157) || (~((y <= 1.35e+234)) && (y <= 8.6e+293)))
		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, -3.1e+69], t$95$1, If[LessEqual[y, -8.8e-141], N[(z * x), $MachinePrecision], If[LessEqual[y, 3.95e-44], x, If[LessEqual[y, 4.2e+75], N[(z * x), $MachinePrecision], If[Or[LessEqual[y, 3.2e+157], And[N[Not[LessEqual[y, 1.35e+234]], $MachinePrecision], LessEqual[y, 8.6e+293]]], t$95$1, N[(y * t), $MachinePrecision]]]]]]]

Derivation

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

Alternative 3: 66.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(x - t\right)\\ \mathbf{if}\;z \leq -0.06:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{-231}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \mathbf{elif}\;z \leq 64000000000:\\ \;\;\;\;\left(y - z\right) \cdot t\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- x t))))
   (if (<= z -0.06)
     t_1
     (if (<= z 6.5e-231)
       (* x (- 1.0 y))
       (if (<= z 64000000000.0) (* (- y z) t) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * (x - t);
	double tmp;
	if (z <= -0.06) {
		tmp = t_1;
	} else if (z <= 6.5e-231) {
		tmp = x * (1.0 - y);
	} else if (z <= 64000000000.0) {
		tmp = (y - 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 = z * (x - t)
    if (z <= (-0.06d0)) then
        tmp = t_1
    else if (z <= 6.5d-231) then
        tmp = x * (1.0d0 - y)
    else if (z <= 64000000000.0d0) then
        tmp = (y - 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 = z * (x - t);
	double tmp;
	if (z <= -0.06) {
		tmp = t_1;
	} else if (z <= 6.5e-231) {
		tmp = x * (1.0 - y);
	} else if (z <= 64000000000.0) {
		tmp = (y - z) * t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * (x - t)
	tmp = 0
	if z <= -0.06:
		tmp = t_1
	elif z <= 6.5e-231:
		tmp = x * (1.0 - y)
	elif z <= 64000000000.0:
		tmp = (y - z) * t
	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 <= -0.06)
		tmp = t_1;
	elseif (z <= 6.5e-231)
		tmp = Float64(x * Float64(1.0 - y));
	elseif (z <= 64000000000.0)
		tmp = Float64(Float64(y - z) * t);
	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 <= -0.06)
		tmp = t_1;
	elseif (z <= 6.5e-231)
		tmp = x * (1.0 - y);
	elseif (z <= 64000000000.0)
		tmp = (y - z) * t;
	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, -0.06], t$95$1, If[LessEqual[z, 6.5e-231], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 64000000000.0], N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 4: 76.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.1 \cdot 10^{+57} \lor \neg \left(t \leq 7 \cdot 10^{+72}\right):\\ \;\;\;\;\left(y - z\right) \cdot t\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \left(y - z\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -1.1e+57) (not (<= t 7e+72)))
   (* (- y z) t)
   (* x (- 1.0 (- y z)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -1.1e+57) || !(t <= 7e+72)) {
		tmp = (y - z) * t;
	} else {
		tmp = x * (1.0 - (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 <= (-1.1d+57)) .or. (.not. (t <= 7d+72))) then
        tmp = (y - z) * t
    else
        tmp = x * (1.0d0 - (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 <= -1.1e+57) || !(t <= 7e+72)) {
		tmp = (y - z) * t;
	} else {
		tmp = x * (1.0 - (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -1.1e+57) or not (t <= 7e+72):
		tmp = (y - z) * t
	else:
		tmp = x * (1.0 - (y - z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -1.1e+57) || !(t <= 7e+72))
		tmp = Float64(Float64(y - z) * t);
	else
		tmp = Float64(x * Float64(1.0 - Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -1.1e+57) || ~((t <= 7e+72)))
		tmp = (y - z) * t;
	else
		tmp = x * (1.0 - (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -1.1e+57], N[Not[LessEqual[t, 7e+72]], $MachinePrecision]], N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision], N[(x * N[(1.0 - N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 84.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1 \lor \neg \left(z \leq 86000000000\right):\\ \;\;\;\;z \cdot \left(x - t\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \left(t - x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.0) (not (<= z 86000000000.0)))
   (* z (- x t))
   (+ x (* y (- t x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.0) || !(z <= 86000000000.0)) {
		tmp = z * (x - t);
	} else {
		tmp = x + (y * (t - 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 <= (-1.0d0)) .or. (.not. (z <= 86000000000.0d0))) then
        tmp = z * (x - t)
    else
        tmp = x + (y * (t - x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.0) || !(z <= 86000000000.0)) {
		tmp = z * (x - t);
	} else {
		tmp = x + (y * (t - x));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.0) or not (z <= 86000000000.0):
		tmp = z * (x - t)
	else:
		tmp = x + (y * (t - x))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.0) || !(z <= 86000000000.0))
		tmp = Float64(z * Float64(x - t));
	else
		tmp = Float64(x + Float64(y * Float64(t - x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.0) || ~((z <= 86000000000.0)))
		tmp = z * (x - t);
	else
		tmp = x + (y * (t - x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.0], N[Not[LessEqual[z, 86000000000.0]], $MachinePrecision]], N[(z * N[(x - t), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 34.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.15 \cdot 10^{+15}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 1200000000:\\ \;\;\;\;z \cdot \left(-t\right)\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{+113}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.15e+15)
   (* z x)
   (if (<= x 1200000000.0) (* z (- t)) (if (<= x 2.4e+113) x (- (* y x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.15e+15) {
		tmp = z * x;
	} else if (x <= 1200000000.0) {
		tmp = z * -t;
	} else if (x <= 2.4e+113) {
		tmp = x;
	} 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 (x <= (-1.15d+15)) then
        tmp = z * x
    else if (x <= 1200000000.0d0) then
        tmp = z * -t
    else if (x <= 2.4d+113) then
        tmp = x
    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 (x <= -1.15e+15) {
		tmp = z * x;
	} else if (x <= 1200000000.0) {
		tmp = z * -t;
	} else if (x <= 2.4e+113) {
		tmp = x;
	} else {
		tmp = -(y * x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.15e+15:
		tmp = z * x
	elif x <= 1200000000.0:
		tmp = z * -t
	elif x <= 2.4e+113:
		tmp = x
	else:
		tmp = -(y * x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.15e+15)
		tmp = Float64(z * x);
	elseif (x <= 1200000000.0)
		tmp = Float64(z * Float64(-t));
	elseif (x <= 2.4e+113)
		tmp = x;
	else
		tmp = Float64(-Float64(y * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.15e+15)
		tmp = z * x;
	elseif (x <= 1200000000.0)
		tmp = z * -t;
	elseif (x <= 2.4e+113)
		tmp = x;
	else
		tmp = -(y * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.15e+15], N[(z * x), $MachinePrecision], If[LessEqual[x, 1200000000.0], N[(z * (-t)), $MachinePrecision], If[LessEqual[x, 2.4e+113], x, (-N[(y * x), $MachinePrecision])]]]

Derivation

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

Alternative 7: 52.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.35 \cdot 10^{+143}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 2600000000:\\ \;\;\;\;\left(y - z\right) \cdot t\\ \mathbf{elif}\;x \leq 1.4 \cdot 10^{+120}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.35e+143)
   (* z x)
   (if (<= x 2600000000.0) (* (- y z) t) (if (<= x 1.4e+120) x (- (* y x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.35e+143) {
		tmp = z * x;
	} else if (x <= 2600000000.0) {
		tmp = (y - z) * t;
	} else if (x <= 1.4e+120) {
		tmp = x;
	} 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 (x <= (-1.35d+143)) then
        tmp = z * x
    else if (x <= 2600000000.0d0) then
        tmp = (y - z) * t
    else if (x <= 1.4d+120) then
        tmp = x
    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 (x <= -1.35e+143) {
		tmp = z * x;
	} else if (x <= 2600000000.0) {
		tmp = (y - z) * t;
	} else if (x <= 1.4e+120) {
		tmp = x;
	} else {
		tmp = -(y * x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.35e+143:
		tmp = z * x
	elif x <= 2600000000.0:
		tmp = (y - z) * t
	elif x <= 1.4e+120:
		tmp = x
	else:
		tmp = -(y * x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.35e+143)
		tmp = Float64(z * x);
	elseif (x <= 2600000000.0)
		tmp = Float64(Float64(y - z) * t);
	elseif (x <= 1.4e+120)
		tmp = x;
	else
		tmp = Float64(-Float64(y * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.35e+143)
		tmp = z * x;
	elseif (x <= 2600000000.0)
		tmp = (y - z) * t;
	elseif (x <= 1.4e+120)
		tmp = x;
	else
		tmp = -(y * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.35e+143], N[(z * x), $MachinePrecision], If[LessEqual[x, 2600000000.0], N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision], If[LessEqual[x, 1.4e+120], x, (-N[(y * x), $MachinePrecision])]]]

Derivation

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

Alternative 8: 37.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.6 \cdot 10^{+91}:\\ \;\;\;\;y \cdot t\\ \mathbf{elif}\;y \leq -7 \cdot 10^{-142}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{-61}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -2.6e+91)
   (* y t)
   (if (<= y -7e-142) (* z x) (if (<= y 1.12e-61) x (* y t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -2.6e+91) {
		tmp = y * t;
	} else if (y <= -7e-142) {
		tmp = z * x;
	} else if (y <= 1.12e-61) {
		tmp = x;
	} 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) :: tmp
    if (y <= (-2.6d+91)) then
        tmp = y * t
    else if (y <= (-7d-142)) then
        tmp = z * x
    else if (y <= 1.12d-61) then
        tmp = x
    else
        tmp = y * t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -2.6e+91) {
		tmp = y * t;
	} else if (y <= -7e-142) {
		tmp = z * x;
	} else if (y <= 1.12e-61) {
		tmp = x;
	} else {
		tmp = y * t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -2.6e+91:
		tmp = y * t
	elif y <= -7e-142:
		tmp = z * x
	elif y <= 1.12e-61:
		tmp = x
	else:
		tmp = y * t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -2.6e+91)
		tmp = Float64(y * t);
	elseif (y <= -7e-142)
		tmp = Float64(z * x);
	elseif (y <= 1.12e-61)
		tmp = x;
	else
		tmp = Float64(y * t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -2.6e+91)
		tmp = y * t;
	elseif (y <= -7e-142)
		tmp = z * x;
	elseif (y <= 1.12e-61)
		tmp = x;
	else
		tmp = y * t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -2.6e+91], N[(y * t), $MachinePrecision], If[LessEqual[y, -7e-142], N[(z * x), $MachinePrecision], If[LessEqual[y, 1.12e-61], x, N[(y * t), $MachinePrecision]]]]

Derivation

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

Alternative 9: 62.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.5 \cdot 10^{-23} \lor \neg \left(t \leq 1.18 \cdot 10^{-113}\right):\\ \;\;\;\;\left(y - z\right) \cdot t\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -9.5e-23) (not (<= t 1.18e-113)))
   (* (- y z) t)
   (* x (- 1.0 y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -9.5e-23) || !(t <= 1.18e-113)) {
		tmp = (y - 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 ((t <= (-9.5d-23)) .or. (.not. (t <= 1.18d-113))) then
        tmp = (y - 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 ((t <= -9.5e-23) || !(t <= 1.18e-113)) {
		tmp = (y - z) * t;
	} else {
		tmp = x * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -9.5e-23) or not (t <= 1.18e-113):
		tmp = (y - z) * t
	else:
		tmp = x * (1.0 - y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -9.5e-23) || !(t <= 1.18e-113))
		tmp = Float64(Float64(y - z) * 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 ((t <= -9.5e-23) || ~((t <= 1.18e-113)))
		tmp = (y - z) * t;
	else
		tmp = x * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -9.5e-23], N[Not[LessEqual[t, 1.18e-113]], $MachinePrecision]], N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 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 11: 38.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.94 \lor \neg \left(z \leq 1\right):\\ \;\;\;\;z \cdot x\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -0.94) (not (<= z 1.0))) (* z x) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -0.94) || !(z <= 1.0)) {
		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 <= (-0.94d0)) .or. (.not. (z <= 1.0d0))) 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 <= -0.94) || !(z <= 1.0)) {
		tmp = z * x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -0.94) or not (z <= 1.0):
		tmp = z * x
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -0.94) || !(z <= 1.0))
		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 <= -0.94) || ~((z <= 1.0)))
		tmp = z * x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -0.94], N[Not[LessEqual[z, 1.0]], $MachinePrecision]], N[(z * x), $MachinePrecision], x]

Derivation

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

Alternative 12: 18.2% 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 2023343 
(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))))