Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t}
\]
↓
\[\frac{\frac{x}{y}}{\frac{y}{x}} + \frac{\frac{z}{t}}{\frac{t}{z}}
\]
(FPCore (x y z t)
:precision binary64
(+ (/ (* x x) (* y y)) (/ (* z z) (* t t)))) ↓
(FPCore (x y z t)
:precision binary64
(+ (/ (/ x y) (/ y x)) (/ (/ 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 ((x / y) / (y / x)) + ((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) / (y / x)) + ((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 ((x / y) / (y / x)) + ((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 ((x / y) / (y / x)) + ((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(Float64(x / y) / Float64(y / x)) + 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) / (y / x)) + ((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[(N[(x / y), $MachinePrecision] / N[(y / x), $MachinePrecision]), $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}
↓
\frac{\frac{x}{y}}{\frac{y}{x}} + \frac{\frac{z}{t}}{\frac{t}{z}}
Alternatives Alternative 1 Error 9.9 Cost 1992
\[\begin{array}{l}
t_1 := \frac{x \cdot x}{y \cdot y}\\
\mathbf{if}\;t_1 \leq 0:\\
\;\;\;\;\frac{z}{t} \cdot \frac{z}{t}\\
\mathbf{elif}\;t_1 \leq 10^{+89}:\\
\;\;\;\;x \cdot \frac{\frac{x}{y}}{y} + z \cdot \frac{z}{t \cdot t}\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\
\end{array}
\]
Alternative 2 Error 11.8 Cost 964
\[\begin{array}{l}
\mathbf{if}\;\frac{x \cdot x}{y \cdot y} \leq 5 \cdot 10^{-240}:\\
\;\;\;\;\frac{z}{t} \cdot \frac{z}{t}\\
\mathbf{else}:\\
\;\;\;\;\frac{x}{y} \cdot \frac{x}{y}\\
\end{array}
\]
Alternative 3 Error 11.7 Cost 964
\[\begin{array}{l}
\mathbf{if}\;\frac{x \cdot x}{y \cdot y} \leq 5 \cdot 10^{-240}:\\
\;\;\;\;\frac{z}{t} \cdot \frac{z}{t}\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\
\end{array}
\]
Alternative 4 Error 0.4 Cost 960
\[\frac{z}{t} \cdot \frac{z}{t} + \frac{x}{y} \cdot \frac{x}{y}
\]
Alternative 5 Error 0.4 Cost 960
\[\frac{\frac{z}{t}}{\frac{t}{z}} + \frac{x}{y} \cdot \frac{x}{y}
\]
Alternative 6 Error 0.4 Cost 960
\[\frac{\frac{x}{y}}{\frac{y}{x}} + \frac{z}{t} \cdot \frac{z}{t}
\]
Alternative 7 Error 31.1 Cost 448
\[x \cdot \frac{\frac{x}{y}}{y}
\]
Alternative 8 Error 27.4 Cost 448
\[\frac{x}{y} \cdot \frac{x}{y}
\]