1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
use std::collections::HashMap;
use kodama::linkage;
pub use kodama::Method;
use ndarray::ArrayView2;
use linfa::dataset::{Dataset, Targets};
use linfa::traits::Transformer;
use linfa::Float;
use linfa_kernel::Kernel;
enum Criterion<T> {
NumClusters(usize),
Distance(T),
}
pub struct HierarchicalCluster<T> {
method: Method,
stopping: Criterion<T>,
}
impl<F: Float> HierarchicalCluster<F> {
pub fn with_method(mut self, method: Method) -> HierarchicalCluster<F> {
self.method = method;
self
}
pub fn num_clusters(mut self, num_clusters: usize) -> HierarchicalCluster<F> {
self.stopping = Criterion::NumClusters(num_clusters);
self
}
pub fn max_distance(mut self, max_distance: F) -> HierarchicalCluster<F> {
self.stopping = Criterion::Distance(max_distance);
self
}
}
impl<'b: 'a, 'a, F: Float>
Transformer<Kernel<ArrayView2<'a, F>>, Dataset<Kernel<ArrayView2<'a, F>>, Vec<usize>>>
for HierarchicalCluster<F>
{
fn transform(
&self,
kernel: Kernel<ArrayView2<'a, F>>,
) -> Dataset<Kernel<ArrayView2<'a, F>>, Vec<usize>> {
let threshold = F::from(1e-6).unwrap();
let mut distance = kernel
.to_upper_triangle()
.into_iter()
.map(|x| {
if x > threshold {
-x.ln()
} else {
-threshold.ln()
}
})
.collect::<Vec<_>>();
let num_observations = kernel.size();
let res = linkage(&mut distance, num_observations, self.method);
let mut clusters = (0..num_observations)
.map(|x| (x, vec![x]))
.collect::<HashMap<_, _>>();
let mut ct = num_observations;
for step in res.steps() {
let should_stop = match self.stopping {
Criterion::NumClusters(max_clusters) => clusters.len() <= max_clusters,
Criterion::Distance(dis) => step.dissimilarity >= dis,
};
if should_stop {
break;
}
let mut ids = Vec::with_capacity(2);
let mut cl = clusters.remove(&step.cluster1).unwrap();
ids.append(&mut cl);
let mut cl = clusters.remove(&step.cluster2).unwrap();
ids.append(&mut cl);
clusters.insert(ct, ids);
ct += 1;
}
let mut tmp = vec![0; num_observations];
for (i, (_, ids)) in clusters.into_iter().enumerate() {
for id in ids {
tmp[id] = i;
}
}
Dataset::new(kernel, tmp)
}
}
impl<'a, F: Float, T: Targets>
Transformer<
Dataset<Kernel<ArrayView2<'a, F>>, T>,
Dataset<Kernel<ArrayView2<'a, F>>, Vec<usize>>,
> for HierarchicalCluster<F>
{
fn transform(
&self,
dataset: Dataset<Kernel<ArrayView2<'a, F>>, T>,
) -> Dataset<Kernel<ArrayView2<'a, F>>, Vec<usize>> {
self.transform(dataset.records)
}
}
impl<T> Default for HierarchicalCluster<T> {
fn default() -> HierarchicalCluster<T> {
HierarchicalCluster {
method: Method::Average,
stopping: Criterion::NumClusters(2),
}
}
}
#[cfg(test)]
mod tests {
use linfa::traits::Transformer;
use linfa_kernel::{Kernel, KernelMethod};
use ndarray::{Array, Axis};
use ndarray_rand::{rand_distr::Normal, RandomExt};
use super::HierarchicalCluster;
#[test]
fn test_blobs() {
let npoints = 10;
let entries = ndarray::stack(
Axis(0),
&[
Array::random((npoints, 2), Normal::new(-1., 0.1).unwrap()).view(),
Array::random((npoints, 2), Normal::new(1., 0.1).unwrap()).view(),
],
)
.unwrap();
let kernel = Kernel::params()
.method(KernelMethod::Gaussian(5.0))
.transform(&entries);
let kernel = HierarchicalCluster::default()
.max_distance(0.1)
.transform(kernel);
let ids = kernel.targets();
let first_cluster_id = &ids[0];
assert!(ids
.iter()
.take(npoints)
.all(|item| item == first_cluster_id));
let second_cluster_id = &ids[npoints];
assert!(ids
.iter()
.skip(npoints)
.all(|item| item == second_cluster_id));
assert_ne!(first_cluster_id, second_cluster_id);
let kernel = HierarchicalCluster::default()
.num_clusters(2)
.transform(kernel);
let ids = kernel.targets();
let first_cluster_id = &ids[0];
assert!(ids
.iter()
.take(npoints)
.all(|item| item == first_cluster_id));
let second_cluster_id = &ids[npoints];
assert!(ids
.iter()
.skip(npoints)
.all(|item| item == second_cluster_id));
assert_ne!(first_cluster_id, second_cluster_id);
}
#[test]
fn test_noise() {
let data = Array::random((100, 2), Normal::new(0., 1.0).unwrap());
let kernel = Kernel::params()
.method(KernelMethod::Linear)
.transform(&data);
dbg!(&kernel.to_upper_triangle());
let predictions = HierarchicalCluster::default()
.max_distance(3.0)
.transform(kernel);
dbg!(&predictions.targets());
}
}