using NNlib: log_fast

# Cost functions

mse(ŷ, y) = sum((ŷ .- y).^2)/length(y)

function crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat)
  return -sum(y .* log_fast.(ŷ)) / size(y, 2)
end

function weighted_crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, w::AbstractVecOrMat)
  return -sum(y .* log_fast.(ŷ) .* w) / size(y, 2)
end



@deprecate logloss(x, y) crossentropy(x, y)

function logitcrossentropy(logŷ::AbstractVecOrMat, y::AbstractVecOrMat)
  logŷ = logŷ .- maximum(logŷ, 1)
  ypred = logŷ .- log_fast.(sum(exp.(logŷ), 1))
  -sum(y .* ypred) / size(y, 2)
end

"""
    normalise(x::AbstractVecOrMat)

Normalise each column of `x` to mean 0 and standard deviation 1.
"""
function normalise(x::AbstractVecOrMat)
  μ′ = mean(x, 1)
  σ′ = std(x, 1, mean = μ′)
  return (x .- μ′) ./ σ′
end