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Linfa's 0.4.0 release (#127)
* Bump version 0.4.0, add release notes * Add t-SNE example to release notes * Add example for t-SNE and start writing preprocessing example * Fix typo * Add example for TF-IDF text preprocessing * Explain that target construction is omitted and traits changes * Add contribute section on the new lapack traits * Link to crates.io in the base crate documentation * Link to crates.io in the base crate documentation * Add mnist example * Run rustfmt * Fix mnist example
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‎CONTRIBUTE.md‎

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/// ...
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}
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```
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## Use the lapack trait bound
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When you want to implement an algorithm which requires the [Lapack](https://docs.rs/ndarray-linalg/0.13.1/ndarray_linalg/types/trait.Lapack.html) bound, then you could add the trait bound to the `linfa::Float` standard bound, e.g. `F: Float + Scalar + Lapack`. If you do that you're currently running into conflicting function definitions of [num_traits::Float](https://docs.rs/num-traits/0.2.14/num_traits/float/trait.Float.html) and [cauchy::Scalar](https://docs.rs/cauchy/0.4.0/cauchy/trait.Scalar.html) with the first defined for real-valued values and the second for complex values.
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If you want to avoid that you can use the `linfa::dataset::{WithLapack, WithoutLapack}` traits, which basically adds the lapack trait bound for a block and then removes it again so that the conflicts can be avoided. For example:
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```rust
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let decomp = covariance.with_lapack().cholesky(UPLO::Lower)?;
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let sol = decomp
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.solve_triangular(UPLO::Lower, Diag::NonUnit, &Array::eye(n_features))?
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.without_lapack();
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```

‎Cargo.toml‎

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[package]
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name = "linfa"
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version = "0.3.1"
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version = "0.4.0"
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authors = [
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"Luca Palmieri <rust@lpalmieri.com>",
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"Lorenz Schmidt <bytesnake@mailbox.org>",

‎algorithms/linfa-bayes/Cargo.toml‎

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[package]
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name = "linfa-bayes"
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version = "0.3.1"
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version = "0.4.0"
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authors = ["VasanthakumarV <vasanth260m12@gmail.com>"]
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description = "Collection of Naive Bayes Algorithms"
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edition = "2018"
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ndarray-stats = "0.4"
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thiserror = "1"
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linfa = { version = "0.3.1", path = "../.." }
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linfa = { version = "0.4.0", path = "../.." }
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[dev-dependencies]
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approx = "0.4"
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linfa-datasets = { version = "0.3.1", path = "../../datasets", features = ["winequality"] }
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linfa-datasets = { version = "0.4.0", path = "../../datasets", features = ["winequality"] }

‎algorithms/linfa-clustering/Cargo.toml‎

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[package]
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name = "linfa-clustering"
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version = "0.3.1"
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version = "0.4.0"
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edition = "2018"
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authors = [
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"Luca Palmieri <rust@lpalmieri.com>",
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rand_isaac = "0.3"
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thiserror = "1"
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partitions = "0.2.4"
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linfa = { version = "0.3.1", path = "../..", features = ["ndarray-linalg"] }
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linfa = { version = "0.4.0", path = "../..", features = ["ndarray-linalg"] }
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[dev-dependencies]
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ndarray-npy = { version = "0.7", default-features = false }

‎algorithms/linfa-clustering/src/gaussian_mixture/algorithm.rs‎

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let n_features = covariances.shape()[1];
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let mut precisions_chol = Array::zeros((n_clusters, n_features, n_features));
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for (k, covariance) in covariances.outer_iter().enumerate() {
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dbg!(&covariance.shape());
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dbg!(&covariance.with_lapack().shape());
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let decomp = covariance.with_lapack().cholesky(UPLO::Lower)?;
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let sol = decomp
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.solve_triangular(UPLO::Lower, Diag::NonUnit, &Array::eye(n_features))?

‎algorithms/linfa-elasticnet/Cargo.toml‎

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[package]
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name = "linfa-elasticnet"
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version = "0.3.1"
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version = "0.4.0"
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authors = [
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"Paul Körbitz / Google <koerbitz@google.com>",
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"Lorenz Schmidt <bytesnake@mailbox.org>"
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approx = "0.4"
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thiserror = "1"
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linfa = { version = "0.3.1", path = "../.." }
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linfa = { version = "0.4.0", path = "../.." }
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[dev-dependencies]
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linfa-datasets = { version = "0.3.1", path = "../../datasets", features = ["diabetes"] }
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linfa-datasets = { version = "0.4.0", path = "../../datasets", features = ["diabetes"] }
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ndarray-rand = "0.13"
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rand_isaac = "0.3"

‎algorithms/linfa-hierarchical/Cargo.toml‎

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[package]
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name = "linfa-hierarchical"
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version = "0.3.1"
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version = "0.4.0"
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authors = ["Lorenz Schmidt <lorenz.schmidt@mailbox.org>"]
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edition = "2018"
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ndarray = { version = "0.14", default-features = false }
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kodama = "0.2"
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linfa = { version = "0.3.1", path = "../.." }
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linfa-kernel = { version = "0.3.1", path = "../linfa-kernel" }
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linfa = { version = "0.4.0", path = "../.." }
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linfa-kernel = { version = "0.4.0", path = "../linfa-kernel" }
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[dev-dependencies]
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rand = "0.8"
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ndarray-rand = "0.13"
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linfa-datasets = { version = "0.3.1", path = "../../datasets", features = ["iris"] }
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linfa-datasets = { version = "0.4.0", path = "../../datasets", features = ["iris"] }

‎algorithms/linfa-ica/Cargo.toml‎

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[package]
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name = "linfa-ica"
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version = "0.3.1"
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version = "0.4.0"
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authors = ["VasanthakumarV <vasanth260m12@gmail.com>"]
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description = "A collection of Independent Component Analysis (ICA) algorithms"
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edition = "2018"
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rand_isaac = "0.3"
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thiserror = "1"
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linfa = { version = "0.3.1", path = "../..", features = ["ndarray-linalg"] }
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linfa = { version = "0.4.0", path = "../..", features = ["ndarray-linalg"] }
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ndarray-npy = { version = "0.7", default-features = false }

‎algorithms/linfa-kernel/Cargo.toml‎

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name = "linfa-kernel"
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version = "0.3.1"
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version = "0.4.0"
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authors = ["Lorenz Schmidt <bytesnake@mailbox.org>"]
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description = "Kernel methods for non-linear algorithms"
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edition = "2018"
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hnsw = "0.6"
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space = "0.10"
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linfa = { version = "0.3.1", path = "../.." }
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linfa = { version = "0.4.0", path = "../.." }

‎algorithms/linfa-linear/Cargo.toml‎

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name = "linfa-linear"
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version = "0.3.1"
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version = "0.4.0"
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authors = [
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"Paul Körbitz / Google <koerbitz@google.com>",
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"VasanthakumarV <vasanth260m12@gmail.com>"
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serde = { version = "1.0", default-features = false, features = ["derive"] }
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linfa = { version = "0.3.1", path = "../.." }
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linfa = { version = "0.4.0", path = "../.." }
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linfa-datasets = { version = "0.3.1", path = "../../datasets", features = ["diabetes"] }
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linfa-datasets = { version = "0.4.0", path = "../../datasets", features = ["diabetes"] }
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approx = "0.4"

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