Class: GeneValidator::HierarchicalClusterization

Inherits:
Object
  • Object
show all
Defined in:
lib/genevalidator/clusterization.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(values) ⇒ HierarchicalClusterization

Object initialization Params: values :vector of values



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# File 'lib/genevalidator/clusterization.rb', line 314

def initialize(values)
  @values = values
  @clusters = []
end

Instance Attribute Details

#clusters ⇒ Object

Returns the value of attribute clusters.



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# File 'lib/genevalidator/clusterization.rb', line 308

def clusters
  @clusters
end

#values ⇒ Object

Returns the value of attribute values.



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# File 'lib/genevalidator/clusterization.rb', line 307

def values
  @values
end

Instance Method Details

#hierarchical_clusterization(no_clusters = 0, distance_method = 0, vec = @values, debug = false) ⇒ Object

Makes an hierarchical clusterization until the most dense cluster is obtained or the distance between clusters is sufficintly big or the desired number of clusters is obtained Params: no_clusters: stop test (number of clusters) distance_method: distance method (method 0 or method 1) vec: a vector of values (by default the values from initialization) debug: display debug information Output: vector of Cluster objects



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# File 'lib/genevalidator/clusterization.rb', line 417

def hierarchical_clusterization(no_clusters = 0, distance_method = 0,
                                vec = @values, debug = false)
  clusters = []
  vec = vec.sort

  if vec.length == 1
    hash    = { vec[0] => 1 }
    cluster = Cluster.new(hash)
    clusters.push(cluster)
    clusters
  end

  # Thresholds
  threshold_distance = (0.25 * (vec.max - vec.min))
  threshold_density  = (0.5 * vec.length).to_i

  # make a histogram from the input vector
  histogram = Hash[vec.group_by { |x| x }.map { |k, vs| [k, vs.length] }]

  # clusters = array of clusters
  # initially each length belongs to a different cluster
  histogram.sort_by { |a| a[0] }.each do |elem|
    warn "len #{elem[0]} appears #{elem[1]} times" if debug
    hash = { elem[0] => elem[1] }
    cluster = Cluster.new(hash)
    clusters.push(cluster)
  end

  clusters.each(&:print) if debug

  return clusters if clusters.length == 1

  # each iteration merge the closest two adiacent cluster
  # the loop stops according to the stop conditions
  iteration = 0
  loop do
    # stop condition 1
    break if no_clusters != 0 && clusters.length == no_clusters

    iteration += iteration
    warn "\nIteration #{iteration}" if debug

    min_distance = 100_000_000
    cluster      = 0
    density      = 0

    clusters[0..clusters.length - 2].each_with_index do |_item, i|
      dist = clusters[i].distance(clusters[i + 1], distance_method)
      warn "distance btwn clusters #{i} and #{i + 1} is #{dist}" if debug
      current_density = clusters[i].density + clusters[i + 1].density
      if dist < min_distance
        min_distance = dist
        cluster = i
        density = current_density
      elsif dist == min_distance && density < current_density
        cluster = i
        density = current_density
      end
    end

    # stop condition 2
    # the distance between the closest clusters exceeds the threshold
    if no_clusters == 0 && (clusters[cluster].mean - clusters[cluster + 1].mean).abs > threshold_distance
      break
    end

    # merge clusters 'cluster' and 'cluster'+1
    warn "clusters to merge #{cluster} and #{cluster + 1}" if debug

    clusters[cluster].add(clusters[cluster + 1])
    clusters.delete_at(cluster + 1)

    if debug
      clusters.each_with_index do |elem, i|
        warn "cluster #{i}"
        elem.print
      end
    end

    # stop condition 3
    # the density of the biggest clusters exceeds the threshold
    if no_clusters == 0 && clusters[cluster].density > threshold_density
      break
    end
  end

  @clusters = clusters
end

#hierarchical_clusterization_2d(no_clusters = 0, distance_method = 0, vec = @values, debug = false) ⇒ Object 



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# File 'lib/genevalidator/clusterization.rb', line 319

def hierarchical_clusterization_2d(no_clusters = 0, distance_method = 0,
                                   vec = @values, debug = false)
  clusters = []

  if vec.length == 1
    hash = { vec[0] => 1 }
    cluster = PairCluster.new(hash)
    clusters.push(cluster)
    clusters
  end

  # Thresholds
  # threshold_distance = (0.25 * (vec.max-vec.min))
  threshold_density = (0.5 * vec.length).to_i

  # make a histogram from the input vector
  histogram = Hash[vec.group_by { |a| a }.map { |k, vs| [k, vs.length] }]

  # clusters = array of clusters
  # initially each length belongs to a different cluster
  histogram.each do |e|
    warn "pair (#{e[0].x} #{e[0].y}) appears #{e[1]} times" if debug
    hash = { e[0] => e[1] }
    cluster = PairCluster.new(hash)
    clusters.push(cluster)
  end

  clusters.each(&:print) if debug

  return clusters if clusters.length == 1

  # each iteration merge the closest two adiacent cluster
  # the loop stops according to the stop conditions
  iteration = 0
  loop do
    # stop condition 1
    break if no_clusters != 0 && clusters.length == no_clusters

    iteration += iteration
    warn "\nIteration #{iteration}" if debug

    min_distance = 100_000_000
    cluster1     = 0
    cluster2     = 0
    density      = 0

    [*(0..(clusters.length - 2))].each do |i|
      [*((i + 1)..(clusters.length - 1))].each do |j|
        dist = clusters[i].distance(clusters[j], distance_method)
        warn "distance between clusters #{i} and #{j} is #{dist}" if debug
        current_density = clusters[i].density + clusters[j].density
        if dist < min_distance
          min_distance = dist
          cluster1     = i
          cluster2     = j
          density      = current_density
        elsif dist == min_distance && density < current_density
          cluster1 = i
          cluster2 = j
          density  = current_density
        end
      end
    end

    # merge clusters 'cluster1' and 'cluster2'
    warn "clusters to merge #{cluster1} and #{cluster2}" if debug

    clusters[cluster1].add(clusters[cluster2])
    clusters.delete_at(cluster2)

    if debug
      clusters.each_with_index do |elem, i|
        warn "cluster #{i}"
        elem.print
      end
    end

    # stop condition 3
    # the density of the biggest clusters exceeds the threshold
    if no_clusters == 0 && clusters[cluster].density > threshold_density
      break
    end
  end

  @clusters = clusters
end

#most_dense_cluster(clusters = @clusters) ⇒ Object

Returns the cluster with the maimum density Params: clusters: list of Clususter objects



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# File 'lib/genevalidator/clusterization.rb', line 510

def most_dense_cluster(clusters = @clusters)
  max_density = 0
  max_density_cluster = 0

  nil if clusters.nil?

  clusters.each_with_index do |item, i|
    if item.density > max_density
      max_density = item.density
      max_density_cluster = i
    end
  end
  clusters[max_density_cluster]
end