Statistics.Distribution.Binomial:directEntropy from math-functions-0.1.5.2

Average Error: 0.3 → 0.3

Time: 6.0s

Precision: binary64

Cost: 6592

Math

\[x \cdot \log x \]

↓

\[x \cdot \log x \]

FPCore

(FPCore (x) :precision binary64 (* x (log x)))

↓

(FPCore (x) :precision binary64 (* x (log x)))

C

double code(double x) {
	return x * log(x);
}

↓

double code(double x) {
	return x * log(x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * log(x)
end function

↓

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * log(x)
end function

Java

public static double code(double x) {
	return x * Math.log(x);
}

↓

public static double code(double x) {
	return x * Math.log(x);
}

Python

def code(x):
	return x * math.log(x)

↓

def code(x):
	return x * math.log(x)

Julia

function code(x)
	return Float64(x * log(x))
end

↓

function code(x)
	return Float64(x * log(x))
end

MATLAB

function tmp = code(x)
	tmp = x * log(x);
end

↓

function tmp = code(x)
	tmp = x * log(x);
end

Wolfram

code[x_] := N[(x * N[Log[x], $MachinePrecision]), $MachinePrecision]

↓

code[x_] := N[(x * N[Log[x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 0.3

   \[x \cdot \log x \]

2. Final simplification 0.3

   \[\leadsto x \cdot \log x \]

Reproduce

herbie shell --seed 2022291 
(FPCore (x)
  :name "Statistics.Distribution.Binomial:directEntropy from math-functions-0.1.5.2"
  :precision binary64
  (* x (log x)))