Class: Rumale::Clustering::KMeans

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::ClusterAnalyzer
Defined in:
lib/rumale/clustering/k_means.rb

Overview

KMeans is a class that implements K-Means cluster analysis. The current implementation uses the Euclidean distance for analyzing the clusters.

Reference

    1. Arthur and S. Vassilvitskii, “k-means++: the advantages of careful seeding,” Proc. SODA’07, pp. 1027–1035, 2007.

Examples:

analyzer = Rumale::Clustering::KMeans.new(n_clusters: 10, max_iter: 50)
cluster_labels = analyzer.fit_predict(samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::ClusterAnalyzer

#score

Constructor Details

#initialize(n_clusters: 8, init: 'k-means++', max_iter: 50, tol: 1.0e-4, random_seed: nil) ⇒ KMeans

Create a new cluster analyzer with K-Means method.

Parameters:

  • n_clusters (Integer) (defaults to: 8) —

    The number of clusters.

  • init (String) (defaults to: 'k-means++') —

    The initialization method for centroids (‘random’ or ‘k-means++’).

  • max_iter (Integer) (defaults to: 50) —

    The maximum number of iterations.

  • tol (Float) (defaults to: 1.0e-4) —

    The tolerance of termination criterion.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator.



38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
 
# File 'lib/rumale/clustering/k_means.rb', line 38

def initialize(n_clusters: 8, init: 'k-means++', max_iter: 50, tol: 1.0e-4, random_seed: nil)
  check_params_integer(n_clusters: n_clusters, max_iter: max_iter)
  check_params_float(tol: tol)
  check_params_string(init: init)
  check_params_type_or_nil(Integer, random_seed: random_seed)
  check_params_positive(n_clusters: n_clusters, max_iter: max_iter)
  @params = {}
  @params[:n_clusters] = n_clusters
  @params[:init] = init == 'random' ? 'random' : 'k-means++'
  @params[:max_iter] = max_iter
  @params[:tol] = tol
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @cluster_centers = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#cluster_centers ⇒ Numo::DFloat (readonly)

Return the centroids.

Returns:

  • (Numo::DFloat) —

    (shape: [n_clusters, n_features])



25
26
27
 
# File 'lib/rumale/clustering/k_means.rb', line 25

def cluster_centers
  @cluster_centers
end

#rng ⇒ Random (readonly)

Return the random generator.

Returns:

  • (Random) 


29
30
31
 
# File 'lib/rumale/clustering/k_means.rb', line 29

def rng
  @rng
end

Instance Method Details

#fit(x) ⇒ KMeans

Analysis clusters with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for cluster analysis.

Returns:

  • (KMeans) —

    The learned cluster analyzer itself.



61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
 
# File 'lib/rumale/clustering/k_means.rb', line 61

def fit(x, _y = nil)
  check_sample_array(x)
  init_cluster_centers(x)
  @params[:max_iter].times do |_t|
    cluster_labels = assign_cluster(x)
    old_centers = @cluster_centers.dup
    @params[:n_clusters].times do |n|
      assigned_bits = cluster_labels.eq(n)
      @cluster_centers[n, true] = x[assigned_bits.where, true].mean(axis: 0) if assigned_bits.count.positive?
    end
    error = Numo::NMath.sqrt(((old_centers - @cluster_centers)**2).sum(axis: 1)).mean
    break if error <= @params[:tol]
  end
  self
end

#fit_predict(x) ⇒ Numo::Int32

Analysis clusters and assign samples to clusters.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for cluster analysis.

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted cluster label per sample.



90
91
92
93
94
 
# File 'lib/rumale/clustering/k_means.rb', line 90

def fit_predict(x)
  check_sample_array(x)
  fit(x)
  predict(x)
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data.



98
99
100
101
102
 
# File 'lib/rumale/clustering/k_means.rb', line 98

def marshal_dump
  { params: @params,
    cluster_centers: @cluster_centers,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


106
107
108
109
110
111
 
# File 'lib/rumale/clustering/k_means.rb', line 106

def marshal_load(obj)
  @params = obj[:params]
  @cluster_centers = obj[:cluster_centers]
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Numo::Int32

Predict cluster labels for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the cluster label.

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted cluster label per sample.



81
82
83
84
 
# File 'lib/rumale/clustering/k_means.rb', line 81

def predict(x)
  check_sample_array(x)
  assign_cluster(x)
end