Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, C

Percentage Accurate: 99.8% → 100.0%

Time: 5.2s

Alternatives: 8

Speedup: 1.4×

• 20.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.25\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.25)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.25d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.25)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.25), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.25\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.25)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.25d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.25)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.25), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ 2 + \frac{x - z}{y \cdot 0.25} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ 2.0 (/ (- x z) (* y 0.25))))

C

double code(double x, double y, double z) {
	return 2.0 + ((x - z) / (y * 0.25));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 2.0d0 + ((x - z) / (y * 0.25d0))
end function

Java

public static double code(double x, double y, double z) {
	return 2.0 + ((x - z) / (y * 0.25));
}

Python

def code(x, y, z):
	return 2.0 + ((x - z) / (y * 0.25))

Julia

function code(x, y, z)
	return Float64(2.0 + Float64(Float64(x - z) / Float64(y * 0.25)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 2.0 + ((x - z) / (y * 0.25));
end

Wolfram

code[x_, y_, z_] := N[(2.0 + N[(N[(x - z), $MachinePrecision] / N[(y * 0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 57.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + \frac{4}{\frac{y}{x}}\\ t_1 := 1 + -4 \cdot \frac{z}{y}\\ \mathbf{if}\;z \leq -2.6 \cdot 10^{+105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -60000000000:\\ \;\;\;\;2\\ \mathbf{elif}\;z \leq -8.4 \cdot 10^{-302}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{-199}:\\ \;\;\;\;2\\ \mathbf{elif}\;z \leq 10^{+37}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ 4.0 (/ y x)))) (t_1 (+ 1.0 (* -4.0 (/ z y)))))
   (if (<= z -2.6e+105)
     t_1
     (if (<= z -60000000000.0)
       2.0
       (if (<= z -8.4e-302)
         t_0
         (if (<= z 5.5e-199) 2.0 (if (<= z 1e+37) t_0 t_1)))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + (4.0 / (y / x));
	double t_1 = 1.0 + (-4.0 * (z / y));
	double tmp;
	if (z <= -2.6e+105) {
		tmp = t_1;
	} else if (z <= -60000000000.0) {
		tmp = 2.0;
	} else if (z <= -8.4e-302) {
		tmp = t_0;
	} else if (z <= 5.5e-199) {
		tmp = 2.0;
	} else if (z <= 1e+37) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + (4.0d0 / (y / x))
    t_1 = 1.0d0 + ((-4.0d0) * (z / y))
    if (z <= (-2.6d+105)) then
        tmp = t_1
    else if (z <= (-60000000000.0d0)) then
        tmp = 2.0d0
    else if (z <= (-8.4d-302)) then
        tmp = t_0
    else if (z <= 5.5d-199) then
        tmp = 2.0d0
    else if (z <= 1d+37) then
        tmp = t_0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + (4.0 / (y / x));
	double t_1 = 1.0 + (-4.0 * (z / y));
	double tmp;
	if (z <= -2.6e+105) {
		tmp = t_1;
	} else if (z <= -60000000000.0) {
		tmp = 2.0;
	} else if (z <= -8.4e-302) {
		tmp = t_0;
	} else if (z <= 5.5e-199) {
		tmp = 2.0;
	} else if (z <= 1e+37) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + (4.0 / (y / x))
	t_1 = 1.0 + (-4.0 * (z / y))
	tmp = 0
	if z <= -2.6e+105:
		tmp = t_1
	elif z <= -60000000000.0:
		tmp = 2.0
	elif z <= -8.4e-302:
		tmp = t_0
	elif z <= 5.5e-199:
		tmp = 2.0
	elif z <= 1e+37:
		tmp = t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(4.0 / Float64(y / x)))
	t_1 = Float64(1.0 + Float64(-4.0 * Float64(z / y)))
	tmp = 0.0
	if (z <= -2.6e+105)
		tmp = t_1;
	elseif (z <= -60000000000.0)
		tmp = 2.0;
	elseif (z <= -8.4e-302)
		tmp = t_0;
	elseif (z <= 5.5e-199)
		tmp = 2.0;
	elseif (z <= 1e+37)
		tmp = t_0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + (4.0 / (y / x));
	t_1 = 1.0 + (-4.0 * (z / y));
	tmp = 0.0;
	if (z <= -2.6e+105)
		tmp = t_1;
	elseif (z <= -60000000000.0)
		tmp = 2.0;
	elseif (z <= -8.4e-302)
		tmp = t_0;
	elseif (z <= 5.5e-199)
		tmp = 2.0;
	elseif (z <= 1e+37)
		tmp = t_0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(4.0 / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.6e+105], t$95$1, If[LessEqual[z, -60000000000.0], 2.0, If[LessEqual[z, -8.4e-302], t$95$0, If[LessEqual[z, 5.5e-199], 2.0, If[LessEqual[z, 1e+37], t$95$0, t$95$1]]]]]]]

Derivation

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

Alternative 3: 57.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + \frac{x \cdot 4}{y}\\ t_1 := 1 + -4 \cdot \frac{z}{y}\\ \mathbf{if}\;z \leq -2.3 \cdot 10^{+105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2000000000:\\ \;\;\;\;2\\ \mathbf{elif}\;z \leq -2.9 \cdot 10^{-302}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.02 \cdot 10^{-198}:\\ \;\;\;\;2\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{+37}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ (* x 4.0) y))) (t_1 (+ 1.0 (* -4.0 (/ z y)))))
   (if (<= z -2.3e+105)
     t_1
     (if (<= z -2000000000.0)
       2.0
       (if (<= z -2.9e-302)
         t_0
         (if (<= z 1.02e-198) 2.0 (if (<= z 3.7e+37) t_0 t_1)))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + ((x * 4.0) / y);
	double t_1 = 1.0 + (-4.0 * (z / y));
	double tmp;
	if (z <= -2.3e+105) {
		tmp = t_1;
	} else if (z <= -2000000000.0) {
		tmp = 2.0;
	} else if (z <= -2.9e-302) {
		tmp = t_0;
	} else if (z <= 1.02e-198) {
		tmp = 2.0;
	} else if (z <= 3.7e+37) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + ((x * 4.0d0) / y)
    t_1 = 1.0d0 + ((-4.0d0) * (z / y))
    if (z <= (-2.3d+105)) then
        tmp = t_1
    else if (z <= (-2000000000.0d0)) then
        tmp = 2.0d0
    else if (z <= (-2.9d-302)) then
        tmp = t_0
    else if (z <= 1.02d-198) then
        tmp = 2.0d0
    else if (z <= 3.7d+37) then
        tmp = t_0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + ((x * 4.0) / y);
	double t_1 = 1.0 + (-4.0 * (z / y));
	double tmp;
	if (z <= -2.3e+105) {
		tmp = t_1;
	} else if (z <= -2000000000.0) {
		tmp = 2.0;
	} else if (z <= -2.9e-302) {
		tmp = t_0;
	} else if (z <= 1.02e-198) {
		tmp = 2.0;
	} else if (z <= 3.7e+37) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + ((x * 4.0) / y)
	t_1 = 1.0 + (-4.0 * (z / y))
	tmp = 0
	if z <= -2.3e+105:
		tmp = t_1
	elif z <= -2000000000.0:
		tmp = 2.0
	elif z <= -2.9e-302:
		tmp = t_0
	elif z <= 1.02e-198:
		tmp = 2.0
	elif z <= 3.7e+37:
		tmp = t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(Float64(x * 4.0) / y))
	t_1 = Float64(1.0 + Float64(-4.0 * Float64(z / y)))
	tmp = 0.0
	if (z <= -2.3e+105)
		tmp = t_1;
	elseif (z <= -2000000000.0)
		tmp = 2.0;
	elseif (z <= -2.9e-302)
		tmp = t_0;
	elseif (z <= 1.02e-198)
		tmp = 2.0;
	elseif (z <= 3.7e+37)
		tmp = t_0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + ((x * 4.0) / y);
	t_1 = 1.0 + (-4.0 * (z / y));
	tmp = 0.0;
	if (z <= -2.3e+105)
		tmp = t_1;
	elseif (z <= -2000000000.0)
		tmp = 2.0;
	elseif (z <= -2.9e-302)
		tmp = t_0;
	elseif (z <= 1.02e-198)
		tmp = 2.0;
	elseif (z <= 3.7e+37)
		tmp = t_0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(N[(x * 4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.3e+105], t$95$1, If[LessEqual[z, -2000000000.0], 2.0, If[LessEqual[z, -2.9e-302], t$95$0, If[LessEqual[z, 1.02e-198], 2.0, If[LessEqual[z, 3.7e+37], t$95$0, t$95$1]]]]]]]

Derivation

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

Alternative 4: 80.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7 \cdot 10^{+144} \lor \neg \left(z \leq 1.9 \cdot 10^{+61}\right):\\ \;\;\;\;1 + -4 \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;2 + 4 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -7e+144) (not (<= z 1.9e+61)))
   (+ 1.0 (* -4.0 (/ z y)))
   (+ 2.0 (* 4.0 (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -7e+144) || !(z <= 1.9e+61)) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 2.0 + (4.0 * (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-7d+144)) .or. (.not. (z <= 1.9d+61))) then
        tmp = 1.0d0 + ((-4.0d0) * (z / y))
    else
        tmp = 2.0d0 + (4.0d0 * (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -7e+144) || !(z <= 1.9e+61)) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 2.0 + (4.0 * (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -7e+144) or not (z <= 1.9e+61):
		tmp = 1.0 + (-4.0 * (z / y))
	else:
		tmp = 2.0 + (4.0 * (x / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -7e+144) || !(z <= 1.9e+61))
		tmp = Float64(1.0 + Float64(-4.0 * Float64(z / y)));
	else
		tmp = Float64(2.0 + Float64(4.0 * Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -7e+144) || ~((z <= 1.9e+61)))
		tmp = 1.0 + (-4.0 * (z / y));
	else
		tmp = 2.0 + (4.0 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -7e+144], N[Not[LessEqual[z, 1.9e+61]], $MachinePrecision]], N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 85.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.9 \cdot 10^{+46} \lor \neg \left(x \leq 7.8 \cdot 10^{+54}\right):\\ \;\;\;\;2 + 4 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;2 + \frac{z}{\frac{y}{-4}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -3.9e+46) (not (<= x 7.8e+54)))
   (+ 2.0 (* 4.0 (/ x y)))
   (+ 2.0 (/ z (/ y -4.0)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.9e+46) || !(x <= 7.8e+54)) {
		tmp = 2.0 + (4.0 * (x / y));
	} else {
		tmp = 2.0 + (z / (y / -4.0));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-3.9d+46)) .or. (.not. (x <= 7.8d+54))) then
        tmp = 2.0d0 + (4.0d0 * (x / y))
    else
        tmp = 2.0d0 + (z / (y / (-4.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.9e+46) || !(x <= 7.8e+54)) {
		tmp = 2.0 + (4.0 * (x / y));
	} else {
		tmp = 2.0 + (z / (y / -4.0));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -3.9e+46) or not (x <= 7.8e+54):
		tmp = 2.0 + (4.0 * (x / y))
	else:
		tmp = 2.0 + (z / (y / -4.0))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -3.9e+46) || !(x <= 7.8e+54))
		tmp = Float64(2.0 + Float64(4.0 * Float64(x / y)));
	else
		tmp = Float64(2.0 + Float64(z / Float64(y / -4.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -3.9e+46) || ~((x <= 7.8e+54)))
		tmp = 2.0 + (4.0 * (x / y));
	else
		tmp = 2.0 + (z / (y / -4.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -3.9e+46], N[Not[LessEqual[x, 7.8e+54]], $MachinePrecision]], N[(2.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 + N[(z / N[(y / -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 53.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{+159}:\\ \;\;\;\;2\\ \mathbf{elif}\;y \leq 6.4 \cdot 10^{+72}:\\ \;\;\;\;1 + -4 \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -8.5e+159) 2.0 (if (<= y 6.4e+72) (+ 1.0 (* -4.0 (/ z y))) 2.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -8.5e+159) {
		tmp = 2.0;
	} else if (y <= 6.4e+72) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-8.5d+159)) then
        tmp = 2.0d0
    else if (y <= 6.4d+72) then
        tmp = 1.0d0 + ((-4.0d0) * (z / y))
    else
        tmp = 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -8.5e+159) {
		tmp = 2.0;
	} else if (y <= 6.4e+72) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 2.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -8.5e+159:
		tmp = 2.0
	elif y <= 6.4e+72:
		tmp = 1.0 + (-4.0 * (z / y))
	else:
		tmp = 2.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -8.5e+159)
		tmp = 2.0;
	elseif (y <= 6.4e+72)
		tmp = Float64(1.0 + Float64(-4.0 * Float64(z / y)));
	else
		tmp = 2.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -8.5e+159)
		tmp = 2.0;
	elseif (y <= 6.4e+72)
		tmp = 1.0 + (-4.0 * (z / y));
	else
		tmp = 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -8.5e+159], 2.0, If[LessEqual[y, 6.4e+72], N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0]]

Derivation

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

Alternative 7: 99.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ 2 + \left(x - z\right) \cdot \frac{4}{y} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ 2.0 (* (- x z) (/ 4.0 y))))

C

double code(double x, double y, double z) {
	return 2.0 + ((x - z) * (4.0 / y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 2.0d0 + ((x - z) * (4.0d0 / y))
end function

Java

public static double code(double x, double y, double z) {
	return 2.0 + ((x - z) * (4.0 / y));
}

Python

def code(x, y, z):
	return 2.0 + ((x - z) * (4.0 / y))

Julia

function code(x, y, z)
	return Float64(2.0 + Float64(Float64(x - z) * Float64(4.0 / y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 2.0 + ((x - z) * (4.0 / y));
end

Wolfram

code[x_, y_, z_] := N[(2.0 + N[(N[(x - z), $MachinePrecision] * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 8: 33.6% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 2 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 2.0)

C

double code(double x, double y, double z) {
	return 2.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 2.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 2.0;
}

Python

def code(x, y, z):
	return 2.0

Julia

function code(x, y, z)
	return 2.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 2.0;
end

Wolfram

code[x_, y_, z_] := 2.0

Derivation

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

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z)
  :name "Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, C"
  :precision binary64
  (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.25)) z)) y)))