Utils

wh_learn(X, Y)

Widrow-Hoff Learning.

Obligatory Arguments

• test_mode::Symbol: which test mode, currently supports :trainonly, :presplit, :carefulsplit and :randomsplit.

Optional Arguments

• eta::Float64=0.1: the learning rate
• n_epochs::Int64=1: the number of epochs to be trained
• weights::Matrix=nothing: the initial weights
• learn_seq::Vector=nothing: the learning sequence
• save_history::Bool=false: if true, a partical training history will be saved
• history_cols::Vector=nothing: the list of column indices you want to saved in history, e.g. [1,32,42] or [2]
• history_rows::Vector=nothing: the list of row indices you want to saved in history, e.g. [1,32,42] or [2]
• verbose::Bool = false: if true, more information will be printed out

make_learn_seq(freq)

Make Widrow-Hoff learning sequence.