jon.recoil.org
forked from
anil.recoil.org/monopam-myspace
My aggregated monorepo of OCaml code, automaintained
tessera-linalg: initial scaffold with matrix helpers
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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tessera-linalg/dune-project
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tessera-linalg/dune-project
···
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(lang dune 3.17)
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(name tessera-linalg)
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(generate_opam_files true)
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(license ISC)
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(package
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(name tessera-linalg)
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(synopsis "PCA and kNN for float32 Bigarray data")
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(description "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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(depends
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(ocaml (>= 5.2))
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(alcotest (and :with-test (>= 0.8)))))
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tessera-linalg/lib/dune
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tessera-linalg/lib/dune
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tessera-linalg/lib/linalg.ml
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tessera-linalg/lib/linalg.ml
···
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type vec = (float, Bigarray.float32_elt, Bigarray.c_layout) Bigarray.Array1.t
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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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let create_mat ~rows ~cols =
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let data = Bigarray.Array1.create Bigarray.float32 Bigarray.c_layout (rows * cols) in
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Bigarray.Array1.fill data 0.0;
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{ data; rows; cols }
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let mat_get m i j =
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Bigarray.Array1.get m.data (i * m.cols + j)
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let mat_set m i j v =
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Bigarray.Array1.set m.data (i * m.cols + j) v
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(* ---- PCA (stub) ---- *)
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type pca_model = {
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mean : float array;
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components : float array array;
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}
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let pca_fit ?(max_samples = 100_000) _mat ~n_components =
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ignore max_samples;
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{ mean = [||]; components = Array.make n_components [||] }
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let pca_transform _model mat =
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create_mat ~rows:mat.rows ~cols:0
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(* ---- kNN (stub) ---- *)
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type knn_model = {
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embeddings : mat;
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labels : int array;
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}
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let knn_fit ~embeddings ~labels =
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{ embeddings; labels }
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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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let knn_predict _model ~k:_ query =
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{ predictions = Array.make query.rows 0;
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confidences = Array.make query.rows 0.0 }
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tessera-linalg/lib/linalg.mli
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tessera-linalg/lib/linalg.mli
···
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type vec = (float, Bigarray.float32_elt, Bigarray.c_layout) Bigarray.Array1.t
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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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val create_mat : rows:int -> cols:int -> mat
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val mat_get : mat -> int -> int -> float
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val mat_set : mat -> int -> int -> float -> unit
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type pca_model
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val pca_fit : ?max_samples:int -> mat -> n_components:int -> pca_model
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val pca_transform : pca_model -> mat -> mat
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type knn_model
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val knn_fit : embeddings:mat -> labels:int array -> knn_model
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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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val knn_predict : knn_model -> k:int -> mat -> knn_result
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tessera-linalg/test/dune
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tessera-linalg/test/dune
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tessera-linalg/test/test_linalg.ml
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tessera-linalg/test/test_linalg.ml
···
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open Linalg
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(* Helper to create a mat from a float array array (row-major) *)
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let mat_of_arrays rows_data =
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let rows = Array.length rows_data in
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let cols = if rows > 0 then Array.length rows_data.(0) else 0 in
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let m = create_mat ~rows ~cols in
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for i = 0 to rows - 1 do
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for j = 0 to cols - 1 do
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mat_set m i j rows_data.(i).(j)
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done
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done;
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m
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let _ = mat_of_arrays
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(* ---- Matrix tests ---- *)
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let test_create_mat_dims () =
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let m = create_mat ~rows:3 ~cols:4 in
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Alcotest.(check int) "rows" 3 m.rows;
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Alcotest.(check int) "cols" 4 m.cols;
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Alcotest.(check int) "data length" 12 (Bigarray.Array1.dim m.data)
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let test_mat_get_set_roundtrip () =
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let m = create_mat ~rows:2 ~cols:3 in
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mat_set m 0 0 1.5;
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mat_set m 1 2 42.0;
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let v1 = mat_get m 0 0 in
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let v2 = mat_get m 1 2 in
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Alcotest.(check (float 1e-3)) "get(0,0)" 1.5 v1;
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Alcotest.(check (float 1e-3)) "get(1,2)" 42.0 v2
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let test_mat_zero_init () =
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let m = create_mat ~rows:2 ~cols:2 in
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Alcotest.(check (float 1e-6)) "zero init" 0.0 (mat_get m 0 0)
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(* ---- Test runner ---- *)
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let () =
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Alcotest.run "tessera-linalg" [
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"matrix", [
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Alcotest.test_case "create_mat dimensions" `Quick test_create_mat_dims;
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Alcotest.test_case "get/set roundtrip" `Quick test_mat_get_set_roundtrip;
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Alcotest.test_case "zero initialization" `Quick test_mat_zero_init;
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];
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]