Fits a k-NN classifier model.

Options

• :algorithm (atom/0) - Algorithm used to compute the k-nearest neighbors. Possible values:

  • :brute - Brute-force search. See Scholar.Neighbors.BruteKNN for more details.

  • :kd_tree - k-d tree. See Scholar.Neighbors.KDTree for more details.

  • :random_projection_forest - Random projection forest. See Scholar.Neighbors.RandomProjectionForest for more details.

  • Module implementing fit(data, opts) and predict(model, query). predict/2 must return tuple containing indices of k-nearest neighbors of query points as well as distances between query points and their k-nearest neighbors.

  The default value is :brute.

• :num_classes (pos_integer/0) - Required. The number of possible classes.

• :weights - Weight function used in prediction. Possible values:

  • :uniform - uniform weights. All points in each neighborhood are weighted equally.

  • :distance - weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.

  The default value is :uniform.

Algorithm-specific options (e.g. :num_neighbors, :metric) should be provided together with the classifier options.

Examples

iex> x = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
iex> y = Nx.tensor([0, 0, 0, 1, 1])
iex> model = Scholar.Neighbors.KNNClassifier.fit(x, y, num_neighbors: 3, num_classes: 2)
iex> model.algorithm
Scholar.Neighbors.BruteKNN.fit(x, num_neighbors: 3)
iex> model.labels
Nx.tensor([0, 0, 0, 1, 1])

iex> x = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
iex> y = Nx.tensor([0, 0, 0, 1, 1])
iex> model = Scholar.Neighbors.KNNClassifier.fit(x, y, algorithm: :kd_tree, num_neighbors: 3, metric: {:minkowski, 1}, num_classes: 2)
iex> model.algorithm
Scholar.Neighbors.KDTree.fit(x, num_neighbors: 3, metric: {:minkowski, 1})
iex> model.labels
Nx.tensor([0, 0, 0, 1, 1])

iex> x = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
iex> y = Nx.tensor([0, 0, 0, 1, 1])
iex> key = Nx.Random.key(12)
iex> model = Scholar.Neighbors.KNNClassifier.fit(x, y, algorithm: :random_projection_forest, num_neighbors: 2, num_classes: 2, num_trees: 4, key: key)
iex> model.algorithm
Scholar.Neighbors.RandomProjectionForest.fit(x, num_neighbors: 2, num_trees: 4, key: key)
iex> model.labels
Nx.tensor([0, 0, 0, 1, 1])

Predicts classes using a k-NN classifier model.

Examples

iex> x_train = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
iex> y_train = Nx.tensor([0, 0, 0, 1, 1])
iex> model = Scholar.Neighbors.KNNClassifier.fit(x_train, y_train, num_neighbors: 3, num_classes: 2)
iex> x = Nx.tensor([[1, 3], [4, 2], [3, 6]])
iex> Scholar.Neighbors.KNNClassifier.predict(model, x)
Nx.tensor([0, 0, 1])

Predicts class probabilities using a k-NN classifier model.

Examples

iex> x_train = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
iex> y_train = Nx.tensor([0, 0, 0, 1, 1])
iex> model = Scholar.Neighbors.KNNClassifier.fit(x_train, y_train, num_neighbors: 3, num_classes: 2)
iex> x = Nx.tensor([[1, 3], [4, 2], [3, 6]])
iex> Scholar.Neighbors.KNNClassifier.predict_probability(model, x)
Nx.tensor(
  [
    [1.0, 0.0],
    [1.0, 0.0],
    [0.3333333432674408, 0.6666666865348816]
  ]
)