# `Scholar.Neighbors.BruteKNN`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/brute_knn.ex#L1)

Brute-Force k-Nearest Neighbor Search Algorithm.

In order to find the k-nearest neighbors the algorithm calculates
the distance between the query point and each of the data samples.
Therefore, its time complexity is $O(MN)$ for $N$ samples and $M$ query points.
It uses $O(BN)$ memory for batch size $B$.
Larger batch sizes will lead to faster predictions, but will consume more memory.

# `fit`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/brute_knn.ex#L76)

Fits a brute-force k-NN model.

## Options

* `:num_neighbors` (`t:pos_integer/0`) - Required. The number of nearest neighbors.

* `:metric` - The function that measures the pairwise distance between two points. Possible values:

  * `{:minkowski, p}` - Minkowski metric. By changing value of `p` parameter (a positive number or `:infinity`)
  we can set Manhattan (`1`), Euclidean (`2`), Chebyshev (`:infinity`), or any arbitrary $L_p$ metric.

  * `:cosine` - Cosine metric.

  * `:euclidean` - Euclidean metric.

  * `:squared_euclidean` - Squared Euclidean metric.

  * `:manhattan` - Manhattan metric.

  * Anonymous function of arity 2 that takes two rank-2 tensors.

  The default value is `&Scholar.Metrics.Distance.pairwise_minkowski/2`.

* `:batch_size` (`t:pos_integer/0`) - The number of samples in a batch.

## Examples

    iex> data = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
    iex> model = Scholar.Neighbors.BruteKNN.fit(data, num_neighbors: 2)
    iex> model.num_neighbors
    2
    iex> model.data
    #Nx.Tensor<
      s32[5][2]
      [
        [1, 2],
        [2, 3],
        [3, 4],
        [4, 5],
        [5, 6]
      ]
    >

# `get_batches`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/brute_knn.ex#L217)

# `predict`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/brute_knn.ex#L131)

Computes nearest neighbors of query tensor using brute-force search.
Returns the neighbors indices and distances from query points.

## Examples

    iex> data = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
    iex> model = Scholar.Neighbors.BruteKNN.fit(data, num_neighbors: 2)
    iex> query = Nx.tensor([[1, 3], [4, 2], [3, 6]])
    iex> {neighbors, distances} = Scholar.Neighbors.BruteKNN.predict(model, query)
    iex> neighbors
    #Nx.Tensor<
      u64[3][2]
      [
        [0, 1],
        [1, 2],
        [3, 2]
      ]
    >
    iex> distances
    #Nx.Tensor<
      f32[3][2]
      [
        [1.0, 1.0],
        [2.2360680103302, 2.2360680103302],
        [1.4142135381698608, 2.0]
      ]
    >

# `predict_n`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/brute_knn.ex#L150)

---

*Consult [api-reference.md](api-reference.md) for complete listing*