[INFO] cloning repository https://github.com/julama/sc [INFO] running `Command { std: "git" "-c" "credential.helper=" "-c" "credential.helper=/workspace/cargo-home/bin/git-credential-null" "clone" "--bare" "https://github.com/julama/sc" "/workspace/cache/git-repos/https%3A%2F%2Fgithub.com%2Fjulama%2Fsc", kill_on_drop: false }` [INFO] [stderr] Cloning into bare repository '/workspace/cache/git-repos/https%3A%2F%2Fgithub.com%2Fjulama%2Fsc'... [INFO] running `Command { std: "git" "rev-parse" "HEAD", kill_on_drop: false }` [INFO] [stdout] cb0c9bc71b6a2bb937e50c89083be4d9a19b31f2 [INFO] documenting julama/sc against beta-2022-05-20 for beta-1.62-rustdoc-1 [INFO] running `Command { std: "git" "clone" "/workspace/cache/git-repos/https%3A%2F%2Fgithub.com%2Fjulama%2Fsc" "/workspace/builds/worker-7/source", kill_on_drop: false }` [INFO] [stderr] Cloning into '/workspace/builds/worker-7/source'... [INFO] [stderr] done. [INFO] validating manifest of git repo https://github.com/julama/sc 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 git repo https://github.com/julama/sc [INFO] finished tweaking git repo https://github.com/julama/sc [INFO] tweaked toml for git repo https://github.com/julama/sc written to /workspace/builds/worker-7/source/Cargo.toml [INFO] crate git repo https://github.com/julama/sc already has a lockfile, it will not be regenerated [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] [stderr] Blocking waiting for file lock on package cache [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7/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] e2188135a79cf361856dac878b34844b99b7118abcc5b8cc1f56cbfac465b8cc [INFO] running `Command { std: "docker" "start" "-a" "e2188135a79cf361856dac878b34844b99b7118abcc5b8cc1f56cbfac465b8cc", kill_on_drop: false }` [INFO] running `Command { std: "docker" "inspect" "e2188135a79cf361856dac878b34844b99b7118abcc5b8cc1f56cbfac465b8cc", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "e2188135a79cf361856dac878b34844b99b7118abcc5b8cc1f56cbfac465b8cc", kill_on_drop: false }` [INFO] [stdout] e2188135a79cf361856dac878b34844b99b7118abcc5b8cc1f56cbfac465b8cc [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7/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] f60a3ec444521df9e7c886254d1be0a7d19eb2840f119c516baeb633d387e57e [INFO] running `Command { std: "docker" "start" "-a" "f60a3ec444521df9e7c886254d1be0a7d19eb2840f119c516baeb633d387e57e", kill_on_drop: false }` [INFO] [stderr] Compiling autocfg v1.0.1 [INFO] [stderr] Compiling libc v0.2.81 [INFO] [stderr] Compiling getrandom v0.1.15 [INFO] [stderr] Checking cfg-if v0.1.10 [INFO] [stderr] Compiling syn v1.0.55 [INFO] [stderr] Compiling serde_derive v1.0.118 [INFO] [stderr] Checking ppv-lite86 v0.2.10 [INFO] [stderr] Compiling serde v1.0.118 [INFO] [stderr] Compiling quote v1.0.8 [INFO] [stderr] Compiling num-traits v0.2.14 [INFO] [stderr] Compiling num-integer v0.1.44 [INFO] [stderr] Compiling num-bigint v0.3.1 [INFO] [stderr] Compiling num-iter v0.1.42 [INFO] [stderr] Compiling num-rational v0.3.2 [INFO] [stderr] Checking rand_core v0.5.1 [INFO] [stderr] Checking rand_chacha v0.2.2 [INFO] [stderr] Checking rand v0.7.3 [INFO] [stderr] Checking num-complex v0.3.1 [INFO] [stderr] Checking rand_distr v0.3.0 [INFO] [stderr] Checking num v0.3.1 [INFO] [stderr] Documenting smartcore v0.1.0 (/opt/rustwide/workdir) [INFO] [stdout] warning: lint `missing_doc_code_examples` has been renamed to `rustdoc::missing_doc_code_examples` [INFO] [stdout] --> src/lib.rs:9:9 [INFO] [stdout] | [INFO] [stdout] 9 | #![warn(missing_doc_code_examples)] [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^ help: use the new name: `rustdoc::missing_doc_code_examples` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(renamed_and_removed_lints)]` on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unresolved link to `T` [INFO] [stdout] --> src/linalg/mod.rs:88:38 [INFO] [stdout] | [INFO] [stdout] 88 | /// 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:121:41 [INFO] [stdout] | [INFO] [stdout] 121 | /// 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: 3 warnings emitted [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished dev [unoptimized + debuginfo] target(s) in 16.53s [INFO] running `Command { std: "docker" "inspect" "f60a3ec444521df9e7c886254d1be0a7d19eb2840f119c516baeb633d387e57e", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "f60a3ec444521df9e7c886254d1be0a7d19eb2840f119c516baeb633d387e57e", kill_on_drop: false }` [INFO] [stdout] f60a3ec444521df9e7c886254d1be0a7d19eb2840f119c516baeb633d387e57e [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7/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" "DOCS_RS=1" "-e" "RUSTC_BOOTSTRAP=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] 6f0f95aac77f522186d97608caa2a788b491072c8fc16a791d795b816a4a5e2d [INFO] running `Command { std: "docker" "start" "-a" "6f0f95aac77f522186d97608caa2a788b491072c8fc16a791d795b816a4a5e2d", kill_on_drop: false }` [INFO] [stderr] Blocking waiting for file lock on package cache [INFO] [stderr] Compiling autocfg v1.0.1 [INFO] [stderr] Compiling libm v0.2.1 [INFO] [stderr] Compiling libc v0.2.81 [INFO] [stderr] Compiling proc-macro2 v1.0.24 [INFO] [stderr] Compiling getrandom v0.1.15 [INFO] [stderr] Checking cfg-if v0.1.10 [INFO] [stderr] Compiling unicode-xid v0.2.1 [INFO] [stderr] Compiling syn v1.0.55 [INFO] [stderr] Compiling serde_derive v1.0.118 [INFO] [stderr] Checking ppv-lite86 v0.2.10 [INFO] [stderr] Compiling serde v1.0.118 [INFO] [stderr] Compiling num-traits v0.2.14 [INFO] [stderr] Compiling num-integer v0.1.44 [INFO] [stderr] Compiling num-bigint v0.3.1 [INFO] [stderr] Compiling num-rational v0.3.2 [INFO] [stderr] Compiling num-iter v0.1.42 [INFO] [stderr] Compiling quote v1.0.8 [INFO] [stderr] Checking rand_core v0.5.1 [INFO] [stderr] Checking rand_chacha v0.2.2 [INFO] [stderr] Checking num-complex v0.3.1 [INFO] [stderr] Checking rand v0.7.3 [INFO] [stderr] Checking rand_distr v0.3.0 [INFO] [stderr] Checking num v0.3.1 [INFO] [stderr] Documenting smartcore v0.1.0 (/opt/rustwide/workdir) [INFO] [stdout] warning: lint `missing_doc_code_examples` has been renamed to `rustdoc::missing_doc_code_examples` [INFO] [stdout] --> src/lib.rs:9:9 [INFO] [stdout] | [INFO] [stdout] 9 | #![warn(missing_doc_code_examples)] [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^ help: use the new name: `rustdoc::missing_doc_code_examples` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(renamed_and_removed_lints)]` on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:74:1 [INFO] [stdout] | [INFO] [stdout] 74 | /// 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(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:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Nearest Neighbors Search Algorithms and Data Structures [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! Nearest neighbor search is a basic computational tool that is particularly relevant to machine learning, [INFO] [stdout] 4 | | //! where it is often believed that highdimensional datasets have low-dimensional intrinsic structure. [INFO] [stdout] ... | [INFO] [stdout] 29 | | //! [INFO] [stdout] 30 | | //! [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:33:1 [INFO] [stdout] | [INFO] [stdout] 33 | /// 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:73:1 [INFO] [stdout] | [INFO] [stdout] 73 | / impl> CoverTree { [INFO] [stdout] 74 | | /// Construct a cover tree. [INFO] [stdout] 75 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 76 | | /// * `distance` - distance metric to use for searching. This function should extend [`Distance`](../../../math/distance/index.html) ... [INFO] [stdout] ... | [INFO] [stdout] 447 | | } [INFO] [stdout] 448 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/algorithm/neighbour/cover_tree.rs:74:5 [INFO] [stdout] | [INFO] [stdout] 74 | / /// Construct a cover tree. [INFO] [stdout] 75 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 76 | | /// * `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:100:5 [INFO] [stdout] | [INFO] [stdout] 100 | / /// Find k nearest neighbors of `p` [INFO] [stdout] 101 | | /// * `p` - look for k nearest points to `p` [INFO] [stdout] 102 | | /// * `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:182:5 [INFO] [stdout] | [INFO] [stdout] 182 | / /// Find all nearest neighbors within radius `radius` from `p` [INFO] [stdout] 183 | | /// * `p` - look for k nearest points to `p` [INFO] [stdout] 184 | | /// * `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:33:1 [INFO] [stdout] | [INFO] [stdout] 33 | /// 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:41:1 [INFO] [stdout] | [INFO] [stdout] 41 | / impl> LinearKNNSearch { [INFO] [stdout] 42 | | /// Initializes algorithm. [INFO] [stdout] 43 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 44 | | /// * `distance` - distance metric to use for searching. This function should extend [`Distance`](../../../math/distance/index.html) ... [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/algorithm/neighbour/linear_search.rs:42:5 [INFO] [stdout] | [INFO] [stdout] 42 | / /// Initializes algorithm. [INFO] [stdout] 43 | | /// * `data` - vector of data points to search for. [INFO] [stdout] 44 | | /// * `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:53:5 [INFO] [stdout] | [INFO] [stdout] 53 | / /// Find k nearest neighbors [INFO] [stdout] 54 | | /// * `from` - look for k nearest points to `from` [INFO] [stdout] 55 | | /// * `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:90:5 [INFO] [stdout] | [INFO] [stdout] 90 | / /// Find all nearest neighbors within radius `radius` from `p` [INFO] [stdout] 91 | | /// * `p` - look for k nearest points to `p` [INFO] [stdout] 92 | | /// * `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:45:1 [INFO] [stdout] | [INFO] [stdout] 45 | / /// Both, KNN classifier and regressor benefits from underlying search algorithms that helps to speed up queries. [INFO] [stdout] 46 | | /// `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:61:1 [INFO] [stdout] | [INFO] [stdout] 61 | / impl KNNAlgorithmName { [INFO] [stdout] 62 | | pub(crate) fn fit, T>>( [INFO] [stdout] 63 | | &self, [INFO] [stdout] 64 | | data: Vec>, [INFO] [stdout] ... | [INFO] [stdout] 75 | | } [INFO] [stdout] 76 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:76:1 [INFO] [stdout] | [INFO] [stdout] 4 | | clippy::type_complexity, [INFO] [stdout] | |__________________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 76| / /// 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:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | /// DBSCAN clustering algorithm [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:54:1 [INFO] [stdout] | [INFO] [stdout] 54 | /// 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>> DBSCAN { [INFO] [stdout] 84 | | /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 85 | | /// * `data` - training instances to cluster [INFO] [stdout] 86 | | /// * `k` - number of clusters [INFO] [stdout] ... | [INFO] [stdout] 174 | | } [INFO] [stdout] 175 | | } [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 | / /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 85 | | /// * `data` - training instances to cluster [INFO] [stdout] 86 | | /// * `k` - number of clusters [INFO] [stdout] 87 | | /// * `parameters` - cluster parameters [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/dbscan.rs:146:5 [INFO] [stdout] | [INFO] [stdout] 146 | / /// Predict clusters for `x` [INFO] [stdout] 147 | | /// * `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:67:1 [INFO] [stdout] | [INFO] [stdout] 67 | /// 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:102:1 [INFO] [stdout] | [INFO] [stdout] 102 | /// 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:114:1 [INFO] [stdout] | [INFO] [stdout] 114 | / impl KMeans { [INFO] [stdout] 115 | | /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 116 | | /// * `data` - training instances to cluster [INFO] [stdout] 117 | | /// * `k` - number of clusters [INFO] [stdout] ... | [INFO] [stdout] 263 | | } [INFO] [stdout] 264 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:115:5 [INFO] [stdout] | [INFO] [stdout] 115 | / /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features. [INFO] [stdout] 116 | | /// * `data` - training instances to cluster [INFO] [stdout] 117 | | /// * `k` - number of clusters [INFO] [stdout] 118 | | /// * `parameters` - cluster parameters [INFO] [stdout] | |___________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/cluster/kmeans.rs:187:5 [INFO] [stdout] | [INFO] [stdout] 187 | / /// Predict clusters for `x` [INFO] [stdout] 188 | | /// * `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:81:1 [INFO] [stdout] | [INFO] [stdout] 12 | | //! [INFO] [stdout] | |____________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 81 | / /// Matrix decomposition algorithms [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:56:1 [INFO] [stdout] | [INFO] [stdout] 56 | /// 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:84:1 [INFO] [stdout] | [INFO] [stdout] 84 | /// PCA parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:99:1 [INFO] [stdout] | [INFO] [stdout] 99 | / impl> PCA { [INFO] [stdout] 100 | | /// Fits PCA to your data. [INFO] [stdout] 101 | | /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 102 | | /// * `n_components` - number of components to keep. [INFO] [stdout] ... | [INFO] [stdout] 226 | | } [INFO] [stdout] 227 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/decomposition/pca.rs:100:5 [INFO] [stdout] | [INFO] [stdout] 100 | / /// Fits PCA to your data. [INFO] [stdout] 101 | | /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 102 | | /// * `n_components` - number of components to keep. [INFO] [stdout] 103 | | /// * `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:206:5 [INFO] [stdout] | [INFO] [stdout] 206 | / /// Run dimensionality reduction for `x` [INFO] [stdout] 207 | | /// * `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/lib.rs:83:1 [INFO] [stdout] | [INFO] [stdout] 17 | | //! * __Classification__: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Supervised Nearest Neighbors (KNN) [INFO] [stdout] | |__________________________________________________________________________________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 83 | / /// 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:61:1 [INFO] [stdout] | [INFO] [stdout] 61 | / /// Parameters of the Random Forest algorithm. [INFO] [stdout] 62 | | /// 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:79:1 [INFO] [stdout] | [INFO] [stdout] 79 | /// 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:120:1 [INFO] [stdout] | [INFO] [stdout] 120 | / impl RandomForestClassifier { [INFO] [stdout] 121 | | /// Build a forest of trees from the training set. [INFO] [stdout] 122 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 123 | | /// * `y` - the target class values [INFO] [stdout] ... | [INFO] [stdout] 217 | | } [INFO] [stdout] 218 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_classifier.rs:121:5 [INFO] [stdout] | [INFO] [stdout] 121 | / /// Build a forest of trees from the training set. [INFO] [stdout] 122 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 123 | | /// * `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:171:5 [INFO] [stdout] | [INFO] [stdout] 171 | / /// Predict class for `x` [INFO] [stdout] 172 | | /// * `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:60:1 [INFO] [stdout] | [INFO] [stdout] 60 | / /// Parameters of the Random Forest Regressor [INFO] [stdout] 61 | | /// 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:75:1 [INFO] [stdout] | [INFO] [stdout] 75 | /// 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:109:1 [INFO] [stdout] | [INFO] [stdout] 109 | / impl RandomForestRegressor { [INFO] [stdout] 110 | | /// Build a forest of trees from the training set. [INFO] [stdout] 111 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 112 | | /// * `y` - the target class values [INFO] [stdout] ... | [INFO] [stdout] 174 | | } [INFO] [stdout] 175 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/ensemble/random_forest_regressor.rs:110:5 [INFO] [stdout] | [INFO] [stdout] 110 | / /// Build a forest of trees from the training set. [INFO] [stdout] 111 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 112 | | /// * `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:140:5 [INFO] [stdout] | [INFO] [stdout] 140 | / /// Predict class for `x` [INFO] [stdout] 141 | | /// * `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/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:7:1 [INFO] [stdout] | [INFO] [stdout] 7 | /// 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:14:1 [INFO] [stdout] | [INFO] [stdout] 14 | /// Type of error [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:31:1 [INFO] [stdout] | [INFO] [stdout] 31 | / impl Failed { [INFO] [stdout] 32 | | ///get type of error [INFO] [stdout] 33 | | #[inline] [INFO] [stdout] 34 | | pub fn error(&self) -> FailedError { [INFO] [stdout] ... | [INFO] [stdout] 67 | | } [INFO] [stdout] 68 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/error/mod.rs:32:5 [INFO] [stdout] | [INFO] [stdout] 32 | ///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:38:5 [INFO] [stdout] | [INFO] [stdout] 38 | /// 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:45:5 [INFO] [stdout] | [INFO] [stdout] 45 | /// 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:53:5 [INFO] [stdout] | [INFO] [stdout] 53 | /// 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:61:5 [INFO] [stdout] | [INFO] [stdout] 61 | /// 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] 113 | | } [INFO] [stdout] 114 | | } [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:116:1 [INFO] [stdout] | [INFO] [stdout] 116 | /// 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:118:5 [INFO] [stdout] | [INFO] [stdout] 118 | /// 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:123:5 [INFO] [stdout] | [INFO] [stdout] 123 | / /// Compute the Cholesky decomposition of a matrix. The input matrix [INFO] [stdout] 124 | | /// 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:161:5 [INFO] [stdout] | [INFO] [stdout] 161 | /// 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:255:5 [INFO] [stdout] | [INFO] [stdout] 255 | /// 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:181:1 [INFO] [stdout] | [INFO] [stdout] 181 | /// 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:189:1 [INFO] [stdout] | [INFO] [stdout] 189 | /// 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:214:1 [INFO] [stdout] | [INFO] [stdout] 214 | / impl DenseMatrix { [INFO] [stdout] 215 | | /// Create new instance of `DenseMatrix` without copying data. [INFO] [stdout] 216 | | /// `values` should be in column-major order. [INFO] [stdout] 217 | | pub fn new(nrows: usize, ncols: usize, values: Vec) -> Self { [INFO] [stdout] ... | [INFO] [stdout] 318 | | } [INFO] [stdout] 319 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/naive/dense_matrix.rs:215:5 [INFO] [stdout] | [INFO] [stdout] 215 | / /// Create new instance of `DenseMatrix` without copying data. [INFO] [stdout] 216 | | /// `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:225:5 [INFO] [stdout] | [INFO] [stdout] 225 | /// 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:230:5 [INFO] [stdout] | [INFO] [stdout] 230 | /// 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:250:5 [INFO] [stdout] | [INFO] [stdout] 250 | / /// Creates new matrix from an array. [INFO] [stdout] 251 | | /// * `nrows` - number of rows in new matrix. [INFO] [stdout] 252 | | /// * `ncols` - number of columns in new matrix. [INFO] [stdout] 253 | | /// * `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:258:5 [INFO] [stdout] | [INFO] [stdout] 258 | / /// Creates new matrix from a vector. [INFO] [stdout] 259 | | /// * `nrows` - number of rows in new matrix. [INFO] [stdout] 260 | | /// * `ncols` - number of columns in new matrix. [INFO] [stdout] 261 | | /// * `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:276:5 [INFO] [stdout] | [INFO] [stdout] 276 | / /// Creates new row vector (_1xN_ matrix) from an array. [INFO] [stdout] 277 | | /// * `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:282:5 [INFO] [stdout] | [INFO] [stdout] 282 | / /// Creates new row vector (_1xN_ matrix) from a vector. [INFO] [stdout] 283 | | /// * `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:292:5 [INFO] [stdout] | [INFO] [stdout] 292 | / /// Creates new column vector (_1xN_ matrix) from an array. [INFO] [stdout] 293 | | /// * `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:298:5 [INFO] [stdout] | [INFO] [stdout] 298 | / /// Creates new column vector (_1xN_ matrix) from a vector. [INFO] [stdout] 299 | | /// * `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:308:5 [INFO] [stdout] | [INFO] [stdout] 308 | / /// Creates new column vector (_1xN_ matrix) from a vector. [INFO] [stdout] 309 | | /// * `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 j in 0..tau.len() { [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:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// 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:71:5 [INFO] [stdout] | [INFO] [stdout] 71 | / /// Get an element of a vector [INFO] [stdout] 72 | | /// * `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:75:5 [INFO] [stdout] | [INFO] [stdout] 75 | / /// Set an element at `i` to `x` [INFO] [stdout] 76 | | /// * `i` - index of an element [INFO] [stdout] 77 | | /// * `x` - new value [INFO] [stdout] | |_________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:80:5 [INFO] [stdout] | [INFO] [stdout] 80 | /// 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:83:5 [INFO] [stdout] | [INFO] [stdout] 83 | /// 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:103:5 [INFO] [stdout] | [INFO] [stdout] 103 | /// 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:106:5 [INFO] [stdout] | [INFO] [stdout] 106 | /// 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:109:5 [INFO] [stdout] | [INFO] [stdout] 109 | /// 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:112:5 [INFO] [stdout] | [INFO] [stdout] 112 | /// 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:115:5 [INFO] [stdout] | [INFO] [stdout] 115 | /// Vector dot product [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:118:5 [INFO] [stdout] | [INFO] [stdout] 118 | /// 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:121:5 [INFO] [stdout] | [INFO] [stdout] 121 | /// 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:124:5 [INFO] [stdout] | [INFO] [stdout] 124 | /// 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:127:5 [INFO] [stdout] | [INFO] [stdout] 127 | /// 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:130:5 [INFO] [stdout] | [INFO] [stdout] 130 | /// 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:133:5 [INFO] [stdout] | [INFO] [stdout] 133 | /// 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:136:5 [INFO] [stdout] | [INFO] [stdout] 136 | /// 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:139:5 [INFO] [stdout] | [INFO] [stdout] 139 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:147:5 [INFO] [stdout] | [INFO] [stdout] 147 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:155:5 [INFO] [stdout] | [INFO] [stdout] 155 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:163:5 [INFO] [stdout] | [INFO] [stdout] 163 | /// Subtract scalar [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:171:5 [INFO] [stdout] | [INFO] [stdout] 171 | /// 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:178:5 [INFO] [stdout] | [INFO] [stdout] 178 | /// 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:185:5 [INFO] [stdout] | [INFO] [stdout] 185 | /// 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:192:5 [INFO] [stdout] | [INFO] [stdout] 192 | /// 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:199:5 [INFO] [stdout] | [INFO] [stdout] 199 | /// 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:202:5 [INFO] [stdout] | [INFO] [stdout] 202 | /// 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:205:5 [INFO] [stdout] | [INFO] [stdout] 205 | /// 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:208:5 [INFO] [stdout] | [INFO] [stdout] 208 | /// 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:211:5 [INFO] [stdout] | [INFO] [stdout] 211 | /// 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:218:5 [INFO] [stdout] | [INFO] [stdout] 218 | /// 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:225:5 [INFO] [stdout] | [INFO] [stdout] 225 | /// 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:232:5 [INFO] [stdout] | [INFO] [stdout] 232 | /// 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:239:5 [INFO] [stdout] | [INFO] [stdout] 239 | /// 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:251:5 [INFO] [stdout] | [INFO] [stdout] 251 | /// 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:255:5 [INFO] [stdout] | [INFO] [stdout] 255 | /// Computes variance. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:270:5 [INFO] [stdout] | [INFO] [stdout] 270 | /// 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:276:1 [INFO] [stdout] | [INFO] [stdout] 276 | /// Generic matrix type. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:278:5 [INFO] [stdout] | [INFO] [stdout] 278 | / /// Row vector that is associated with this matrix type, [INFO] [stdout] 279 | | /// e.g. if we have an implementation of sparce matrix [INFO] [stdout] 280 | | /// we should have an associated sparce vector type that [INFO] [stdout] 281 | | /// 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:284:5 [INFO] [stdout] | [INFO] [stdout] 284 | /// 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:287:5 [INFO] [stdout] | [INFO] [stdout] 287 | /// 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:290:5 [INFO] [stdout] | [INFO] [stdout] 290 | / /// Get an element of the matrix. [INFO] [stdout] 291 | | /// * `row` - row number [INFO] [stdout] 292 | | /// * `col` - column number [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 | / /// Get a vector with elements of the `row`'th row [INFO] [stdout] 296 | | /// * `row` - row number [INFO] [stdout] | |____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:299:5 [INFO] [stdout] | [INFO] [stdout] 299 | / /// Get the `row`'th row [INFO] [stdout] 300 | | /// * `row` - row number [INFO] [stdout] | |____________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linalg/mod.rs:303:5 [INFO] [stdout] | [INFO] [stdout] 303 | / /// Copies a vector with elements of the `row`'th row into `result` [INFO] [stdout] 304 | | /// * `row` - row number [INFO] [stdout] 305 | | /// * `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:308:5 [INFO] [stdout] | [INFO] [stdout] 308 | / /// Get a vector with elements of the `col`'th column [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 | / /// Copies a vector with elements of the `col`'th column into `result` [INFO] [stdout] 313 | | /// * `col` - column number [INFO] [stdout] 314 | | /// * `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:317:5 [INFO] [stdout] | [INFO] [stdout] 317 | /// 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:320:5 [INFO] [stdout] | [INFO] [stdout] 320 | /// 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:323:5 [INFO] [stdout] | [INFO] [stdout] 323 | /// 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:326:5 [INFO] [stdout] | [INFO] [stdout] 326 | /// 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:329:5 [INFO] [stdout] | [INFO] [stdout] 329 | /// 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:332:5 [INFO] [stdout] | [INFO] [stdout] 332 | /// 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:408:5 [INFO] [stdout] | [INFO] [stdout] 408 | /// 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:411:5 [INFO] [stdout] | [INFO] [stdout] 411 | /// 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:414:5 [INFO] [stdout] | [INFO] [stdout] 414 | /// 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:417:5 [INFO] [stdout] | [INFO] [stdout] 417 | /// 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:420:5 [INFO] [stdout] | [INFO] [stdout] 420 | /// 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:423:5 [INFO] [stdout] | [INFO] [stdout] 423 | /// 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:426:5 [INFO] [stdout] | [INFO] [stdout] 426 | /// 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:429:5 [INFO] [stdout] | [INFO] [stdout] 429 | /// 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:432:5 [INFO] [stdout] | [INFO] [stdout] 432 | /// 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:435:5 [INFO] [stdout] | [INFO] [stdout] 435 | /// 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:442:5 [INFO] [stdout] | [INFO] [stdout] 442 | /// 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:449:5 [INFO] [stdout] | [INFO] [stdout] 449 | /// 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:456:5 [INFO] [stdout] | [INFO] [stdout] 456 | /// 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:463:5 [INFO] [stdout] | [INFO] [stdout] 463 | /// 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:466:5 [INFO] [stdout] | [INFO] [stdout] 466 | /// 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:469:5 [INFO] [stdout] | [INFO] [stdout] 469 | /// 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:472:5 [INFO] [stdout] | [INFO] [stdout] 472 | /// 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:475:5 [INFO] [stdout] | [INFO] [stdout] 475 | /// 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:482:5 [INFO] [stdout] | [INFO] [stdout] 482 | /// 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:489:5 [INFO] [stdout] | [INFO] [stdout] 489 | /// 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:496:5 [INFO] [stdout] | [INFO] [stdout] 496 | /// 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:503:5 [INFO] [stdout] | [INFO] [stdout] 503 | /// 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:506:5 [INFO] [stdout] | [INFO] [stdout] 506 | /// 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:509:5 [INFO] [stdout] | [INFO] [stdout] 509 | /// 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:512:5 [INFO] [stdout] | [INFO] [stdout] 512 | /// 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:515:5 [INFO] [stdout] | [INFO] [stdout] 515 | /// 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:518:5 [INFO] [stdout] | [INFO] [stdout] 518 | /// 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:521:5 [INFO] [stdout] | [INFO] [stdout] 521 | /// 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:542:5 [INFO] [stdout] | [INFO] [stdout] 542 | /// 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:545:5 [INFO] [stdout] | [INFO] [stdout] 545 | /// 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:548:5 [INFO] [stdout] | [INFO] [stdout] 548 | /// 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:555:5 [INFO] [stdout] | [INFO] [stdout] 555 | /// 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:558:5 [INFO] [stdout] | [INFO] [stdout] 558 | /// 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:561:5 [INFO] [stdout] | [INFO] [stdout] 561 | /// 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:578:5 [INFO] [stdout] | [INFO] [stdout] 578 | /// 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:581:5 [INFO] [stdout] | [INFO] [stdout] 581 | /// 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:584:5 [INFO] [stdout] | [INFO] [stdout] 584 | /// 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:609:5 [INFO] [stdout] | [INFO] [stdout] 609 | /// 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:613:1 [INFO] [stdout] | [INFO] [stdout] 613 | /// 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:88:1 [INFO] [stdout] | [INFO] [stdout] 21 | | //! * __Evaluation Metrics__: Accuracy, AUC, Recall, Precision, F1, Mean Absolute Error, Mean Squared Error, R2 [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 88 | / /// 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/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:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | /// Lasso regression parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:50:1 [INFO] [stdout] | [INFO] [stdout] 50 | /// Lasso regressor [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:82:1 [INFO] [stdout] | [INFO] [stdout] 82 | / impl> Lasso { [INFO] [stdout] 83 | | /// Fits Lasso regression to your data. [INFO] [stdout] 84 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 85 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 184 | | } [INFO] [stdout] 185 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/lasso.rs:83:5 [INFO] [stdout] | [INFO] [stdout] 83 | / /// Fits Lasso regression to your data. [INFO] [stdout] 84 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 85 | | /// * `y` - target values [INFO] [stdout] 86 | | /// * `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:149:5 [INFO] [stdout] | [INFO] [stdout] 149 | / /// Predict target values from `x` [INFO] [stdout] 150 | | /// * `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:158:5 [INFO] [stdout] | [INFO] [stdout] 158 | /// 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:163:5 [INFO] [stdout] | [INFO] [stdout] 163 | /// 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:72:1 [INFO] [stdout] | [INFO] [stdout] 72 | /// 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:80:1 [INFO] [stdout] | [INFO] [stdout] 80 | /// 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:87:1 [INFO] [stdout] | [INFO] [stdout] 87 | /// Linear Regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:110:1 [INFO] [stdout] | [INFO] [stdout] 110 | / impl> LinearRegression { [INFO] [stdout] 111 | | /// Fits Linear Regression to your data. [INFO] [stdout] 112 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 113 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 164 | | } [INFO] [stdout] 165 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/linear_regression.rs:111:5 [INFO] [stdout] | [INFO] [stdout] 111 | / /// Fits Linear Regression to your data. [INFO] [stdout] 112 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 113 | | /// * `y` - target values [INFO] [stdout] 114 | | /// * `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:147:5 [INFO] [stdout] | [INFO] [stdout] 147 | / /// Predict target values from `x` [INFO] [stdout] 148 | | /// * `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:156:5 [INFO] [stdout] | [INFO] [stdout] 156 | /// 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:161:5 [INFO] [stdout] | [INFO] [stdout] 161 | /// 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:69:1 [INFO] [stdout] | [INFO] [stdout] 69 | /// Logistic Regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:210:1 [INFO] [stdout] | [INFO] [stdout] 210 | / impl> LogisticRegression { [INFO] [stdout] 211 | | /// Fits Logistic Regression to your data. [INFO] [stdout] 212 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 213 | | /// * `y` - target class values [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/linear/logistic_regression.rs:211:5 [INFO] [stdout] | [INFO] [stdout] 211 | / /// Fits Logistic Regression to your data. [INFO] [stdout] 212 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 213 | | /// * `y` - target class values [INFO] [stdout] | |___________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/logistic_regression.rs:286:5 [INFO] [stdout] | [INFO] [stdout] 286 | / /// Predict class labels for samples in `x`. [INFO] [stdout] 287 | | /// * `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:320:5 [INFO] [stdout] | [INFO] [stdout] 320 | /// 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:325:5 [INFO] [stdout] | [INFO] [stdout] 325 | /// 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:92:1 [INFO] [stdout] | [INFO] [stdout] 92 | /// Ridge regression [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:117:1 [INFO] [stdout] | [INFO] [stdout] 117 | / impl> RidgeRegression { [INFO] [stdout] 118 | | /// Fits ridge regression to your data. [INFO] [stdout] 119 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 120 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 230 | | } [INFO] [stdout] 231 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/linear/ridge_regression.rs:118:5 [INFO] [stdout] | [INFO] [stdout] 118 | / /// Fits ridge regression to your data. [INFO] [stdout] 119 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 120 | | /// * `y` - target values [INFO] [stdout] 121 | | /// * `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:213:5 [INFO] [stdout] | [INFO] [stdout] 213 | / /// Predict target values from `x` [INFO] [stdout] 214 | | /// * `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:222:5 [INFO] [stdout] | [INFO] [stdout] 222 | /// 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:227:5 [INFO] [stdout] | [INFO] [stdout] 227 | /// Get estimate of intercept [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:90:1 [INFO] [stdout] | [INFO] [stdout] 90 | /// 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:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// 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:31:1 [INFO] [stdout] | [INFO] [stdout] 31 | / impl Euclidian { [INFO] [stdout] 32 | | #[inline] [INFO] [stdout] 33 | | pub(crate) fn squared_distance(x: &Vec, y: &Vec) -> T { [INFO] [stdout] 34 | | if x.len() != y.len() { [INFO] [stdout] ... | [INFO] [stdout] 45 | | } [INFO] [stdout] 46 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/hamming.rs:28:1 [INFO] [stdout] | [INFO] [stdout] 28 | /// 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:54:1 [INFO] [stdout] | [INFO] [stdout] 54 | /// Mahalanobis distance. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mahalanobis.rs:64:1 [INFO] [stdout] | [INFO] [stdout] 64 | / impl> Mahalanobis { [INFO] [stdout] 65 | | /// Constructs new instance of `Mahalanobis` from given dataset [INFO] [stdout] 66 | | /// * `data` - a matrix of _NxM_ where _N_ is number of observations and _M_ is number of attributes [INFO] [stdout] 67 | | pub fn new(data: &M) -> Mahalanobis { [INFO] [stdout] ... | [INFO] [stdout] 87 | | } [INFO] [stdout] 88 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/mahalanobis.rs:65:5 [INFO] [stdout] | [INFO] [stdout] 65 | / /// Constructs new instance of `Mahalanobis` from given dataset [INFO] [stdout] 66 | | /// * `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:77:5 [INFO] [stdout] | [INFO] [stdout] 77 | / /// Constructs new instance of `Mahalanobis` from given covariance matrix [INFO] [stdout] 78 | | /// * `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:26:1 [INFO] [stdout] | [INFO] [stdout] 26 | /// Manhattan distance [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/math/distance/minkowski.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | /// 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:24:1 [INFO] [stdout] | [INFO] [stdout] 24 | /// Accuracy metric. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/accuracy.rs:28:1 [INFO] [stdout] | [INFO] [stdout] 28 | / impl Accuracy { [INFO] [stdout] 29 | | /// Function that calculated accuracy score. [INFO] [stdout] 30 | | /// * `y_true` - cround truth (correct) labels [INFO] [stdout] 31 | | /// * `y_pred` - predicted labels, as returned by a classifier. [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/metrics/accuracy.rs:29:5 [INFO] [stdout] | [INFO] [stdout] 29 | / /// Function that calculated accuracy score. [INFO] [stdout] 30 | | /// * `y_true` - cround truth (correct) labels [INFO] [stdout] 31 | | /// * `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:29:1 [INFO] [stdout] | [INFO] [stdout] 29 | /// 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:33:1 [INFO] [stdout] | [INFO] [stdout] 33 | / impl AUC { [INFO] [stdout] 34 | | /// AUC score. [INFO] [stdout] 35 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 36 | | /// * `y_pred_probabilities` - probability estimates, as returned by a classifier. [INFO] [stdout] ... | [INFO] [stdout] 87 | | } [INFO] [stdout] 88 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/auc.rs:34:5 [INFO] [stdout] | [INFO] [stdout] 34 | / /// AUC score. [INFO] [stdout] 35 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 36 | | /// * `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:8:1 [INFO] [stdout] | [INFO] [stdout] 8 | /// 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:11:1 [INFO] [stdout] | [INFO] [stdout] 11 | / impl HCVScore { [INFO] [stdout] 12 | | /// Computes Homogeneity, completeness and V-Measure scores at once. [INFO] [stdout] 13 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 14 | | /// * `labels_pred` - cluster labels to evaluate. [INFO] [stdout] ... | [INFO] [stdout] 37 | | } [INFO] [stdout] 38 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/cluster_hcv.rs:12:5 [INFO] [stdout] | [INFO] [stdout] 12 | / /// Computes Homogeneity, completeness and V-Measure scores at once. [INFO] [stdout] 13 | | /// * `labels_true` - ground truth class labels to be used as a reference. [INFO] [stdout] 14 | | /// * `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:28:1 [INFO] [stdout] | [INFO] [stdout] 28 | /// F-measure [INFO] [stdout] | ^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/f1.rs:35:1 [INFO] [stdout] | [INFO] [stdout] 35 | / impl F1 { [INFO] [stdout] 36 | | /// Computes F1 score [INFO] [stdout] 37 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 38 | | /// * `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/f1.rs:36:5 [INFO] [stdout] | [INFO] [stdout] 36 | / /// Computes F1 score [INFO] [stdout] 37 | | /// * `y_true` - cround truth (correct) labels. [INFO] [stdout] 38 | | /// * `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:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// 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:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | / impl MeanAbsoluteError { [INFO] [stdout] 31 | | /// Computes mean absolute error [INFO] [stdout] 32 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 33 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] ... | [INFO] [stdout] 50 | | } [INFO] [stdout] 51 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_absolute_error.rs:31:5 [INFO] [stdout] | [INFO] [stdout] 31 | / /// Computes mean absolute error [INFO] [stdout] 32 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 33 | | /// * `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:27:1 [INFO] [stdout] | [INFO] [stdout] 27 | /// 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:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | / impl MeanSquareError { [INFO] [stdout] 31 | | /// Computes mean squared error [INFO] [stdout] 32 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 33 | | /// * `y_pred` - Estimated target values. [INFO] [stdout] ... | [INFO] [stdout] 50 | | } [INFO] [stdout] 51 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/mean_squared_error.rs:31:5 [INFO] [stdout] | [INFO] [stdout] 31 | / /// Computes mean squared error [INFO] [stdout] 32 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 33 | | /// * `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:26:1 [INFO] [stdout] | [INFO] [stdout] 26 | /// Precision metric. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/precision.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | / impl Precision { [INFO] [stdout] 31 | | /// Calculated precision 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] 71 | | } [INFO] [stdout] 72 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/precision.rs:31:5 [INFO] [stdout] | [INFO] [stdout] 31 | / /// Calculated precision 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/r2.rs:26:1 [INFO] [stdout] | [INFO] [stdout] 26 | /// 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:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | / impl R2 { [INFO] [stdout] 31 | | /// Computes R2 score [INFO] [stdout] 32 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 33 | | /// * `y_pred` - Estimated target values. [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/metrics/r2.rs:31:5 [INFO] [stdout] | [INFO] [stdout] 31 | / /// Computes R2 score [INFO] [stdout] 32 | | /// * `y_true` - Ground truth (correct) target values. [INFO] [stdout] 33 | | /// * `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:26:1 [INFO] [stdout] | [INFO] [stdout] 26 | /// Recall metric. [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/recall.rs:30:1 [INFO] [stdout] | [INFO] [stdout] 30 | / impl Recall { [INFO] [stdout] 31 | | /// Calculated recall 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] 71 | | } [INFO] [stdout] 72 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/metrics/recall.rs:31:5 [INFO] [stdout] | [INFO] [stdout] 31 | / /// Calculated recall 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/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:1:1 [INFO] [stdout] | [INFO] [stdout] 1 | / //! # Model Selection methods [INFO] [stdout] 2 | | //! [INFO] [stdout] 3 | | //! In statistics and machine learning we usually split our data into multiple subsets: training data and testing data (and sometimes to ... [INFO] [stdout] 4 | | //! and fit our model on the train data, in order to make predictions on the test data. We do that to avoid overfitting or underfitting m... [INFO] [stdout] ... | [INFO] [stdout] 9 | | //! [INFO] [stdout] 10 | | //! In SmartCore you can split your data into training and test datasets using `train_test_split` function. [INFO] [stdout] | |___________________________________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:19:1 [INFO] [stdout] | [INFO] [stdout] 19 | / /// Splits data into 2 disjoint datasets. [INFO] [stdout] 20 | | /// * `x` - features, matrix of size _NxM_ where _N_ is number of samples and _M_ is number of attributes. [INFO] [stdout] 21 | | /// * `y` - target values, should be of size _M_ [INFO] [stdout] 22 | | /// * `test_size`, (0, 1] - the proportion of the dataset to include in the test split. [INFO] [stdout] | |_______________________________________________________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:85:1 [INFO] [stdout] | [INFO] [stdout] 85 | / /// [INFO] [stdout] 86 | | /// KFold Cross-Validation [INFO] [stdout] 87 | | /// [INFO] [stdout] | |___^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:89:5 [INFO] [stdout] | [INFO] [stdout] 89 | /// Returns integer indices corresponding to test sets [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:92:5 [INFO] [stdout] | [INFO] [stdout] 92 | /// Returns masksk corresponding to test sets [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/model_selection/mod.rs:95:5 [INFO] [stdout] | [INFO] [stdout] 95 | / /// Return a tuple containing the the training set indices for that split and [INFO] [stdout] 96 | | /// 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:100:1 [INFO] [stdout] | [INFO] [stdout] 100 | / /// [INFO] [stdout] 101 | | /// An implementation of KFold [INFO] [stdout] 102 | | /// [INFO] [stdout] | |___^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:95:1 [INFO] [stdout] | [INFO] [stdout] 95 | /// Supervised learning algorithms based on applying the Bayes theorem with the independence assumptions between predictors [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:97:1 [INFO] [stdout] | [INFO] [stdout] 33 | | //! smartcore = "0.1.0" [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 97 | / /// 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:44:1 [INFO] [stdout] | [INFO] [stdout] 44 | /// `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:55:1 [INFO] [stdout] | [INFO] [stdout] 55 | /// 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:98:1 [INFO] [stdout] | [INFO] [stdout] 98 | / impl, T>> KNNClassifier { [INFO] [stdout] 99 | | /// Fits KNN classifier to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 100 | | /// * `x` - training data [INFO] [stdout] 101 | | /// * `y` - vector with target values (classes) of length N [INFO] [stdout] ... | [INFO] [stdout] 183 | | } [INFO] [stdout] 184 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_classifier.rs:99:5 [INFO] [stdout] | [INFO] [stdout] 99 | / /// Fits KNN classifier to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 100 | | /// * `x` - training data [INFO] [stdout] 101 | | /// * `y` - vector with target values (classes) of length N [INFO] [stdout] 102 | | /// * `distance` - a function that defines a distance between each pair of point in training data. [INFO] [stdout] 103 | | /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait. [INFO] [stdout] 104 | | /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions. [INFO] [stdout] 105 | | /// * `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:150:5 [INFO] [stdout] | [INFO] [stdout] 150 | / /// Estimates the class labels for the provided data. [INFO] [stdout] 151 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 152 | | /// 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:46:1 [INFO] [stdout] | [INFO] [stdout] 46 | /// `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:57:1 [INFO] [stdout] | [INFO] [stdout] 57 | /// 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:91:1 [INFO] [stdout] | [INFO] [stdout] 91 | / impl, T>> KNNRegressor { [INFO] [stdout] 92 | | /// Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 93 | | /// * `x` - training data [INFO] [stdout] 94 | | /// * `y` - vector with real values [INFO] [stdout] ... | [INFO] [stdout] 161 | | } [INFO] [stdout] 162 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/neighbors/knn_regressor.rs:92:5 [INFO] [stdout] | [INFO] [stdout] 92 | / /// Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features. [INFO] [stdout] 93 | | /// * `x` - training data [INFO] [stdout] 94 | | /// * `y` - vector with real values [INFO] [stdout] 95 | | /// * `distance` - a function that defines a distance between each pair of point in training data. [INFO] [stdout] 96 | | /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait. [INFO] [stdout] 97 | | /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions. [INFO] [stdout] 98 | | /// * `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:134:5 [INFO] [stdout] | [INFO] [stdout] 134 | / /// Predict the target for the provided data. [INFO] [stdout] 135 | | /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features. [INFO] [stdout] 136 | | /// 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:50:1 [INFO] [stdout] | [INFO] [stdout] 50 | /// 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:59:1 [INFO] [stdout] | [INFO] [stdout] 59 | / impl KNNWeightFunction { [INFO] [stdout] 60 | | fn calc_weights(&self, distances: Vec) -> std::vec::Vec { [INFO] [stdout] 61 | | match *self { [INFO] [stdout] 62 | | KNNWeightFunction::Distance => { [INFO] [stdout] ... | [INFO] [stdout] 76 | | } [INFO] [stdout] 77 | | } [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] 24 | | //! we do recommend to go with one of the popular linear algebra libraries available in Rust. At this moment we support these packages: [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 100| / /// Support Vector Machines [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:96:1 [INFO] [stdout] | [INFO] [stdout] 96 | /// SVC Parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:111:1 [INFO] [stdout] | [INFO] [stdout] 111 | /// Support Vector Classifier [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:161:1 [INFO] [stdout] | [INFO] [stdout] 161 | / impl, K: Kernel> SVC { [INFO] [stdout] 162 | | /// Fits SVC to your data. [INFO] [stdout] 163 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 164 | | /// * `y` - class labels [INFO] [stdout] ... | [INFO] [stdout] 245 | | } [INFO] [stdout] 246 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svc.rs:162:5 [INFO] [stdout] | [INFO] [stdout] 162 | / /// Fits SVC to your data. [INFO] [stdout] 163 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 164 | | /// * `y` - class labels [INFO] [stdout] 165 | | /// * `kernel` - the kernel function [INFO] [stdout] 166 | | /// * `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:215:5 [INFO] [stdout] | [INFO] [stdout] 215 | / /// Predicts estimated class labels from `x` [INFO] [stdout] 216 | | /// * `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:86:1 [INFO] [stdout] | [INFO] [stdout] 86 | /// SVR Parameters [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:102:1 [INFO] [stdout] | [INFO] [stdout] 102 | /// 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:148:1 [INFO] [stdout] | [INFO] [stdout] 148 | / impl, K: Kernel> SVR { [INFO] [stdout] 149 | | /// Fits SVR to your data. [INFO] [stdout] 150 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 151 | | /// * `y` - target values [INFO] [stdout] ... | [INFO] [stdout] 202 | | } [INFO] [stdout] 203 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/svr.rs:149:5 [INFO] [stdout] | [INFO] [stdout] 149 | / /// Fits SVR to your data. [INFO] [stdout] 150 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 151 | | /// * `y` - target values [INFO] [stdout] 152 | | /// * `kernel` - the kernel function [INFO] [stdout] 153 | | /// * `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:180:5 [INFO] [stdout] | [INFO] [stdout] 180 | / /// Predict target values from `x` [INFO] [stdout] 181 | | /// * `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:34:1 [INFO] [stdout] | [INFO] [stdout] 34 | /// 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:36:5 [INFO] [stdout] | [INFO] [stdout] 36 | /// 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:40:1 [INFO] [stdout] | [INFO] [stdout] 40 | /// 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:43:1 [INFO] [stdout] | [INFO] [stdout] 43 | / impl Kernels { [INFO] [stdout] 44 | | /// Linear kernel [INFO] [stdout] 45 | | pub fn linear() -> LinearKernel { [INFO] [stdout] 46 | | LinearKernel {} [INFO] [stdout] ... | [INFO] [stdout] 92 | | } [INFO] [stdout] 93 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:44:5 [INFO] [stdout] | [INFO] [stdout] 44 | /// Linear kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:49:5 [INFO] [stdout] | [INFO] [stdout] 49 | /// 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:54:5 [INFO] [stdout] | [INFO] [stdout] 54 | / /// Polynomial kernel [INFO] [stdout] 55 | | /// * `degree` - degree of the polynomial [INFO] [stdout] 56 | | /// * `gamma` - kernel coefficient [INFO] [stdout] 57 | | /// * `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:66:5 [INFO] [stdout] | [INFO] [stdout] 66 | / /// Polynomial kernel [INFO] [stdout] 67 | | /// * `degree` - degree of the polynomial [INFO] [stdout] 68 | | /// * `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:78:5 [INFO] [stdout] | [INFO] [stdout] 78 | / /// Sigmoid kernel [INFO] [stdout] 79 | | /// * `gamma` - kernel coefficient [INFO] [stdout] 80 | | /// * `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:85:5 [INFO] [stdout] | [INFO] [stdout] 85 | / /// Sigmoid kernel [INFO] [stdout] 86 | | /// * `gamma` - kernel coefficient [INFO] [stdout] | |__________________________________________^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:95:1 [INFO] [stdout] | [INFO] [stdout] 95 | /// Linear Kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:99:1 [INFO] [stdout] | [INFO] [stdout] 99 | /// 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:105:1 [INFO] [stdout] | [INFO] [stdout] 105 | /// Polynomial kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/svm/mod.rs:115:1 [INFO] [stdout] | [INFO] [stdout] 115 | /// Sigmoid (hyperbolic tangent) kernel [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/lib.rs:102:1 [INFO] [stdout] | [INFO] [stdout] 20 | | //! * __Distance Metrics__: Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis [INFO] [stdout] | |_________________________________________________________________________________________________________________^ [INFO] [stdout] ... [INFO] [stdout] 102| / /// 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:79:1 [INFO] [stdout] | [INFO] [stdout] 79 | /// 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:91:1 [INFO] [stdout] | [INFO] [stdout] 91 | /// 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:101:1 [INFO] [stdout] | [INFO] [stdout] 101 | /// 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:272:1 [INFO] [stdout] | [INFO] [stdout] 272 | / impl DecisionTreeClassifier { [INFO] [stdout] 273 | | /// Build a decision tree classifier from the training data. [INFO] [stdout] 274 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 275 | | /// * `y` - the target class values [INFO] [stdout] ... | [INFO] [stdout] 590 | | } [INFO] [stdout] 591 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_classifier.rs:273:5 [INFO] [stdout] | [INFO] [stdout] 273 | / /// Build a decision tree classifier from the training data. [INFO] [stdout] 274 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 275 | | /// * `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:353:5 [INFO] [stdout] | [INFO] [stdout] 353 | / /// Predict class value for `x`. [INFO] [stdout] 354 | | /// * `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:74:1 [INFO] [stdout] | [INFO] [stdout] 74 | /// 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:84:1 [INFO] [stdout] | [INFO] [stdout] 84 | /// 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:192:1 [INFO] [stdout] | [INFO] [stdout] 192 | / impl DecisionTreeRegressor { [INFO] [stdout] 193 | | /// Build a decision tree regressor from the training data. [INFO] [stdout] 194 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 195 | | /// * `y` - the target values [INFO] [stdout] ... | [INFO] [stdout] 462 | | } [INFO] [stdout] 463 | | } [INFO] [stdout] | |_^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: missing code example in this documentation [INFO] [stdout] --> src/tree/decision_tree_regressor.rs:193:5 [INFO] [stdout] | [INFO] [stdout] 193 | / /// Build a decision tree regressor from the training data. [INFO] [stdout] 194 | | /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation. [INFO] [stdout] 195 | | /// * `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:259:5 [INFO] [stdout] | [INFO] [stdout] 259 | / /// Predict regression value 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: unresolved link to `T` [INFO] [stdout] --> src/linalg/mod.rs:88:38 [INFO] [stdout] | [INFO] [stdout] 88 | /// 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:121:41 [INFO] [stdout] | [INFO] [stdout] 121 | /// 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: 381 warnings emitted [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished dev [unoptimized + debuginfo] target(s) in 15.71s [INFO] running `Command { std: "docker" "inspect" "6f0f95aac77f522186d97608caa2a788b491072c8fc16a791d795b816a4a5e2d", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "6f0f95aac77f522186d97608caa2a788b491072c8fc16a791d795b816a4a5e2d", kill_on_drop: false }` [INFO] [stdout] 6f0f95aac77f522186d97608caa2a788b491072c8fc16a791d795b816a4a5e2d