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add feature gates
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@sinhrks
sinhrks committed Dec 19, 2016
1 parent c262ac4 commit dcbbd89
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3 changes: 3 additions & 0 deletions Cargo.toml
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Expand Up @@ -11,7 +11,10 @@ readme = "README.md"
license = "MIT"

[features]
default = ["datasets"]
stats = []
datasets = []
test = ["datasets"]

[dependencies]
num = { version = "0.1.35", default-features = false }
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303 changes: 151 additions & 152 deletions src/datasets/mod.rs
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Expand Up @@ -32,164 +32,163 @@ impl<D, T> Dataset<D, T> where D: Clone + Debug, T: Clone + Debug {
/// Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
/// Irvine, CA: University of California, School of Information and Computer Science.
pub fn load_iris() -> Dataset<Matrix<f64>, Vector<usize>> {
let data: Vec<f64> = vec![5.1, 3.5, 1.4, 0.2,
4.9, 3.0, 1.4, 0.2,
4.7, 3.2, 1.3, 0.2,
4.6, 3.1, 1.5, 0.2,
5.0, 3.6, 1.4, 0.2,
5.4, 3.9, 1.7, 0.4,
4.6, 3.4, 1.4, 0.3,
5.0, 3.4, 1.5, 0.2,
4.4, 2.9, 1.4, 0.2,
4.9, 3.1, 1.5, 0.1,
5.4, 3.7, 1.5, 0.2,
4.8, 3.4, 1.6, 0.2,
4.8, 3.0, 1.4, 0.1,
4.3, 3.0, 1.1, 0.1,
5.8, 4.0, 1.2, 0.2,
5.7, 4.4, 1.5, 0.4,
5.4, 3.9, 1.3, 0.4,
5.1, 3.5, 1.4, 0.3,
5.7, 3.8, 1.7, 0.3,
5.1, 3.8, 1.5, 0.3,
5.4, 3.4, 1.7, 0.2,
5.1, 3.7, 1.5, 0.4,
4.6, 3.6, 1.0, 0.2,
5.1, 3.3, 1.7, 0.5,
4.8, 3.4, 1.9, 0.2,
5.0, 3.0, 1.6, 0.2,
5.0, 3.4, 1.6, 0.4,
5.2, 3.5, 1.5, 0.2,
5.2, 3.4, 1.4, 0.2,
4.7, 3.2, 1.6, 0.2,
4.8, 3.1, 1.6, 0.2,
5.4, 3.4, 1.5, 0.4,
5.2, 4.1, 1.5, 0.1,
5.5, 4.2, 1.4, 0.2,
4.9, 3.1, 1.5, 0.1,
5.0, 3.2, 1.2, 0.2,
5.5, 3.5, 1.3, 0.2,
4.9, 3.1, 1.5, 0.1,
4.4, 3.0, 1.3, 0.2,
5.1, 3.4, 1.5, 0.2,
5.0, 3.5, 1.3, 0.3,
4.5, 2.3, 1.3, 0.3,
4.4, 3.2, 1.3, 0.2,
5.0, 3.5, 1.6, 0.6,
5.1, 3.8, 1.9, 0.4,
4.8, 3.0, 1.4, 0.3,
5.1, 3.8, 1.6, 0.2,
4.6, 3.2, 1.4, 0.2,
5.3, 3.7, 1.5, 0.2,
5.0, 3.3, 1.4, 0.2,
7.0, 3.2, 4.7, 1.4,
6.4, 3.2, 4.5, 1.5,
6.9, 3.1, 4.9, 1.5,
5.5, 2.3, 4.0, 1.3,
6.5, 2.8, 4.6, 1.5,
5.7, 2.8, 4.5, 1.3,
6.3, 3.3, 4.7, 1.6,
4.9, 2.4, 3.3, 1.0,
6.6, 2.9, 4.6, 1.3,
5.2, 2.7, 3.9, 1.4,
5.0, 2.0, 3.5, 1.0,
5.9, 3.0, 4.2, 1.5,
6.0, 2.2, 4.0, 1.0,
6.1, 2.9, 4.7, 1.4,
5.6, 2.9, 3.6, 1.3,
6.7, 3.1, 4.4, 1.4,
5.6, 3.0, 4.5, 1.5,
5.8, 2.7, 4.1, 1.0,
6.2, 2.2, 4.5, 1.5,
5.6, 2.5, 3.9, 1.1,
5.9, 3.2, 4.8, 1.8,
6.1, 2.8, 4.0, 1.3,
6.3, 2.5, 4.9, 1.5,
6.1, 2.8, 4.7, 1.2,
6.4, 2.9, 4.3, 1.3,
6.6, 3.0, 4.4, 1.4,
6.8, 2.8, 4.8, 1.4,
6.7, 3.0, 5.0, 1.7,
6.0, 2.9, 4.5, 1.5,
5.7, 2.6, 3.5, 1.0,
5.5, 2.4, 3.8, 1.1,
5.5, 2.4, 3.7, 1.0,
5.8, 2.7, 3.9, 1.2,
6.0, 2.7, 5.1, 1.6,
5.4, 3.0, 4.5, 1.5,
6.0, 3.4, 4.5, 1.6,
6.7, 3.1, 4.7, 1.5,
6.3, 2.3, 4.4, 1.3,
5.6, 3.0, 4.1, 1.3,
5.5, 2.5, 4.0, 1.3,
5.5, 2.6, 4.4, 1.2,
6.1, 3.0, 4.6, 1.4,
5.8, 2.6, 4.0, 1.2,
5.0, 2.3, 3.3, 1.0,
5.6, 2.7, 4.2, 1.3,
5.7, 3.0, 4.2, 1.2,
5.7, 2.9, 4.2, 1.3,
6.2, 2.9, 4.3, 1.3,
5.1, 2.5, 3.0, 1.1,
5.7, 2.8, 4.1, 1.3,
6.3, 3.3, 6.0, 2.5,
5.8, 2.7, 5.1, 1.9,
7.1, 3.0, 5.9, 2.1,
6.3, 2.9, 5.6, 1.8,
6.5, 3.0, 5.8, 2.2,
7.6, 3.0, 6.6, 2.1,
4.9, 2.5, 4.5, 1.7,
7.3, 2.9, 6.3, 1.8,
6.7, 2.5, 5.8, 1.8,
7.2, 3.6, 6.1, 2.5,
6.5, 3.2, 5.1, 2.0,
6.4, 2.7, 5.3, 1.9,
6.8, 3.0, 5.5, 2.1,
5.7, 2.5, 5.0, 2.0,
5.8, 2.8, 5.1, 2.4,
6.4, 3.2, 5.3, 2.3,
6.5, 3.0, 5.5, 1.8,
7.7, 3.8, 6.7, 2.2,
7.7, 2.6, 6.9, 2.3,
6.0, 2.2, 5.0, 1.5,
6.9, 3.2, 5.7, 2.3,
5.6, 2.8, 4.9, 2.0,
7.7, 2.8, 6.7, 2.0,
6.3, 2.7, 4.9, 1.8,
6.7, 3.3, 5.7, 2.1,
7.2, 3.2, 6.0, 1.8,
6.2, 2.8, 4.8, 1.8,
6.1, 3.0, 4.9, 1.8,
6.4, 2.8, 5.6, 2.1,
7.2, 3.0, 5.8, 1.6,
7.4, 2.8, 6.1, 1.9,
7.9, 3.8, 6.4, 2.0,
6.4, 2.8, 5.6, 2.2,
6.3, 2.8, 5.1, 1.5,
6.1, 2.6, 5.6, 1.4,
7.7, 3.0, 6.1, 2.3,
6.3, 3.4, 5.6, 2.4,
6.4, 3.1, 5.5, 1.8,
6.0, 3.0, 4.8, 1.8,
6.9, 3.1, 5.4, 2.1,
6.7, 3.1, 5.6, 2.4,
6.9, 3.1, 5.1, 2.3,
5.8, 2.7, 5.1, 1.9,
6.8, 3.2, 5.9, 2.3,
6.7, 3.3, 5.7, 2.5,
6.7, 3.0, 5.2, 2.3,
6.3, 2.5, 5.0, 1.9,
6.5, 3.0, 5.2, 2.0,
6.2, 3.4, 5.4, 2.3,
5.9, 3.0, 5.1, 1.8];

let data: Matrix<f64> = matrix![5.1, 3.5, 1.4, 0.2;
4.9, 3.0, 1.4, 0.2;
4.7, 3.2, 1.3, 0.2;
4.6, 3.1, 1.5, 0.2;
5.0, 3.6, 1.4, 0.2;
5.4, 3.9, 1.7, 0.4;
4.6, 3.4, 1.4, 0.3;
5.0, 3.4, 1.5, 0.2;
4.4, 2.9, 1.4, 0.2;
4.9, 3.1, 1.5, 0.1;
5.4, 3.7, 1.5, 0.2;
4.8, 3.4, 1.6, 0.2;
4.8, 3.0, 1.4, 0.1;
4.3, 3.0, 1.1, 0.1;
5.8, 4.0, 1.2, 0.2;
5.7, 4.4, 1.5, 0.4;
5.4, 3.9, 1.3, 0.4;
5.1, 3.5, 1.4, 0.3;
5.7, 3.8, 1.7, 0.3;
5.1, 3.8, 1.5, 0.3;
5.4, 3.4, 1.7, 0.2;
5.1, 3.7, 1.5, 0.4;
4.6, 3.6, 1.0, 0.2;
5.1, 3.3, 1.7, 0.5;
4.8, 3.4, 1.9, 0.2;
5.0, 3.0, 1.6, 0.2;
5.0, 3.4, 1.6, 0.4;
5.2, 3.5, 1.5, 0.2;
5.2, 3.4, 1.4, 0.2;
4.7, 3.2, 1.6, 0.2;
4.8, 3.1, 1.6, 0.2;
5.4, 3.4, 1.5, 0.4;
5.2, 4.1, 1.5, 0.1;
5.5, 4.2, 1.4, 0.2;
4.9, 3.1, 1.5, 0.1;
5.0, 3.2, 1.2, 0.2;
5.5, 3.5, 1.3, 0.2;
4.9, 3.1, 1.5, 0.1;
4.4, 3.0, 1.3, 0.2;
5.1, 3.4, 1.5, 0.2;
5.0, 3.5, 1.3, 0.3;
4.5, 2.3, 1.3, 0.3;
4.4, 3.2, 1.3, 0.2;
5.0, 3.5, 1.6, 0.6;
5.1, 3.8, 1.9, 0.4;
4.8, 3.0, 1.4, 0.3;
5.1, 3.8, 1.6, 0.2;
4.6, 3.2, 1.4, 0.2;
5.3, 3.7, 1.5, 0.2;
5.0, 3.3, 1.4, 0.2;
7.0, 3.2, 4.7, 1.4;
6.4, 3.2, 4.5, 1.5;
6.9, 3.1, 4.9, 1.5;
5.5, 2.3, 4.0, 1.3;
6.5, 2.8, 4.6, 1.5;
5.7, 2.8, 4.5, 1.3;
6.3, 3.3, 4.7, 1.6;
4.9, 2.4, 3.3, 1.0;
6.6, 2.9, 4.6, 1.3;
5.2, 2.7, 3.9, 1.4;
5.0, 2.0, 3.5, 1.0;
5.9, 3.0, 4.2, 1.5;
6.0, 2.2, 4.0, 1.0;
6.1, 2.9, 4.7, 1.4;
5.6, 2.9, 3.6, 1.3;
6.7, 3.1, 4.4, 1.4;
5.6, 3.0, 4.5, 1.5;
5.8, 2.7, 4.1, 1.0;
6.2, 2.2, 4.5, 1.5;
5.6, 2.5, 3.9, 1.1;
5.9, 3.2, 4.8, 1.8;
6.1, 2.8, 4.0, 1.3;
6.3, 2.5, 4.9, 1.5;
6.1, 2.8, 4.7, 1.2;
6.4, 2.9, 4.3, 1.3;
6.6, 3.0, 4.4, 1.4;
6.8, 2.8, 4.8, 1.4;
6.7, 3.0, 5.0, 1.7;
6.0, 2.9, 4.5, 1.5;
5.7, 2.6, 3.5, 1.0;
5.5, 2.4, 3.8, 1.1;
5.5, 2.4, 3.7, 1.0;
5.8, 2.7, 3.9, 1.2;
6.0, 2.7, 5.1, 1.6;
5.4, 3.0, 4.5, 1.5;
6.0, 3.4, 4.5, 1.6;
6.7, 3.1, 4.7, 1.5;
6.3, 2.3, 4.4, 1.3;
5.6, 3.0, 4.1, 1.3;
5.5, 2.5, 4.0, 1.3;
5.5, 2.6, 4.4, 1.2;
6.1, 3.0, 4.6, 1.4;
5.8, 2.6, 4.0, 1.2;
5.0, 2.3, 3.3, 1.0;
5.6, 2.7, 4.2, 1.3;
5.7, 3.0, 4.2, 1.2;
5.7, 2.9, 4.2, 1.3;
6.2, 2.9, 4.3, 1.3;
5.1, 2.5, 3.0, 1.1;
5.7, 2.8, 4.1, 1.3;
6.3, 3.3, 6.0, 2.5;
5.8, 2.7, 5.1, 1.9;
7.1, 3.0, 5.9, 2.1;
6.3, 2.9, 5.6, 1.8;
6.5, 3.0, 5.8, 2.2;
7.6, 3.0, 6.6, 2.1;
4.9, 2.5, 4.5, 1.7;
7.3, 2.9, 6.3, 1.8;
6.7, 2.5, 5.8, 1.8;
7.2, 3.6, 6.1, 2.5;
6.5, 3.2, 5.1, 2.0;
6.4, 2.7, 5.3, 1.9;
6.8, 3.0, 5.5, 2.1;
5.7, 2.5, 5.0, 2.0;
5.8, 2.8, 5.1, 2.4;
6.4, 3.2, 5.3, 2.3;
6.5, 3.0, 5.5, 1.8;
7.7, 3.8, 6.7, 2.2;
7.7, 2.6, 6.9, 2.3;
6.0, 2.2, 5.0, 1.5;
6.9, 3.2, 5.7, 2.3;
5.6, 2.8, 4.9, 2.0;
7.7, 2.8, 6.7, 2.0;
6.3, 2.7, 4.9, 1.8;
6.7, 3.3, 5.7, 2.1;
7.2, 3.2, 6.0, 1.8;
6.2, 2.8, 4.8, 1.8;
6.1, 3.0, 4.9, 1.8;
6.4, 2.8, 5.6, 2.1;
7.2, 3.0, 5.8, 1.6;
7.4, 2.8, 6.1, 1.9;
7.9, 3.8, 6.4, 2.0;
6.4, 2.8, 5.6, 2.2;
6.3, 2.8, 5.1, 1.5;
6.1, 2.6, 5.6, 1.4;
7.7, 3.0, 6.1, 2.3;
6.3, 3.4, 5.6, 2.4;
6.4, 3.1, 5.5, 1.8;
6.0, 3.0, 4.8, 1.8;
6.9, 3.1, 5.4, 2.1;
6.7, 3.1, 5.6, 2.4;
6.9, 3.1, 5.1, 2.3;
5.8, 2.7, 5.1, 1.9;
6.8, 3.2, 5.9, 2.3;
6.7, 3.3, 5.7, 2.5;
6.7, 3.0, 5.2, 2.3;
6.3, 2.5, 5.0, 1.9;
6.5, 3.0, 5.2, 2.0;
6.2, 3.4, 5.4, 2.3;
5.9, 3.0, 5.1, 1.8];
let target: Vec<usize> = vec![0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2];

Dataset{ data: Matrix::new(150, 4, data),
Dataset{ data: data,
target: Vector::new(target) }
}
1 change: 1 addition & 0 deletions src/lib.rs
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Expand Up @@ -222,5 +222,6 @@ pub mod analysis {
pub mod score;
}

#[cfg(feature = "datasets")]
/// Module for datasets.
pub mod datasets;
2 changes: 1 addition & 1 deletion tests/datasets.rs
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@@ -1,7 +1,7 @@
extern crate rusty_machine as rm;

use rm::datasets;
use rm::prelude::*;
use rm::linalg::BaseMatrix;

#[test]
fn test_iris() {
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5 changes: 4 additions & 1 deletion tests/lib.rs
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Expand Up @@ -10,4 +10,7 @@ pub mod learning {
pub mod optim {
mod grad_desc;
}
}
}

#[cfg(feature = "datasets")]
pub mod datasets;

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