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fix: formatting

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This commit is contained in:
Volodymyr Orlov
2020-08-27 11:40:11 -07:00
parent aa458d22fa
commit f73b349f57
4 changed files with 59 additions and 40 deletions
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src/lib.rs
+20 -20
View File
@@ -2,9 +2,9 @@
#![warn(missing_doc_code_examples)]
//! # SmartCore
//!
//! Welcome to SmartCore library, the most complete machine learning library for Rust!
//!
//!
//! Welcome to SmartCore library, the most complete machine learning library for Rust!
//!
//! In SmartCore you will find implementation of these ML algorithms:
//! * Regression: Linear Regression (OLS), Decision Tree Regressor, Random Forest Regressor
//! * Classification: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Unsupervised Nearest Neighbors (KNN)
@@ -12,33 +12,33 @@
//! * Matrix decomposition: PCA, LU, QR, SVD, EVD
//! * Distance Metrics: Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis
//! * Evaluation Metrics: Accuracy, AUC, Recall, Precision, F1, Mean Absolute Error, Mean Squared Error, R2
//!
//!
//! Most of algorithms implemented in SmartCore operate on n-dimentional arrays. While you can use Rust vectors with all functions defined in this library
//! we do recommend to go with one of the popular linear algebra libraries available in Rust. At this moment we support these packages:
//! * [ndarray](https://docs.rs/ndarray)
//! * [nalgebra](https://docs.rs/nalgebra/)
//!
//!
//! ## Getting Started
//!
//!
//! To start using SmartCore simply add the following to your Cargo.toml file:
//! ```ignore
//! [dependencies]
//! smartcore = "0.1.0"
//! ```
//!
//!
//! All ML algorithms in SmartCore are grouped into these generic categories:
//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
//! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition.
//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
//! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition.
//! * [Linear Models](linear/index.html), regression and classification methods where output is assumed to have linear relation to explanatory variables
//! * [Ensemble Models](ensemble/index.html), variety of regression and classification ensemble models
//! * [Tree-based Models](tree/index.html), classification and regression trees
//! * [Nearest Neighbors](neighbors/index.html), K Nearest Neighbors for classification and regression
//!
//! Each category is assigned to a separate module.
//!
//!
//! Each category is assigned to a separate module.
//!
//! For example, KNN classifier is defined in [smartcore::neighbors::knn](neighbors/knn/index.html). To train and run it using standard Rust vectors you will
//! run this code:
//!
//!
//! ```
//! // DenseMatrix defenition
//! use smartcore::linalg::naive::dense_matrix::*;
@@ -46,20 +46,20 @@
//! use smartcore::neighbors::knn::*;
//! // Various distance metrics
//! use smartcore::math::distance::*;
//!
//!
//! // Turn Rust vectors with samples into a matrix
//! let x = DenseMatrix::from_array(&[
//! &[1., 2.],
//! &[3., 4.],
//! &[5., 6.],
//! &[7., 8.],
//! &[1., 2.],
//! &[3., 4.],
//! &[5., 6.],
//! &[7., 8.],
//! &[9., 10.]]);
//! // Our classes are defined as a Vector
//! let y = vec![2., 2., 2., 3., 3.];
//!
//!
//! // Train classifier
//! let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
//!
//!
//! // Predict classes
//! let y_hat = knn.predict(&x);
//! ```
src/linear/linear_regression.rs
+24 -6
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@@ -12,7 +12,7 @@ pub enum LinearRegressionSolverName {
}
#[derive(Serialize, Deserialize, Debug)]
pub struct LinearRegressionParameters {
pub struct LinearRegressionParameters {
solver: LinearRegressionSolverName,
}
@@ -25,8 +25,8 @@ pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
impl Default for LinearRegressionParameters {
fn default() -> Self {
LinearRegressionParameters {
solver: LinearRegressionSolverName::SVD
LinearRegressionParameters {
solver: LinearRegressionSolverName::SVD,
}
}
}
@@ -39,7 +39,11 @@ impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
}
impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
pub fn fit(x: &M, y: &M::RowVector, parameters: LinearRegressionParameters) -> LinearRegression<T, M> {
pub fn fit(
x: &M,
y: &M::RowVector,
parameters: LinearRegressionParameters,
) -> LinearRegression<T, M> {
let y_m = M::from_row_vector(y.clone());
let b = y_m.transpose();
let (x_nrows, num_attributes) = x.shape();
@@ -103,7 +107,14 @@ mod tests {
114.2, 115.7, 116.9,
]);
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
let y_hat_qr = LinearRegression::fit(
&x,
&y,
LinearRegressionParameters {
solver: LinearRegressionSolverName::QR,
},
)
.predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
@@ -143,7 +154,14 @@ mod tests {
114.2, 115.7, 116.9,
];
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
let y_hat_qr = LinearRegression::fit(
&x,
&y,
LinearRegressionParameters {
solver: LinearRegressionSolverName::QR,
},
)
.predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
src/neighbors/knn.rs
+14 -13
View File
@@ -13,9 +13,9 @@ pub enum KNNAlgorithmName {
}
#[derive(Serialize, Deserialize, Debug)]
pub struct KNNClassifierParameters {
pub struct KNNClassifierParameters {
pub algorithm: KNNAlgorithmName,
pub k: usize
pub k: usize,
}
#[derive(Serialize, Deserialize, Debug)]
@@ -34,9 +34,9 @@ enum KNNAlgorithm<T: FloatExt, D: Distance<Vec<T>, T>> {
impl Default for KNNClassifierParameters {
fn default() -> Self {
KNNClassifierParameters {
KNNClassifierParameters {
algorithm: KNNAlgorithmName::CoverTree,
k: 3
k: 3,
}
}
}
@@ -93,7 +93,7 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
x: &M,
y: &M::RowVector,
distance: D,
parameters: KNNClassifierParameters
parameters: KNNClassifierParameters,
) -> KNNClassifier<T, D> {
let y_m = M::from_row_vector(y.clone());
@@ -118,7 +118,10 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
)
);
assert!(parameters.k > 1, format!("k should be > 1, k=[{}]", parameters.k));
assert!(
parameters.k > 1,
format!("k should be > 1, k=[{}]", parameters.k)
);
KNNClassifier {
classes: classes,
@@ -169,7 +172,10 @@ mod tests {
&x,
&y,
Distances::euclidian(),
KNNClassifierParameters{k: 3, algorithm: KNNAlgorithmName::LinearSearch}
KNNClassifierParameters {
k: 3,
algorithm: KNNAlgorithmName::LinearSearch,
},
);
let r = knn.predict(&x);
assert_eq!(5, Vec::len(&r));
@@ -181,12 +187,7 @@ mod tests {
let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(
&x,
&y,
Distances::euclidian(),
Default::default()
);
let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
src/optimization/mod.rs
+1 -1
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@@ -5,7 +5,7 @@ pub type F<'a, T, X> = dyn for<'b> Fn(&'b X) -> T + 'a;
pub type DF<'a, X> = dyn for<'b> Fn(&'b mut X, &'b X) + 'a;
#[derive(Debug, PartialEq)]
pub enum FunctionOrder {
pub enum FunctionOrder {
SECOND,
THIRD,
}