Class: Rumale::Manifold::LocallyLinearEmbedding
- Inherits:
-
Base::Estimator
- Object
- Base::Estimator
- Rumale::Manifold::LocallyLinearEmbedding
- Includes:
- Base::Transformer
- Defined in:
- lib/rumale/manifold/locally_linear_embedding.rb
Overview
LocallyLinearEmbedding is a class that implements Locally Linear Embedding.
Reference
-
Roweis, S., and Saul, L., “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” J. of Science, vol. 290, pp. 2323-2326, 2000.
Instance Attribute Summary collapse
-
#embedding ⇒ Numo::DFloat
readonly
Return the data in representation space.
Instance Method Summary collapse
-
#fit(x) ⇒ LocallyLinearEmbedding
Fit the model with given training data.
-
#fit_transform(x) ⇒ Numo::DFloat
Fit the model with training data, and then transform them with the learned model.
-
#initialize(n_components: 2, n_neighbors: 10, reg_param: 1e-3) ⇒ LocallyLinearEmbedding
constructor
Create a new transformer with Locally Linear Embedding.
-
#transform(x) ⇒ Numo::DFloat
Transform the given data with the learned model.
Constructor Details
#initialize(n_components: 2, n_neighbors: 10, reg_param: 1e-3) ⇒ LocallyLinearEmbedding
Create a new transformer with Locally Linear Embedding.
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# File 'lib/rumale/manifold/locally_linear_embedding.rb', line 33 def initialize(n_components: 2, n_neighbors: 10, reg_param: 1e-3) super() @params = { n_components: n_components, n_neighbors: [1, n_neighbors].max, reg_param: reg_param } end |
Instance Attribute Details
#embedding ⇒ Numo::DFloat (readonly)
Return the data in representation space.
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# File 'lib/rumale/manifold/locally_linear_embedding.rb', line 26 def @embedding end |
Instance Method Details
#fit(x) ⇒ LocallyLinearEmbedding
Fit the model with given training data.
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# File 'lib/rumale/manifold/locally_linear_embedding.rb', line 47 def fit(x, _y = nil) raise 'LocallyLinearEmbedding#fit requires Numo::Linalg but that is not loaded' unless enable_linalg?(warning: false) x = Rumale::Validation.check_convert_sample_array(x) n_samples = x.shape[0] tol = @params[:reg_param].fdiv(@params[:n_neighbors]) distance_mat = Rumale::PairwiseMetric.squared_error(x) neighbor_ids = neighbor_ids(distance_mat, @params[:n_neighbors], true) affinity_mat = Numo::DFloat.eye(n_samples) n_samples.times do |n| x_local = x[neighbor_ids[n, true], true] - x[n, true] gram_mat = x_local.dot(x_local.transpose) gram_mat += tol * gram_mat.trace * Numo::DFloat.eye(@params[:n_neighbors]) weights = Numo::Linalg.solve(gram_mat, Numo::DFloat.ones(@params[:n_neighbors])) weights /= weights.sum + 1e-8 affinity_mat[n, neighbor_ids[n, true]] -= weights end kernel_mat = affinity_mat.transpose.dot(affinity_mat) _, eig_vecs = Numo::Linalg.eigh(kernel_mat, vals_range: 1...(1 + @params[:n_components])) @embedding = @params[:n_components] == 1 ? eig_vecs[true, 0].dup : eig_vecs.dup @x_train = x.dup self end |
#fit_transform(x) ⇒ Numo::DFloat
Fit the model with training data, and then transform them with the learned model.
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# File 'lib/rumale/manifold/locally_linear_embedding.rb', line 81 def fit_transform(x, _y = nil) unless enable_linalg?(warning: false) raise 'LocallyLinearEmbedding#fit_transform requires Numo::Linalg but that is not loaded' end fit(x).transform(x) end |
#transform(x) ⇒ Numo::DFloat
Transform the given data with the learned model.
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# File 'lib/rumale/manifold/locally_linear_embedding.rb', line 93 def transform(x) x = Rumale::Validation.check_convert_sample_array(x) n_samples = x.shape[0] tol = @params[:reg_param].fdiv(@params[:n_neighbors]) distance_mat = Rumale::PairwiseMetric.squared_error(x, @x_train) neighbor_ids = neighbor_ids(distance_mat, @params[:n_neighbors], false) weight_mat = Numo::DFloat.zeros(n_samples, @x_train.shape[0]) n_samples.times do |n| x_local = @x_train[neighbor_ids[n, true], true] - x[n, true] gram_mat = x_local.dot(x_local.transpose) gram_mat += tol * weight_mat.trace * Numo::DFloat.eye(@params[:n_neighbors]) weights = Numo::Linalg.solve(gram_mat, Numo::DFloat.ones(@params[:n_neighbors])) weights /= weights.sum + 1e-8 weight_mat[n, neighbor_ids[n, true]] = weights end weight_mat.dot(@embedding) end |