View Source Scholar.Cluster.AffinityPropagation (Scholar v0.3.1)

Model representing affinity propagation clustering. The first dimension of :clusters_centers is set to the number of samples in the dataset. The artificial centers are filled with :infinity values. To fillter them out use prune function.

The algorithm has a time complexity of the order $O(N^2T)$, where $N$ is the number of samples and $T$ is the number of iterations until convergence. Further, the memory complexity is of the order $O(N^2)$.

Summary

Functions

fit(data, opts \\ [])

Cluster the dataset using affinity propagation.

predict(model, x)

Predict the closest cluster each sample in x belongs to.

prune(model)

Optionally prune clusters, indices, and labels to only valid entries.

Functions

fit(data, opts \\ [])

Cluster the dataset using affinity propagation.

Options

:iterations (pos_integer/0) - Number of iterations of the algorithm. The default value is 300.
:damping_factor (float/0) - Damping factor in the range [0.5, 1.0) is the extent to which the current value is maintained relative to incoming values (weighted 1 - damping). The default value is 0.5.
:preference - How to compute the preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of clusters is influenced by this option.
The preferences is either an atom, each is a Nx reduction function to apply on the input similarities (such as :reduce_min, :median, :mean, etc) or a float.
The default value is :reduce_min.
:key - Determines random number generation for centroid initialization. If the key is not provided, it is set to Nx.Random.key(System.system_time()).
:learning_loop_unroll (boolean/0) - If true, the learning loop is unrolled. The default value is false.
:converge_after (pos_integer/0) - Number of iterations with no change in the number of estimated clusters that stops the convergence. The default value is 15.

Return Values

The function returns a struct with the following parameters:

:clusters_centers - Cluster centers from the initial data.
:cluster_centers_indices - Indices of cluster centers.
:num_clusters - Number of clusters.

Examples

iex> key = Nx.Random.key(42)
iex> x = Nx.tensor([[12,5,78,2], [9,3,81,-2], [-1,3,6,1], [1,-2,5,2]])
iex> Scholar.Cluster.AffinityPropagation.fit(x, key: key)
%Scholar.Cluster.AffinityPropagation{
  labels: Nx.tensor([0, 0, 2, 2]),
  cluster_centers_indices: Nx.tensor([0, -1, 2, -1]),
  cluster_centers: Nx.tensor(
    [
      [12.0, 5.0, 78.0, 2.0],
      [:infinity, :infinity, :infinity, :infinity],
      [-1.0, 3.0, 6.0, 1.0],
      [:infinity, :infinity, :infinity, :infinity]
    ]
  ),
  num_clusters: Nx.tensor(2, type: :u64),
  iterations: Nx.tensor(22, type: :s64)
}

predict(model, x)

Predict the closest cluster each sample in x belongs to.

Examples

iex> key = Nx.Random.key(42)
iex> x = Nx.tensor([[12,5,78,2], [9,3,81,-2], [-1,3,6,1], [1,-2,5,2]])
iex> model = Scholar.Cluster.AffinityPropagation.fit(x, key: key)
iex> model = Scholar.Cluster.AffinityPropagation.prune(model)
iex> Scholar.Cluster.AffinityPropagation.predict(model, Nx.tensor([[10,3,50,6], [8,3,8,2]]))
#Nx.Tensor<
  s64[2]
  [0, 1]
>

prune(model)

Optionally prune clusters, indices, and labels to only valid entries.

It returns an updated and pruned model.

Examples

iex> key = Nx.Random.key(42)
iex> x = Nx.tensor([[12,5,78,2], [9,3,81,-2], [-1,3,6,1], [1,-2,5,2]])
iex> model = Scholar.Cluster.AffinityPropagation.fit(x, key: key)
iex> Scholar.Cluster.AffinityPropagation.prune(model)
%Scholar.Cluster.AffinityPropagation{
  labels: Nx.tensor([0, 0, 1, 1]),
  cluster_centers_indices: Nx.tensor([0, 2]),
  cluster_centers: Nx.tensor(
    [
      [12.0, 5.0, 78.0, 2.0],
      [-1.0, 3.0, 6.0, 1.0]
    ]
  ),
  num_clusters: Nx.tensor(2, type: :u64),
  iterations: Nx.tensor(22, type: :s64)
}