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Autoformat crate #198

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30 changes: 17 additions & 13 deletions benches/examples/cross_validation.rs
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
use rusty_machine::linalg::{Matrix, BaseMatrix};
use rusty_machine::learning::{LearningResult, SupModel};
use rusty_machine::analysis::score::row_accuracy;
use rusty_machine::analysis::cross_validation::k_fold_validate;
use rand::{thread_rng, Rng};
use test::{Bencher, black_box};
use rusty_machine::analysis::cross_validation::k_fold_validate;
use rusty_machine::analysis::score::row_accuracy;
use rusty_machine::learning::{LearningResult, SupModel};
use rusty_machine::linalg::{BaseMatrix, Matrix};
use test::{black_box, Bencher};

fn generate_data(rows: usize, cols: usize) -> Matrix<f64> {
let mut rng = thread_rng();
Expand All @@ -22,14 +22,14 @@ fn generate_data(rows: usize, cols: usize) -> Matrix<f64> {
/// matrices when trained. Its prediction for each row is the
/// sum of the row's elements plus the precalculated training sum.
struct DummyModel {
sum: f64
sum: f64,
}

impl SupModel<Matrix<f64>, Matrix<f64>> for DummyModel {
fn predict(&self, inputs: &Matrix<f64>) -> LearningResult<Matrix<f64>> {
let predictions: Vec<f64> = inputs
.row_iter()
.map(|row| { self.sum + sum(row.iter()) })
.map(|row| self.sum + sum(row.iter()))
.collect();
Ok(Matrix::new(inputs.rows(), 1, predictions))
}
Expand All @@ -40,12 +40,12 @@ impl SupModel<Matrix<f64>, Matrix<f64>> for DummyModel {
}
}

fn sum<'a, I: Iterator<Item=&'a f64>>(x: I) -> f64 {
fn sum<'a, I: Iterator<Item = &'a f64>>(x: I) -> f64 {
x.fold(0f64, |acc, x| acc + x)
}

macro_rules! bench {
($name:ident: $params:expr) => {
($name:ident : $params:expr) => {
#[bench]
fn $name(b: &mut Bencher) {
let (rows, cols, k) = $params;
Expand All @@ -54,12 +54,16 @@ macro_rules! bench {

b.iter(|| {
let mut model = DummyModel { sum: 0f64 };
let _ = black_box(
k_fold_validate(&mut model, &inputs, &targets, k, row_accuracy)
);
let _ = black_box(k_fold_validate(
&mut model,
&inputs,
&targets,
k,
row_accuracy,
));
});
}
}
};
}

bench!(bench_10_10_3: (10, 10, 3));
Expand Down
22 changes: 11 additions & 11 deletions benches/examples/k_means.rs
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Original file line number Diff line number Diff line change
@@ -1,19 +1,19 @@
use rusty_machine::linalg::{Matrix, BaseMatrix};
use rusty_machine::learning::k_means::KMeansClassifier;
use rusty_machine::learning::UnSupModel;
use rusty_machine::linalg::{BaseMatrix, Matrix};

use rand::thread_rng;
use rand::distributions::IndependentSample;
use rand::distributions::normal::Normal;
use rand::distributions::IndependentSample;
use rand::thread_rng;

use test::{Bencher, black_box};
use test::{black_box, Bencher};

fn generate_data(centroids: &Matrix<f64>, points_per_centroid: usize, noise: f64) -> Matrix<f64> {
assert!(centroids.cols() > 0, "Centroids cannot be empty.");
assert!(centroids.rows() > 0, "Centroids cannot be empty.");
assert!(noise >= 0f64, "Noise must be non-negative.");
let mut raw_cluster_data = Vec::with_capacity(centroids.rows() * points_per_centroid *
centroids.cols());
let mut raw_cluster_data =
Vec::with_capacity(centroids.rows() * points_per_centroid * centroids.cols());

let mut rng = thread_rng();
let normal_rv = Normal::new(0f64, noise);
Expand All @@ -32,14 +32,15 @@ fn generate_data(centroids: &Matrix<f64>, points_per_centroid: usize, noise: f64
}
}

Matrix::new(centroids.rows() * points_per_centroid,
centroids.cols(),
raw_cluster_data)
Matrix::new(
centroids.rows() * points_per_centroid,
centroids.cols(),
raw_cluster_data,
)
}

#[bench]
fn k_means_train(b: &mut Bencher) {

const SAMPLES_PER_CENTROID: usize = 2000;
// Choose two cluster centers, at (-0.5, -0.5) and (0, 0.5).
let centroids = Matrix::new(2, 2, vec![-0.5, -0.5, 0.0, 0.5]);
Expand All @@ -55,7 +56,6 @@ fn k_means_train(b: &mut Bencher) {

#[bench]
fn k_means_predict(b: &mut Bencher) {

const SAMPLES_PER_CENTROID: usize = 2000;
// Choose two cluster centers, at (-0.5, -0.5) and (0, 0.5).
let centroids = Matrix::new(2, 2, vec![-0.5, -0.5, 0.0, 0.5]);
Expand Down
24 changes: 12 additions & 12 deletions benches/examples/nnet.rs
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Original file line number Diff line number Diff line change
@@ -1,15 +1,15 @@
use test::{Bencher, black_box};
use test::{black_box, Bencher};

use rand::{random, Closed01};
use std::vec::Vec;

use rusty_machine::learning::nnet::{NeuralNet, BCECriterion};
use rusty_machine::learning::toolkit::regularization::Regularization;
use rusty_machine::learning::toolkit::activ_fn::Sigmoid;
use rusty_machine::learning::nnet::{BCECriterion, NeuralNet};
use rusty_machine::learning::optim::grad_desc::StochasticGD;
use rusty_machine::learning::toolkit::activ_fn::Sigmoid;
use rusty_machine::learning::toolkit::regularization::Regularization;

use rusty_machine::linalg::Matrix;
use rusty_machine::learning::SupModel;
use rusty_machine::linalg::Matrix;

fn generate_data() -> (Matrix<f64>, Matrix<f64>, Matrix<f64>) {
const THRESHOLD: f64 = 0.7;
Expand All @@ -34,12 +34,7 @@ fn generate_data() -> (Matrix<f64>, Matrix<f64>, Matrix<f64>) {
let inputs = Matrix::new(SAMPLES, 2, input_data);
let targets = Matrix::new(SAMPLES, 1, label_data);

let test_cases = vec![
0.0, 0.0,
0.0, 1.0,
1.0, 1.0,
1.0, 0.0,
];
let test_cases = vec![0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0];
let test_inputs = Matrix::new(test_cases.len() / 2, 2, test_cases);

(inputs, targets, test_inputs)
Expand All @@ -52,7 +47,12 @@ fn nnet_and_gate_train(b: &mut Bencher) {
let criterion = BCECriterion::new(Regularization::L2(0.));

b.iter(|| {
let mut model = black_box(NeuralNet::mlp(layers, criterion, StochasticGD::default(), Sigmoid));
let mut model = black_box(NeuralNet::mlp(
layers,
criterion,
StochasticGD::default(),
Sigmoid,
));
let _ = black_box(model.train(&inputs, &targets).unwrap());
})
}
Expand Down
23 changes: 12 additions & 11 deletions benches/examples/svm.rs
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Original file line number Diff line number Diff line change
@@ -1,23 +1,21 @@
use rusty_machine::learning::svm::SVM;
// Necessary for the training trait.
use rusty_machine::learning::SupModel;
use rusty_machine::learning::toolkit::kernel::HyperTan;
use rusty_machine::learning::SupModel;

use rusty_machine::linalg::Matrix;
use rusty_machine::linalg::Vector;

use test::{Bencher, black_box};
use test::{black_box, Bencher};

fn generate_data() -> (Matrix<f64>, Vector<f64>) {
// Training data
let inputs = Matrix::new(11, 1, vec![
-0.1, -2., -9., -101., -666.7,
0., 0.1, 1., 11., 99., 456.7
]);
let targets = Vector::new(vec![
-1., -1., -1., -1., -1.,
1., 1., 1., 1., 1., 1.
]);
let inputs = Matrix::new(
11,
1,
vec![-0.1, -2., -9., -101., -666.7, 0., 0.1, 1., 11., 99., 456.7],
);
let targets = Vector::new(vec![-1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1.]);

(inputs, targets)
}
Expand All @@ -43,7 +41,10 @@ fn svm_sign_learner_train(b: &mut Bencher) {
fn svm_sign_learner_predict(b: &mut Bencher) {
let (inputs, targets) = generate_data();

let test_data = (-1000..1000).filter(|&x| x % 100 == 0).map(|x| x as f64).collect::<Vec<_>>();
let test_data = (-1000..1000)
.filter(|&x| x % 100 == 0)
.map(|x| x as f64)
.collect::<Vec<_>>();
let test_inputs = Matrix::new(test_data.len(), 1, test_data);
let mut svm_mod = SVM::new(HyperTan::new(100., 0.), 0.3);
let _ = svm_mod.train(&inputs, &targets);
Expand Down
2 changes: 1 addition & 1 deletion benches/lib.rs
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Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
#![feature(test)]

extern crate rand;
extern crate rusty_machine;
extern crate test;
extern crate rand;

mod examples {
mod cross_validation;
Expand Down
31 changes: 16 additions & 15 deletions examples/k-means_generating_cluster.rs
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Original file line number Diff line number Diff line change
@@ -1,23 +1,20 @@
extern crate rusty_machine;
extern crate rand;
extern crate rusty_machine;

use rusty_machine::linalg::{Matrix, BaseMatrix};
use rusty_machine::learning::k_means::KMeansClassifier;
use rusty_machine::learning::UnSupModel;
use rusty_machine::linalg::{BaseMatrix, Matrix};

use rand::thread_rng;
use rand::distributions::IndependentSample;
use rand::distributions::normal::Normal;
use rand::distributions::IndependentSample;
use rand::thread_rng;

fn generate_data(centroids: &Matrix<f64>,
points_per_centroid: usize,
noise: f64)
-> Matrix<f64> {
fn generate_data(centroids: &Matrix<f64>, points_per_centroid: usize, noise: f64) -> Matrix<f64> {
assert!(centroids.cols() > 0, "Centroids cannot be empty.");
assert!(centroids.rows() > 0, "Centroids cannot be empty.");
assert!(noise >= 0f64, "Noise must be non-negative.");
let mut raw_cluster_data = Vec::with_capacity(centroids.rows() * points_per_centroid *
centroids.cols());
let mut raw_cluster_data =
Vec::with_capacity(centroids.rows() * points_per_centroid * centroids.cols());

let mut rng = thread_rng();
let normal_rv = Normal::new(0f64, noise);
Expand All @@ -36,18 +33,22 @@ fn generate_data(centroids: &Matrix<f64>,
}
}

Matrix::new(centroids.rows() * points_per_centroid,
centroids.cols(),
raw_cluster_data)
Matrix::new(
centroids.rows() * points_per_centroid,
centroids.cols(),
raw_cluster_data,
)
}

fn main() {
println!("K-Means clustering example:");

const SAMPLES_PER_CENTROID: usize = 2000;

println!("Generating {0} samples from each centroids:",
SAMPLES_PER_CENTROID);
println!(
"Generating {0} samples from each centroids:",
SAMPLES_PER_CENTROID
);
// Choose two cluster centers, at (-0.5, -0.5) and (0, 0.5).
let centroids = Matrix::new(2, 2, vec![-0.5, -0.5, 0.0, 0.5]);
println!("{}", centroids);
Expand Down
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