Average Error: 34.0 → 0.4
Time: 4.9s
Precision: binary64
\[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
↓
\[{\left(\frac{x}{y}\right)}^{2} + \frac{\frac{z}{t}}{\frac{t}{z}} \]
(FPCore (x y z t)
 :precision binary64
 (+ (/ (* x x) (* y y)) (/ (* z z) (* t t))))
↓
(FPCore (x y z t)
 :precision binary64
 (+ (pow (/ x y) 2.0) (/ (/ z t) (/ t z))))
double code(double x, double y, double z, double t) {
	return ((x * x) / (y * y)) + ((z * z) / (t * t));
}
↓
double code(double x, double y, double z, double t) {
	return pow((x / y), 2.0) + ((z / t) / (t / z));
}

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 * y)) + ((z * z) / (t * t))
end function
↓
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) ** 2.0d0) + ((z / t) / (t / z))
end function

public static double code(double x, double y, double z, double t) {
	return ((x * x) / (y * y)) + ((z * z) / (t * t));
}
↓
public static double code(double x, double y, double z, double t) {
	return Math.pow((x / y), 2.0) + ((z / t) / (t / z));
}

def code(x, y, z, t):
	return ((x * x) / (y * y)) + ((z * z) / (t * t))
↓
def code(x, y, z, t):
	return math.pow((x / y), 2.0) + ((z / t) / (t / z))

function code(x, y, z, t)
	return Float64(Float64(Float64(x * x) / Float64(y * y)) + Float64(Float64(z * z) / Float64(t * t)))
end
↓
function code(x, y, z, t)
	return Float64((Float64(x / y) ^ 2.0) + Float64(Float64(z / t) / Float64(t / z)))
end

function tmp = code(x, y, z, t)
	tmp = ((x * x) / (y * y)) + ((z * z) / (t * t));
end
↓
function tmp = code(x, y, z, t)
	tmp = ((x / y) ^ 2.0) + ((z / t) / (t / z));
end

code[x_, y_, z_, t_] := N[(N[(N[(x * x), $MachinePrecision] / N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(N[(z * z), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_, t_] := N[(N[Power[N[(x / y), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(z / t), $MachinePrecision] / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t}
↓
{\left(\frac{x}{y}\right)}^{2} + \frac{\frac{z}{t}}{\frac{t}{z}}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original34.0
Target0.4
Herbie0.4
\[{\left(\frac{x}{y}\right)}^{2} + {\left(\frac{z}{t}\right)}^{2} \]

Derivation

  1. Initial program 34.0

    \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]

  2. Simplified13.8

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

  3. Applied egg-rr0.4

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

  4. Applied egg-rr0.4

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

  5. Final simplification0.4

    \[\leadsto {\left(\frac{x}{y}\right)}^{2} + \frac{\frac{z}{t}}{\frac{t}{z}} \]

Reproduce

herbie shell --seed 2022170 
(FPCore (x y z t)
  :name "Graphics.Rasterific.Svg.PathConverter:arcToSegments from rasterific-svg-0.2.3.1"
  :precision binary64

  :herbie-target
  (+ (pow (/ x y) 2.0) (pow (/ z t) 2.0))

  (+ (/ (* x x) (* y y)) (/ (* z z) (* t t))))