jon.recoil.org
forked from
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My aggregated monorepo of OCaml code, automaintained
tessera-linalg: polish docs and generate opam file
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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tessera-linalg/lib/linalg.mli
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tessera-linalg/lib/linalg.mli
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(** PCA and kNN for float32 Bigarray data.
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Portable OCaml implementations of PCA dimensionality reduction and
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k-nearest-neighbors classification. Operates on flat float32 Bigarray
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data with explicit shapes.
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{2 Example: embedding classification}
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{[
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(* PCA: reduce 128-dim embeddings to 3 components *)
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let pca = Linalg.pca_fit embeddings ~n_components:3 in
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let projected = Linalg.pca_transform pca embeddings in
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(* kNN: classify using labeled training points *)
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let model = Linalg.knn_fit ~embeddings:train ~labels in
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let result = Linalg.knn_predict model ~k:5 projected in
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(* result.predictions.(i) is the class, result.confidences.(i) in [0,1] *)
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]} *)
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(** {1 Data types} *)
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type vec = (float, Bigarray.float32_elt, Bigarray.c_layout) Bigarray.Array1.t
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(** A flat float32 vector backed by Bigarray. *)
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type mat = {
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data : vec;
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rows : int;
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cols : int;
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}
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(** A 2D float32 matrix in row-major order. Element [(i, j)] is at flat
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index [i * cols + j] in {!data}. *)
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(** {1 Matrix operations} *)
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val create_mat : rows:int -> cols:int -> mat
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(** Create a zero-initialized matrix. *)
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val mat_get : mat -> int -> int -> float
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(** [mat_get m row col] returns element at [(row, col)]. *)
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val mat_set : mat -> int -> int -> float -> unit
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(** [mat_set m row col v] sets element at [(row, col)] to [v]. *)
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(** {1 PCA} *)
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type pca_model
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(** A fitted PCA model storing the mean vector and principal components. *)
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val pca_fit : ?max_samples:int -> mat -> n_components:int -> pca_model
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(** Fit PCA on a data matrix of shape [(n_samples, n_features)].
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@param max_samples Limit rows used for covariance estimation
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(default: 100,000). Rows are sampled evenly if the matrix is larger.
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@param n_components Number of principal components to compute.
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Uses power iteration on the covariance matrix, which is efficient for
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the typical case of 128 features (128x128 covariance). *)
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val pca_transform : pca_model -> mat -> mat
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(** Project data through a fitted PCA model.
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Input: [(n_samples, n_features)]. Output: [(n_samples, n_components)]. *)
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(** {1 kNN} *)
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type knn_model
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(** A fitted kNN model storing training embeddings and labels. *)
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val knn_fit : embeddings:mat -> labels:int array -> knn_model
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(** Create a kNN model from labeled training data.
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@param embeddings Training data of shape [(n_training, n_features)].
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@param labels Integer class labels, length [n_training]. *)
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type knn_result = {
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predictions : int array;
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confidences : float array;
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}
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(** Classification output. [predictions.(i)] is the predicted class for
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sample [i]. [confidences.(i)] is in [\[0, 1\]] — the fraction of
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distance-weighted votes for the winning class. *)
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val knn_predict : knn_model -> k:int -> mat -> knn_result
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(** Classify samples using k-nearest neighbors with distance weighting.
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Input: [(n_samples, n_features)]. [k] is capped to the number of
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training points. Weights are [1 / (distance + epsilon)] to handle
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exact matches gracefully. *)
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tessera-linalg/tessera-linalg.opam
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tessera-linalg/tessera-linalg.opam
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# This file is generated by dune, edit dune-project instead
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opam-version: "2.0"
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synopsis: "PCA and kNN for float32 Bigarray data"
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description:
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"Portable OCaml implementations of PCA dimensionality reduction and k-nearest-neighbors classification. Operates on flat float32 Bigarray data with explicit shapes."
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license: "ISC"
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depends: [
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"dune" {>= "3.17"}
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"ocaml" {>= "5.2"}
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"alcotest" {with-test & >= "0.8"}
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"odoc" {with-doc}
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]
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build: [
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["dune" "subst"] {dev}
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[
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"dune"
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"build"
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"-p"
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name
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"-j"
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jobs
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"@install"
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"@runtest" {with-test}
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"@doc" {with-doc}
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]
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]