[INFO] fetching crate smartcore 0.2.1... [INFO] documenting smartcore-0.2.1 against beta-2022-05-20 for beta-1.62-rustdoc-1 [INFO] extracting crate smartcore 0.2.1 into /workspace/builds/worker-15/source [INFO] validating manifest of crates.io crate smartcore 0.2.1 on toolchain beta-2022-05-20 [INFO] running `Command { std: "/workspace/cargo-home/bin/cargo" "+beta-2022-05-20" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }` [INFO] started tweaking crates.io crate smartcore 0.2.1 [INFO] finished tweaking crates.io crate smartcore 0.2.1 [INFO] tweaked toml for crates.io crate smartcore 0.2.1 written to /workspace/builds/worker-15/source/Cargo.toml [INFO] running `Command { std: "/workspace/cargo-home/bin/cargo" "+beta-2022-05-20" "generate-lockfile" "--manifest-path" "Cargo.toml" "-Zno-index-update", kill_on_drop: false }` [INFO] running `Command { std: "/workspace/cargo-home/bin/cargo" "+beta-2022-05-20" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }` [INFO] [stderr] Blocking waiting for file lock on package cache [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-15/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-15/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:d190cb871061d98bc6d0581d85cb2ecb09a0f8a142ba5463de30be9999fc3251" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2022-05-20" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }` [INFO] [stdout] 4fc88608d4df32c4fbe61ca669657a464646d3e870a295c452cdde3dea7a5054 [INFO] running `Command { std: "docker" "start" "-a" "4fc88608d4df32c4fbe61ca669657a464646d3e870a295c452cdde3dea7a5054", kill_on_drop: false }` [INFO] running `Command { std: "docker" "inspect" "4fc88608d4df32c4fbe61ca669657a464646d3e870a295c452cdde3dea7a5054", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "4fc88608d4df32c4fbe61ca669657a464646d3e870a295c452cdde3dea7a5054", kill_on_drop: false }` [INFO] [stdout] 4fc88608d4df32c4fbe61ca669657a464646d3e870a295c452cdde3dea7a5054 [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-15/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-15/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:d190cb871061d98bc6d0581d85cb2ecb09a0f8a142ba5463de30be9999fc3251" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2022-05-20" "doc" "--frozen" "--no-deps" "--document-private-items" "--message-format=json", kill_on_drop: false }` [INFO] [stdout] 798741188bcf12926ac86e7cecf5a9d7bc45900c96ea2abd377e84215204cd47 [INFO] running `Command { std: "docker" "start" "-a" "798741188bcf12926ac86e7cecf5a9d7bc45900c96ea2abd377e84215204cd47", kill_on_drop: false }` [INFO] [stderr] Compiling autocfg v1.1.0 [INFO] [stderr] Compiling libm v0.2.2 [INFO] [stderr] Compiling libc v0.2.126 [INFO] [stderr] Compiling num-traits v0.2.15 [INFO] [stderr] Compiling num-integer v0.1.45 [INFO] [stderr] Compiling num-bigint v0.4.3 [INFO] [stderr] Compiling num-rational v0.4.0 [INFO] [stderr] Compiling num-iter v0.1.43 [INFO] [stderr] Checking getrandom v0.2.6 [INFO] [stderr] Checking rand_core v0.6.3 [INFO] [stderr] Checking rand_chacha v0.3.1 [INFO] [stderr] Checking rand v0.8.5 [INFO] [stderr] Checking num-complex v0.4.1 [INFO] [stderr] Checking rand_distr v0.4.3 [INFO] [stderr] Checking num v0.4.0 [INFO] [stderr] Documenting smartcore v0.2.1 (/opt/rustwide/workdir) [INFO] [stdout] warning: unresolved link to `T` [INFO] [stdout] --> src/linalg/mod.rs:89:38 [INFO] [stdout] | [INFO] [stdout] 89 | /// Create a new vector from a &[T] [INFO] [stdout] | ^ no item named `T` in scope [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(rustdoc::broken_intra_doc_links)]` on by default [INFO] [stdout] = help: to escape `[` and `]` characters, add '\' before them like `\[` or `\]` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: this URL is not a hyperlink [INFO] [stdout] --> src/linalg/mod.rs:122:41 [INFO] [stdout] | [INFO] [stdout] 122 | /// Returns [L2 norm] of the vector(https://en.wikipedia.org/wiki/Matrix_norm). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ help: use an automatic link instead: `` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(rustdoc::bare_urls)]` on by default [INFO] [stdout] = note: bare URLs are not automatically turned into clickable links [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: 2 warnings emitted [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished dev [unoptimized + debuginfo] target(s) in 7.27s [INFO] running `Command { std: "docker" "inspect" "798741188bcf12926ac86e7cecf5a9d7bc45900c96ea2abd377e84215204cd47", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "798741188bcf12926ac86e7cecf5a9d7bc45900c96ea2abd377e84215204cd47", kill_on_drop: false }` [INFO] [stdout] 798741188bcf12926ac86e7cecf5a9d7bc45900c96ea2abd377e84215204cd47 [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-15/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-15/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTC_BOOTSTRAP=1" "-e" "DOCS_RS=1" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:d190cb871061d98bc6d0581d85cb2ecb09a0f8a142ba5463de30be9999fc3251" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2022-05-20" "rustdoc" "--lib" "-Zrustdoc-map" "--frozen" "--message-format=json" "--" "-Z" "unstable-options" "--document-private-items", kill_on_drop: false }` [INFO] [stdout] 64c171b1df8a5554249fb66f4fe6988d72f5a4068449fa566cca1a64f784d5b7 [INFO] running `Command { std: "docker" "start" "-a" "64c171b1df8a5554249fb66f4fe6988d72f5a4068449fa566cca1a64f784d5b7", kill_on_drop: false }` [INFO] [stderr] Compiling autocfg v1.1.0 [INFO] [stderr] Compiling libm v0.2.2 [INFO] [stderr] Compiling libc v0.2.126 [INFO] [stderr] Checking cfg-if v1.0.0 [INFO] [stderr] Checking ppv-lite86 v0.2.16 [INFO] [stderr] Compiling num-traits v0.2.15 [INFO] [stderr] Compiling num-integer v0.1.45 [INFO] [stderr] Compiling num-bigint v0.4.3 [INFO] [stderr] Compiling num-iter v0.1.43 [INFO] [stderr] Compiling num-rational v0.4.0 [INFO] [stderr] Checking getrandom v0.2.6 [INFO] [stderr] Checking rand_core v0.6.3 [INFO] [stderr] Checking rand_chacha v0.3.1 [INFO] [stderr] Checking num-complex v0.4.1 [INFO] [stderr] Checking rand v0.8.5 [INFO] [stderr] Checking rand_distr v0.4.3 [INFO] [stderr] Checking num v0.4.0 [INFO] [stderr] Documenting smartcore v0.2.1 (/opt/rustwide/workdir) [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// Various algorithms and helper methods that are used elsewhere in SmartCore [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] note: the lint level is defined here [INFO] [stdout] --> src/lib.rs:9:9 [INFO] [stdout] | [INFO] [stdout] 9 | #![warn(rustdoc::missing_doc_code_examples)] [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/mod.rs:2:1 [INFO] [stdout] | [INFO] [stdout] 2 | / //! # Nearest Neighbors Search Algorithms and Data Structures [INFO] [stdout] 3 | | //! [INFO] [stdout] 4 | | //! Nearest neighbor search is a basic computational tool that is particularly relevant to machine learning, [INFO] [stdout] 5 | | //! where it is often believed that highdimensional datasets have low-dimensional intrinsic structure. [INFO] [stdout] ... | [INFO] [stdout] 30 | | //! [INFO] [stdout] 31 | | //! [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | /// Implements Cover Tree algorithm [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:77:1 [INFO] [stdout] | [INFO] [stdout] 77 | / impl> CoverTree { [INFO] [stdout] 78 | | /// Construct a cover tree. [INFO] [stdout] 79 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 80 | | /// * `distance` - distance metric to use for searching. This function should extend [`Distance`](../../../math/distance/index.html) ... [INFO] [stdout] ... | [INFO] [stdout] 453 | | } [INFO] [stdout] 454 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:78:5 [INFO] [stdout] | [INFO] [stdout] 78 | / /// Construct a cover tree. [INFO] [stdout] 79 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 80 | | /// * `distance` - distance metric to use for searching. This function should extend [`Distance`](../../../math/distance/index.html) interface. [INFO] [stdout] | |___________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:104:5 [INFO] [stdout] | [INFO] [stdout] 104 | / /// Find k nearest neighbors of `p` [INFO] [stdout] 105 | | /// * `p` - look for k nearest points to `p` [INFO] [stdout] 106 | | /// * `k` - the number of nearest neighbors to return [INFO] [stdout] | |_________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:189:5 [INFO] [stdout] | [INFO] [stdout] 189 | / /// Find all nearest neighbors within radius `radius` from `p` [INFO] [stdout] 190 | | /// * `p` - look for k nearest points to `p` [INFO] [stdout] 191 | | /// * `radius` - radius of the search [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/linear_search.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | /// Implements Linear Search algorithm, see [KNN algorithms](../index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/linear_search.rs:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | / impl> LinearKNNSearch { [INFO] [stdout] 45 | | /// Initializes algorithm. [INFO] [stdout] 46 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 47 | | /// * `distance` - distance metric to use for searching. This function should extend [`Distance`](../../../math/distance/index.html) ... [INFO] [stdout] ... | [INFO] [stdout] 115 | | } [INFO] [stdout] 116 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/linear_search.rs:45:5 [INFO] [stdout] | [INFO] [stdout] 45 | / /// Initializes algorithm. [INFO] [stdout] 46 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 47 | | /// * `distance` - distance metric to use for searching. This function should extend [`Distance`](../../../math/distance/index.html) interface. [INFO] [stdout] | |___________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/linear_search.rs:56:5 [INFO] [stdout] | [INFO] [stdout] 56 | / /// Find k nearest neighbors [INFO] [stdout] 57 | | /// * `from` - look for k nearest points to `from` [INFO] [stdout] 58 | | /// * `k` - the number of nearest neighbors to return [INFO] [stdout] | |_________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/linear_search.rs:93:5 [INFO] [stdout] | [INFO] [stdout] 93 | / /// Find all nearest neighbors within radius `radius` from `p` [INFO] [stdout] 94 | | /// * `p` - look for k nearest points to `p` [INFO] [stdout] 95 | | /// * `radius` - radius of the search [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/mod.rs:47:1 [INFO] [stdout] | [INFO] [stdout] 47 | / /// Both, KNN classifier and regressor benefits from underlying search algorithms that helps to speed up queries. [INFO] [stdout] 48 | | /// `KNNAlgorithmName` maintains a list of supported search algorithms, see [KNN algorithms](../algorithm/neighbour/index.html) [INFO] [stdout] | |_______________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/mod.rs:65:1 [INFO] [stdout] | [INFO] [stdout] 65 | / impl KNNAlgorithmName { [INFO] [stdout] 66 | | pub(crate) fn fit, T>>( [INFO] [stdout] 67 | | &self, [INFO] [stdout] 68 | | data: Vec>, [INFO] [stdout] ... | [INFO] [stdout] 79 | | } [INFO] [stdout] 80 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Common Interfaces and API [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! This module provides interfaces and uniform API with simple conventions [INFO] [stdout] 4 | | //! that are used in other modules for supervised and unsupervised learning. [INFO] [stdout] | |____________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:8:1 [INFO] [stdout] | [INFO] [stdout] 8 | /// An estimator for unsupervised learning, that provides method `fit` to learn from data [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:10:5 [INFO] [stdout] | [INFO] [stdout] 10 | / /// Fit a model to a training dataset, estimate model's parameters. [INFO] [stdout] 11 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 12 | | /// * `parameters` - hyperparameters of an algorithm [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:19:1 [INFO] [stdout] | [INFO] [stdout] 19 | /// An estimator for supervised learning, , that provides method `fit` to learn from data and training values [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:21:5 [INFO] [stdout] | [INFO] [stdout] 21 | / /// Fit a model to a training dataset, estimate model's parameters. [INFO] [stdout] 22 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 23 | | /// * `y` - target training values of size _N_. [INFO] [stdout] 24 | | /// * `parameters` - hyperparameters of an algorithm [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:31:1 [INFO] [stdout] | [INFO] [stdout] 31 | /// Implements method predict that estimates target value from new data [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:33:5 [INFO] [stdout] | [INFO] [stdout] 33 | / /// Estimate target values from new data. [INFO] [stdout] 34 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] | |________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:38:1 [INFO] [stdout] | [INFO] [stdout] 38 | /// Implements method transform that filters or modifies input data [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/api.rs:40:5 [INFO] [stdout] | [INFO] [stdout] 40 | / /// Transform data by modifying or filtering it [INFO] [stdout] 41 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] | |________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:72:1 [INFO] [stdout] | [INFO] [stdout] 4 | | clippy::many_single_char_names, [INFO] [stdout] | |__________________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 72| / /// Algorithms for clustering of unlabeled data [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:58:1 [INFO] [stdout] | [INFO] [stdout] 58 | /// DBSCAN clustering algorithm [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// DBSCAN clustering algorithm parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 83 | / impl, T>> DBSCANParameters { [INFO] [stdout] 84 | | /// a function that defines a distance between each pair of point in training data. [INFO] [stdout] 85 | | /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait. [INFO] [stdout] 86 | | /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions. [INFO] [stdout] ... | [INFO] [stdout] 109 | | } [INFO] [stdout] 110 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:84:5 [INFO] [stdout] | [INFO] [stdout] 84 | / /// a function that defines a distance between each pair of point in training data. [INFO] [stdout] 85 | | /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait. [INFO] [stdout] 86 | | /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions. [INFO] [stdout] | |_______________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:95:5 [INFO] [stdout] | [INFO] [stdout] 95 | /// The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:100:5 [INFO] [stdout] | [INFO] [stdout] 100 | /// The maximum distance between two samples for one to be considered as in the neighborhood of the other. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:105:5 [INFO] [stdout] | [INFO] [stdout] 105 | /// KNN algorithm to use. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:148:1 [INFO] [stdout] | [INFO] [stdout] 148 | / impl, T>> DBSCAN { [INFO] [stdout] 149 | | /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 150 | | /// * `data` - training instances to cluster [INFO] [stdout] 151 | | /// * `k` - number of clusters [INFO] [stdout] ... | [INFO] [stdout] 261 | | } [INFO] [stdout] 262 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:149:5 [INFO] [stdout] | [INFO] [stdout] 149 | / /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 150 | | /// * `data` - training instances to cluster [INFO] [stdout] 151 | | /// * `k` - number of clusters [INFO] [stdout] 152 | | /// * `parameters` - cluster parameters [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:233:5 [INFO] [stdout] | [INFO] [stdout] 233 | / /// Predict clusters for `x` [INFO] [stdout] 234 | | /// * `x` - matrix with new data to transform of size _KxM_ , where _K_ is number of new samples and _M_ is number of features. [INFO] [stdout] | |___________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// K-Means clustering algorithm [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:105:1 [INFO] [stdout] | [INFO] [stdout] 105 | /// K-Means clustering algorithm parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:113:1 [INFO] [stdout] | [INFO] [stdout] 113 | / impl KMeansParameters { [INFO] [stdout] 114 | | /// Number of clusters. [INFO] [stdout] 115 | | pub fn with_k(mut self, k: usize) -> Self { [INFO] [stdout] 116 | | self.k = k; [INFO] [stdout] ... | [INFO] [stdout] 123 | | } [INFO] [stdout] 124 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:114:5 [INFO] [stdout] | [INFO] [stdout] 114 | /// Number of clusters. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:119:5 [INFO] [stdout] | [INFO] [stdout] 119 | /// Maximum number of iterations of the k-means algorithm for a single run. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:147:1 [INFO] [stdout] | [INFO] [stdout] 147 | / impl KMeans { [INFO] [stdout] 148 | | /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 149 | | /// * `data` - training instances to cluster [INFO] [stdout] 150 | | /// * `parameters` - cluster parameters [INFO] [stdout] ... | [INFO] [stdout] 294 | | } [INFO] [stdout] 295 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:148:5 [INFO] [stdout] | [INFO] [stdout] 148 | / /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 149 | | /// * `data` - training instances to cluster [INFO] [stdout] 150 | | /// * `parameters` - cluster parameters [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:218:5 [INFO] [stdout] | [INFO] [stdout] 218 | / /// Predict clusters for `x` [INFO] [stdout] 219 | | /// * `x` - matrix with new data to transform of size _KxM_ , where _K_ is number of new samples and _M_ is number of features. [INFO] [stdout] | |___________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:74:1 [INFO] [stdout] | [INFO] [stdout] 3 | | clippy::too_many_arguments, [INFO] [stdout] | |____________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 74| / /// Various datasets [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/boston.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # The Boston Housing Dataset [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! | Number of Instances | Number of Attributes | Missing Values? | Associated Tasks: | [INFO] [stdout] 4 | | //! |-|-|-|-| [INFO] [stdout] ... | [INFO] [stdout] 25 | | //! | MEDV, Median value of owner-occupied homes in $1000's | Numerical | Yes | [INFO] [stdout] 26 | | //! [INFO] [stdout] | |___^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/boston.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | /// Get dataset [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/breast_cancer.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Breast Cancer Wisconsin (Diagnostic) Data Set [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! Diagnostic Wisconsin Breast Cancer database [INFO] [stdout] 4 | | //! [INFO] [stdout] ... | [INFO] [stdout] 27 | | //! The mean, standard error, and "worst" or largest (mean of the three worst/largest values) of these features were computed for each im... [INFO] [stdout] 28 | | //! For instance, field 0 is Mean Radius, field 10 is Radius SE, field 20 is Worst Radius. [INFO] [stdout] | |__________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/breast_cancer.rs:32:1 [INFO] [stdout] | [INFO] [stdout] 32 | /// Get dataset [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/diabetes.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Diabetes Data [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! | Number of Instances | Number of Attributes | Missing Values? | Associated Tasks: | [INFO] [stdout] 4 | | //! |-|-|-|-| [INFO] [stdout] ... | [INFO] [stdout] 20 | | //! ## References: [INFO] [stdout] 21 | | //! * ["Least Angle Regression", Efron B., Hastie T., Johnstone I., Tibshirani R., 2004, Annals of Statistics (with discussion), 407-499](http://statweb.stanford.edu/~tibs/ftp/lars.pdf) [INFO] [stdout] | |_________________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/diabetes.rs:25:1 [INFO] [stdout] | [INFO] [stdout] 25 | /// Get dataset [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/digits.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Optical Recognition of Handwritten Digits Data Set [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! | Number of Instances | Number of Attributes | Missing Values? | Associated Tasks: | [INFO] [stdout] 4 | | //! |-|-|-|-| [INFO] [stdout] ... | [INFO] [stdout] 10 | | //! All input attributes are integers in the range 0..16. [INFO] [stdout] 11 | | //! [INFO] [stdout] | |___^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/digits.rs:15:1 [INFO] [stdout] | [INFO] [stdout] 15 | /// Get dataset [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/generator.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Dataset Generators [INFO] [stdout] 2 | | //! [INFO] [stdout] | |___^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/generator.rs:9:1 [INFO] [stdout] | [INFO] [stdout] 9 | /// Generate `num_centers` clusters of normally distributed points [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/generator.rs:50:1 [INFO] [stdout] | [INFO] [stdout] 50 | /// Make a large circle containing a smaller circle in 2d. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/generator.rs:91:1 [INFO] [stdout] | [INFO] [stdout] 91 | /// Make two interleaving half circles in 2d [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/iris.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # The Iris Dataset flower [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! | Number of Instances | Number of Attributes | Missing Values? | Associated Tasks: | [INFO] [stdout] 4 | | //! |-|-|-|-| [INFO] [stdout] ... | [INFO] [stdout] 16 | | //! | Class | Nominal | Yes | [INFO] [stdout] 17 | | //! [INFO] [stdout] | |___^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/iris.rs:21:1 [INFO] [stdout] | [INFO] [stdout] 21 | /// Get dataset [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/mod.rs:19:1 [INFO] [stdout] | [INFO] [stdout] 19 | /// Dataset [INFO] [stdout] | ^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/mod.rs:38:1 [INFO] [stdout] | [INFO] [stdout] 38 | / impl Dataset { [INFO] [stdout] 39 | | /// Reshape data into a two-dimensional matrix [INFO] [stdout] 40 | | pub fn as_matrix(&self) -> Vec> { [INFO] [stdout] 41 | | let mut result: Vec> = Vec::with_capacity(self.num_samples); [INFO] [stdout] ... | [INFO] [stdout] 52 | | } [INFO] [stdout] 53 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/dataset/mod.rs:39:5 [INFO] [stdout] | [INFO] [stdout] 39 | /// Reshape data into a two-dimensional matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:77:1 [INFO] [stdout] | [INFO] [stdout] 12 | | //! [INFO] [stdout] | |____________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 77 | / /// Matrix decomposition algorithms [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:58:1 [INFO] [stdout] | [INFO] [stdout] 58 | /// Principal components analysis algorithm [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:87:1 [INFO] [stdout] | [INFO] [stdout] 87 | /// PCA parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:96:1 [INFO] [stdout] | [INFO] [stdout] 96 | / impl PCAParameters { [INFO] [stdout] 97 | | /// Number of components to keep. [INFO] [stdout] 98 | | pub fn with_n_components(mut self, n_components: usize) -> Self { [INFO] [stdout] 99 | | self.n_components = n_components; [INFO] [stdout] ... | [INFO] [stdout] 107 | | } [INFO] [stdout] 108 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:97:5 [INFO] [stdout] | [INFO] [stdout] 97 | /// Number of components to keep. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:102:5 [INFO] [stdout] | [INFO] [stdout] 102 | / /// By default, covariance matrix is used to compute principal components. [INFO] [stdout] 103 | | /// Enable this flag if you want to use correlation matrix instead. [INFO] [stdout] | |_______________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:131:1 [INFO] [stdout] | [INFO] [stdout] 131 | / impl> PCA { [INFO] [stdout] 132 | | /// Fits PCA to your data. [INFO] [stdout] 133 | | /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 134 | | /// * `n_components` - number of components to keep. [INFO] [stdout] ... | [INFO] [stdout] 266 | | } [INFO] [stdout] 267 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:132:5 [INFO] [stdout] | [INFO] [stdout] 132 | / /// Fits PCA to your data. [INFO] [stdout] 133 | | /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 134 | | /// * `n_components` - number of components to keep. [INFO] [stdout] 135 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:241:5 [INFO] [stdout] | [INFO] [stdout] 241 | / /// Run dimensionality reduction for `x` [INFO] [stdout] 242 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:263:5 [INFO] [stdout] | [INFO] [stdout] 263 | /// Get a projection matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:57:1 [INFO] [stdout] | [INFO] [stdout] 57 | /// SVD [INFO] [stdout] | ^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:73:1 [INFO] [stdout] | [INFO] [stdout] 73 | /// SVD parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:85:1 [INFO] [stdout] | [INFO] [stdout] 85 | / impl SVDParameters { [INFO] [stdout] 86 | | /// Number of components to keep. [INFO] [stdout] 87 | | pub fn with_n_components(mut self, n_components: usize) -> Self { [INFO] [stdout] 88 | | self.n_components = n_components; [INFO] [stdout] 89 | | self [INFO] [stdout] 90 | | } [INFO] [stdout] 91 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:86:5 [INFO] [stdout] | [INFO] [stdout] 86 | /// Number of components to keep. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:105:1 [INFO] [stdout] | [INFO] [stdout] 105 | / impl> SVD { [INFO] [stdout] 106 | | /// Fits SVD to your data. [INFO] [stdout] 107 | | /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 108 | | /// * `n_components` - number of components to keep. [INFO] [stdout] ... | [INFO] [stdout] 148 | | } [INFO] [stdout] 149 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:106:5 [INFO] [stdout] | [INFO] [stdout] 106 | / /// Fits SVD to your data. [INFO] [stdout] 107 | | /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 108 | | /// * `n_components` - number of components to keep. [INFO] [stdout] 109 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:130:5 [INFO] [stdout] | [INFO] [stdout] 130 | / /// Run dimensionality reduction for `x` [INFO] [stdout] 131 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/svd.rs:145:5 [INFO] [stdout] | [INFO] [stdout] 145 | /// Get a projection matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:79:1 [INFO] [stdout] | [INFO] [stdout] 17 | | //! [INFO] [stdout] | |__________________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 79 | / /// Ensemble methods, including Random Forest classifier and regressor [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:64:1 [INFO] [stdout] | [INFO] [stdout] 64 | / /// Parameters of the Random Forest algorithm. [INFO] [stdout] 65 | | /// Some parameters here are passed directly into base estimator. [INFO] [stdout] | |_________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:87:1 [INFO] [stdout] | [INFO] [stdout] 87 | /// Random Forest Classifier [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:97:1 [INFO] [stdout] | [INFO] [stdout] 97 | / impl RandomForestClassifierParameters { [INFO] [stdout] 98 | | /// Split criteria to use when building a tree. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] 99 | | pub fn with_criterion(mut self, criterion: SplitCriterion) -> Self { [INFO] [stdout] 100 | | self.criterion = criterion; [INFO] [stdout] ... | [INFO] [stdout] 139 | | } [INFO] [stdout] 140 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:98:5 [INFO] [stdout] | [INFO] [stdout] 98 | /// Split criteria to use when building a tree. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:103:5 [INFO] [stdout] | [INFO] [stdout] 103 | /// Tree max depth. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:108:5 [INFO] [stdout] | [INFO] [stdout] 108 | /// The minimum number of samples required to be at a leaf node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:113:5 [INFO] [stdout] | [INFO] [stdout] 113 | /// The minimum number of samples required to split an internal node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:118:5 [INFO] [stdout] | [INFO] [stdout] 118 | /// The number of trees in the forest. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:123:5 [INFO] [stdout] | [INFO] [stdout] 123 | /// Number of random sample of predictors to use as split candidates. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:129:5 [INFO] [stdout] | [INFO] [stdout] 129 | /// Whether to keep samples used for tree generation. This is required for OOB prediction. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:135:5 [INFO] [stdout] | [INFO] [stdout] 135 | /// Seed used for bootstrap sampling and feature selection for each tree. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:196:1 [INFO] [stdout] | [INFO] [stdout] 196 | / impl RandomForestClassifier { [INFO] [stdout] 197 | | /// Build a forest of trees from the training set. [INFO] [stdout] 198 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 199 | | /// * `y` - the target class values [INFO] [stdout] ... | [INFO] [stdout] 340 | | } [INFO] [stdout] 341 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:197:5 [INFO] [stdout] | [INFO] [stdout] 197 | / /// Build a forest of trees from the training set. [INFO] [stdout] 198 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 199 | | /// * `y` - the target class values [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:259:5 [INFO] [stdout] | [INFO] [stdout] 259 | / /// Predict class for `x` [INFO] [stdout] 260 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:283:5 [INFO] [stdout] | [INFO] [stdout] 283 | /// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:64:1 [INFO] [stdout] | [INFO] [stdout] 64 | / /// Parameters of the Random Forest Regressor [INFO] [stdout] 65 | | /// Some parameters here are passed directly into base estimator. [INFO] [stdout] | |_________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 83 | /// Random Forest Regressor [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:92:1 [INFO] [stdout] | [INFO] [stdout] 92 | / impl RandomForestRegressorParameters { [INFO] [stdout] 93 | | /// Tree max depth. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] 94 | | pub fn with_max_depth(mut self, max_depth: u16) -> Self { [INFO] [stdout] 95 | | self.max_depth = Some(max_depth); [INFO] [stdout] ... | [INFO] [stdout] 129 | | } [INFO] [stdout] 130 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:93:5 [INFO] [stdout] | [INFO] [stdout] 93 | /// Tree max depth. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:98:5 [INFO] [stdout] | [INFO] [stdout] 98 | /// The minimum number of samples required to be at a leaf node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:103:5 [INFO] [stdout] | [INFO] [stdout] 103 | /// The minimum number of samples required to split an internal node. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:108:5 [INFO] [stdout] | [INFO] [stdout] 108 | /// The number of trees in the forest. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:113:5 [INFO] [stdout] | [INFO] [stdout] 113 | /// Number of random sample of predictors to use as split candidates. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:119:5 [INFO] [stdout] | [INFO] [stdout] 119 | /// Whether to keep samples used for tree generation. This is required for OOB prediction. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:125:5 [INFO] [stdout] | [INFO] [stdout] 125 | /// Seed used for bootstrap sampling and feature selection for each tree. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:179:1 [INFO] [stdout] | [INFO] [stdout] 179 | / impl RandomForestRegressor { [INFO] [stdout] 180 | | /// Build a forest of trees from the training set. [INFO] [stdout] 181 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 182 | | /// * `y` - the target class values [INFO] [stdout] ... | [INFO] [stdout] 296 | | } [INFO] [stdout] 297 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:180:5 [INFO] [stdout] | [INFO] [stdout] 180 | / /// Build a forest of trees from the training set. [INFO] [stdout] 181 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 182 | | /// * `y` - the target class values [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:224:5 [INFO] [stdout] | [INFO] [stdout] 224 | / /// Predict class for `x` [INFO] [stdout] 225 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:250:5 [INFO] [stdout] | [INFO] [stdout] 250 | /// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | //! # Custom warnings and errors [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:8:1 [INFO] [stdout] | [INFO] [stdout] 8 | /// Generic error to be raised when something goes wrong. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:16:1 [INFO] [stdout] | [INFO] [stdout] 16 | /// Type of error [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | / impl Failed { [INFO] [stdout] 36 | | ///get type of error [INFO] [stdout] 37 | | #[inline] [INFO] [stdout] 38 | | pub fn error(&self) -> FailedError { [INFO] [stdout] ... | [INFO] [stdout] 71 | | } [INFO] [stdout] 72 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:36:5 [INFO] [stdout] | [INFO] [stdout] 36 | ///get type of error [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:42:5 [INFO] [stdout] | [INFO] [stdout] 42 | /// new instance of `FailedError::FitError` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:49:5 [INFO] [stdout] | [INFO] [stdout] 49 | /// new instance of `FailedError::PredictFailed` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:57:5 [INFO] [stdout] | [INFO] [stdout] 57 | /// new instance of `FailedError::TransformFailed` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:65:5 [INFO] [stdout] | [INFO] [stdout] 65 | /// new instance of `err` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:41:1 [INFO] [stdout] | [INFO] [stdout] 41 | /// Results of Cholesky decomposition. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:47:1 [INFO] [stdout] | [INFO] [stdout] 47 | / impl> Cholesky { [INFO] [stdout] 48 | | pub(crate) fn new(R: M) -> Cholesky { [INFO] [stdout] 49 | | Cholesky { R, t: PhantomData } [INFO] [stdout] 50 | | } [INFO] [stdout] ... | [INFO] [stdout] 112 | | } [INFO] [stdout] 113 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:52:5 [INFO] [stdout] | [INFO] [stdout] 52 | /// Get lower triangular matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:67:5 [INFO] [stdout] | [INFO] [stdout] 67 | /// Get upper triangular matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:115:1 [INFO] [stdout] | [INFO] [stdout] 115 | /// Trait that implements Cholesky decomposition routine for any matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:117:5 [INFO] [stdout] | [INFO] [stdout] 117 | /// Compute the Cholesky decomposition of a matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:122:5 [INFO] [stdout] | [INFO] [stdout] 122 | / /// Compute the Cholesky decomposition of a matrix. The input matrix [INFO] [stdout] 123 | | /// will be used for factorization. [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/cholesky.rs:160:5 [INFO] [stdout] | [INFO] [stdout] 160 | /// Solves Ax = b [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/evd.rs:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | /// Results of eigen decomposition [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/evd.rs:54:1 [INFO] [stdout] | [INFO] [stdout] 54 | /// Trait that implements EVD decomposition routine for any matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/evd.rs:56:5 [INFO] [stdout] | [INFO] [stdout] 56 | / /// Compute the eigen decomposition of a square matrix. [INFO] [stdout] 57 | | /// * `symmetric` - whether the matrix is symmetric [INFO] [stdout] | |_______________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/evd.rs:62:5 [INFO] [stdout] | [INFO] [stdout] 62 | / /// Compute the eigen decomposition of a square matrix. The input matrix [INFO] [stdout] 63 | | /// will be used for factorization. [INFO] [stdout] 64 | | /// * `symmetric` - whether the matrix is symmetric [INFO] [stdout] | |_______________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/high_order.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! In this module you will find composite of matrix operations that are used elsewhere [INFO] [stdout] 2 | | //! for improved efficiency. [INFO] [stdout] | |____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/high_order.rs:7:1 [INFO] [stdout] | [INFO] [stdout] 7 | /// High order matrix operations. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:45:1 [INFO] [stdout] | [INFO] [stdout] 45 | /// Result of LU decomposition. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:54:1 [INFO] [stdout] | [INFO] [stdout] 54 | / impl> LU { [INFO] [stdout] 55 | | pub(crate) fn new(LU: M, pivot: Vec, _pivot_sign: i8) -> LU { [INFO] [stdout] 56 | | let (_, n) = LU.shape(); [INFO] [stdout] 57 | | [INFO] [stdout] ... | [INFO] [stdout] 190 | | } [INFO] [stdout] 191 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:75:5 [INFO] [stdout] | [INFO] [stdout] 75 | /// Get lower triangular matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:93:5 [INFO] [stdout] | [INFO] [stdout] 93 | /// Get upper triangular matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:111:5 [INFO] [stdout] | [INFO] [stdout] 111 | /// Pivot vector [INFO] [stdout] | ^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:123:5 [INFO] [stdout] | [INFO] [stdout] 123 | /// Returns matrix inverse [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:193:1 [INFO] [stdout] | [INFO] [stdout] 193 | /// Trait that implements LU decomposition routine for any matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:195:5 [INFO] [stdout] | [INFO] [stdout] 195 | /// Compute the LU decomposition of a square matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:200:5 [INFO] [stdout] | [INFO] [stdout] 200 | / /// Compute the LU decomposition of a square matrix. The input matrix [INFO] [stdout] 201 | | /// will be used for factorization. [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/lu.rs:252:5 [INFO] [stdout] | [INFO] [stdout] 252 | /// Solves Ax = b [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/mod.rs:25:1 [INFO] [stdout] | [INFO] [stdout] 25 | /// Add this module to use Dense Matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:198:1 [INFO] [stdout] | [INFO] [stdout] 198 | /// Column-major, dense matrix. See [Simple Dense Matrix](../index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:206:1 [INFO] [stdout] | [INFO] [stdout] 206 | /// Column-major, dense matrix. See [Simple Dense Matrix](../index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:231:1 [INFO] [stdout] | [INFO] [stdout] 231 | / impl DenseMatrix { [INFO] [stdout] 232 | | /// Create new instance of `DenseMatrix` without copying data. [INFO] [stdout] 233 | | /// `values` should be in column-major order. [INFO] [stdout] 234 | | pub fn new(nrows: usize, ncols: usize, values: Vec) -> Self { [INFO] [stdout] ... | [INFO] [stdout] 335 | | } [INFO] [stdout] 336 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:232:5 [INFO] [stdout] | [INFO] [stdout] 232 | / /// Create new instance of `DenseMatrix` without copying data. [INFO] [stdout] 233 | | /// `values` should be in column-major order. [INFO] [stdout] | |_________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:242:5 [INFO] [stdout] | [INFO] [stdout] 242 | /// New instance of `DenseMatrix` from 2d array. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:247:5 [INFO] [stdout] | [INFO] [stdout] 247 | /// New instance of `DenseMatrix` from 2d vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:267:5 [INFO] [stdout] | [INFO] [stdout] 267 | / /// Creates new matrix from an array. [INFO] [stdout] 268 | | /// * `nrows` - number of rows in new matrix. [INFO] [stdout] 269 | | /// * `ncols` - number of columns in new matrix. [INFO] [stdout] 270 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:275:5 [INFO] [stdout] | [INFO] [stdout] 275 | / /// Creates new matrix from a vector. [INFO] [stdout] 276 | | /// * `nrows` - number of rows in new matrix. [INFO] [stdout] 277 | | /// * `ncols` - number of columns in new matrix. [INFO] [stdout] 278 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:293:5 [INFO] [stdout] | [INFO] [stdout] 293 | / /// Creates new row vector (_1xN_ matrix) from an array. [INFO] [stdout] 294 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:299:5 [INFO] [stdout] | [INFO] [stdout] 299 | / /// Creates new row vector (_1xN_ matrix) from a vector. [INFO] [stdout] 300 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:309:5 [INFO] [stdout] | [INFO] [stdout] 309 | / /// Creates new column vector (_1xN_ matrix) from an array. [INFO] [stdout] 310 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:315:5 [INFO] [stdout] | [INFO] [stdout] 315 | / /// Creates new column vector (_1xN_ matrix) from a vector. [INFO] [stdout] 316 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:325:5 [INFO] [stdout] | [INFO] [stdout] 325 | / /// Creates new column vector (_1xN_ matrix) from a vector. [INFO] [stdout] 326 | | /// * `values` - values to initialize the matrix. [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:37:1 [INFO] [stdout] | [INFO] [stdout] 37 | /// Results of QR decomposition. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | / impl> QR { [INFO] [stdout] 45 | | pub(crate) fn new(QR: M, tau: Vec) -> QR { [INFO] [stdout] 46 | | let mut singular = false; [INFO] [stdout] 47 | | for tau_elem in tau.iter() { [INFO] [stdout] ... | [INFO] [stdout] 139 | | } [INFO] [stdout] 140 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:57:5 [INFO] [stdout] | [INFO] [stdout] 57 | /// Get upper triangular matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:70:5 [INFO] [stdout] | [INFO] [stdout] 70 | /// Get an orthogonal matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:142:1 [INFO] [stdout] | [INFO] [stdout] 142 | /// Trait that implements QR decomposition routine for any matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:144:5 [INFO] [stdout] | [INFO] [stdout] 144 | /// Compute the QR decomposition of a matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:149:5 [INFO] [stdout] | [INFO] [stdout] 149 | / /// Compute the QR decomposition of a matrix. The input matrix [INFO] [stdout] 150 | | /// will be used for factorization. [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/qr.rs:188:5 [INFO] [stdout] | [INFO] [stdout] 188 | /// Solves Ax = b [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Various Statistical Methods [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! This module provides reference implementations for various statistical functions. [INFO] [stdout] 4 | | //! Concrete implementations of the `BaseMatrix` trait are free to override these methods for better performance. [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:9:1 [INFO] [stdout] | [INFO] [stdout] 9 | /// Defines baseline implementations for various statistical functions [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:11:5 [INFO] [stdout] | [INFO] [stdout] 11 | /// Computes the arithmetic mean along the specified axis. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:38:5 [INFO] [stdout] | [INFO] [stdout] 38 | /// Computes variance along the specified axis. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:70:5 [INFO] [stdout] | [INFO] [stdout] 70 | /// Computes the standard deviation along the specified axis. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:86:5 [INFO] [stdout] | [INFO] [stdout] 86 | /// standardize values by removing the mean and scaling to unit variance [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/stats.rs:107:1 [INFO] [stdout] | [INFO] [stdout] 107 | /// Defines baseline implementations for various matrix processing functions [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:41:1 [INFO] [stdout] | [INFO] [stdout] 41 | /// Results of SVD decomposition [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:56:1 [INFO] [stdout] | [INFO] [stdout] 56 | / impl> SVD { [INFO] [stdout] 57 | | /// Diagonal matrix with singular values [INFO] [stdout] 58 | | pub fn S(&self) -> M { [INFO] [stdout] 59 | | let mut s = M::zeros(self.U.shape().1, self.V.shape().0); [INFO] [stdout] ... | [INFO] [stdout] 66 | | } [INFO] [stdout] 67 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:57:5 [INFO] [stdout] | [INFO] [stdout] 57 | /// Diagonal matrix with singular values [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// Trait that implements SVD decomposition routine for any matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:71:5 [INFO] [stdout] | [INFO] [stdout] 71 | /// Solves Ax = b. Overrides original matrix in the process. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:76:5 [INFO] [stdout] | [INFO] [stdout] 76 | /// Solves Ax = b [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:81:5 [INFO] [stdout] | [INFO] [stdout] 81 | /// Compute the SVD decomposition of a matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:86:5 [INFO] [stdout] | [INFO] [stdout] 86 | / /// Compute the SVD decomposition of a matrix. The input matrix [INFO] [stdout] 87 | | /// will be used for factorization. [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/svd.rs:427:1 [INFO] [stdout] | [INFO] [stdout] 427 | / impl> SVD { [INFO] [stdout] 428 | | pub(crate) fn new(U: M, V: M, s: Vec) -> SVD { [INFO] [stdout] 429 | | let m = U.shape().0; [INFO] [stdout] 430 | | let n = V.shape().0; [INFO] [stdout] ... | [INFO] [stdout] 478 | | } [INFO] [stdout] 479 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:70:1 [INFO] [stdout] | [INFO] [stdout] 70 | /// Column or row vector [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:72:5 [INFO] [stdout] | [INFO] [stdout] 72 | / /// Get an element of a vector [INFO] [stdout] 73 | | /// * `i` - index of an element [INFO] [stdout] | |___________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:76:5 [INFO] [stdout] | [INFO] [stdout] 76 | / /// Set an element at `i` to `x` [INFO] [stdout] 77 | | /// * `i` - index of an element [INFO] [stdout] 78 | | /// * `x` - new value [INFO] [stdout] | |_________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:81:5 [INFO] [stdout] | [INFO] [stdout] 81 | /// Get number of elevemnt in the vector [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:84:5 [INFO] [stdout] | [INFO] [stdout] 84 | /// Returns true if the vector is empty. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:104:5 [INFO] [stdout] | [INFO] [stdout] 104 | /// Return a vector with the elements of the one-dimensional array. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:107:5 [INFO] [stdout] | [INFO] [stdout] 107 | /// Create new vector with zeros of size `len`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:110:5 [INFO] [stdout] | [INFO] [stdout] 110 | /// Create new vector with ones of size `len`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:113:5 [INFO] [stdout] | [INFO] [stdout] 113 | /// Create new vector of size `len` where each element is set to `value`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:116:5 [INFO] [stdout] | [INFO] [stdout] 116 | /// Vector dot product [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:119:5 [INFO] [stdout] | [INFO] [stdout] 119 | /// Returns True if matrices are element-wise equal within a tolerance `error`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:122:5 [INFO] [stdout] | [INFO] [stdout] 122 | /// Returns [L2 norm] of the vector(https://en.wikipedia.org/wiki/Matrix_norm). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:125:5 [INFO] [stdout] | [INFO] [stdout] 125 | /// Returns [vectors norm](https://en.wikipedia.org/wiki/Matrix_norm) of order `p`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:128:5 [INFO] [stdout] | [INFO] [stdout] 128 | /// Divide single element of the vector by `x`, write result to original vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:131:5 [INFO] [stdout] | [INFO] [stdout] 131 | /// Multiply single element of the vector by `x`, write result to original vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:134:5 [INFO] [stdout] | [INFO] [stdout] 134 | /// Add single element of the vector to `x`, write result to original vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:137:5 [INFO] [stdout] | [INFO] [stdout] 137 | /// Subtract `x` from single element of the vector, write result to original vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:140:5 [INFO] [stdout] | [INFO] [stdout] 140 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:148:5 [INFO] [stdout] | [INFO] [stdout] 148 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:156:5 [INFO] [stdout] | [INFO] [stdout] 156 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:164:5 [INFO] [stdout] | [INFO] [stdout] 164 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:172:5 [INFO] [stdout] | [INFO] [stdout] 172 | /// Add vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:179:5 [INFO] [stdout] | [INFO] [stdout] 179 | /// Subtract vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:186:5 [INFO] [stdout] | [INFO] [stdout] 186 | /// Multiply vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:193:5 [INFO] [stdout] | [INFO] [stdout] 193 | /// Divide vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:200:5 [INFO] [stdout] | [INFO] [stdout] 200 | /// Add vectors, element-wise, overriding original vector with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:203:5 [INFO] [stdout] | [INFO] [stdout] 203 | /// Subtract vectors, element-wise, overriding original vector with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:206:5 [INFO] [stdout] | [INFO] [stdout] 206 | /// Multiply vectors, element-wise, overriding original vector with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:209:5 [INFO] [stdout] | [INFO] [stdout] 209 | /// Divide vectors, element-wise, overriding original vector with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:212:5 [INFO] [stdout] | [INFO] [stdout] 212 | /// Add vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:219:5 [INFO] [stdout] | [INFO] [stdout] 219 | /// Subtract vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:226:5 [INFO] [stdout] | [INFO] [stdout] 226 | /// Multiply vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:233:5 [INFO] [stdout] | [INFO] [stdout] 233 | /// Divide vectors, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:240:5 [INFO] [stdout] | [INFO] [stdout] 240 | /// Calculates sum of all elements of the vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:252:5 [INFO] [stdout] | [INFO] [stdout] 252 | /// Computes the arithmetic mean. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:256:5 [INFO] [stdout] | [INFO] [stdout] 256 | /// Computes variance. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:271:5 [INFO] [stdout] | [INFO] [stdout] 271 | /// Computes the standard deviation. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:276:5 [INFO] [stdout] | [INFO] [stdout] 276 | /// Copies content of `other` vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:279:5 [INFO] [stdout] | [INFO] [stdout] 279 | /// Take elements from an array. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:293:1 [INFO] [stdout] | [INFO] [stdout] 293 | /// Generic matrix type. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:295:5 [INFO] [stdout] | [INFO] [stdout] 295 | / /// Row vector that is associated with this matrix type, [INFO] [stdout] 296 | | /// e.g. if we have an implementation of sparce matrix [INFO] [stdout] 297 | | /// we should have an associated sparce vector type that [INFO] [stdout] 298 | | /// represents a row in this matrix. [INFO] [stdout] | |________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:301:5 [INFO] [stdout] | [INFO] [stdout] 301 | /// Transforms row vector `vec` into a 1xM matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:304:5 [INFO] [stdout] | [INFO] [stdout] 304 | /// Transforms 1-d matrix of 1xM into a row vector. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:307:5 [INFO] [stdout] | [INFO] [stdout] 307 | / /// Get an element of the matrix. [INFO] [stdout] 308 | | /// * `row` - row number [INFO] [stdout] 309 | | /// * `col` - column number [INFO] [stdout] | |_______________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:312:5 [INFO] [stdout] | [INFO] [stdout] 312 | / /// Get a vector with elements of the `row`'th row [INFO] [stdout] 313 | | /// * `row` - row number [INFO] [stdout] | |____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:316:5 [INFO] [stdout] | [INFO] [stdout] 316 | / /// Get the `row`'th row [INFO] [stdout] 317 | | /// * `row` - row number [INFO] [stdout] | |____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:320:5 [INFO] [stdout] | [INFO] [stdout] 320 | / /// Copies a vector with elements of the `row`'th row into `result` [INFO] [stdout] 321 | | /// * `row` - row number [INFO] [stdout] 322 | | /// * `result` - receiver for the row [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:325:5 [INFO] [stdout] | [INFO] [stdout] 325 | / /// Get a vector with elements of the `col`'th column [INFO] [stdout] 326 | | /// * `col` - column number [INFO] [stdout] | |_______________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:329:5 [INFO] [stdout] | [INFO] [stdout] 329 | / /// Copies a vector with elements of the `col`'th column into `result` [INFO] [stdout] 330 | | /// * `col` - column number [INFO] [stdout] 331 | | /// * `result` - receiver for the col [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:334:5 [INFO] [stdout] | [INFO] [stdout] 334 | /// Set an element at `col`, `row` to `x` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:337:5 [INFO] [stdout] | [INFO] [stdout] 337 | /// Create an identity matrix of size `size` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:340:5 [INFO] [stdout] | [INFO] [stdout] 340 | /// Create new matrix with zeros of size `nrows` by `ncols`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:343:5 [INFO] [stdout] | [INFO] [stdout] 343 | /// Create new matrix with ones of size `nrows` by `ncols`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:346:5 [INFO] [stdout] | [INFO] [stdout] 346 | /// Create new matrix of size `nrows` by `ncols` where each element is set to `value`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:349:5 [INFO] [stdout] | [INFO] [stdout] 349 | /// Return the shape of an array. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:425:5 [INFO] [stdout] | [INFO] [stdout] 425 | /// Returns True if matrices are element-wise equal within a tolerance `error`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:428:5 [INFO] [stdout] | [INFO] [stdout] 428 | /// Add matrices, element-wise, overriding original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:431:5 [INFO] [stdout] | [INFO] [stdout] 431 | /// Subtract matrices, element-wise, overriding original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:434:5 [INFO] [stdout] | [INFO] [stdout] 434 | /// Multiply matrices, element-wise, overriding original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:437:5 [INFO] [stdout] | [INFO] [stdout] 437 | /// Divide matrices, element-wise, overriding original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:440:5 [INFO] [stdout] | [INFO] [stdout] 440 | /// Divide single element of the matrix by `x`, write result to original matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:443:5 [INFO] [stdout] | [INFO] [stdout] 443 | /// Multiply single element of the matrix by `x`, write result to original matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:446:5 [INFO] [stdout] | [INFO] [stdout] 446 | /// Add single element of the matrix to `x`, write result to original matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:449:5 [INFO] [stdout] | [INFO] [stdout] 449 | /// Subtract `x` from single element of the matrix, write result to original matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:452:5 [INFO] [stdout] | [INFO] [stdout] 452 | /// Add matrices, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:459:5 [INFO] [stdout] | [INFO] [stdout] 459 | /// Subtract matrices, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:466:5 [INFO] [stdout] | [INFO] [stdout] 466 | /// Multiply matrices, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:473:5 [INFO] [stdout] | [INFO] [stdout] 473 | /// Divide matrices, element-wise [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:480:5 [INFO] [stdout] | [INFO] [stdout] 480 | /// Add `scalar` to the matrix, override original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:483:5 [INFO] [stdout] | [INFO] [stdout] 483 | /// Subtract `scalar` from the elements of matrix, override original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:486:5 [INFO] [stdout] | [INFO] [stdout] 486 | /// Multiply `scalar` by the elements of matrix, override original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:489:5 [INFO] [stdout] | [INFO] [stdout] 489 | /// Divide elements of the matrix by `scalar`, override original matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:492:5 [INFO] [stdout] | [INFO] [stdout] 492 | /// Add `scalar` to the matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:499:5 [INFO] [stdout] | [INFO] [stdout] 499 | /// Subtract `scalar` from the elements of matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:506:5 [INFO] [stdout] | [INFO] [stdout] 506 | /// Multiply `scalar` by the elements of matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:513:5 [INFO] [stdout] | [INFO] [stdout] 513 | /// Divide elements of the matrix by `scalar`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:520:5 [INFO] [stdout] | [INFO] [stdout] 520 | /// Reverse or permute the axes of the matrix, return new matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:523:5 [INFO] [stdout] | [INFO] [stdout] 523 | /// Create new `nrows` by `ncols` matrix and populate it with random samples from a uniform distribution over [0, 1). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:526:5 [INFO] [stdout] | [INFO] [stdout] 526 | /// Returns [L2 norm](https://en.wikipedia.org/wiki/Matrix_norm). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:529:5 [INFO] [stdout] | [INFO] [stdout] 529 | /// Returns [matrix norm](https://en.wikipedia.org/wiki/Matrix_norm) of order `p`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:532:5 [INFO] [stdout] | [INFO] [stdout] 532 | /// Returns the average of the matrix columns. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:535:5 [INFO] [stdout] | [INFO] [stdout] 535 | /// Numerical negative, element-wise. Overrides original matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:538:5 [INFO] [stdout] | [INFO] [stdout] 538 | /// Numerical negative, element-wise. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:559:5 [INFO] [stdout] | [INFO] [stdout] 559 | /// Copies content of `other` matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:562:5 [INFO] [stdout] | [INFO] [stdout] 562 | /// Calculate the absolute value element-wise. Overrides original matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:565:5 [INFO] [stdout] | [INFO] [stdout] 565 | /// Calculate the absolute value element-wise. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:572:5 [INFO] [stdout] | [INFO] [stdout] 572 | /// Calculates sum of all elements of the matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:575:5 [INFO] [stdout] | [INFO] [stdout] 575 | /// Calculates max of all elements of the matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:578:5 [INFO] [stdout] | [INFO] [stdout] 578 | /// Calculates min of all elements of the matrix. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:595:5 [INFO] [stdout] | [INFO] [stdout] 595 | /// Calculates [Softmax function](https://en.wikipedia.org/wiki/Softmax_function). Overrides the matrix with result. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:598:5 [INFO] [stdout] | [INFO] [stdout] 598 | /// Raises elements of the matrix to the power of `p` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:601:5 [INFO] [stdout] | [INFO] [stdout] 601 | /// Returns new matrix with elements raised to the power of `p` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:626:5 [INFO] [stdout] | [INFO] [stdout] 626 | /// Calculates the covariance matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:629:5 [INFO] [stdout] | [INFO] [stdout] 629 | /// Take elements from an array along an axis. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:656:1 [INFO] [stdout] | [INFO] [stdout] 656 | /// Generic matrix with additional mixins like various factorization methods. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:84:1 [INFO] [stdout] | [INFO] [stdout] 21 | | //! * [nalgebra](https://docs.rs/nalgebra/) [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 84 | / /// Supervised classification and regression models that assume linear relationship between dependent and explanatory variables. [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:70:1 [INFO] [stdout] | [INFO] [stdout] 70 | /// Elastic net parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:88:1 [INFO] [stdout] | [INFO] [stdout] 88 | /// Elastic net [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:96:1 [INFO] [stdout] | [INFO] [stdout] 96 | / impl ElasticNetParameters { [INFO] [stdout] 97 | | /// Regularization parameter. [INFO] [stdout] 98 | | pub fn with_alpha(mut self, alpha: T) -> Self { [INFO] [stdout] 99 | | self.alpha = alpha; [INFO] [stdout] ... | [INFO] [stdout] 123 | | } [INFO] [stdout] 124 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:97:5 [INFO] [stdout] | [INFO] [stdout] 97 | /// Regularization parameter. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:102:5 [INFO] [stdout] | [INFO] [stdout] 102 | / /// The elastic net mixing parameter, with 0 <= l1_ratio <= 1. [INFO] [stdout] 103 | | /// For l1_ratio = 0 the penalty is an L2 penalty. [INFO] [stdout] 104 | | /// For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2. [INFO] [stdout] | |______________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:109:5 [INFO] [stdout] | [INFO] [stdout] 109 | /// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:114:5 [INFO] [stdout] | [INFO] [stdout] 114 | /// The tolerance for the optimization [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:119:5 [INFO] [stdout] | [INFO] [stdout] 119 | /// The maximum number of iterations [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:159:1 [INFO] [stdout] | [INFO] [stdout] 159 | / impl> ElasticNet { [INFO] [stdout] 160 | | /// Fits elastic net regression to your data. [INFO] [stdout] 161 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 162 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 285 | | } [INFO] [stdout] 286 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:160:5 [INFO] [stdout] | [INFO] [stdout] 160 | / /// Fits elastic net regression to your data. [INFO] [stdout] 161 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 162 | | /// * `y` - target values [INFO] [stdout] 163 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:226:5 [INFO] [stdout] | [INFO] [stdout] 226 | / /// Predict target values from `x` [INFO] [stdout] 227 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:235:5 [INFO] [stdout] | [INFO] [stdout] 235 | /// Get estimates regression coefficients [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/elastic_net.rs:240:5 [INFO] [stdout] | [INFO] [stdout] 240 | /// Get estimate of intercept [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Lasso [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! [Linear regression](../linear_regression/index.html) is the standard algorithm for predicting a quantitative response \\(y\\) on the ... [INFO] [stdout] 4 | | //! that assumes that there is approximately a linear relationship between \\(X\\) and \\(y\\). [INFO] [stdout] ... | [INFO] [stdout] 23 | | //! [INFO] [stdout] 24 | | //! [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:37:1 [INFO] [stdout] | [INFO] [stdout] 37 | /// Lasso regression parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:54:1 [INFO] [stdout] | [INFO] [stdout] 54 | /// Lasso regressor [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:60:1 [INFO] [stdout] | [INFO] [stdout] 60 | / impl LassoParameters { [INFO] [stdout] 61 | | /// Regularization parameter. [INFO] [stdout] 62 | | pub fn with_alpha(mut self, alpha: T) -> Self { [INFO] [stdout] 63 | | self.alpha = alpha; [INFO] [stdout] ... | [INFO] [stdout] 80 | | } [INFO] [stdout] 81 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:61:5 [INFO] [stdout] | [INFO] [stdout] 61 | /// Regularization parameter. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:66:5 [INFO] [stdout] | [INFO] [stdout] 66 | /// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:71:5 [INFO] [stdout] | [INFO] [stdout] 71 | /// The tolerance for the optimization [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:76:5 [INFO] [stdout] | [INFO] [stdout] 76 | /// The maximum number of iterations [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:115:1 [INFO] [stdout] | [INFO] [stdout] 115 | / impl> Lasso { [INFO] [stdout] 116 | | /// Fits Lasso regression to your data. [INFO] [stdout] 117 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 118 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 220 | | } [INFO] [stdout] 221 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:116:5 [INFO] [stdout] | [INFO] [stdout] 116 | / /// Fits Lasso regression to your data. [INFO] [stdout] 117 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 118 | | /// * `y` - target values [INFO] [stdout] 119 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:185:5 [INFO] [stdout] | [INFO] [stdout] 185 | / /// Predict target values from `x` [INFO] [stdout] 186 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:194:5 [INFO] [stdout] | [INFO] [stdout] 194 | /// Get estimates regression coefficients [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:199:5 [INFO] [stdout] | [INFO] [stdout] 199 | /// Get estimate of intercept [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:75:1 [INFO] [stdout] | [INFO] [stdout] 75 | /// Approach to use for estimation of regression coefficients. QR is more efficient but SVD is more stable. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 83 | /// Linear Regression parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:91:1 [INFO] [stdout] | [INFO] [stdout] 91 | /// Linear Regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:100:1 [INFO] [stdout] | [INFO] [stdout] 100 | / impl LinearRegressionParameters { [INFO] [stdout] 101 | | /// Solver to use for estimation of regression coefficients. [INFO] [stdout] 102 | | pub fn with_solver(mut self, solver: LinearRegressionSolverName) -> Self { [INFO] [stdout] 103 | | self.solver = solver; [INFO] [stdout] 104 | | self [INFO] [stdout] 105 | | } [INFO] [stdout] 106 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:101:5 [INFO] [stdout] | [INFO] [stdout] 101 | /// Solver to use for estimation of regression coefficients. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:141:1 [INFO] [stdout] | [INFO] [stdout] 141 | / impl> LinearRegression { [INFO] [stdout] 142 | | /// Fits Linear Regression to your data. [INFO] [stdout] 143 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 144 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 195 | | } [INFO] [stdout] 196 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:142:5 [INFO] [stdout] | [INFO] [stdout] 142 | / /// Fits Linear Regression to your data. [INFO] [stdout] 143 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 144 | | /// * `y` - target values [INFO] [stdout] 145 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:178:5 [INFO] [stdout] | [INFO] [stdout] 178 | / /// Predict target values from `x` [INFO] [stdout] 179 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:187:5 [INFO] [stdout] | [INFO] [stdout] 187 | /// Get estimates regression coefficients [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:192:5 [INFO] [stdout] | [INFO] [stdout] 192 | /// Get estimate of intercept [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:72:1 [INFO] [stdout] | [INFO] [stdout] 72 | /// Solver options for Logistic regression. Right now only LBFGS solver is supported. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:78:1 [INFO] [stdout] | [INFO] [stdout] 78 | /// Logistic Regression parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:88:1 [INFO] [stdout] | [INFO] [stdout] 88 | /// Logistic Regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:120:1 [INFO] [stdout] | [INFO] [stdout] 120 | / impl LogisticRegressionParameters { [INFO] [stdout] 121 | | /// Solver to use for estimation of regression coefficients. [INFO] [stdout] 122 | | pub fn with_solver(mut self, solver: LogisticRegressionSolverName) -> Self { [INFO] [stdout] 123 | | self.solver = solver; [INFO] [stdout] ... | [INFO] [stdout] 130 | | } [INFO] [stdout] 131 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:121:5 [INFO] [stdout] | [INFO] [stdout] 121 | /// Solver to use for estimation of regression coefficients. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:126:5 [INFO] [stdout] | [INFO] [stdout] 126 | /// Regularization parameter. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:308:1 [INFO] [stdout] | [INFO] [stdout] 308 | / impl> LogisticRegression { [INFO] [stdout] 309 | | /// Fits Logistic Regression to your data. [INFO] [stdout] 310 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 311 | | /// * `y` - target class values [INFO] [stdout] ... | [INFO] [stdout] 445 | | } [INFO] [stdout] 446 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:309:5 [INFO] [stdout] | [INFO] [stdout] 309 | / /// Fits Logistic Regression to your data. [INFO] [stdout] 310 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 311 | | /// * `y` - target class values [INFO] [stdout] 312 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |____________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:389:5 [INFO] [stdout] | [INFO] [stdout] 389 | / /// Predict class labels for samples in `x`. [INFO] [stdout] 390 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:423:5 [INFO] [stdout] | [INFO] [stdout] 423 | /// Get estimates regression coefficients [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:428:5 [INFO] [stdout] | [INFO] [stdout] 428 | /// Get estimate of intercept [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:72:1 [INFO] [stdout] | [INFO] [stdout] 72 | /// Approach to use for estimation of regression coefficients. Cholesky is more efficient but SVD is more stable. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:80:1 [INFO] [stdout] | [INFO] [stdout] 80 | /// Ridge Regression parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:93:1 [INFO] [stdout] | [INFO] [stdout] 93 | /// Ridge regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:102:1 [INFO] [stdout] | [INFO] [stdout] 102 | / impl RidgeRegressionParameters { [INFO] [stdout] 103 | | /// Regularization parameter. [INFO] [stdout] 104 | | pub fn with_alpha(mut self, alpha: T) -> Self { [INFO] [stdout] 105 | | self.alpha = alpha; [INFO] [stdout] ... | [INFO] [stdout] 117 | | } [INFO] [stdout] 118 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:103:5 [INFO] [stdout] | [INFO] [stdout] 103 | /// Regularization parameter. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:108:5 [INFO] [stdout] | [INFO] [stdout] 108 | /// Solver to use for estimation of regression coefficients. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:113:5 [INFO] [stdout] | [INFO] [stdout] 113 | /// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:155:1 [INFO] [stdout] | [INFO] [stdout] 155 | / impl> RidgeRegression { [INFO] [stdout] 156 | | /// Fits ridge regression to your data. [INFO] [stdout] 157 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 158 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 268 | | } [INFO] [stdout] 269 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:156:5 [INFO] [stdout] | [INFO] [stdout] 156 | / /// Fits ridge regression to your data. [INFO] [stdout] 157 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 158 | | /// * `y` - target values [INFO] [stdout] 159 | | /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:251:5 [INFO] [stdout] | [INFO] [stdout] 251 | / /// Predict target values from `x` [INFO] [stdout] 252 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:260:5 [INFO] [stdout] | [INFO] [stdout] 260 | /// Get estimates regression coefficients [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:265:5 [INFO] [stdout] | [INFO] [stdout] 265 | /// Get estimate of intercept [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:86:1 [INFO] [stdout] | [INFO] [stdout] 86 | /// Helper methods and classes, including definitions of distance metrics [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/mod.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / /// Multitude of distance metrics are defined here [INFO] [stdout] 2 | | pub mod distance; [INFO] [stdout] 3 | | pub mod num; [INFO] [stdout] 4 | | pub(crate) mod vector; [INFO] [stdout] ... | [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/euclidian.rs:28:1 [INFO] [stdout] | [INFO] [stdout] 28 | /// Euclidean distance is a measure of the true straight line distance between two points in Euclidean n-space. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/euclidian.rs:33:1 [INFO] [stdout] | [INFO] [stdout] 33 | / impl Euclidian { [INFO] [stdout] 34 | | #[inline] [INFO] [stdout] 35 | | pub(crate) fn squared_distance(x: &[T], y: &[T]) -> T { [INFO] [stdout] 36 | | if x.len() != y.len() { [INFO] [stdout] ... | [INFO] [stdout] 47 | | } [INFO] [stdout] 48 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/hamming.rs:29:1 [INFO] [stdout] | [INFO] [stdout] 29 | /// While comparing two integer-valued vectors of equal length, Hamming distance is the number of bit positions in which the two bits are different [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mahalanobis.rs:55:1 [INFO] [stdout] | [INFO] [stdout] 55 | /// Mahalanobis distance. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mahalanobis.rs:66:1 [INFO] [stdout] | [INFO] [stdout] 66 | / impl> Mahalanobis { [INFO] [stdout] 67 | | /// Constructs new instance of `Mahalanobis` from given dataset [INFO] [stdout] 68 | | /// * `data` - a matrix of _NxM_ where _N_ is number of observations and _M_ is number of attributes [INFO] [stdout] 69 | | pub fn new(data: &M) -> Mahalanobis { [INFO] [stdout] ... | [INFO] [stdout] 89 | | } [INFO] [stdout] 90 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mahalanobis.rs:67:5 [INFO] [stdout] | [INFO] [stdout] 67 | / /// Constructs new instance of `Mahalanobis` from given dataset [INFO] [stdout] 68 | | /// * `data` - a matrix of _NxM_ where _N_ is number of observations and _M_ is number of attributes [INFO] [stdout] | |________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mahalanobis.rs:79:5 [INFO] [stdout] | [INFO] [stdout] 79 | / /// Constructs new instance of `Mahalanobis` from given covariance matrix [INFO] [stdout] 80 | | /// * `cov` - a covariance matrix [INFO] [stdout] | |_____________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/manhattan.rs:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// Manhattan distance [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/minkowski.rs:31:1 [INFO] [stdout] | [INFO] [stdout] 31 | /// Defines the Minkowski distance of order `p` [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | /// Distance metric, a function that calculates distance between two points [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:32:5 [INFO] [stdout] | [INFO] [stdout] 32 | /// Calculates distance between _a_ and _b_ [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:36:1 [INFO] [stdout] | [INFO] [stdout] 36 | /// Multitude of distance metric functions [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:39:1 [INFO] [stdout] | [INFO] [stdout] 39 | / impl Distances { [INFO] [stdout] 40 | | /// Euclidian distance, see [`Euclidian`](euclidian/index.html) [INFO] [stdout] 41 | | pub fn euclidian() -> euclidian::Euclidian { [INFO] [stdout] 42 | | euclidian::Euclidian {} [INFO] [stdout] ... | [INFO] [stdout] 64 | | } [INFO] [stdout] 65 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:40:5 [INFO] [stdout] | [INFO] [stdout] 40 | /// Euclidian distance, see [`Euclidian`](euclidian/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:45:5 [INFO] [stdout] | [INFO] [stdout] 45 | / /// Minkowski distance, see [`Minkowski`](minkowski/index.html) [INFO] [stdout] 46 | | /// * `p` - function order. Should be >= 1 [INFO] [stdout] | |______________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:51:5 [INFO] [stdout] | [INFO] [stdout] 51 | /// Manhattan distance, see [`Manhattan`](manhattan/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:56:5 [INFO] [stdout] | [INFO] [stdout] 56 | /// Hamming distance, see [`Hamming`](hamming/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mod.rs:61:5 [INFO] [stdout] | [INFO] [stdout] 61 | /// Mahalanobis distance, see [`Mahalanobis`](mahalanobis/index.html) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Real Number [INFO] [stdout] 2 | | //! Most algorithms in SmartCore rely on basic linear algebra operations like dot product, matrix decomposition and other subroutines tha... [INFO] [stdout] 3 | | //! This module defines real number and some useful functions that are used in [Linear Algebra](../../linalg/index.html) module. [INFO] [stdout] | |________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:11:1 [INFO] [stdout] | [INFO] [stdout] 11 | / /// Defines real number [INFO] [stdout] 12 | | /// [INFO] [stdout] | |_______________________________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:26:5 [INFO] [stdout] | [INFO] [stdout] 26 | /// Copy sign from `sign` - another real number [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:29:5 [INFO] [stdout] | [INFO] [stdout] 29 | /// Calculates natural \\( \ln(1+e^x) \\) without overflow. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:32:5 [INFO] [stdout] | [INFO] [stdout] 32 | /// Efficient implementation of Sigmoid function, \\( S(x) = \frac{1}{1 + e^{-x}} \\), see [Sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:35:5 [INFO] [stdout] | [INFO] [stdout] 35 | /// Returns pseudorandom number between 0 and 1 [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:38:5 [INFO] [stdout] | [INFO] [stdout] 38 | /// Returns 2 [INFO] [stdout] | ^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:41:5 [INFO] [stdout] | [INFO] [stdout] 41 | /// Returns .5 [INFO] [stdout] | ^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:44:5 [INFO] [stdout] | [INFO] [stdout] 44 | /// Returns \\( x^2 \\) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/num.rs:49:5 [INFO] [stdout] | [INFO] [stdout] 49 | /// Raw transmutation to u64 [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/accuracy.rs:25:1 [INFO] [stdout] | [INFO] [stdout] 25 | /// Accuracy metric. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/accuracy.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | / impl Accuracy { [INFO] [stdout] 31 | | /// Function that calculated accuracy score. [INFO] [stdout] 32 | | /// * `y_true` - cround truth (correct) labels [INFO] [stdout] 33 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] ... | [INFO] [stdout] 53 | | } [INFO] [stdout] 54 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/accuracy.rs:31:5 [INFO] [stdout] | [INFO] [stdout] 31 | / /// Function that calculated accuracy score. [INFO] [stdout] 32 | | /// * `y_true` - cround truth (correct) labels [INFO] [stdout] 33 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |___________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/auc.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | /// Area Under the Receiver Operating Characteristic Curve (ROC AUC) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/auc.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | / impl AUC { [INFO] [stdout] 36 | | /// AUC score. [INFO] [stdout] 37 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 38 | | /// * `y_pred_probabilities` - probability estimates, as returned by a classifier. [INFO] [stdout] ... | [INFO] [stdout] 89 | | } [INFO] [stdout] 90 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/auc.rs:36:5 [INFO] [stdout] | [INFO] [stdout] 36 | / /// AUC score. [INFO] [stdout] 37 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 38 | | /// * `y_pred_probabilities` - probability estimates, as returned by a classifier. [INFO] [stdout] | |______________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:58:1 [INFO] [stdout] | [INFO] [stdout] 58 | /// Compute the homogeneity, completeness and V-Measure scores. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/cluster_hcv.rs:10:1 [INFO] [stdout] | [INFO] [stdout] 10 | /// Homogeneity, completeness and V-Measure scores. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/cluster_hcv.rs:13:1 [INFO] [stdout] | [INFO] [stdout] 13 | / impl HCVScore { [INFO] [stdout] 14 | | /// Computes Homogeneity, completeness and V-Measure scores at once. [INFO] [stdout] 15 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 16 | | /// * `labels_pred` - cluster labels to evaluate. [INFO] [stdout] ... | [INFO] [stdout] 39 | | } [INFO] [stdout] 40 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/cluster_hcv.rs:14:5 [INFO] [stdout] | [INFO] [stdout] 14 | / /// Computes Homogeneity, completeness and V-Measure scores at once. [INFO] [stdout] 15 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 16 | | /// * `labels_pred` - cluster labels to evaluate. [INFO] [stdout] | |_________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/f1.rs:29:1 [INFO] [stdout] | [INFO] [stdout] 29 | /// F-measure [INFO] [stdout] | ^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/f1.rs:37:1 [INFO] [stdout] | [INFO] [stdout] 37 | / impl F1 { [INFO] [stdout] 38 | | /// Computes F1 score [INFO] [stdout] 39 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 40 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] ... | [INFO] [stdout] 55 | | } [INFO] [stdout] 56 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/f1.rs:38:5 [INFO] [stdout] | [INFO] [stdout] 38 | / /// Computes F1 score [INFO] [stdout] 39 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 40 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |___________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_absolute_error.rs:29:1 [INFO] [stdout] | [INFO] [stdout] 29 | /// Mean Absolute Error [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_absolute_error.rs:32:1 [INFO] [stdout] | [INFO] [stdout] 32 | / impl MeanAbsoluteError { [INFO] [stdout] 33 | | /// Computes mean absolute error [INFO] [stdout] 34 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 35 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] ... | [INFO] [stdout] 52 | | } [INFO] [stdout] 53 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_absolute_error.rs:33:5 [INFO] [stdout] | [INFO] [stdout] 33 | / /// Computes mean absolute error [INFO] [stdout] 34 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 35 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] | |_____________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_squared_error.rs:29:1 [INFO] [stdout] | [INFO] [stdout] 29 | /// Mean Squared Error [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_squared_error.rs:32:1 [INFO] [stdout] | [INFO] [stdout] 32 | / impl MeanSquareError { [INFO] [stdout] 33 | | /// Computes mean squared error [INFO] [stdout] 34 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 35 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] ... | [INFO] [stdout] 52 | | } [INFO] [stdout] 53 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_squared_error.rs:33:5 [INFO] [stdout] | [INFO] [stdout] 33 | / /// Computes mean squared error [INFO] [stdout] 34 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 35 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] | |_____________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/precision.rs:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// Precision metric. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/precision.rs:32:1 [INFO] [stdout] | [INFO] [stdout] 32 | / impl Precision { [INFO] [stdout] 33 | | /// Calculated precision score [INFO] [stdout] 34 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 35 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] ... | [INFO] [stdout] 73 | | } [INFO] [stdout] 74 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/precision.rs:33:5 [INFO] [stdout] | [INFO] [stdout] 33 | / /// Calculated precision score [INFO] [stdout] 34 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 35 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |___________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/r2.rs:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// Coefficient of Determination (R2) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/r2.rs:32:1 [INFO] [stdout] | [INFO] [stdout] 32 | / impl R2 { [INFO] [stdout] 33 | | /// Computes R2 score [INFO] [stdout] 34 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 35 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] ... | [INFO] [stdout] 66 | | } [INFO] [stdout] 67 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/r2.rs:33:5 [INFO] [stdout] | [INFO] [stdout] 33 | / /// Computes R2 score [INFO] [stdout] 34 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 35 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] | |_____________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/recall.rs:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// Recall metric. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/recall.rs:32:1 [INFO] [stdout] | [INFO] [stdout] 32 | / impl Recall { [INFO] [stdout] 33 | | /// Calculated recall score [INFO] [stdout] 34 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 35 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] ... | [INFO] [stdout] 73 | | } [INFO] [stdout] 74 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/recall.rs:33:5 [INFO] [stdout] | [INFO] [stdout] 33 | / /// Calculated recall score [INFO] [stdout] 34 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 35 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |___________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:77:1 [INFO] [stdout] | [INFO] [stdout] 77 | /// Use these metrics to compare classification models. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:80:1 [INFO] [stdout] | [INFO] [stdout] 80 | /// Metrics for regression models. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 83 | /// Cluster metrics. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:86:1 [INFO] [stdout] | [INFO] [stdout] 86 | / impl ClassificationMetrics { [INFO] [stdout] 87 | | /// Accuracy score, see [accuracy](accuracy/index.html). [INFO] [stdout] 88 | | pub fn accuracy() -> accuracy::Accuracy { [INFO] [stdout] 89 | | accuracy::Accuracy {} [INFO] [stdout] ... | [INFO] [stdout] 110 | | } [INFO] [stdout] 111 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:87:5 [INFO] [stdout] | [INFO] [stdout] 87 | /// Accuracy score, see [accuracy](accuracy/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:92:5 [INFO] [stdout] | [INFO] [stdout] 92 | /// Recall, see [recall](recall/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:97:5 [INFO] [stdout] | [INFO] [stdout] 97 | /// Precision, see [precision](precision/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:102:5 [INFO] [stdout] | [INFO] [stdout] 102 | /// F1 score, also known as balanced F-score or F-measure, see [F1](f1/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:107:5 [INFO] [stdout] | [INFO] [stdout] 107 | /// Area Under the Receiver Operating Characteristic Curve (ROC AUC), see [AUC](auc/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:113:1 [INFO] [stdout] | [INFO] [stdout] 113 | / impl RegressionMetrics { [INFO] [stdout] 114 | | /// Mean squared error, see [mean squared error](mean_squared_error/index.html). [INFO] [stdout] 115 | | pub fn mean_squared_error() -> mean_squared_error::MeanSquareError { [INFO] [stdout] 116 | | mean_squared_error::MeanSquareError {} [INFO] [stdout] ... | [INFO] [stdout] 127 | | } [INFO] [stdout] 128 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:114:5 [INFO] [stdout] | [INFO] [stdout] 114 | /// Mean squared error, see [mean squared error](mean_squared_error/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:119:5 [INFO] [stdout] | [INFO] [stdout] 119 | /// Mean absolute error, see [mean absolute error](mean_absolute_error/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:124:5 [INFO] [stdout] | [INFO] [stdout] 124 | /// Coefficient of determination (R2), see [R2](r2/index.html). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:130:1 [INFO] [stdout] | [INFO] [stdout] 130 | / impl ClusterMetrics { [INFO] [stdout] 131 | | /// Homogeneity and completeness and V-Measure scores at once. [INFO] [stdout] 132 | | pub fn hcv_score() -> cluster_hcv::HCVScore { [INFO] [stdout] 133 | | cluster_hcv::HCVScore {} [INFO] [stdout] 134 | | } [INFO] [stdout] 135 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:131:5 [INFO] [stdout] | [INFO] [stdout] 131 | /// Homogeneity and completeness and V-Measure scores at once. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:137:1 [INFO] [stdout] | [INFO] [stdout] 137 | / /// Function that calculated accuracy score, see [accuracy](accuracy/index.html). [INFO] [stdout] 138 | | /// * `y_true` - cround truth (correct) labels [INFO] [stdout] 139 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |_______________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:144:1 [INFO] [stdout] | [INFO] [stdout] 144 | / /// Calculated recall score, see [recall](recall/index.html) [INFO] [stdout] 145 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 146 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |_______________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:151:1 [INFO] [stdout] | [INFO] [stdout] 151 | / /// Calculated precision score, see [precision](precision/index.html). [INFO] [stdout] 152 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 153 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |_______________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:158:1 [INFO] [stdout] | [INFO] [stdout] 158 | / /// Computes F1 score, see [F1](f1/index.html). [INFO] [stdout] 159 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 160 | | /// * `y_pred` - predicted labels, as returned by a classifier. [INFO] [stdout] | |_______________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:165:1 [INFO] [stdout] | [INFO] [stdout] 165 | / /// AUC score, see [AUC](auc/index.html). [INFO] [stdout] 166 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 167 | | /// * `y_pred_probabilities` - probability estimates, as returned by a classifier. [INFO] [stdout] | |__________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:172:1 [INFO] [stdout] | [INFO] [stdout] 172 | / /// Computes mean squared error, see [mean squared error](mean_squared_error/index.html). [INFO] [stdout] 173 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 174 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:179:1 [INFO] [stdout] | [INFO] [stdout] 179 | / /// Computes mean absolute error, see [mean absolute error](mean_absolute_error/index.html). [INFO] [stdout] 180 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 181 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:186:1 [INFO] [stdout] | [INFO] [stdout] 186 | / /// Computes R2 score, see [R2](r2/index.html). [INFO] [stdout] 187 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 188 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] | |_________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:193:1 [INFO] [stdout] | [INFO] [stdout] 193 | / /// Homogeneity metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0). [INFO] [stdout] 194 | | /// A cluster result satisfies homogeneity if all of its clusters contain only data points which are members of a single class. [INFO] [stdout] 195 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 196 | | /// * `labels_pred` - cluster labels to evaluate. [INFO] [stdout] | |_________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:203:1 [INFO] [stdout] | [INFO] [stdout] 203 | / /// [INFO] [stdout] 204 | | /// Completeness metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0). [INFO] [stdout] 205 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 206 | | /// * `labels_pred` - cluster labels to evaluate. [INFO] [stdout] | |_________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mod.rs:213:1 [INFO] [stdout] | [INFO] [stdout] 213 | / /// The harmonic mean between homogeneity and completeness. [INFO] [stdout] 214 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 215 | | /// * `labels_pred` - cluster labels to evaluate. [INFO] [stdout] | |_________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:117:1 [INFO] [stdout] | [INFO] [stdout] 117 | /// An interface for the K-Folds cross-validator [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:119:5 [INFO] [stdout] | [INFO] [stdout] 119 | /// An iterator over indices that split data into training and test set. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:121:5 [INFO] [stdout] | [INFO] [stdout] 121 | / /// Return a tuple containing the the training set indices for that split and [INFO] [stdout] 122 | | /// the testing set indices for that split. [INFO] [stdout] | |_______________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:124:5 [INFO] [stdout] | [INFO] [stdout] 124 | /// Returns the number of splits [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:128:1 [INFO] [stdout] | [INFO] [stdout] 128 | / /// Splits data into 2 disjoint datasets. [INFO] [stdout] 129 | | /// * `x` - features, matrix of size _NxM_ where _N_ is number of samples and _M_ is number of attributes. [INFO] [stdout] 130 | | /// * `y` - target values, should be of size _N_ [INFO] [stdout] 131 | | /// * `test_size`, (0, 1] - the proportion of the dataset to include in the test split. [INFO] [stdout] 132 | | /// * `shuffle`, - whether or not to shuffle the data before splitting [INFO] [stdout] | |______________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:173:1 [INFO] [stdout] | [INFO] [stdout] 173 | /// Cross validation results. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:182:1 [INFO] [stdout] | [INFO] [stdout] 182 | / impl CrossValidationResult { [INFO] [stdout] 183 | | /// Average test score [INFO] [stdout] 184 | | pub fn mean_test_score(&self) -> T { [INFO] [stdout] 185 | | self.test_score.sum() / T::from_usize(self.test_score.len()).unwrap() [INFO] [stdout] ... | [INFO] [stdout] 190 | | } [INFO] [stdout] 191 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:183:5 [INFO] [stdout] | [INFO] [stdout] 183 | /// Average test score [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:187:5 [INFO] [stdout] | [INFO] [stdout] 187 | /// Average training score [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:193:1 [INFO] [stdout] | [INFO] [stdout] 193 | / /// Evaluate an estimator by cross-validation using given metric. [INFO] [stdout] 194 | | /// * `fit_estimator` - a `fit` function of an estimator [INFO] [stdout] 195 | | /// * `x` - features, matrix of size _NxM_ where _N_ is number of samples and _M_ is number of attributes. [INFO] [stdout] 196 | | /// * `y` - target values, should be of size _N_ [INFO] [stdout] 197 | | /// * `parameters` - parameters of selected estimator. Use `Default::default()` for default parameters. [INFO] [stdout] 198 | | /// * `cv` - the cross-validation splitting strategy, should be an instance of [`BaseKFold`](./trait.BaseKFold.html) [INFO] [stdout] 199 | | /// * `score` - a metric to use for evaluation, see [metrics](../metrics/index.html) [INFO] [stdout] | |____________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:239:1 [INFO] [stdout] | [INFO] [stdout] 239 | / /// Generate cross-validated estimates for each input data point. [INFO] [stdout] 240 | | /// The data is split according to the cv parameter. Each sample belongs to exactly one test set, and its prediction is computed with an ... [INFO] [stdout] 241 | | /// * `fit_estimator` - a `fit` function of an estimator [INFO] [stdout] 242 | | /// * `x` - features, matrix of size _NxM_ where _N_ is number of samples and _M_ is number of attributes. [INFO] [stdout] 243 | | /// * `y` - target values, should be of size _N_ [INFO] [stdout] 244 | | /// * `parameters` - parameters of selected estimator. Use `Default::default()` for default parameters. [INFO] [stdout] 245 | | /// * `cv` - the cross-validation splitting strategy, should be an instance of [`BaseKFold`](./trait.BaseKFold.html) [INFO] [stdout] | |____________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:91:1 [INFO] [stdout] | [INFO] [stdout] 37 | | //! * [Nearest Neighbors](neighbors/index.html), K Nearest Neighbors for classification and regression [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 91 | / /// Supervised learning algorithms based on applying the Bayes theorem with the independence assumptions between predictors [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:113:1 [INFO] [stdout] | [INFO] [stdout] 113 | /// `BernoulliNB` parameters. Use `Default::default()` for default values. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:125:1 [INFO] [stdout] | [INFO] [stdout] 125 | / impl BernoulliNBParameters { [INFO] [stdout] 126 | | /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). [INFO] [stdout] 127 | | pub fn with_alpha(mut self, alpha: T) -> Self { [INFO] [stdout] 128 | | self.alpha = alpha; [INFO] [stdout] ... | [INFO] [stdout] 140 | | } [INFO] [stdout] 141 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:126:5 [INFO] [stdout] | [INFO] [stdout] 126 | /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:131:5 [INFO] [stdout] | [INFO] [stdout] 131 | /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:136:5 [INFO] [stdout] | [INFO] [stdout] 136 | /// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:252:1 [INFO] [stdout] | [INFO] [stdout] 252 | / /// BernoulliNB implements the naive Bayes algorithm for data that follows the Bernoulli [INFO] [stdout] 253 | | /// distribution. [INFO] [stdout] | |_________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:275:1 [INFO] [stdout] | [INFO] [stdout] 275 | / impl> BernoulliNB { [INFO] [stdout] 276 | | /// Fits BernoulliNB with given data [INFO] [stdout] 277 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 278 | | /// features. [INFO] [stdout] ... | [INFO] [stdout] 342 | | } [INFO] [stdout] 343 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:276:5 [INFO] [stdout] | [INFO] [stdout] 276 | / /// Fits BernoulliNB with given data [INFO] [stdout] 277 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 278 | | /// features. [INFO] [stdout] 279 | | /// * `y` - vector with target values (classes) of length N. [INFO] [stdout] 280 | | /// * `parameters` - additional parameters like class priors, alpha for smoothing and [INFO] [stdout] 281 | | /// binarizing threshold. [INFO] [stdout] | |_____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:305:5 [INFO] [stdout] | [INFO] [stdout] 305 | / /// Estimates the class labels for the provided data. [INFO] [stdout] 306 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 307 | | /// Returns a vector of size N with class estimates. [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:316:5 [INFO] [stdout] | [INFO] [stdout] 316 | / /// Class labels known to the classifier. [INFO] [stdout] 317 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:322:5 [INFO] [stdout] | [INFO] [stdout] 322 | / /// Number of training samples observed in each class. [INFO] [stdout] 323 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:328:5 [INFO] [stdout] | [INFO] [stdout] 328 | /// Number of features of each sample [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:333:5 [INFO] [stdout] | [INFO] [stdout] 333 | / /// Number of samples encountered for each (class, feature) [INFO] [stdout] 334 | | /// Returns a 2d vector of shape (n_classes, n_features) [INFO] [stdout] | |____________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/bernoulli.rs:339:5 [INFO] [stdout] | [INFO] [stdout] 339 | /// Empirical log probability of features given a class [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:242:1 [INFO] [stdout] | [INFO] [stdout] 242 | /// `CategoricalNB` parameters. Use `Default::default()` for default values. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:250:1 [INFO] [stdout] | [INFO] [stdout] 250 | / impl CategoricalNBParameters { [INFO] [stdout] 251 | | /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). [INFO] [stdout] 252 | | pub fn with_alpha(mut self, alpha: T) -> Self { [INFO] [stdout] 253 | | self.alpha = alpha; [INFO] [stdout] 254 | | self [INFO] [stdout] 255 | | } [INFO] [stdout] 256 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:251:5 [INFO] [stdout] | [INFO] [stdout] 251 | /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:264:1 [INFO] [stdout] | [INFO] [stdout] 264 | /// CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:289:1 [INFO] [stdout] | [INFO] [stdout] 289 | / impl> CategoricalNB { [INFO] [stdout] 290 | | /// Fits CategoricalNB with given data [INFO] [stdout] 291 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 292 | | /// features. [INFO] [stdout] ... | [INFO] [stdout] 346 | | } [INFO] [stdout] 347 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:290:5 [INFO] [stdout] | [INFO] [stdout] 290 | / /// Fits CategoricalNB with given data [INFO] [stdout] 291 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 292 | | /// features. [INFO] [stdout] 293 | | /// * `y` - vector with target values (classes) of length N. [INFO] [stdout] 294 | | /// * `parameters` - additional parameters like alpha for smoothing [INFO] [stdout] | |_______________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:306:5 [INFO] [stdout] | [INFO] [stdout] 306 | / /// Estimates the class labels for the provided data. [INFO] [stdout] 307 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 308 | | /// Returns a vector of size N with class estimates. [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:313:5 [INFO] [stdout] | [INFO] [stdout] 313 | / /// Class labels known to the classifier. [INFO] [stdout] 314 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:319:5 [INFO] [stdout] | [INFO] [stdout] 319 | / /// Number of training samples observed in each class. [INFO] [stdout] 320 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:325:5 [INFO] [stdout] | [INFO] [stdout] 325 | /// Number of features of each sample [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:330:5 [INFO] [stdout] | [INFO] [stdout] 330 | /// Number of features of each sample [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:335:5 [INFO] [stdout] | [INFO] [stdout] 335 | / /// Holds arrays of shape (n_classes, n_categories of respective feature) [INFO] [stdout] 336 | | /// for each feature. Each array provides the number of samples [INFO] [stdout] 337 | | /// encountered for each class and category of the specific feature. [INFO] [stdout] | |________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/categorical.rs:341:5 [INFO] [stdout] | [INFO] [stdout] 341 | / /// Holds arrays of shape (n_classes, n_categories of respective feature) [INFO] [stdout] 342 | | /// for each feature. Each array provides the empirical log probability [INFO] [stdout] 343 | | /// of categories given the respective feature and class, ``P(x_i|y)``. [INFO] [stdout] | |___________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:77:1 [INFO] [stdout] | [INFO] [stdout] 77 | /// `GaussianNB` parameters. Use `Default::default()` for default values. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:85:1 [INFO] [stdout] | [INFO] [stdout] 85 | / impl GaussianNBParameters { [INFO] [stdout] 86 | | /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data [INFO] [stdout] 87 | | pub fn with_priors(mut self, priors: Vec) -> Self { [INFO] [stdout] 88 | | self.priors = Some(priors); [INFO] [stdout] 89 | | self [INFO] [stdout] 90 | | } [INFO] [stdout] 91 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:86:5 [INFO] [stdout] | [INFO] [stdout] 86 | /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:182:1 [INFO] [stdout] | [INFO] [stdout] 182 | / /// GaussianNB implements the naive Bayes algorithm for data that follows the Gaussian [INFO] [stdout] 183 | | /// distribution. [INFO] [stdout] | |_________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:204:1 [INFO] [stdout] | [INFO] [stdout] 204 | / impl> GaussianNB { [INFO] [stdout] 205 | | /// Fits GaussianNB with given data [INFO] [stdout] 206 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 207 | | /// features. [INFO] [stdout] ... | [INFO] [stdout] 255 | | } [INFO] [stdout] 256 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:205:5 [INFO] [stdout] | [INFO] [stdout] 205 | / /// Fits GaussianNB with given data [INFO] [stdout] 206 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 207 | | /// features. [INFO] [stdout] 208 | | /// * `y` - vector with target values (classes) of length N. [INFO] [stdout] 209 | | /// * `parameters` - additional parameters like class priors. [INFO] [stdout] | |_________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:220:5 [INFO] [stdout] | [INFO] [stdout] 220 | / /// Estimates the class labels for the provided data. [INFO] [stdout] 221 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 222 | | /// Returns a vector of size N with class estimates. [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:227:5 [INFO] [stdout] | [INFO] [stdout] 227 | / /// Class labels known to the classifier. [INFO] [stdout] 228 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:233:5 [INFO] [stdout] | [INFO] [stdout] 233 | / /// Number of training samples observed in each class. [INFO] [stdout] 234 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:239:5 [INFO] [stdout] | [INFO] [stdout] 239 | / /// Probability of each class [INFO] [stdout] 240 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:245:5 [INFO] [stdout] | [INFO] [stdout] 245 | / /// Mean of each feature per class [INFO] [stdout] 246 | | /// Returns a 2d vector of shape (n_classes, n_features). [INFO] [stdout] | |_____________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/gaussian.rs:251:5 [INFO] [stdout] | [INFO] [stdout] 251 | / /// Variance of each feature per class [INFO] [stdout] 252 | | /// Returns a 2d vector of shape (n_classes, n_features). [INFO] [stdout] | |_____________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:85:1 [INFO] [stdout] | [INFO] [stdout] 85 | /// `MultinomialNB` parameters. Use `Default::default()` for default values. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:95:1 [INFO] [stdout] | [INFO] [stdout] 95 | / impl MultinomialNBParameters { [INFO] [stdout] 96 | | /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). [INFO] [stdout] 97 | | pub fn with_alpha(mut self, alpha: T) -> Self { [INFO] [stdout] 98 | | self.alpha = alpha; [INFO] [stdout] ... | [INFO] [stdout] 105 | | } [INFO] [stdout] 106 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:96:5 [INFO] [stdout] | [INFO] [stdout] 96 | /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:101:5 [INFO] [stdout] | [INFO] [stdout] 101 | /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:215:1 [INFO] [stdout] | [INFO] [stdout] 215 | /// MultinomialNB implements the naive Bayes algorithm for multinomially distributed data. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:240:1 [INFO] [stdout] | [INFO] [stdout] 240 | / impl> MultinomialNB { [INFO] [stdout] 241 | | /// Fits MultinomialNB with given data [INFO] [stdout] 242 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 243 | | /// features. [INFO] [stdout] ... | [INFO] [stdout] 292 | | } [INFO] [stdout] 293 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:241:5 [INFO] [stdout] | [INFO] [stdout] 241 | / /// Fits MultinomialNB with given data [INFO] [stdout] 242 | | /// * `x` - training data of size NxM where N is the number of samples and M is the number of [INFO] [stdout] 243 | | /// features. [INFO] [stdout] 244 | | /// * `y` - vector with target values (classes) of length N. [INFO] [stdout] 245 | | /// * `parameters` - additional parameters like class priors, alpha for smoothing and [INFO] [stdout] 246 | | /// binarizing threshold. [INFO] [stdout] | |_____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:258:5 [INFO] [stdout] | [INFO] [stdout] 258 | / /// Estimates the class labels for the provided data. [INFO] [stdout] 259 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 260 | | /// Returns a vector of size N with class estimates. [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:265:5 [INFO] [stdout] | [INFO] [stdout] 265 | / /// Class labels known to the classifier. [INFO] [stdout] 266 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:271:5 [INFO] [stdout] | [INFO] [stdout] 271 | / /// Number of training samples observed in each class. [INFO] [stdout] 272 | | /// Returns a vector of size n_classes. [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:277:5 [INFO] [stdout] | [INFO] [stdout] 277 | / /// Empirical log probability of features given a class, P(x_i|y). [INFO] [stdout] 278 | | /// Returns a 2d vector of shape (n_classes, n_features) [INFO] [stdout] | |____________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:283:5 [INFO] [stdout] | [INFO] [stdout] 283 | /// Number of features of each sample [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/naive_bayes/multinomial.rs:288:5 [INFO] [stdout] | [INFO] [stdout] 288 | / /// Number of samples encountered for each (class, feature) [INFO] [stdout] 289 | | /// Returns a 2d vector of shape (n_classes, n_features) [INFO] [stdout] | |____________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:93:1 [INFO] [stdout] | [INFO] [stdout] 33 | | //! * [Matrix Decomposition](decomposition/index.html), various methods for matrix decomposition. [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 93 | / /// Supervised neighbors-based learning methods [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:48:1 [INFO] [stdout] | [INFO] [stdout] 48 | /// `KNNClassifier` parameters. Use `Default::default()` for default values. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:66:1 [INFO] [stdout] | [INFO] [stdout] 66 | /// K Nearest Neighbors Classifier [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:77:1 [INFO] [stdout] | [INFO] [stdout] 77 | / impl, T>> KNNClassifierParameters { [INFO] [stdout] 78 | | /// number of training samples to consider when estimating class for new point. Default value is 3. [INFO] [stdout] 79 | | pub fn with_k(mut self, k: usize) -> Self { [INFO] [stdout] 80 | | self.k = k; [INFO] [stdout] ... | [INFO] [stdout] 107 | | } [INFO] [stdout] 108 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:78:5 [INFO] [stdout] | [INFO] [stdout] 78 | /// number of training samples to consider when estimating class for new point. Default value is 3. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:83:5 [INFO] [stdout] | [INFO] [stdout] 83 | / /// a function that defines a distance between each pair of point in training data. [INFO] [stdout] 84 | | /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait. [INFO] [stdout] 85 | | /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions. [INFO] [stdout] | |_______________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:98:5 [INFO] [stdout] | [INFO] [stdout] 98 | /// backend search algorithm. See [`knn search algorithms`](../../algorithm/neighbour/index.html). `CoverTree` is default. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:103:5 [INFO] [stdout] | [INFO] [stdout] 103 | /// weighting function that is used to calculate estimated class value. Default function is `KNNWeightFunction::Uniform`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:165:1 [INFO] [stdout] | [INFO] [stdout] 165 | / impl, T>> KNNClassifier { [INFO] [stdout] 166 | | /// Fits KNN classifier to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 167 | | /// * `x` - training data [INFO] [stdout] 168 | | /// * `y` - vector with target values (classes) of length N [INFO] [stdout] ... | [INFO] [stdout] 246 | | } [INFO] [stdout] 247 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:166:5 [INFO] [stdout] | [INFO] [stdout] 166 | / /// Fits KNN classifier to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 167 | | /// * `x` - training data [INFO] [stdout] 168 | | /// * `y` - vector with target values (classes) of length N [INFO] [stdout] 169 | | /// * `parameters` - additional parameters like search algorithm and k [INFO] [stdout] | |__________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:213:5 [INFO] [stdout] | [INFO] [stdout] 213 | / /// Estimates the class labels for the provided data. [INFO] [stdout] 214 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 215 | | /// Returns a vector of size N with class estimates. [INFO] [stdout] | |________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:51:1 [INFO] [stdout] | [INFO] [stdout] 51 | /// `KNNRegressor` parameters. Use `Default::default()` for default values. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// K Nearest Neighbors Regressor [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:79:1 [INFO] [stdout] | [INFO] [stdout] 79 | / impl, T>> KNNRegressorParameters { [INFO] [stdout] 80 | | /// number of training samples to consider when estimating class for new point. Default value is 3. [INFO] [stdout] 81 | | pub fn with_k(mut self, k: usize) -> Self { [INFO] [stdout] 82 | | self.k = k; [INFO] [stdout] ... | [INFO] [stdout] 109 | | } [INFO] [stdout] 110 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:80:5 [INFO] [stdout] | [INFO] [stdout] 80 | /// number of training samples to consider when estimating class for new point. Default value is 3. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:85:5 [INFO] [stdout] | [INFO] [stdout] 85 | / /// a function that defines a distance between each pair of point in training data. [INFO] [stdout] 86 | | /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait. [INFO] [stdout] 87 | | /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions. [INFO] [stdout] | |_______________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:100:5 [INFO] [stdout] | [INFO] [stdout] 100 | /// backend search algorithm. See [`knn search algorithms`](../../algorithm/neighbour/index.html). `CoverTree` is default. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:105:5 [INFO] [stdout] | [INFO] [stdout] 105 | /// weighting function that is used to calculate estimated class value. Default function is `KNNWeightFunction::Uniform`. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:159:1 [INFO] [stdout] | [INFO] [stdout] 159 | / impl, T>> KNNRegressor { [INFO] [stdout] 160 | | /// Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 161 | | /// * `x` - training data [INFO] [stdout] 162 | | /// * `y` - vector with real values [INFO] [stdout] ... | [INFO] [stdout] 225 | | } [INFO] [stdout] 226 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:160:5 [INFO] [stdout] | [INFO] [stdout] 160 | / /// Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 161 | | /// * `x` - training data [INFO] [stdout] 162 | | /// * `y` - vector with real values [INFO] [stdout] 163 | | /// * `parameters` - additional parameters like search algorithm and k [INFO] [stdout] | |__________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:198:5 [INFO] [stdout] | [INFO] [stdout] 198 | / /// Predict the target for the provided data. [INFO] [stdout] 199 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 200 | | /// Returns a vector of size N with estimates. [INFO] [stdout] | |__________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/mod.rs:51:1 [INFO] [stdout] | [INFO] [stdout] 51 | /// Weight function that is used to determine estimated value. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/mod.rs:61:1 [INFO] [stdout] | [INFO] [stdout] 61 | / impl KNNWeightFunction { [INFO] [stdout] 62 | | fn calc_weights(&self, distances: Vec) -> std::vec::Vec { [INFO] [stdout] 63 | | match *self { [INFO] [stdout] 64 | | KNNWeightFunction::Distance => { [INFO] [stdout] ... | [INFO] [stdout] 78 | | } [INFO] [stdout] 79 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:96:1 [INFO] [stdout] | [INFO] [stdout] 96 | /// Preprocessing utilities [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | /// OneHotEncoder Parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | / impl OneHotEncoderParams { [INFO] [stdout] 45 | | /// Generate parameters from categorical variable column numbers [INFO] [stdout] 46 | | pub fn from_cat_idx(categorical_params: &[usize]) -> Self { [INFO] [stdout] 47 | | Self { [INFO] [stdout] ... | [INFO] [stdout] 51 | | } [INFO] [stdout] 52 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:45:5 [INFO] [stdout] | [INFO] [stdout] 45 | /// Generate parameters from categorical variable column numbers [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:97:1 [INFO] [stdout] | [INFO] [stdout] 97 | /// Encode Categorical variavbles of data matrix to one-hot [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:104:1 [INFO] [stdout] | [INFO] [stdout] 104 | / impl OneHotEncoder { [INFO] [stdout] 105 | | /// Create an encoder instance with categories infered from data matrix [INFO] [stdout] 106 | | pub fn fit(data: &M, params: OneHotEncoderParams) -> Result [INFO] [stdout] 107 | | where [INFO] [stdout] ... | [INFO] [stdout] 218 | | } [INFO] [stdout] 219 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:105:5 [INFO] [stdout] | [INFO] [stdout] 105 | /// Create an encoder instance with categories infered from data matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/categorical.rs:156:5 [INFO] [stdout] | [INFO] [stdout] 156 | /// Transform categorical variables to one-hot encoded and return a new matrix [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/mod.rs:4:1 [INFO] [stdout] | [INFO] [stdout] 3 | | mod data_traits; [INFO] [stdout] | |_______________________________________________________________________^ [INFO] [stdout] 4 | / /// Encode a series (column, array) of categorical variables as one-hot vectors [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:60:1 [INFO] [stdout] | [INFO] [stdout] 60 | / impl CategoryMapper [INFO] [stdout] 61 | | where [INFO] [stdout] 62 | | C: Hash + Eq + Clone, [INFO] [stdout] 63 | | { [INFO] [stdout] ... | [INFO] [stdout] 176 | | } [INFO] [stdout] 177 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:64:5 [INFO] [stdout] | [INFO] [stdout] 64 | /// Get the number of categories in the mapper [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:69:5 [INFO] [stdout] | [INFO] [stdout] 69 | /// Fit an encoder to a lable iterator [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:89:5 [INFO] [stdout] | [INFO] [stdout] 89 | /// Build an encoder from a predefined (category -> class number) map [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:102:5 [INFO] [stdout] | [INFO] [stdout] 102 | /// Build an encoder from a predefined positional category-class num vector [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:116:5 [INFO] [stdout] | [INFO] [stdout] 116 | /// Get label num of a category [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:121:5 [INFO] [stdout] | [INFO] [stdout] 121 | /// Return category corresponding to label num [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:126:5 [INFO] [stdout] | [INFO] [stdout] 126 | /// List all categories (position = category number) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:131:5 [INFO] [stdout] | [INFO] [stdout] 131 | /// Get one-hot encoding of the category [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:141:5 [INFO] [stdout] | [INFO] [stdout] 141 | /// Invert one-hot vector, back to the category [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/preprocessing/series_encoder.rs:167:5 [INFO] [stdout] | [INFO] [stdout] 167 | /// Get ordinal encoding of the catergory [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:98:1 [INFO] [stdout] | [INFO] [stdout] 24 | | //! [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 98 | / /// Support Vector Machines [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:91:1 [INFO] [stdout] | [INFO] [stdout] 91 | /// SVC Parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:114:1 [INFO] [stdout] | [INFO] [stdout] 114 | /// Support Vector Classifier [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:155:1 [INFO] [stdout] | [INFO] [stdout] 155 | / impl, K: Kernel> SVCParameters { [INFO] [stdout] 156 | | /// Number of epochs. [INFO] [stdout] 157 | | pub fn with_epoch(mut self, epoch: usize) -> Self { [INFO] [stdout] 158 | | self.epoch = epoch; [INFO] [stdout] ... | [INFO] [stdout] 180 | | } [INFO] [stdout] 181 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:156:5 [INFO] [stdout] | [INFO] [stdout] 156 | /// Number of epochs. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:161:5 [INFO] [stdout] | [INFO] [stdout] 161 | /// Regularization parameter. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:166:5 [INFO] [stdout] | [INFO] [stdout] 166 | /// Tolerance for stopping criterion. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:171:5 [INFO] [stdout] | [INFO] [stdout] 171 | /// The kernel function. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:211:1 [INFO] [stdout] | [INFO] [stdout] 211 | / impl, K: Kernel> SVC { [INFO] [stdout] 212 | | /// Fits SVC to your data. [INFO] [stdout] 213 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 214 | | /// * `y` - class labels [INFO] [stdout] ... | [INFO] [stdout] 293 | | } [INFO] [stdout] 294 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:212:5 [INFO] [stdout] | [INFO] [stdout] 212 | / /// Fits SVC to your data. [INFO] [stdout] 213 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 214 | | /// * `y` - class labels [INFO] [stdout] 215 | | /// * `parameters` - optional parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |___________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:263:5 [INFO] [stdout] | [INFO] [stdout] 263 | / /// Predicts estimated class labels from `x` [INFO] [stdout] 264 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 83 | /// SVR Parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:107:1 [INFO] [stdout] | [INFO] [stdout] 107 | /// Epsilon-Support Vector Regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:144:1 [INFO] [stdout] | [INFO] [stdout] 144 | / impl, K: Kernel> SVRParameters { [INFO] [stdout] 145 | | /// Epsilon in the epsilon-SVR model. [INFO] [stdout] 146 | | pub fn with_eps(mut self, eps: T) -> Self { [INFO] [stdout] 147 | | self.eps = eps; [INFO] [stdout] ... | [INFO] [stdout] 169 | | } [INFO] [stdout] 170 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:145:5 [INFO] [stdout] | [INFO] [stdout] 145 | /// Epsilon in the epsilon-SVR model. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:150:5 [INFO] [stdout] | [INFO] [stdout] 150 | /// Regularization parameter. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:155:5 [INFO] [stdout] | [INFO] [stdout] 155 | /// Tolerance for stopping criterion. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:160:5 [INFO] [stdout] | [INFO] [stdout] 160 | /// The kernel function. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:200:1 [INFO] [stdout] | [INFO] [stdout] 200 | / impl, K: Kernel> SVR { [INFO] [stdout] 201 | | /// Fits SVR to your data. [INFO] [stdout] 202 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 203 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 253 | | } [INFO] [stdout] 254 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:201:5 [INFO] [stdout] | [INFO] [stdout] 201 | / /// Fits SVR to your data. [INFO] [stdout] 202 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 203 | | /// * `y` - target values [INFO] [stdout] 204 | | /// * `kernel` - the kernel function [INFO] [stdout] 205 | | /// * `parameters` - optional parameters, use `Default::default()` to set parameters to default values. [INFO] [stdout] | |___________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:231:5 [INFO] [stdout] | [INFO] [stdout] 231 | / /// Predict target values from `x` [INFO] [stdout] 232 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | /// Defines a kernel function [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:37:5 [INFO] [stdout] | [INFO] [stdout] 37 | /// Apply kernel function to x_i and x_j [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:41:1 [INFO] [stdout] | [INFO] [stdout] 41 | /// Pre-defined kernel functions [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | / impl Kernels { [INFO] [stdout] 45 | | /// Linear kernel [INFO] [stdout] 46 | | pub fn linear() -> LinearKernel { [INFO] [stdout] 47 | | LinearKernel {} [INFO] [stdout] ... | [INFO] [stdout] 93 | | } [INFO] [stdout] 94 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:45:5 [INFO] [stdout] | [INFO] [stdout] 45 | /// Linear kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:50:5 [INFO] [stdout] | [INFO] [stdout] 50 | /// Radial basis function kernel (Gaussian) [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:55:5 [INFO] [stdout] | [INFO] [stdout] 55 | / /// Polynomial kernel [INFO] [stdout] 56 | | /// * `degree` - degree of the polynomial [INFO] [stdout] 57 | | /// * `gamma` - kernel coefficient [INFO] [stdout] 58 | | /// * `coef0` - independent term in kernel function [INFO] [stdout] | |_______________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:67:5 [INFO] [stdout] | [INFO] [stdout] 67 | / /// Polynomial kernel [INFO] [stdout] 68 | | /// * `degree` - degree of the polynomial [INFO] [stdout] 69 | | /// * `n_features` - number of features in vector [INFO] [stdout] | |_____________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:79:5 [INFO] [stdout] | [INFO] [stdout] 79 | / /// Sigmoid kernel [INFO] [stdout] 80 | | /// * `gamma` - kernel coefficient [INFO] [stdout] 81 | | /// * `coef0` - independent term in kernel function [INFO] [stdout] | |_______________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:86:5 [INFO] [stdout] | [INFO] [stdout] 86 | / /// Sigmoid kernel [INFO] [stdout] 87 | | /// * `gamma` - kernel coefficient [INFO] [stdout] | |__________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:96:1 [INFO] [stdout] | [INFO] [stdout] 96 | /// Linear Kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:101:1 [INFO] [stdout] | [INFO] [stdout] 101 | /// Radial basis function (Gaussian) kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:109:1 [INFO] [stdout] | [INFO] [stdout] 109 | /// Polynomial kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:121:1 [INFO] [stdout] | [INFO] [stdout] 121 | /// Sigmoid (hyperbolic tangent) kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:100:1 [INFO] [stdout] | [INFO] [stdout] 20 | | //! * [ndarray](https://docs.rs/ndarray) [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 100| / /// Supervised tree-based learning methods [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 83 | /// Parameters of Decision Tree [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:95:1 [INFO] [stdout] | [INFO] [stdout] 95 | /// Decision Tree [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:106:1 [INFO] [stdout] | [INFO] [stdout] 106 | /// The function to measure the quality of a split. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:170:1 [INFO] [stdout] | [INFO] [stdout] 170 | / impl DecisionTreeClassifierParameters { [INFO] [stdout] 171 | | /// Split criteria to use when building a tree. [INFO] [stdout] 172 | | pub fn with_criterion(mut self, criterion: SplitCriterion) -> Self { [INFO] [stdout] 173 | | self.criterion = criterion; [INFO] [stdout] ... | [INFO] [stdout] 190 | | } [INFO] [stdout] 191 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:171:5 [INFO] [stdout] | [INFO] [stdout] 171 | /// Split criteria to use when building a tree. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:176:5 [INFO] [stdout] | [INFO] [stdout] 176 | /// The maximum depth of the tree. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:181:5 [INFO] [stdout] | [INFO] [stdout] 181 | /// The minimum number of samples required to be at a leaf node. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:186:5 [INFO] [stdout] | [INFO] [stdout] 186 | /// The minimum number of samples required to split an internal node. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:321:1 [INFO] [stdout] | [INFO] [stdout] 321 | / impl DecisionTreeClassifier { [INFO] [stdout] 322 | | /// Build a decision tree classifier from the training data. [INFO] [stdout] 323 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 324 | | /// * `y` - the target class values [INFO] [stdout] ... | [INFO] [stdout] 646 | | } [INFO] [stdout] 647 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:322:5 [INFO] [stdout] | [INFO] [stdout] 322 | / /// Build a decision tree classifier from the training data. [INFO] [stdout] 323 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 324 | | /// * `y` - the target class values [INFO] [stdout] | |_______________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:410:5 [INFO] [stdout] | [INFO] [stdout] 410 | / /// Predict class value for `x`. [INFO] [stdout] 411 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:78:1 [INFO] [stdout] | [INFO] [stdout] 78 | /// Parameters of Regression Tree [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:88:1 [INFO] [stdout] | [INFO] [stdout] 88 | /// Regression Tree [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:109:1 [INFO] [stdout] | [INFO] [stdout] 109 | / impl DecisionTreeRegressorParameters { [INFO] [stdout] 110 | | /// The maximum depth of the tree. [INFO] [stdout] 111 | | pub fn with_max_depth(mut self, max_depth: u16) -> Self { [INFO] [stdout] 112 | | self.max_depth = Some(max_depth); [INFO] [stdout] ... | [INFO] [stdout] 124 | | } [INFO] [stdout] 125 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:110:5 [INFO] [stdout] | [INFO] [stdout] 110 | /// The maximum depth of the tree. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:115:5 [INFO] [stdout] | [INFO] [stdout] 115 | /// The minimum number of samples required to be at a leaf node. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:120:5 [INFO] [stdout] | [INFO] [stdout] 120 | /// The minimum number of samples required to split an internal node. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:235:1 [INFO] [stdout] | [INFO] [stdout] 235 | / impl DecisionTreeRegressor { [INFO] [stdout] 236 | | /// Build a decision tree regressor from the training data. [INFO] [stdout] 237 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 238 | | /// * `y` - the target values [INFO] [stdout] ... | [INFO] [stdout] 512 | | } [INFO] [stdout] 513 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:236:5 [INFO] [stdout] | [INFO] [stdout] 236 | / /// Build a decision tree regressor from the training data. [INFO] [stdout] 237 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 238 | | /// * `y` - the target values [INFO] [stdout] | |_________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:310:5 [INFO] [stdout] | [INFO] [stdout] 310 | / /// Predict regression value for `x`. [INFO] [stdout] 311 | | /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features. [INFO] [stdout] | |_____________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unresolved link to `T` [INFO] [stdout] --> src/linalg/mod.rs:89:38 [INFO] [stdout] | [INFO] [stdout] 89 | /// Create a new vector from a &[T] [INFO] [stdout] | ^ no item named `T` in scope [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(rustdoc::broken_intra_doc_links)]` on by default [INFO] [stdout] = help: to escape `[` and `]` characters, add '\' before them like `\[` or `\]` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: this URL is not a hyperlink [INFO] [stdout] --> src/linalg/mod.rs:122:41 [INFO] [stdout] | [INFO] [stdout] 122 | /// Returns [L2 norm] of the vector(https://en.wikipedia.org/wiki/Matrix_norm). [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ help: use an automatic link instead: `` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(rustdoc::bare_urls)]` on by default [INFO] [stdout] = note: bare URLs are not automatically turned into clickable links [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: 583 warnings emitted [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished dev [unoptimized + debuginfo] target(s) in 9.35s [INFO] running `Command { std: "docker" "inspect" "64c171b1df8a5554249fb66f4fe6988d72f5a4068449fa566cca1a64f784d5b7", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "64c171b1df8a5554249fb66f4fe6988d72f5a4068449fa566cca1a64f784d5b7", kill_on_drop: false }` [INFO] [stdout] 64c171b1df8a5554249fb66f4fe6988d72f5a4068449fa566cca1a64f784d5b7