#!/usr/bin/env python3

#######################################################################
# 
#
# Author: Garry Morrison
# email: garry@semantic-db.org
# Date: 2015-05-10
# Update:
# Copyright: closed for now
#
# Usage:
#
#######################################################################


import sys

from the_semantic_db_code import *
from the_semantic_db_functions import *
from the_semantic_db_processor import *

C = context_list("average categorize")

C.learn("op","x","a")
C.learn("op","y","b")
C.learn("op","z","c")

C.learn("op","u",ket("a",0.7))

def average_categorize(context,op,t,phi,ave):
  one = context.relevant_kets(op)
  print("one:",one)
  out_list = []
  for x in one:
    print("x:",x)
    r = x.apply_op(context,op)
    print("r:",r)
    best_k = -1
    best_simm = 0
    for k,sp in enumerate(out_list):
      similarity = silent_simm(r,sp)
      if similarity > best_simm:
        best_k = k
        best_simm = similarity
    print("best k:",best_k)
    print("best simm:",best_simm)

    if best_k == -1 or best_simm < t:
      out_list.append(r)
    else:
      k = best_k
#      out_list[k] += r
      out_list[k] += r.multiply(best_simm)  # reweight based on result of simm.
  for k,sp in enumerate(out_list):
    print("sp:",sp)
    context.learn(ave,phi + ": " + str(k+1),sp)
  return ket("average categorize")

average_categorize(C,"op",0.6,"phi","ave")
print(C.dump_universe())
#sys.exit(0)

print("-----------------------------------------------------")
# let's try for a bigger example!

C = context_list("average categorize letters")
load_sw(C,"sw-examples/H-I-pat-rec.sw")
print(C.dump_universe())

def pixels_to_string(context,one):
  data = one.apply_op(context,"pixels")
  I = 5
  J = 7

  string = ""
  for j in range(1,J+1):
    for i in range(1,I+1):
      elt = ket("pixel: " + str(j) + ": " + str(i))
      coeff = data.find_value(elt)
      c = str(int(coeff))
      if coeff == 0:
        c = ' '
      string += c + ' '
    string += "\n"
  return string.rstrip('\n')

#print(pixels_to_string(C,ket("letter: H")))

# create our averages:
# average_categorize(C,"pixels",0.65,"phi","ave")
average_categorize(C,"pixels",0.65,"phi","pixels")
# save our pixels:
save_sw(C,"sw-examples/saved-average-categorize-pixels.sw")

# now try our averages:
for x in C.relevant_kets("pixels"):
  print("x:",x)
  print(pixels_to_string(C,x))
  print()

sys.exit(0)
print("-----------------------------------------------------")
# let's try for a bigger example!

C = context_list("average categorize webpages")
#load_sw(C,"sw-examples/improved-fragment-webpages.sw")
load_sw(C,"sw-examples/saved-label-training-data-for-website-fragments.sw")

average_categorize(C,"training-hash-4B",0.7,"phi","average-cat")

print(C.dump_universe())
save_sw(C,"sw-examples/saved-average-categorize.sw")