# The MIT License (MIT)
#
# Copyright (c) 2019 Anamarija Lonza
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

setwd("C:/Users/Nana/Desktop/ZA ANALIZU NA ENG/NOVO")


source("libraries.r")

itemNumber <- 58
iterations <- 1000
seeds <- c()

#calculates spinglass analysis for a graph using a set seed
calculate_spinglass_with_seed <- function(igraph)
{
	seeds <- c(seeds, .Random.seed)
  spinglass <- spinglass.community(igraph)
  return(spinglass)
}

#returns a spinglass membership for a graph using a set seed
membership_for_spinglass_with_seed <- function(igraph)
{
  spinglass <- calculate_spinglass_with_seed(igraph)
  membership <- spinglass$membership
  return(membership)
}

#gives a matrix with the number of clusters obtained in each iteration
create_groupings_matrix <- function(igraph)
{
  groupingsMatrix <- matrix(NA, nrow = 1, ncol = iterations)

  for(i in 1:iterations)
  {
    membership <- membership_for_spinglass_with_seed(igraph)
    groupingsMatrix[1, i] <- max(membership)
  }

  return(groupingsMatrix)
}

#returns the number of clusters which shows up in the most iterations
determine_most_common_grouping <- function(groupingsMatrix)
{
  mean(as.vector(groupingsMatrix))
  max(as.vector(groupingsMatrix))
  min(as.vector(groupingsMatrix))

  mostCommonGrouping <- median(as.vector(groupingsMatrix))
  return(mostCommonGrouping)
}

#for all groupings - if a grouping is one of the most common groupings, add its index to list
find_all_most_common_grouping_indexes <- function(groupingsMatrix, mostCommonGrouping)
{
  mostCommonGroupingIndexes <- list()

  for(i in 1:ncol(groupingsMatrix))
    if(groupingsMatrix[1, i] == mostCommonGrouping)
      mostCommonGroupingIndexes <- c(mostCommonGroupingIndexes, i)

    return(mostCommonGroupingIndexes)
}

#makes a new matrix consisting only of spinglass solutions with the most common number of clusters
#that we determined using the determine_most_common_grouping function
create_matrix_of_all_solutions_with_most_common_grouping <- function(igraph, groupingsMatrix, mostCommonGrouping)
{
  mostCommonGroupingIndexes <- find_all_most_common_grouping_indexes(groupingsMatrix, mostCommonGrouping)

  totalGroupings <- length(mostCommonGroupingIndexes)

  mostCommonGroupings <- matrix(NA, nrow = totalGroupings, ncol = itemNumber)
  count <- 1

  for (i in mostCommonGroupingIndexes)
  {
  	set.seed(seeds[i])
    membership <- membership_for_spinglass_with_seed(igraph)

    mostCommonGroupings[count,] <- membership
    count <- count+1
  }

  return(mostCommonGroupings)
}