# Winter Storm 2015 Modeling Workshop
# Word Learning Model
# ==learner.R==
# defines a parallel word learner with optional cost
# or benefit to future learning from learned words
# learner(vocabulary = when words are due to be learned
#	      penalty = how much learning a word changes the difficulty
#					   of unlearned words)
#		  penalty > 0 slows learning, < 0 speeds learning
# =================================================================
# McMurray, B. (2007). Defusing the childhood vocabulary explosion. Science, 317(5838), 631-631.
# 
# code author: Rachael Richardson
# rachaelr@umd.edu

# turn on for noisy output
chatter = TRUE

learner <- function(vocabulary, offset = 0)	{

	TOTALWORDS = length(vocabulary)

	# boolean record of whether or not words have been learned
	wordslearned <- seq(0,0,length = TOTALWORDS)
	
	# record of total words known at each learning step
	wordsknownbystep <- seq(0,0,length = TOTALWORDS)
	
	# total number of learning steps taken
	timesteps <- 0
	
	# define learner's initial state with sequence of zeros
	learner = seq(0,0,length = TOTALWORDS)

	# continue until all words have been learned
	while( sum(wordslearned) < TOTALWORDS ) {
		
		# for each word that has not been learned 
		for( j in which( wordslearned == 0 )) {
			
			# if the unlearned word has reached its specified threshold it is now learned
			if( learner[j] >= vocabulary[j] ) {
				
				# mark word j as learned
				wordslearned[j] = 1
				
				# words learned incur a cost or benefit to future learning
 				vocabulary[-j] = vocabulary[-j] + offset
		
			}  # end if
			
		}  # end for

		# batch update the record of total words known at this time step
		wordsknownbystep[timesteps] = sum(wordslearned)
			
		# increment the time step
		timesteps = timesteps + 1
		
		if(chatter) {
			cat("epoch",learner[j],":  unlearned words: ", sum( wordslearned == 0 )," total progress: ", sum(wordslearned), "/", TOTALWORDS, "\n")
                }  # end if

                # add a point to all words
		learner = learner + 1	
		
	}  # end while

	return (wordsknownbystep)
	
}  # end learner