#!/usr/bin/env python
#Author: Shriphani Palakodety
#Mail: spalakod@cs.purdue.edu
#Mail2: shriphani@shriphani.com
#Hebb's Rule implementation.


#Each neuron has 3 attributes: 

#Activation = 1 or 0.
#Data = A letter of the alphabet in this case
#Weight with respect to other cells. 
#Initiall weights are 0

import string
import copy

class Neuron:
	def __init__(self, a, weight_dict):
		'''A Represents The Cell's Content'''

		self.content = a #what letter of the alphabet a cell represents
		self.weights = weight_dict #only contains contents of each neuron.
		self.state = 0 #activation state initially on.
		
	def __str__(self):
		return "Activation: "+str(self.state)+"\nWeights: "+str(self.weights)

	
class Network:

	def __init__(self):
		self.neurons = []
		self.flush = {} #weight calculator while updating activities
		self.possibilities = []

		for char in string.uppercase:
			self.flush[char] = 0
		
		for letter in string.uppercase:
			weights = copy.copy(self.flush)
			weights.pop(letter)
			a = Neuron(letter, weights)
			self.neurons.append(a)

	def recvInput(self):
		a = raw_input("Sensory Interface: ") #Get a word
		
		while a != "QUIT":
			b = list(a)
			for i in xrange(len(b)):
				for neuron in self.neurons:
					char = b[i]
					if neuron.content == char:
						neuron.state = 1
						reqd_list = b[0:i]+b[i+1:]
						for char in string.uppercase:
							if char in reqd_list:
								neuron.weights[char]+=1
							else:
								if neuron.weights.has_key(char):
									neuron.weights[char]-=1
								else:
									continue


			self.checkInfo()
			self.possibleInfo()
			print "The possible interpretations of this input might be anagrams of the subsets of: \n"
			print self.possibilities
			a = raw_input("Sensory Interface: ")

	def checkInfo(self):
		output = []
		possibilities = {}
		for neuron in self.neurons:
			if neuron.state == 1:
				for info in neuron.weights.keys():
					self.flush[info] += neuron.weights[info]
		print self.flush
					
		for neuron in self.neurons:
			if self.flush[neuron.content] > 0:
				neuron.state = 1
			else:
				neuron.state = 0
		
		for char in self.flush.keys():
			self.flush[char] = 0

	def possibleInfo(self):
		self.possibilities = []
		for neuron in self.neurons:
			if neuron.state == 1:
				self.possibilities.append(neuron.content)
		
	
words = Network()

words.recvInput()