Logistic regression 2

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\[\log \left(1 + e^{x}\right) - x \cdot y \]
(FPCore (x y) :precision binary64 (- (log (+ 1.0 (exp x))) (* x y)))
double code(double x, double y) {
	return log((1.0 + exp(x))) - (x * y);
}
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = log((1.0d0 + exp(x))) - (x * y)
end function
public static double code(double x, double y) {
	return Math.log((1.0 + Math.exp(x))) - (x * y);
}
def code(x, y):
	return math.log((1.0 + math.exp(x))) - (x * y)
function code(x, y)
	return Float64(log(Float64(1.0 + exp(x))) - Float64(x * y))
end
function tmp = code(x, y)
	tmp = log((1.0 + exp(x))) - (x * y);
end
code[x_, y_] := N[(N[Log[N[(1.0 + N[Exp[x], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - N[(x * y), $MachinePrecision]), $MachinePrecision]
\log \left(1 + e^{x}\right) - x \cdot y