smartcorelib · Mec-iS · Aug 23, 2022 · Aug 15, 2022 · Aug 15, 2022 · Aug 15, 2022
@@ -17,6 +17,7 @@ default = ["datasets"]
 ndarray-bindings = ["ndarray"]
 nalgebra-bindings = ["nalgebra"]
 datasets = []
+fp_bench = []
 
 [dependencies]
 ndarray = { version = "0.15", optional = true }
@@ -26,6 +27,7 @@ num = "0.4"
 rand = "0.8"
 rand_distr = "0.4"
 serde = { version = "1", features = ["derive"], optional = true }
+itertools = "0.10.3"
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
 getrandom = { version = "0.2", features = ["js"] }
@@ -46,3 +48,8 @@ harness = false
 name = "naive_bayes"
 harness = false
 required-features = ["ndarray-bindings", "nalgebra-bindings"]
+
+[[bench]]
+name = "fastpair"
+harness = false
+required-features = ["fp_bench"]
@@ -0,0 +1,56 @@
+use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
+
+// to run this bench you have to change the declaraion in mod.rs ---> pub mod fastpair;
+use smartcore::algorithm::neighbour::fastpair::FastPair;
+use smartcore::linalg::naive::dense_matrix::*;
+use std::time::Duration;
+
+fn closest_pair_bench(n: usize, m: usize) -> () {
+    let x = DenseMatrix::<f64>::rand(n, m);
+    let fastpair = FastPair::new(&x);
+    let result = fastpair.unwrap();
+
+    result.closest_pair();
+}
+
+fn closest_pair_brute_bench(n: usize, m: usize) -> () {
+    let x = DenseMatrix::<f64>::rand(n, m);
+    let fastpair = FastPair::new(&x);
+    let result = fastpair.unwrap();
+
+    result.closest_pair_brute();
+}
+
+fn bench_fastpair(c: &mut Criterion) {
+    let mut group = c.benchmark_group("FastPair");
+
+    // with full samples size (100) the test will take too long
+    group.significance_level(0.1).sample_size(30);
+    // increase from default 5.0 secs
+    group.measurement_time(Duration::from_secs(60));
+
+    for n_samples in [100_usize, 1000_usize].iter() {
+        for n_features in [10_usize, 100_usize, 1000_usize].iter() {
+            group.bench_with_input(
+                BenchmarkId::from_parameter(format!(
+                    "fastpair --- n_samples: {}, n_features: {}",
+                    n_samples, n_features
+                )),
+                n_samples,
+                |b, _| b.iter(|| closest_pair_bench(*n_samples, *n_features)),
+            );
+            group.bench_with_input(
+                BenchmarkId::from_parameter(format!(
+                    "brute --- n_samples: {}, n_features: {}",
+                    n_samples, n_features
+                )),
+                n_samples,
+                |b, _| b.iter(|| closest_pair_brute_bench(*n_samples, *n_features)),
+            );
+        }
+    }
+    group.finish();
+}
+
+criterion_group!(benches, bench_fastpair);
+criterion_main!(benches);
@@ -0,0 +1,48 @@
+//!
+//! Dissimilarities for vector-vector distance
+//!
+//! Representing distances as pairwise dissimilarities, so to build a
+//! graph of closest neighbours. This representation can be reused for
+//! different implementations (initially used in this library for FastPair).
+use std::cmp::{Eq, Ordering, PartialOrd};
+
+#[cfg(feature = "serde")]
+use serde::{Deserialize, Serialize};
+
+use crate::math::num::RealNumber;
+
+///
+/// The edge of the subgraph is defined by `PairwiseDistance`.
+/// The calling algorithm can store a list of distsances as
+/// a list of these structures.
+///
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone, Copy)]
+pub struct PairwiseDistance<T: RealNumber> {
+    /// index of the vector in the original `Matrix` or list
+    pub node: usize,
+
+    /// index of the closest neighbor in the original `Matrix` or same list
+    pub neighbour: Option<usize>,
+
+    /// measure of distance, according to the algorithm distance function
+    /// if the distance is None, the edge has value "infinite" or max distance
+    /// each algorithm has to match
+    pub distance: Option<T>,
+}
+
+impl<T: RealNumber> Eq for PairwiseDistance<T> {}
+
+impl<T: RealNumber> PartialEq for PairwiseDistance<T> {
+    fn eq(&self, other: &Self) -> bool {
+        self.node == other.node
+            && self.neighbour == other.neighbour
+            && self.distance == other.distance
+    }
+}
+
+impl<T: RealNumber> PartialOrd for PairwiseDistance<T> {
+    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
+        self.distance.partial_cmp(&other.distance)
+    }
+}