Graphics.Rasterific.Shading:$sradialGradientWithFocusShader from Rasterific-0.6.1, B

Percentage Accurate: 90.5% → 91.2%

Time: 10.5s

Alternatives: 5

Speedup: 1.0×

• 50.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (- (* x x) (* (* y 4.0) (- (* z z) t))))

C

double code(double x, double y, double z, double t) {
	return (x * x) - ((y * 4.0) * ((z * z) - t));
}

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 * x) - ((y * 4.0d0) * ((z * z) - t))
end function

Java

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

Python

def code(x, y, z, t):
	return (x * x) - ((y * 4.0) * ((z * z) - t))

Julia

function code(x, y, z, t)
	return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * Float64(Float64(z * z) - t)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * x) - ((y * 4.0) * ((z * z) - t));
end

Wolfram

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

Initial Program: 90.5% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (- (* x x) (* (* y 4.0) (- (* z z) t))))

C

double code(double x, double y, double z, double t) {
	return (x * x) - ((y * 4.0) * ((z * z) - t));
}

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 * x) - ((y * 4.0d0) * ((z * z) - t))
end function

Java

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

Python

def code(x, y, z, t):
	return (x * x) - ((y * 4.0) * ((z * z) - t))

Julia

function code(x, y, z, t)
	return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * Float64(Float64(z * z) - t)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * x) - ((y * 4.0) * ((z * z) - t));
end

Wolfram

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

Alternative 1: 91.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \cdot z \leq 5 \cdot 10^{+257}:\\ \;\;\;\;x \cdot x + \left(y \cdot 4\right) \cdot \left(t - z \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x - t \cdot \left(y \cdot \left(-4 + \left(z \cdot 2\right) \cdot \frac{z \cdot 2}{t}\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (* z z) 5e+257)
   (+ (* x x) (* (* y 4.0) (- t (* z z))))
   (- (* x x) (* t (* y (+ -4.0 (* (* z 2.0) (/ (* z 2.0) t))))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z * z) <= 5e+257) {
		tmp = (x * x) + ((y * 4.0) * (t - (z * z)));
	} else {
		tmp = (x * x) - (t * (y * (-4.0 + ((z * 2.0) * ((z * 2.0) / t)))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z * z) <= 5d+257) then
        tmp = (x * x) + ((y * 4.0d0) * (t - (z * z)))
    else
        tmp = (x * x) - (t * (y * ((-4.0d0) + ((z * 2.0d0) * ((z * 2.0d0) / t)))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z * z) <= 5e+257) {
		tmp = (x * x) + ((y * 4.0) * (t - (z * z)));
	} else {
		tmp = (x * x) - (t * (y * (-4.0 + ((z * 2.0) * ((z * 2.0) / t)))));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z * z) <= 5e+257:
		tmp = (x * x) + ((y * 4.0) * (t - (z * z)))
	else:
		tmp = (x * x) - (t * (y * (-4.0 + ((z * 2.0) * ((z * 2.0) / t)))))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(z * z) <= 5e+257)
		tmp = Float64(Float64(x * x) + Float64(Float64(y * 4.0) * Float64(t - Float64(z * z))));
	else
		tmp = Float64(Float64(x * x) - Float64(t * Float64(y * Float64(-4.0 + Float64(Float64(z * 2.0) * Float64(Float64(z * 2.0) / t))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z * z) <= 5e+257)
		tmp = (x * x) + ((y * 4.0) * (t - (z * z)));
	else
		tmp = (x * x) - (t * (y * (-4.0 + ((z * 2.0) * ((z * 2.0) / t)))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(z * z), $MachinePrecision], 5e+257], N[(N[(x * x), $MachinePrecision] + N[(N[(y * 4.0), $MachinePrecision] * N[(t - N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x * x), $MachinePrecision] - N[(t * N[(y * N[(-4.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(N[(z * 2.0), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 5.00000000000000028e257

   1. Initial program 98.8%

      \[x \cdot x - \left(y \cdot 4\right) \cdot \left(z \cdot z - t\right) \]
   2. Add Preprocessing

   if 5.00000000000000028e257 < (*.f64 z z)

   1. Initial program 82.9%

      \[x \cdot x - \left(y \cdot 4\right) \cdot \left(z \cdot z - t\right) \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 82.9%

      \[\leadsto x \cdot x - \color{blue}{t \cdot \left(-4 \cdot y + 4 \cdot \frac{y \cdot {z}^{2}}{t}\right)} \]
   4. Step-by-step derivation

      1. *-commutative 82.9%

         \[\leadsto x \cdot x - t \cdot \left(\color{blue}{y \cdot -4} + 4 \cdot \frac{y \cdot {z}^{2}}{t}\right) \]

      2. associate-/l* 82.9%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot -4 + 4 \cdot \color{blue}{\left(y \cdot \frac{{z}^{2}}{t}\right)}\right) \]

      3. associate-*r* 82.9%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot -4 + \color{blue}{\left(4 \cdot y\right) \cdot \frac{{z}^{2}}{t}}\right) \]

      4. *-commutative 82.9%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot -4 + \color{blue}{\left(y \cdot 4\right)} \cdot \frac{{z}^{2}}{t}\right) \]

      5. associate-/l* 82.9%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot -4 + \color{blue}{\frac{\left(y \cdot 4\right) \cdot {z}^{2}}{t}}\right) \]

      6. associate-*l* 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot -4 + \frac{\color{blue}{y \cdot \left(4 \cdot {z}^{2}\right)}}{t}\right) \]

      7. associate-/l* 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot -4 + \color{blue}{y \cdot \frac{4 \cdot {z}^{2}}{t}}\right) \]

      8. distribute-lft-out 81.6%

         \[\leadsto x \cdot x - t \cdot \color{blue}{\left(y \cdot \left(-4 + \frac{4 \cdot {z}^{2}}{t}\right)\right)} \]

   5. Simplified 81.6%

      \[\leadsto x \cdot x - \color{blue}{t \cdot \left(y \cdot \left(-4 + \frac{4 \cdot {z}^{2}}{t}\right)\right)} \]
   6. Step-by-step derivation

      1. add-sqr-sqrt 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{\color{blue}{\sqrt{4 \cdot {z}^{2}} \cdot \sqrt{4 \cdot {z}^{2}}}}{t}\right)\right) \]

      2. *-un-lft-identity 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{\sqrt{4 \cdot {z}^{2}} \cdot \sqrt{4 \cdot {z}^{2}}}{\color{blue}{1 \cdot t}}\right)\right) \]

      3. times-frac 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \color{blue}{\frac{\sqrt{4 \cdot {z}^{2}}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}}\right)\right) \]

      4. *-commutative 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{\sqrt{\color{blue}{{z}^{2} \cdot 4}}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}\right)\right) \]

      5. sqrt-prod 81.6%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{\color{blue}{\sqrt{{z}^{2}} \cdot \sqrt{4}}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}\right)\right) \]

      6. sqrt-pow1 49.3%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{\color{blue}{{z}^{\left(\frac{2}{2}\right)}} \cdot \sqrt{4}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}\right)\right) \]

      7. metadata-eval 49.3%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{{z}^{\color{blue}{1}} \cdot \sqrt{4}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}\right)\right) \]

      8. pow1 49.3%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{\color{blue}{z} \cdot \sqrt{4}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}\right)\right) \]

      9. metadata-eval 49.3%

         \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot \color{blue}{2}}{1} \cdot \frac{\sqrt{4 \cdot {z}^{2}}}{t}\right)\right) \]

      10. *-commutative 49.3%

          \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot 2}{1} \cdot \frac{\sqrt{\color{blue}{{z}^{2} \cdot 4}}}{t}\right)\right) \]

      11. sqrt-prod 50.6%

          \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot 2}{1} \cdot \frac{\color{blue}{\sqrt{{z}^{2}} \cdot \sqrt{4}}}{t}\right)\right) \]

      12. sqrt-pow1 89.2%

          \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot 2}{1} \cdot \frac{\color{blue}{{z}^{\left(\frac{2}{2}\right)}} \cdot \sqrt{4}}{t}\right)\right) \]

      13. metadata-eval 89.2%

          \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot 2}{1} \cdot \frac{{z}^{\color{blue}{1}} \cdot \sqrt{4}}{t}\right)\right) \]

      14. pow1 89.2%

          \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot 2}{1} \cdot \frac{\color{blue}{z} \cdot \sqrt{4}}{t}\right)\right) \]

      15. metadata-eval 89.2%

          \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \frac{z \cdot 2}{1} \cdot \frac{z \cdot \color{blue}{2}}{t}\right)\right) \]

   7. Applied egg-rr 89.2%

      \[\leadsto x \cdot x - t \cdot \left(y \cdot \left(-4 + \color{blue}{\frac{z \cdot 2}{1} \cdot \frac{z \cdot 2}{t}}\right)\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 5 \cdot 10^{+257}:\\ \;\;\;\;x \cdot x + \left(y \cdot 4\right) \cdot \left(t - z \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x - t \cdot \left(y \cdot \left(-4 + \left(z \cdot 2\right) \cdot \frac{z \cdot 2}{t}\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 92.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x, \left(z \cdot z - t\right) \cdot \left(y \cdot -4\right)\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (fma x x (* (- (* z z) t) (* y -4.0))))

C

double code(double x, double y, double z, double t) {
	return fma(x, x, (((z * z) - t) * (y * -4.0)));
}

Julia

function code(x, y, z, t)
	return fma(x, x, Float64(Float64(Float64(z * z) - t) * Float64(y * -4.0)))
end

Wolfram

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

Derivation

1. Initial program 94.4%

   \[x \cdot x - \left(y \cdot 4\right) \cdot \left(z \cdot z - t\right) \]
2. Step-by-step derivation

   1. fma-neg 95.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -\left(y \cdot 4\right) \cdot \left(z \cdot z - t\right)\right)} \]

   2. distribute-lft-neg-in 95.9%

      \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{\left(-y \cdot 4\right) \cdot \left(z \cdot z - t\right)}\right) \]

   3. *-commutative 95.9%

      \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{\left(z \cdot z - t\right) \cdot \left(-y \cdot 4\right)}\right) \]

   4. distribute-rgt-neg-in 95.9%

      \[\leadsto \mathsf{fma}\left(x, x, \left(z \cdot z - t\right) \cdot \color{blue}{\left(y \cdot \left(-4\right)\right)}\right) \]

   5. metadata-eval 95.9%

      \[\leadsto \mathsf{fma}\left(x, x, \left(z \cdot z - t\right) \cdot \left(y \cdot \color{blue}{-4}\right)\right) \]

3. Simplified 95.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, \left(z \cdot z - t\right) \cdot \left(y \cdot -4\right)\right)} \]
4. Add Preprocessing

5. Final simplification 95.9%

   \[\leadsto \mathsf{fma}\left(x, x, \left(z \cdot z - t\right) \cdot \left(y \cdot -4\right)\right) \]
6. Add Preprocessing

Alternative 3: 90.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return (x * x) + ((y * 4.0) * (t - (z * 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 * x) + ((y * 4.0d0) * (t - (z * z)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 94.4%

   \[x \cdot x - \left(y \cdot 4\right) \cdot \left(z \cdot z - t\right) \]
2. Add Preprocessing

3. Final simplification 94.4%

   \[\leadsto x \cdot x + \left(y \cdot 4\right) \cdot \left(t - z \cdot z\right) \]
4. Add Preprocessing

Alternative 4: 66.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ x \cdot x - y \cdot \left(t \cdot -4\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (* x x) (* y (* t -4.0))))

C

double code(double x, double y, double z, double t) {
	return (x * x) - (y * (t * -4.0));
}

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 * x) - (y * (t * (-4.0d0)))
end function

Java

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

Python

def code(x, y, z, t):
	return (x * x) - (y * (t * -4.0))

Julia

function code(x, y, z, t)
	return Float64(Float64(x * x) - Float64(y * Float64(t * -4.0)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * x) - (y * (t * -4.0));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x * x), $MachinePrecision] - N[(y * N[(t * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.4%

   \[x \cdot x - \left(y \cdot 4\right) \cdot \left(z \cdot z - t\right) \]
2. Add Preprocessing

3. Taylor expanded in z around 0 69.5%

   \[\leadsto x \cdot x - \color{blue}{-4 \cdot \left(t \cdot y\right)} \]
4. Step-by-step derivation

   1. *-commutative 69.5%

      \[\leadsto x \cdot x - \color{blue}{\left(t \cdot y\right) \cdot -4} \]

   2. *-commutative 69.5%

      \[\leadsto x \cdot x - \color{blue}{\left(y \cdot t\right)} \cdot -4 \]

   3. associate-*l* 69.5%

      \[\leadsto x \cdot x - \color{blue}{y \cdot \left(t \cdot -4\right)} \]

5. Simplified 69.5%

   \[\leadsto x \cdot x - \color{blue}{y \cdot \left(t \cdot -4\right)} \]

6. Final simplification 69.5%

   \[\leadsto x \cdot x - y \cdot \left(t \cdot -4\right) \]
7. Add Preprocessing

Alternative 5: 30.7% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ 4 \cdot \left(t \cdot y\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (* 4.0 (* t y)))

C

double code(double x, double y, double z, double t) {
	return 4.0 * (t * y);
}

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 = 4.0d0 * (t * y)
end function

Java

public static double code(double x, double y, double z, double t) {
	return 4.0 * (t * y);
}

Python

def code(x, y, z, t):
	return 4.0 * (t * y)

Julia

function code(x, y, z, t)
	return Float64(4.0 * Float64(t * y))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = 4.0 * (t * y);
end

Wolfram

code[x_, y_, z_, t_] := N[(4.0 * N[(t * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.4%

   \[x \cdot x - \left(y \cdot 4\right) \cdot \left(z \cdot z - t\right) \]
2. Add Preprocessing

3. Taylor expanded in t around inf 33.4%

   \[\leadsto \color{blue}{4 \cdot \left(t \cdot y\right)} \]
4. Step-by-step derivation

   1. *-commutative 33.4%

      \[\leadsto 4 \cdot \color{blue}{\left(y \cdot t\right)} \]

5. Simplified 33.4%

   \[\leadsto \color{blue}{4 \cdot \left(y \cdot t\right)} \]

6. Final simplification 33.4%

   \[\leadsto 4 \cdot \left(t \cdot y\right) \]
7. Add Preprocessing

Developer target: 90.5% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (- (* x x) (* 4.0 (* y (- (* z z) t)))))

C

double code(double x, double y, double z, double t) {
	return (x * x) - (4.0 * (y * ((z * z) - t)));
}

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 * x) - (4.0d0 * (y * ((z * z) - t)))
end function

Java

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

Python

def code(x, y, z, t):
	return (x * x) - (4.0 * (y * ((z * z) - t)))

Julia

function code(x, y, z, t)
	return Float64(Float64(x * x) - Float64(4.0 * Float64(y * Float64(Float64(z * z) - t))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x * x) - (4.0 * (y * ((z * z) - t)));
end

Wolfram

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

Reproduce

herbie shell --seed 2024078 
(FPCore (x y z t)
  :name "Graphics.Rasterific.Shading:$sradialGradientWithFocusShader from Rasterific-0.6.1, B"
  :precision binary64

  :alt
  (- (* x x) (* 4.0 (* y (- (* z z) t))))

  (- (* x x) (* (* y 4.0) (- (* z z) t))))