[INFO] fetching crate sklears-discriminant-analysis 0.1.0-alpha.1... [INFO] checking sklears-discriminant-analysis-0.1.0-alpha.1 against master#e22dab387f6b4f6a87dfc54ac2f6013dddb41e68 for pr-149195 [INFO] extracting crate sklears-discriminant-analysis 0.1.0-alpha.1 into /workspace/builds/worker-6-tc1/source [INFO] started tweaking crates.io crate sklears-discriminant-analysis 0.1.0-alpha.1 [INFO] finished tweaking crates.io crate sklears-discriminant-analysis 0.1.0-alpha.1 [INFO] tweaked toml for crates.io crate sklears-discriminant-analysis 0.1.0-alpha.1 written to /workspace/builds/worker-6-tc1/source/Cargo.toml [INFO] validating manifest of crates.io crate sklears-discriminant-analysis 0.1.0-alpha.1 on toolchain e22dab387f6b4f6a87dfc54ac2f6013dddb41e68 [INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+e22dab387f6b4f6a87dfc54ac2f6013dddb41e68" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }` [INFO] crate crates.io crate sklears-discriminant-analysis 0.1.0-alpha.1 already has a lockfile, it will not be regenerated [INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+e22dab387f6b4f6a87dfc54ac2f6013dddb41e68" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }` [INFO] [stderr] Updating crates.io index [INFO] [stderr] Downloading crates ... [INFO] [stderr] Downloaded deflate64 v0.1.9 [INFO] [stderr] Downloaded friedrich v0.5.0 [INFO] [stderr] Downloaded zip v5.1.1 [INFO] [stderr] Downloaded proptest v1.8.0 [INFO] [stderr] Downloaded sklears-utils v0.1.0-alpha.1 [INFO] [stderr] Downloaded serde-pickle v1.2.0 [INFO] [stderr] Downloaded scirs2-sparse v0.1.0-rc.1 [INFO] [stderr] Downloaded scirs2-optimize v0.1.0-rc.1 [INFO] [stderr] Downloaded sklears-core v0.1.0-alpha.1 [INFO] [stderr] Downloaded scirs2-linalg v0.1.0-rc.1 [INFO] [stderr] Downloaded numrs2 v0.1.0-beta.3 [INFO] [stderr] Downloaded scirs2-core v0.1.0-rc.1 [INFO] [stderr] Downloaded scirs2-stats v0.1.0-rc.1 [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc1/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@sha256:94a0c148923f5b2b52a63ef0eeb1882ad339ab61bce784c8077cbe41c61feb6c" "/opt/rustwide/cargo-home/bin/cargo" "+e22dab387f6b4f6a87dfc54ac2f6013dddb41e68" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }` [INFO] [stdout] f03bd565f90e8be8ace77bf74bfc224f87dcee8c7a6b733593ac0e43dc4cccea [INFO] running `Command { std: "docker" "start" "-a" "f03bd565f90e8be8ace77bf74bfc224f87dcee8c7a6b733593ac0e43dc4cccea", kill_on_drop: false }` [INFO] running `Command { std: "docker" "inspect" "f03bd565f90e8be8ace77bf74bfc224f87dcee8c7a6b733593ac0e43dc4cccea", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "f03bd565f90e8be8ace77bf74bfc224f87dcee8c7a6b733593ac0e43dc4cccea", kill_on_drop: false }` [INFO] [stdout] f03bd565f90e8be8ace77bf74bfc224f87dcee8c7a6b733593ac0e43dc4cccea [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc1/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=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-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@sha256:94a0c148923f5b2b52a63ef0eeb1882ad339ab61bce784c8077cbe41c61feb6c" "/opt/rustwide/cargo-home/bin/cargo" "+e22dab387f6b4f6a87dfc54ac2f6013dddb41e68" "check" "--frozen" "--all" "--all-targets" "--message-format=json", kill_on_drop: false }` [INFO] [stdout] cd66fc18701ab55f177c5cf7f7ffe58107d585c6914dc9aefd5650bc59139941 [INFO] running `Command { std: "docker" "start" "-a" "cd66fc18701ab55f177c5cf7f7ffe58107d585c6914dc9aefd5650bc59139941", kill_on_drop: false }` [INFO] [stderr] Compiling proc-macro2 v1.0.101 [INFO] [stderr] Checking rand_chacha v0.3.1 [INFO] [stderr] Checking getrandom v0.3.3 [INFO] [stderr] Checking bytemuck v1.23.2 [INFO] [stderr] Checking rayon v1.11.0 [INFO] [stderr] Checking regex-automata v0.4.11 [INFO] [stderr] Checking num-rational v0.4.2 [INFO] [stderr] Compiling libc v0.2.176 [INFO] [stderr] Checking safe_arch v0.7.4 [INFO] [stderr] Checking rand v0.8.5 [INFO] [stderr] Checking rand_core v0.9.3 [INFO] [stderr] 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v0.11.3 [INFO] [stderr] Checking nalgebra v0.33.2 [INFO] [stderr] Checking nalgebra v0.30.1 [INFO] [stderr] Checking statrs v0.18.0 [INFO] [stderr] Checking friedrich v0.5.0 [INFO] [stderr] Checking scirs2-linalg v0.1.0-rc.1 [INFO] [stderr] Checking scirs2-sparse v0.1.0-rc.1 [INFO] [stderr] Checking scirs2-stats v0.1.0-rc.1 [INFO] [stderr] Checking scirs2-optimize v0.1.0-rc.1 [INFO] [stderr] Checking numrs2 v0.1.0-beta.3 [INFO] [stderr] Checking sklears-core v0.1.0-alpha.1 [INFO] [stderr] Checking sklears-utils v0.1.0-alpha.1 [INFO] [stderr] Checking sklears-discriminant-analysis v0.1.0-alpha.1 (/opt/rustwide/workdir) [INFO] [stdout] warning: unused doc comment [INFO] [stdout] --> src/one_vs_rest.rs:67:9 [INFO] [stdout] | [INFO] [stdout] 67 | /// BinaryClassifier [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] 68 | BinaryClassifier::LDA(LinearDiscriminantAnalysis::new()) [INFO] [stdout] | -------------------------------------------------------- rustdoc does not generate documentation for expressions [INFO] [stdout] | [INFO] [stdout] = help: use `//` for a plain comment [INFO] [stdout] = note: `#[warn(unused_doc_comments)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unexpected `cfg` condition value: `avx512` [INFO] [stdout] --> src/simd_optimizations.rs:38:25 [INFO] [stdout] | [INFO] [stdout] 38 | && cfg!(feature = "avx512"), [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: expected values for `feature` are: `default` and `serde` [INFO] [stdout] = help: consider adding `avx512` as a feature in `Cargo.toml` [INFO] [stdout] = note: see for more information about checking conditional configuration [INFO] [stdout] = note: `#[warn(unexpected_cfgs)]` on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unexpected `cfg` condition value: `avx2` [INFO] [stdout] --> src/simd_optimizations.rs:40:25 [INFO] [stdout] | [INFO] [stdout] 40 | && cfg!(feature = "avx2"), [INFO] [stdout] | ^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: expected values for `feature` are: `default` and `serde` [INFO] [stdout] = help: consider adding `avx2` as a feature in `Cargo.toml` [INFO] [stdout] = note: see for more information about checking conditional configuration [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unexpected `cfg` condition value: `sse` [INFO] [stdout] --> src/simd_optimizations.rs:42:25 [INFO] [stdout] | [INFO] [stdout] 42 | && cfg!(feature = "sse"), [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: expected values for `feature` are: `default` and `serde` [INFO] [stdout] = help: consider adding `sse` as a feature in `Cargo.toml` [INFO] [stdout] = note: see for more information about checking conditional configuration [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused doc comment [INFO] [stdout] --> src/one_vs_rest.rs:67:9 [INFO] [stdout] | [INFO] [stdout] 67 | /// BinaryClassifier [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] 68 | BinaryClassifier::LDA(LinearDiscriminantAnalysis::new()) [INFO] [stdout] | -------------------------------------------------------- rustdoc does not generate documentation for expressions [INFO] [stdout] | [INFO] [stdout] = help: use `//` for a plain comment [INFO] [stdout] = note: `#[warn(unused_doc_comments)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unexpected `cfg` condition value: `avx512` [INFO] [stdout] --> src/simd_optimizations.rs:38:25 [INFO] [stdout] | [INFO] [stdout] 38 | && cfg!(feature = "avx512"), [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: expected values for `feature` are: `default` and `serde` [INFO] [stdout] = help: consider adding `avx512` as a feature in `Cargo.toml` [INFO] [stdout] = note: see for more information about checking conditional configuration [INFO] [stdout] = note: `#[warn(unexpected_cfgs)]` on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unexpected `cfg` condition value: `avx2` [INFO] [stdout] --> src/simd_optimizations.rs:40:25 [INFO] [stdout] | [INFO] [stdout] 40 | && cfg!(feature = "avx2"), [INFO] [stdout] | ^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: expected values for `feature` are: `default` and `serde` [INFO] [stdout] = help: consider adding `avx2` as a feature in `Cargo.toml` [INFO] [stdout] = note: see for more information about checking conditional configuration [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unexpected `cfg` condition value: `sse` [INFO] [stdout] --> src/simd_optimizations.rs:42:25 [INFO] [stdout] | [INFO] [stdout] 42 | && cfg!(feature = "sse"), [INFO] [stdout] | ^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: expected values for `feature` are: `default` and `serde` [INFO] [stdout] = help: consider adding `sse` as a feature in `Cargo.toml` [INFO] [stdout] = note: see for more information about checking conditional configuration [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused import: `Estimator` [INFO] [stdout] --> src/out_of_core.rs:23:14 [INFO] [stdout] | [INFO] [stdout] 23 | traits::{Estimator, Fit, Predict, PredictProba, Trained}, [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_imports)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/adaptive_discriminant.rs:252:14 [INFO] [stdout] | [INFO] [stdout] 252 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_label` [INFO] [stdout] --> src/adaptive_discriminant.rs:630:26 [INFO] [stdout] | [INFO] [stdout] 630 | for (class_idx, &class_label) in data.classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_label` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_label` [INFO] [stdout] --> src/adaptive_discriminant.rs:680:26 [INFO] [stdout] | [INFO] [stdout] 680 | for (class_idx, &class_label) in classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_label` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/core.rs:85:18 [INFO] [stdout] | [INFO] [stdout] 85 | fn fit(self, x: &ArrayBase, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/bayesian_discriminant/core.rs:85:41 [INFO] [stdout] | [INFO] [stdout] 85 | fn fit(self, x: &ArrayBase, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:73:9 [INFO] [stdout] | [INFO] [stdout] 73 | x: &ArrayBase, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:84:23 [INFO] [stdout] | [INFO] [stdout] 84 | fn predict(&self, x: &ArrayBase) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:93:29 [INFO] [stdout] | [INFO] [stdout] 93 | fn predict_proba(&self, x: &ArrayBase) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:102:25 [INFO] [stdout] | [INFO] [stdout] 102 | fn transform(&self, x: &ArrayBase) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `densities` [INFO] [stdout] --> src/boundary_adjustment.rs:498:21 [INFO] [stdout] | [INFO] [stdout] 498 | let densities = [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_densities` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `cost_matrix` [INFO] [stdout] --> src/boundary_adjustment.rs:510:17 [INFO] [stdout] | [INFO] [stdout] 510 | cost_matrix, [INFO] [stdout] | ^^^^^^^^^^^ help: try ignoring the field: `cost_matrix: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class` [INFO] [stdout] --> src/canonical_discriminant.rs:196:18 [INFO] [stdout] | [INFO] [stdout] 196 | for (i, &class) in self.classes.iter().enumerate() { [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_class` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_idx` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:378:14 [INFO] [stdout] | [INFO] [stdout] 378 | for (class_idx, &class_label) in classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_idx` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:432:14 [INFO] [stdout] | [INFO] [stdout] 432 | for (class_idx, &class_label) in classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:733:14 [INFO] [stdout] | [INFO] [stdout] 733 | for (i, (&true_label, &pred_label)) in y_true.iter().zip(y_pred.iter()).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `prob` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:866:30 [INFO] [stdout] | [INFO] [stdout] 866 | for (j, &prob) in proba_row.iter().enumerate() { [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_prob` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `alpha` [INFO] [stdout] --> src/deep_discriminant.rs:1014:21 [INFO] [stdout] | [INFO] [stdout] 1014 | if let Some(alpha) = self.config.mixup_alpha { [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_alpha` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x_batch` [INFO] [stdout] --> src/deep_discriminant.rs:1221:9 [INFO] [stdout] | [INFO] [stdout] 1221 | x_batch: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_x_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y_batch` [INFO] [stdout] --> src/deep_discriminant.rs:1222:9 [INFO] [stdout] | [INFO] [stdout] 1222 | y_batch: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_y_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `outputs` [INFO] [stdout] --> src/deep_discriminant.rs:1223:9 [INFO] [stdout] | [INFO] [stdout] 1223 | outputs: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_outputs` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `momentum` [INFO] [stdout] --> src/deep_discriminant.rs:1228:13 [INFO] [stdout] | [INFO] [stdout] 1228 | let momentum = self.config.training.momentum; [INFO] [stdout] | ^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_momentum` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: value assigned to `feature_score` is never read [INFO] [stdout] --> src/diagonal_lda.rs:219:37 [INFO] [stdout] | [INFO] [stdout] 219 | let mut feature_score = 0.0; [INFO] [stdout] | ^^^ [INFO] [stdout] | [INFO] [stdout] = help: maybe it is overwritten before being read? [INFO] [stdout] = note: `#[warn(unused_assignments)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/diagonal_lda.rs:286:18 [INFO] [stdout] | [INFO] [stdout] 286 | for (i, mut sample) in x_processed.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/diagonal_lda.rs:408:18 [INFO] [stdout] | [INFO] [stdout] 408 | for (i, mut sample) in x_processed.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `idx` [INFO] [stdout] --> src/discriminant_locality_alignment.rs:394:22 [INFO] [stdout] | [INFO] [stdout] 394 | for (idx, &j) in neighbors.iter().enumerate() { [INFO] [stdout] | ^^^ help: if this is intentional, prefix it with an underscore: `_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `chunk_sizes` [INFO] [stdout] --> src/distributed_discriminant.rs:135:13 [INFO] [stdout] | [INFO] [stdout] 135 | let chunk_sizes: Vec = chunks.iter().map(|(_, y)| y.len()).collect(); [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_chunk_sizes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `chunk_sizes` [INFO] [stdout] --> src/distributed_discriminant.rs:250:13 [INFO] [stdout] | [INFO] [stdout] 250 | let chunk_sizes: Vec = chunks.iter().map(|(_, y)| y.len()).collect(); [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_chunk_sizes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `k_neighbors` [INFO] [stdout] --> src/ensemble_imbalanced.rs:268:9 [INFO] [stdout] | [INFO] [stdout] 268 | k_neighbors: usize, [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_k_neighbors` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/ensemble_imbalanced.rs:325:9 [INFO] [stdout] | [INFO] [stdout] 325 | x: &Array2, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `sample_weights` [INFO] [stdout] --> src/ensemble_imbalanced.rs:327:9 [INFO] [stdout] | [INFO] [stdout] 327 | sample_weights: Option<&Array1>, [INFO] [stdout] | ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_sample_weights` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `iteration` [INFO] [stdout] --> src/ensemble_imbalanced.rs:416:21 [INFO] [stdout] | [INFO] [stdout] 416 | for iteration in 0..*n_estimators { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_iteration` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/feature_ranking.rs:368:13 [INFO] [stdout] | [INFO] [stdout] 368 | let classes = self.unique_classes(y)?; [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/heteroscedastic.rs:277:14 [INFO] [stdout] | [INFO] [stdout] 277 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `mu` [INFO] [stdout] --> src/heteroscedastic.rs:365:13 [INFO] [stdout] | [INFO] [stdout] 365 | let mu = trace / n_features as Float; [INFO] [stdout] | ^^ help: if this is intentional, prefix it with an underscore: `_mu` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `sample_cov` [INFO] [stdout] --> src/heteroscedastic.rs:376:51 [INFO] [stdout] | [INFO] [stdout] 376 | fn oas_shrinkage(&self, data: &Array2, sample_cov: &Array2) -> Result { [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_sample_cov` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unreachable pattern [INFO] [stdout] --> src/information_theoretic.rs:341:13 [INFO] [stdout] | [INFO] [stdout] 341 | _ => { [INFO] [stdout] | ^ no value can reach this [INFO] [stdout] | [INFO] [stdout] note: multiple earlier patterns match some of the same values [INFO] [stdout] --> src/information_theoretic.rs:341:13 [INFO] [stdout] | [INFO] [stdout] 309 | DiscretizationMethod::EqualWidth => { [INFO] [stdout] | -------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 320 | DiscretizationMethod::EqualFrequency => { [INFO] [stdout] | ------------------------------------ matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 332 | DiscretizationMethod::KMeans => self.kmeans_discretization(&feature_values, n_bins)?, [INFO] [stdout] | ---------------------------- matches some of the same values [INFO] [stdout] 333 | DiscretizationMethod::EntropyBased => { [INFO] [stdout] | ---------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 341 | _ => { [INFO] [stdout] | ^ ...and 1 other patterns collectively make this unreachable [INFO] [stdout] = note: `#[warn(unreachable_patterns)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/information_theoretic.rs:376:9 [INFO] [stdout] | [INFO] [stdout] 376 | classes: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unreachable pattern [INFO] [stdout] --> src/information_theoretic.rs:456:17 [INFO] [stdout] | [INFO] [stdout] 456 | _ => { [INFO] [stdout] | ^ no value can reach this [INFO] [stdout] | [INFO] [stdout] note: multiple earlier patterns match some of the same values [INFO] [stdout] --> src/information_theoretic.rs:456:17 [INFO] [stdout] | [INFO] [stdout] 417 | InformationCriterion::MutualInformation => { [INFO] [stdout] | --------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 420 | InformationCriterion::InformationGain => { [INFO] [stdout] | ------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 423 | InformationCriterion::NormalizedMutualInformation => self [INFO] [stdout] | ------------------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 430 | InformationCriterion::ConditionalMutualInformation => self [INFO] [stdout] | -------------------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 456 | _ => { [INFO] [stdout] | ^ ...and 2 other patterns collectively make this unreachable [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `iteration` [INFO] [stdout] --> src/information_theoretic.rs:1031:13 [INFO] [stdout] | [INFO] [stdout] 1031 | for iteration in 0..10 { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_iteration` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/kernel_discriminant.rs:829:13 [INFO] [stdout] | [INFO] [stdout] 829 | let n_features = x.ncols(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:223:14 [INFO] [stdout] | [INFO] [stdout] 223 | let (n_samples, n_features) = data.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:339:14 [INFO] [stdout] | [INFO] [stdout] 339 | let (n_samples, _) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `overall_mean` [INFO] [stdout] --> src/lda.rs:342:13 [INFO] [stdout] | [INFO] [stdout] 342 | let overall_mean = x.mean_axis(Axis(0)).unwrap(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_overall_mean` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:416:14 [INFO] [stdout] | [INFO] [stdout] 416 | let (n_samples, _) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable `alpha` is assigned to, but never used [INFO] [stdout] --> src/lda.rs:809:13 [INFO] [stdout] | [INFO] [stdout] 809 | let mut alpha = 0.0; [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: consider using `_alpha` instead [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: value assigned to `alpha` is never read [INFO] [stdout] --> src/lda.rs:817:17 [INFO] [stdout] | [INFO] [stdout] 817 | alpha += val * val; [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = help: maybe it is overwritten before being read? [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:832:14 [INFO] [stdout] | [INFO] [stdout] 832 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `start_idx` [INFO] [stdout] --> src/lda.rs:882:21 [INFO] [stdout] | [INFO] [stdout] 882 | let start_idx = fold * fold_size; [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_start_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `end_idx` [INFO] [stdout] --> src/lda.rs:883:21 [INFO] [stdout] | [INFO] [stdout] 883 | let end_idx = if fold == k_folds - 1 { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_end_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/locally_linear_discriminant.rs:399:17 [INFO] [stdout] | [INFO] [stdout] 399 | let classes: Vec = { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `xi` [INFO] [stdout] --> src/manifold_discriminant.rs:280:17 [INFO] [stdout] | [INFO] [stdout] 280 | let xi = x.row(i); [INFO] [stdout] | ^^ help: if this is intentional, prefix it with an underscore: `_xi` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `covariances` [INFO] [stdout] --> src/manifold_discriminant.rs:676:17 [INFO] [stdout] | [INFO] [stdout] 676 | covariances, [INFO] [stdout] | ^^^^^^^^^^^ help: try ignoring the field: `covariances: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `covariances` [INFO] [stdout] --> src/manifold_discriminant.rs:757:17 [INFO] [stdout] | [INFO] [stdout] 757 | covariances, [INFO] [stdout] | ^^^^^^^^^^^ help: try ignoring the field: `covariances: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `precision` [INFO] [stdout] --> src/minimum_volume_ellipsoid.rs:342:27 [INFO] [stdout] | [INFO] [stdout] 342 | if let Ok(precision) = self.invert_matrix(&covariance) { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_precision` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `k` [INFO] [stdout] --> src/mixture.rs:150:13 [INFO] [stdout] | [INFO] [stdout] 150 | for k in 0..n_components { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_k` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_classes` [INFO] [stdout] --> src/mixture_experts.rs:543:13 [INFO] [stdout] | [INFO] [stdout] 543 | let n_classes = classes.len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_experts` [INFO] [stdout] --> src/mixture_experts.rs:645:13 [INFO] [stdout] | [INFO] [stdout] 645 | let n_experts = self.experts().len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_experts` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/trained.rs:69:23 [INFO] [stdout] | [INFO] [stdout] 69 | fn predict(&self, x: &Vec>) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/trained.rs:76:29 [INFO] [stdout] | [INFO] [stdout] 76 | fn predict_proba(&self, x: &Vec>) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/trained.rs:83:25 [INFO] [stdout] | [INFO] [stdout] 83 | fn transform(&self, x: &Vec>) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/untrained.rs:46:18 [INFO] [stdout] | [INFO] [stdout] 46 | fn fit(self, x: &Vec>, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/multi_view_discriminant/untrained.rs:46:42 [INFO] [stdout] | [INFO] [stdout] 46 | fn fit(self, x: &Vec>, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:375:14 [INFO] [stdout] | [INFO] [stdout] 375 | for (i, mut row) in x_standardized.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_classes` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:667:13 [INFO] [stdout] | [INFO] [stdout] 667 | let n_classes = classes.len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_classes` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:700:13 [INFO] [stdout] | [INFO] [stdout] 700 | let n_classes = classes.len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:737:14 [INFO] [stdout] | [INFO] [stdout] 737 | for (i, mut row) in x_standardized.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:831:14 [INFO] [stdout] | [INFO] [stdout] 831 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `epoch` [INFO] [stdout] --> src/neural_discriminant.rs:603:13 [INFO] [stdout] | [INFO] [stdout] 603 | for epoch in 0..self.config.training.max_epochs { [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_epoch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/neural_discriminant.rs:652:13 [INFO] [stdout] | [INFO] [stdout] 652 | let n_samples = outputs.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x_batch` [INFO] [stdout] --> src/neural_discriminant.rs:734:9 [INFO] [stdout] | [INFO] [stdout] 734 | x_batch: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_x_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y_batch` [INFO] [stdout] --> src/neural_discriminant.rs:735:9 [INFO] [stdout] | [INFO] [stdout] 735 | y_batch: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_y_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `outputs` [INFO] [stdout] --> src/neural_discriminant.rs:736:9 [INFO] [stdout] | [INFO] [stdout] 736 | outputs: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_outputs` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/one_vs_one.rs:263:13 [INFO] [stdout] | [INFO] [stdout] 263 | let n_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/one_vs_rest.rs:289:13 [INFO] [stdout] | [INFO] [stdout] 289 | let n_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_new_samples` [INFO] [stdout] --> src/online_discriminant.rs:326:13 [INFO] [stdout] | [INFO] [stdout] 326 | let n_new_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_new_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `new_labels` [INFO] [stdout] --> src/online_discriminant.rs:371:17 [INFO] [stdout] | [INFO] [stdout] 371 | let new_labels = label_buffer.clone(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_new_labels` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_new_samples` [INFO] [stdout] --> src/online_discriminant.rs:424:13 [INFO] [stdout] | [INFO] [stdout] 424 | let n_new_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_new_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: value assigned to `drift_score` is never read [INFO] [stdout] --> src/online_discriminant.rs:509:31 [INFO] [stdout] | [INFO] [stdout] 509 | let mut drift_score = 0.0; [INFO] [stdout] | ^^^ [INFO] [stdout] | [INFO] [stdout] = help: maybe it is overwritten before being read? [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/online_discriminant.rs:622:13 [INFO] [stdout] | [INFO] [stdout] 622 | let n_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/out_of_core.rs:186:36 [INFO] [stdout] | [INFO] [stdout] 186 | fn calculate_chunk_size(&self, n_samples: usize, n_features: usize) -> Result { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `mean` [INFO] [stdout] --> src/out_of_core.rs:453:14 [INFO] [stdout] | [INFO] [stdout] 453 | let (mean, covariance) = self.compute_incremental_statistics(&data_manager)?; [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_mean` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `covariance` [INFO] [stdout] --> src/out_of_core.rs:453:20 [INFO] [stdout] | [INFO] [stdout] 453 | let (mean, covariance) = self.compute_incremental_statistics(&data_manager)?; [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_covariance` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n` [INFO] [stdout] --> src/parallel_eigen.rs:100:13 [INFO] [stdout] | [INFO] [stdout] 100 | let n = matrix.nrows(); [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_n` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n` [INFO] [stdout] --> src/parallel_eigen.rs:207:13 [INFO] [stdout] | [INFO] [stdout] 207 | let n = matrix.nrows(); [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_n` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `total_loss` [INFO] [stdout] --> src/penalized_discriminant_analysis.rs:442:17 [INFO] [stdout] | [INFO] [stdout] 442 | let total_loss = loss + penalty; [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_total_loss` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/penalized_discriminant_analysis.rs:653:13 [INFO] [stdout] | [INFO] [stdout] 653 | let n_features = x.ncols(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/qda.rs:130:14 [INFO] [stdout] | [INFO] [stdout] 130 | let (n_samples, n_features) = data.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/random_projection_discriminant_analysis.rs:626:27 [INFO] [stdout] | [INFO] [stdout] 626 | pub fn fit(&mut self, x: &Array2, y: &Array1) -> Result<()> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/random_projection_discriminant_analysis.rs:675:27 [INFO] [stdout] | [INFO] [stdout] 675 | pub fn fit(&mut self, x: &Array2, y: &Array1) -> Result<()> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/robust_adaptive.rs:622:13 [INFO] [stdout] | [INFO] [stdout] 622 | let n_features = trained.pooled_covariance.nrows(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/simd_optimizations.rs:476:13 [INFO] [stdout] | [INFO] [stdout] 476 | let n_features = data.ncols(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x_vec` [INFO] [stdout] --> src/simd_optimizations.rs:802:17 [INFO] [stdout] | [INFO] [stdout] 802 | let x_vec = _mm256_loadu_pd(x.as_ptr().add(i)); [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_x_vec` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/sure_independence_screening.rs:132:13 [INFO] [stdout] | [INFO] [stdout] 132 | let classes: Vec = { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n` [INFO] [stdout] --> src/sure_independence_screening.rs:212:13 [INFO] [stdout] | [INFO] [stdout] 212 | let n = x_ranks.len() as Float; [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_n` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/temporal_discriminant.rs:424:14 [INFO] [stdout] | [INFO] [stdout] 424 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/temporal_discriminant.rs:424:25 [INFO] [stdout] | [INFO] [stdout] 424 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_time_steps` [INFO] [stdout] --> src/temporal_discriminant.rs:424:37 [INFO] [stdout] | [INFO] [stdout] 424 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_time_steps` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_time_steps` [INFO] [stdout] --> src/temporal_discriminant.rs:795:37 [INFO] [stdout] | [INFO] [stdout] 795 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_time_steps` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `degree` [INFO] [stdout] --> src/temporal_discriminant.rs:831:39 [INFO] [stdout] | [INFO] [stdout] 831 | TrendMethod::Polynomial { degree } => { [INFO] [stdout] | ^^^^^^ help: try ignoring the field: `degree: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/temporal_discriminant.rs:1009:14 [INFO] [stdout] | [INFO] [stdout] 1009 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/temporal_discriminant.rs:1124:9 [INFO] [stdout] | [INFO] [stdout] 1124 | y: &Array1, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/temporal_discriminant.rs:1125:9 [INFO] [stdout] | [INFO] [stdout] 1125 | classes: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/temporal_discriminant.rs:1129:22 [INFO] [stdout] | [INFO] [stdout] 1129 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_time_steps` [INFO] [stdout] --> src/temporal_discriminant.rs:1129:45 [INFO] [stdout] | [INFO] [stdout] 1129 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_time_steps` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/temporal_discriminant.rs:1144:9 [INFO] [stdout] | [INFO] [stdout] 1144 | y: &Array1, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `temporal_means` [INFO] [stdout] --> src/temporal_discriminant.rs:1146:9 [INFO] [stdout] | [INFO] [stdout] 1146 | temporal_means: &Array2, [INFO] [stdout] | ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_temporal_means` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `temporal_covariances` [INFO] [stdout] --> src/temporal_discriminant.rs:1147:9 [INFO] [stdout] | [INFO] [stdout] 1147 | temporal_covariances: &[Array2], [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_temporal_covariances` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `pattern` [INFO] [stdout] --> src/temporal_discriminant.rs:1235:29 [INFO] [stdout] | [INFO] [stdout] 1235 | for (class_idx, pattern) in temporal_patterns.iter().enumerate() { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_pattern` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: type `ExpertModel` is more private than the item `MixtureOfExpertsDiscriminantAnalysis::::experts` [INFO] [stdout] --> src/mixture_experts.rs:157:5 [INFO] [stdout] | [INFO] [stdout] 157 | pub fn experts(&self) -> &Vec { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ method `MixtureOfExpertsDiscriminantAnalysis::::experts` is reachable at visibility `pub` [INFO] [stdout] | [INFO] [stdout] note: but type `ExpertModel` is only usable at visibility `pub(self)` [INFO] [stdout] --> src/mixture_experts.rs:192:1 [INFO] [stdout] | [INFO] [stdout] 192 | struct ExpertModel { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] = note: `#[warn(private_interfaces)]` on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: type `GatingNetwork` is more private than the item `MixtureOfExpertsDiscriminantAnalysis::::gating_network` [INFO] [stdout] --> src/mixture_experts.rs:161:5 [INFO] [stdout] | [INFO] [stdout] 161 | pub fn gating_network(&self) -> &GatingNetwork { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ method `MixtureOfExpertsDiscriminantAnalysis::::gating_network` is reachable at visibility `pub` [INFO] [stdout] | [INFO] [stdout] note: but type `GatingNetwork` is only usable at visibility `pub(self)` [INFO] [stdout] --> src/mixture_experts.rs:252:1 [INFO] [stdout] | [INFO] [stdout] 252 | struct GatingNetwork { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused import: `Estimator` [INFO] [stdout] --> src/out_of_core.rs:23:14 [INFO] [stdout] | [INFO] [stdout] 23 | traits::{Estimator, Fit, Predict, PredictProba, Trained}, [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_imports)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/adaptive_discriminant.rs:252:14 [INFO] [stdout] | [INFO] [stdout] 252 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_label` [INFO] [stdout] --> src/adaptive_discriminant.rs:630:26 [INFO] [stdout] | [INFO] [stdout] 630 | for (class_idx, &class_label) in data.classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_label` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_label` [INFO] [stdout] --> src/adaptive_discriminant.rs:680:26 [INFO] [stdout] | [INFO] [stdout] 680 | for (class_idx, &class_label) in classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_label` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/core.rs:85:18 [INFO] [stdout] | [INFO] [stdout] 85 | fn fit(self, x: &ArrayBase, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/bayesian_discriminant/core.rs:85:41 [INFO] [stdout] | [INFO] [stdout] 85 | fn fit(self, x: &ArrayBase, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:73:9 [INFO] [stdout] | [INFO] [stdout] 73 | x: &ArrayBase, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:84:23 [INFO] [stdout] | [INFO] [stdout] 84 | fn predict(&self, x: &ArrayBase) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:93:29 [INFO] [stdout] | [INFO] [stdout] 93 | fn predict_proba(&self, x: &ArrayBase) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/bayesian_discriminant/trained.rs:102:25 [INFO] [stdout] | [INFO] [stdout] 102 | fn transform(&self, x: &ArrayBase) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `densities` [INFO] [stdout] --> src/boundary_adjustment.rs:498:21 [INFO] [stdout] | [INFO] [stdout] 498 | let densities = [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_densities` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `cost_matrix` [INFO] [stdout] --> src/boundary_adjustment.rs:510:17 [INFO] [stdout] | [INFO] [stdout] 510 | cost_matrix, [INFO] [stdout] | ^^^^^^^^^^^ help: try ignoring the field: `cost_matrix: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class` [INFO] [stdout] --> src/canonical_discriminant.rs:196:18 [INFO] [stdout] | [INFO] [stdout] 196 | for (i, &class) in self.classes.iter().enumerate() { [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_class` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_idx` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:378:14 [INFO] [stdout] | [INFO] [stdout] 378 | for (class_idx, &class_label) in classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `class_idx` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:432:14 [INFO] [stdout] | [INFO] [stdout] 432 | for (class_idx, &class_label) in classes.iter().enumerate() { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_class_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:733:14 [INFO] [stdout] | [INFO] [stdout] 733 | for (i, (&true_label, &pred_label)) in y_true.iter().zip(y_pred.iter()).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `prob` [INFO] [stdout] --> src/cost_sensitive_discriminant_analysis.rs:866:30 [INFO] [stdout] | [INFO] [stdout] 866 | for (j, &prob) in proba_row.iter().enumerate() { [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_prob` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `alpha` [INFO] [stdout] --> src/deep_discriminant.rs:1014:21 [INFO] [stdout] | [INFO] [stdout] 1014 | if let Some(alpha) = self.config.mixup_alpha { [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_alpha` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x_batch` [INFO] [stdout] --> src/deep_discriminant.rs:1221:9 [INFO] [stdout] | [INFO] [stdout] 1221 | x_batch: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_x_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y_batch` [INFO] [stdout] --> src/deep_discriminant.rs:1222:9 [INFO] [stdout] | [INFO] [stdout] 1222 | y_batch: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_y_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `outputs` [INFO] [stdout] --> src/deep_discriminant.rs:1223:9 [INFO] [stdout] | [INFO] [stdout] 1223 | outputs: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_outputs` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `momentum` [INFO] [stdout] --> src/deep_discriminant.rs:1228:13 [INFO] [stdout] | [INFO] [stdout] 1228 | let momentum = self.config.training.momentum; [INFO] [stdout] | ^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_momentum` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: value assigned to `feature_score` is never read [INFO] [stdout] --> src/diagonal_lda.rs:219:37 [INFO] [stdout] | [INFO] [stdout] 219 | let mut feature_score = 0.0; [INFO] [stdout] | ^^^ [INFO] [stdout] | [INFO] [stdout] = help: maybe it is overwritten before being read? [INFO] [stdout] = note: `#[warn(unused_assignments)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/diagonal_lda.rs:286:18 [INFO] [stdout] | [INFO] [stdout] 286 | for (i, mut sample) in x_processed.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/diagonal_lda.rs:408:18 [INFO] [stdout] | [INFO] [stdout] 408 | for (i, mut sample) in x_processed.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `idx` [INFO] [stdout] --> src/discriminant_locality_alignment.rs:394:22 [INFO] [stdout] | [INFO] [stdout] 394 | for (idx, &j) in neighbors.iter().enumerate() { [INFO] [stdout] | ^^^ help: if this is intentional, prefix it with an underscore: `_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `chunk_sizes` [INFO] [stdout] --> src/distributed_discriminant.rs:135:13 [INFO] [stdout] | [INFO] [stdout] 135 | let chunk_sizes: Vec = chunks.iter().map(|(_, y)| y.len()).collect(); [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_chunk_sizes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `chunk_sizes` [INFO] [stdout] --> src/distributed_discriminant.rs:250:13 [INFO] [stdout] | [INFO] [stdout] 250 | let chunk_sizes: Vec = chunks.iter().map(|(_, y)| y.len()).collect(); [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_chunk_sizes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `k_neighbors` [INFO] [stdout] --> src/ensemble_imbalanced.rs:268:9 [INFO] [stdout] | [INFO] [stdout] 268 | k_neighbors: usize, [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_k_neighbors` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/ensemble_imbalanced.rs:325:9 [INFO] [stdout] | [INFO] [stdout] 325 | x: &Array2, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `sample_weights` [INFO] [stdout] --> src/ensemble_imbalanced.rs:327:9 [INFO] [stdout] | [INFO] [stdout] 327 | sample_weights: Option<&Array1>, [INFO] [stdout] | ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_sample_weights` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `iteration` [INFO] [stdout] --> src/ensemble_imbalanced.rs:416:21 [INFO] [stdout] | [INFO] [stdout] 416 | for iteration in 0..*n_estimators { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_iteration` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/ensemble_imbalanced.rs:621:13 [INFO] [stdout] | [INFO] [stdout] 621 | let x = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0]]; [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/feature_ranking.rs:368:13 [INFO] [stdout] | [INFO] [stdout] 368 | let classes = self.unique_classes(y)?; [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/heteroscedastic.rs:277:14 [INFO] [stdout] | [INFO] [stdout] 277 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `mu` [INFO] [stdout] --> src/heteroscedastic.rs:365:13 [INFO] [stdout] | [INFO] [stdout] 365 | let mu = trace / n_features as Float; [INFO] [stdout] | ^^ help: if this is intentional, prefix it with an underscore: `_mu` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `sample_cov` [INFO] [stdout] --> src/heteroscedastic.rs:376:51 [INFO] [stdout] | [INFO] [stdout] 376 | fn oas_shrinkage(&self, data: &Array2, sample_cov: &Array2) -> Result { [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_sample_cov` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unreachable pattern [INFO] [stdout] --> src/information_theoretic.rs:341:13 [INFO] [stdout] | [INFO] [stdout] 341 | _ => { [INFO] [stdout] | ^ no value can reach this [INFO] [stdout] | [INFO] [stdout] note: multiple earlier patterns match some of the same values [INFO] [stdout] --> src/information_theoretic.rs:341:13 [INFO] [stdout] | [INFO] [stdout] 309 | DiscretizationMethod::EqualWidth => { [INFO] [stdout] | -------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 320 | DiscretizationMethod::EqualFrequency => { [INFO] [stdout] | ------------------------------------ matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 332 | DiscretizationMethod::KMeans => self.kmeans_discretization(&feature_values, n_bins)?, [INFO] [stdout] | ---------------------------- matches some of the same values [INFO] [stdout] 333 | DiscretizationMethod::EntropyBased => { [INFO] [stdout] | ---------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 341 | _ => { [INFO] [stdout] | ^ ...and 1 other patterns collectively make this unreachable [INFO] [stdout] = note: `#[warn(unreachable_patterns)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/information_theoretic.rs:376:9 [INFO] [stdout] | [INFO] [stdout] 376 | classes: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unreachable pattern [INFO] [stdout] --> src/information_theoretic.rs:456:17 [INFO] [stdout] | [INFO] [stdout] 456 | _ => { [INFO] [stdout] | ^ no value can reach this [INFO] [stdout] | [INFO] [stdout] note: multiple earlier patterns match some of the same values [INFO] [stdout] --> src/information_theoretic.rs:456:17 [INFO] [stdout] | [INFO] [stdout] 417 | InformationCriterion::MutualInformation => { [INFO] [stdout] | --------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 420 | InformationCriterion::InformationGain => { [INFO] [stdout] | ------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 423 | InformationCriterion::NormalizedMutualInformation => self [INFO] [stdout] | ------------------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 430 | InformationCriterion::ConditionalMutualInformation => self [INFO] [stdout] | -------------------------------------------------- matches some of the same values [INFO] [stdout] ... [INFO] [stdout] 456 | _ => { [INFO] [stdout] | ^ ...and 2 other patterns collectively make this unreachable [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `iteration` [INFO] [stdout] --> src/information_theoretic.rs:1031:13 [INFO] [stdout] | [INFO] [stdout] 1031 | for iteration in 0..10 { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_iteration` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/kernel_discriminant.rs:829:13 [INFO] [stdout] | [INFO] [stdout] 829 | let n_features = x.ncols(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:223:14 [INFO] [stdout] | [INFO] [stdout] 223 | let (n_samples, n_features) = data.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:339:14 [INFO] [stdout] | [INFO] [stdout] 339 | let (n_samples, _) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `overall_mean` [INFO] [stdout] --> src/lda.rs:342:13 [INFO] [stdout] | [INFO] [stdout] 342 | let overall_mean = x.mean_axis(Axis(0)).unwrap(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_overall_mean` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:416:14 [INFO] [stdout] | [INFO] [stdout] 416 | let (n_samples, _) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable `alpha` is assigned to, but never used [INFO] [stdout] --> src/lda.rs:809:13 [INFO] [stdout] | [INFO] [stdout] 809 | let mut alpha = 0.0; [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: consider using `_alpha` instead [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: value assigned to `alpha` is never read [INFO] [stdout] --> src/lda.rs:817:17 [INFO] [stdout] | [INFO] [stdout] 817 | alpha += val * val; [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = help: maybe it is overwritten before being read? [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/lda.rs:832:14 [INFO] [stdout] | [INFO] [stdout] 832 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `start_idx` [INFO] [stdout] --> src/lda.rs:882:21 [INFO] [stdout] | [INFO] [stdout] 882 | let start_idx = fold * fold_size; [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_start_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `end_idx` [INFO] [stdout] --> src/lda.rs:883:21 [INFO] [stdout] | [INFO] [stdout] 883 | let end_idx = if fold == k_folds - 1 { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_end_idx` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/locally_linear_discriminant.rs:399:17 [INFO] [stdout] | [INFO] [stdout] 399 | let classes: Vec = { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `xi` [INFO] [stdout] --> src/manifold_discriminant.rs:280:17 [INFO] [stdout] | [INFO] [stdout] 280 | let xi = x.row(i); [INFO] [stdout] | ^^ help: if this is intentional, prefix it with an underscore: `_xi` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `covariances` [INFO] [stdout] --> src/manifold_discriminant.rs:676:17 [INFO] [stdout] | [INFO] [stdout] 676 | covariances, [INFO] [stdout] | ^^^^^^^^^^^ help: try ignoring the field: `covariances: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `covariances` [INFO] [stdout] --> src/manifold_discriminant.rs:757:17 [INFO] [stdout] | [INFO] [stdout] 757 | covariances, [INFO] [stdout] | ^^^^^^^^^^^ help: try ignoring the field: `covariances: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `precision` [INFO] [stdout] --> src/minimum_volume_ellipsoid.rs:342:27 [INFO] [stdout] | [INFO] [stdout] 342 | if let Ok(precision) = self.invert_matrix(&covariance) { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_precision` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `k` [INFO] [stdout] --> src/mixture.rs:150:13 [INFO] [stdout] | [INFO] [stdout] 150 | for k in 0..n_components { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_k` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_classes` [INFO] [stdout] --> src/mixture_experts.rs:543:13 [INFO] [stdout] | [INFO] [stdout] 543 | let n_classes = classes.len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_experts` [INFO] [stdout] --> src/mixture_experts.rs:645:13 [INFO] [stdout] | [INFO] [stdout] 645 | let n_experts = self.experts().len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_experts` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/trained.rs:69:23 [INFO] [stdout] | [INFO] [stdout] 69 | fn predict(&self, x: &Vec>) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/trained.rs:76:29 [INFO] [stdout] | [INFO] [stdout] 76 | fn predict_proba(&self, x: &Vec>) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/trained.rs:83:25 [INFO] [stdout] | [INFO] [stdout] 83 | fn transform(&self, x: &Vec>) -> Result> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/multi_view_discriminant/untrained.rs:46:18 [INFO] [stdout] | [INFO] [stdout] 46 | fn fit(self, x: &Vec>, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/multi_view_discriminant/untrained.rs:46:42 [INFO] [stdout] | [INFO] [stdout] 46 | fn fit(self, x: &Vec>, y: &Array1) -> Result { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:375:14 [INFO] [stdout] | [INFO] [stdout] 375 | for (i, mut row) in x_standardized.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_classes` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:667:13 [INFO] [stdout] | [INFO] [stdout] 667 | let n_classes = classes.len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_classes` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:700:13 [INFO] [stdout] | [INFO] [stdout] 700 | let n_classes = classes.len(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `i` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:737:14 [INFO] [stdout] | [INFO] [stdout] 737 | for (i, mut row) in x_standardized.axis_iter_mut(Axis(0)).enumerate() { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_i` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/nearest_shrunken_centroids.rs:831:14 [INFO] [stdout] | [INFO] [stdout] 831 | let (n_samples, n_features) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `epoch` [INFO] [stdout] --> src/neural_discriminant.rs:603:13 [INFO] [stdout] | [INFO] [stdout] 603 | for epoch in 0..self.config.training.max_epochs { [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_epoch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/neural_discriminant.rs:652:13 [INFO] [stdout] | [INFO] [stdout] 652 | let n_samples = outputs.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x_batch` [INFO] [stdout] --> src/neural_discriminant.rs:734:9 [INFO] [stdout] | [INFO] [stdout] 734 | x_batch: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_x_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y_batch` [INFO] [stdout] --> src/neural_discriminant.rs:735:9 [INFO] [stdout] | [INFO] [stdout] 735 | y_batch: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_y_batch` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `outputs` [INFO] [stdout] --> src/neural_discriminant.rs:736:9 [INFO] [stdout] | [INFO] [stdout] 736 | outputs: &Array2, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_outputs` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/one_vs_one.rs:263:13 [INFO] [stdout] | [INFO] [stdout] 263 | let n_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/one_vs_rest.rs:289:13 [INFO] [stdout] | [INFO] [stdout] 289 | let n_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_new_samples` [INFO] [stdout] --> src/online_discriminant.rs:326:13 [INFO] [stdout] | [INFO] [stdout] 326 | let n_new_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_new_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `new_labels` [INFO] [stdout] --> src/online_discriminant.rs:371:17 [INFO] [stdout] | [INFO] [stdout] 371 | let new_labels = label_buffer.clone(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_new_labels` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_new_samples` [INFO] [stdout] --> src/online_discriminant.rs:424:13 [INFO] [stdout] | [INFO] [stdout] 424 | let n_new_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_new_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: value assigned to `drift_score` is never read [INFO] [stdout] --> src/online_discriminant.rs:509:31 [INFO] [stdout] | [INFO] [stdout] 509 | let mut drift_score = 0.0; [INFO] [stdout] | ^^^ [INFO] [stdout] | [INFO] [stdout] = help: maybe it is overwritten before being read? [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/online_discriminant.rs:622:13 [INFO] [stdout] | [INFO] [stdout] 622 | let n_samples = x.nrows(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/out_of_core.rs:186:36 [INFO] [stdout] | [INFO] [stdout] 186 | fn calculate_chunk_size(&self, n_samples: usize, n_features: usize) -> Result { [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `mean` [INFO] [stdout] --> src/out_of_core.rs:453:14 [INFO] [stdout] | [INFO] [stdout] 453 | let (mean, covariance) = self.compute_incremental_statistics(&data_manager)?; [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_mean` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `covariance` [INFO] [stdout] --> src/out_of_core.rs:453:20 [INFO] [stdout] | [INFO] [stdout] 453 | let (mean, covariance) = self.compute_incremental_statistics(&data_manager)?; [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_covariance` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `predictions` [INFO] [stdout] --> src/out_of_core.rs:733:13 [INFO] [stdout] | [INFO] [stdout] 733 | let predictions = stream.predict(&test_data); [INFO] [stdout] | ^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_predictions` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n` [INFO] [stdout] --> src/parallel_eigen.rs:100:13 [INFO] [stdout] | [INFO] [stdout] 100 | let n = matrix.nrows(); [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_n` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n` [INFO] [stdout] --> src/parallel_eigen.rs:207:13 [INFO] [stdout] | [INFO] [stdout] 207 | let n = matrix.nrows(); [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_n` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `total_loss` [INFO] [stdout] --> src/penalized_discriminant_analysis.rs:442:17 [INFO] [stdout] | [INFO] [stdout] 442 | let total_loss = loss + penalty; [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_total_loss` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/penalized_discriminant_analysis.rs:653:13 [INFO] [stdout] | [INFO] [stdout] 653 | let n_features = x.ncols(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/qda.rs:130:14 [INFO] [stdout] | [INFO] [stdout] 130 | let (n_samples, n_features) = data.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/random_projection_discriminant_analysis.rs:626:27 [INFO] [stdout] | [INFO] [stdout] 626 | pub fn fit(&mut self, x: &Array2, y: &Array1) -> Result<()> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x` [INFO] [stdout] --> src/random_projection_discriminant_analysis.rs:675:27 [INFO] [stdout] | [INFO] [stdout] 675 | pub fn fit(&mut self, x: &Array2, y: &Array1) -> Result<()> { [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_x` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/robust_adaptive.rs:622:13 [INFO] [stdout] | [INFO] [stdout] 622 | let n_features = trained.pooled_covariance.nrows(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/simd_optimizations.rs:476:13 [INFO] [stdout] | [INFO] [stdout] 476 | let n_features = data.ncols(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `x_vec` [INFO] [stdout] --> src/simd_optimizations.rs:802:17 [INFO] [stdout] | [INFO] [stdout] 802 | let x_vec = _mm256_loadu_pd(x.as_ptr().add(i)); [INFO] [stdout] | ^^^^^ help: if this is intentional, prefix it with an underscore: `_x_vec` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/sure_independence_screening.rs:132:13 [INFO] [stdout] | [INFO] [stdout] 132 | let classes: Vec = { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n` [INFO] [stdout] --> src/sure_independence_screening.rs:212:13 [INFO] [stdout] | [INFO] [stdout] 212 | let n = x_ranks.len() as Float; [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_n` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/temporal_discriminant.rs:424:14 [INFO] [stdout] | [INFO] [stdout] 424 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_features` [INFO] [stdout] --> src/temporal_discriminant.rs:424:25 [INFO] [stdout] | [INFO] [stdout] 424 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_features` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_time_steps` [INFO] [stdout] --> src/temporal_discriminant.rs:424:37 [INFO] [stdout] | [INFO] [stdout] 424 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_time_steps` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_time_steps` [INFO] [stdout] --> src/temporal_discriminant.rs:795:37 [INFO] [stdout] | [INFO] [stdout] 795 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_time_steps` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `degree` [INFO] [stdout] --> src/temporal_discriminant.rs:831:39 [INFO] [stdout] | [INFO] [stdout] 831 | TrendMethod::Polynomial { degree } => { [INFO] [stdout] | ^^^^^^ help: try ignoring the field: `degree: _` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/temporal_discriminant.rs:1009:14 [INFO] [stdout] | [INFO] [stdout] 1009 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/temporal_discriminant.rs:1124:9 [INFO] [stdout] | [INFO] [stdout] 1124 | y: &Array1, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `classes` [INFO] [stdout] --> src/temporal_discriminant.rs:1125:9 [INFO] [stdout] | [INFO] [stdout] 1125 | classes: &Array1, [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_classes` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_samples` [INFO] [stdout] --> src/temporal_discriminant.rs:1129:22 [INFO] [stdout] | [INFO] [stdout] 1129 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_samples` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `n_time_steps` [INFO] [stdout] --> src/temporal_discriminant.rs:1129:45 [INFO] [stdout] | [INFO] [stdout] 1129 | let (n_samples, n_features, n_time_steps) = x.dim(); [INFO] [stdout] | ^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_n_time_steps` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `y` [INFO] [stdout] --> src/temporal_discriminant.rs:1144:9 [INFO] [stdout] | [INFO] [stdout] 1144 | y: &Array1, [INFO] [stdout] | ^ help: if this is intentional, prefix it with an underscore: `_y` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `temporal_means` [INFO] [stdout] --> src/temporal_discriminant.rs:1146:9 [INFO] [stdout] | [INFO] [stdout] 1146 | temporal_means: &Array2, [INFO] [stdout] | ^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_temporal_means` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `temporal_covariances` [INFO] [stdout] --> src/temporal_discriminant.rs:1147:9 [INFO] [stdout] | [INFO] [stdout] 1147 | temporal_covariances: &[Array2], [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ help: if this is intentional, prefix it with an underscore: `_temporal_covariances` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `pattern` [INFO] [stdout] --> src/temporal_discriminant.rs:1235:29 [INFO] [stdout] | [INFO] [stdout] 1235 | for (class_idx, pattern) in temporal_patterns.iter().enumerate() { [INFO] [stdout] | ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_pattern` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: type `ExpertModel` is more private than the item `mixture_experts::MixtureOfExpertsDiscriminantAnalysis::::experts` [INFO] [stdout] --> src/mixture_experts.rs:157:5 [INFO] [stdout] | [INFO] [stdout] 157 | pub fn experts(&self) -> &Vec { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ method `mixture_experts::MixtureOfExpertsDiscriminantAnalysis::::experts` is reachable at visibility `pub` [INFO] [stdout] | [INFO] [stdout] note: but type `ExpertModel` is only usable at visibility `pub(self)` [INFO] [stdout] --> src/mixture_experts.rs:192:1 [INFO] [stdout] | [INFO] [stdout] 192 | struct ExpertModel { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^ [INFO] [stdout] = note: `#[warn(private_interfaces)]` on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: type `GatingNetwork` is more private than the item `mixture_experts::MixtureOfExpertsDiscriminantAnalysis::::gating_network` [INFO] [stdout] --> src/mixture_experts.rs:161:5 [INFO] [stdout] | [INFO] [stdout] 161 | pub fn gating_network(&self) -> &GatingNetwork { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ method `mixture_experts::MixtureOfExpertsDiscriminantAnalysis::::gating_network` is reachable at visibility `pub` [INFO] [stdout] | [INFO] [stdout] note: but type `GatingNetwork` is only usable at visibility `pub(self)` [INFO] [stdout] --> src/mixture_experts.rs:252:1 [INFO] [stdout] | [INFO] [stdout] 252 | struct GatingNetwork { [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished `dev` profile [unoptimized + debuginfo] target(s) in 3m 03s [INFO] running `Command { std: "docker" "inspect" "cd66fc18701ab55f177c5cf7f7ffe58107d585c6914dc9aefd5650bc59139941", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "cd66fc18701ab55f177c5cf7f7ffe58107d585c6914dc9aefd5650bc59139941", kill_on_drop: false }` [INFO] [stdout] cd66fc18701ab55f177c5cf7f7ffe58107d585c6914dc9aefd5650bc59139941