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use crate::plugins::discrete::tracker::*;
use crate::*;
use rand::prelude::*;
use rand::thread_rng;
use rayon::iter::repeatn;
pub struct DirichletBaseline {
sample_rate: usize,
sequence_len: usize,
num_sequences: usize,
prior_weight: f64,
observation_weight: f64,
}
impl Default for DirichletBaseline {
fn default() -> DirichletBaseline {
DirichletBaseline {
sample_rate: 100,
sequence_len: 0,
num_sequences: 8,
prior_weight: 1.0,
observation_weight: 1.0,
}
}
}
impl DirichletBaseline {
pub fn set_sequence_len(&mut self, sequence_len: usize) {
self.sequence_len = sequence_len;
}
pub fn set_num_sequences(&mut self, num_sequences: usize) {
self.num_sequences = num_sequences;
}
pub fn set_prior_weight(&mut self, prior_weight: f64) {
self.prior_weight = prior_weight;
}
pub fn set_observation_weight(&mut self, observation_weight: f64) {
self.observation_weight = observation_weight;
}
pub fn set_sample_rate(&mut self, sample_rate: usize) {
self.sample_rate = sample_rate;
}
pub fn train<D: PointCloud>(
&self,
reader: CoverTreeReader<D>,
) -> GokoResult<KLDivergenceBaseline> {
let point_indexes = reader.point_cloud().reference_indexes();
let sequence_len = if self.sequence_len == 0 {
point_indexes.len()
} else {
self.sequence_len
};
let results: Vec<Vec<KLDivergenceStats>> = repeatn(reader, self.num_sequences)
.map(|reader| {
let mut tracker = BayesCategoricalTracker::new(
0,
reader,
);
(&point_indexes[..])
.choose_multiple(&mut thread_rng(), sequence_len)
.enumerate()
.filter_map(|(i, pi)| {
tracker.add_path(tracker.reader().known_path(*pi).unwrap());
if i % self.sample_rate == 0 {
Some(tracker.kl_div_stats())
} else {
None
}
})
.collect()
})
.collect();
let len = results[0].len();
let mut sequence_len = Vec::with_capacity(len);
let mut stats: Vec<KLDivergenceBaselineStats> =
std::iter::repeat_with(KLDivergenceBaselineStats::default)
.take(len)
.collect();
for i in 0..len {
for result_vec in &results {
stats[i].add(&result_vec[i]);
}
sequence_len.push(results[0][i].sequence_len);
}
Ok(KLDivergenceBaseline {
num_sequences: results.len(),
stats,
sequence_len,
})
}
}
#[derive(Debug, Default)]
pub struct KLDivergenceBaselineStats {
pub max: (f64, f64),
pub min: (f64, f64),
pub nz_count: (f64, f64),
pub moment1_nz: (f64, f64),
pub moment2_nz: (f64, f64),
}
impl KLDivergenceBaselineStats {
pub(crate) fn add(&mut self, stats: &KLDivergenceStats) {
self.max.0 += stats.max;
self.max.1 += stats.max * stats.max;
self.min.0 += stats.min;
self.min.1 += stats.min * stats.min;
self.nz_count.0 += stats.nz_count as f64;
self.nz_count.1 += (stats.nz_count * stats.nz_count) as f64;
self.moment1_nz.0 += stats.moment1_nz;
self.moment1_nz.1 += stats.moment1_nz * stats.moment1_nz;
self.moment2_nz.0 += stats.moment2_nz;
self.moment2_nz.1 += stats.moment2_nz * stats.moment2_nz;
}
fn to_mean_var(&self, count: f64) -> KLDivergenceBaselineStats {
let max_mean = self.max.0 / count;
let min_mean = self.min.0 / count;
let nz_count_mean = self.nz_count.0 / count;
let moment1_nz_mean = self.moment1_nz.0 / count;
let moment2_nz_mean = self.moment2_nz.0 / count;
let max_var = self.max.1 / count - max_mean * max_mean;
let min_var = self.min.1 / count - min_mean * min_mean;
let nz_count_var = self.nz_count.1 / count - nz_count_mean * nz_count_mean;
let moment1_nz_var = self.moment1_nz.1 / count - moment1_nz_mean * moment1_nz_mean;
let moment2_nz_var = self.moment2_nz.1 / count - moment2_nz_mean * moment2_nz_mean;
KLDivergenceBaselineStats {
max: (max_mean, max_var),
min: (min_mean, min_var),
nz_count: (nz_count_mean, nz_count_var),
moment1_nz: (moment1_nz_mean, moment1_nz_var),
moment2_nz: (moment2_nz_mean, moment2_nz_var),
}
}
fn interpolate(mut self, other: &Self, w: f64) -> Self {
self.max.0 += w * (other.max.0 - self.max.0);
self.max.1 += w * (other.max.1 - self.max.1);
self.min.0 += w * (other.min.0 - self.min.0);
self.min.1 += w * (other.min.1 - self.min.1);
self.nz_count.0 += w * (other.nz_count.0 - self.nz_count.0);
self.nz_count.1 += w * (other.nz_count.1 - self.nz_count.1);
self.moment1_nz.0 += w * (other.moment1_nz.0 - self.moment1_nz.0);
self.moment1_nz.1 += w * (other.moment1_nz.1 - self.moment1_nz.1);
self.moment2_nz.0 += w * (other.moment2_nz.0 - self.moment2_nz.0);
self.moment2_nz.1 += w * (other.moment2_nz.1 - self.moment2_nz.1);
self
}
}
pub struct KLDivergenceBaseline {
pub num_sequences: usize,
pub sequence_len: Vec<usize>,
pub stats: Vec<KLDivergenceBaselineStats>,
}
impl KLDivergenceBaseline {
pub fn stats(&self, i: usize) -> KLDivergenceBaselineStats {
match self.sequence_len.binary_search(&i) {
Ok(index) => self.stats[index].to_mean_var(self.num_sequences as f64),
Err(index) => {
if index == 0 {
KLDivergenceBaselineStats::default()
} else if index == self.sequence_len.len() {
let stats1 = self.stats[index - 2].to_mean_var(self.num_sequences as f64);
let stats2 = self.stats[index - 1].to_mean_var(self.num_sequences as f64);
let weight = ((i - self.sequence_len[index - 2]) as f64)
/ ((self.sequence_len[index - 1] - self.sequence_len[index - 2]) as f64);
stats1.interpolate(&stats2, weight)
} else {
let stats1 = self.stats[index - 1].to_mean_var(self.num_sequences as f64);
let stats2 = self.stats[index].to_mean_var(self.num_sequences as f64);
let weight = ((i - self.sequence_len[index - 1]) as f64)
/ ((self.sequence_len[index] - self.sequence_len[index - 1]) as f64);
stats1.interpolate(&stats2, weight)
}
}
}
}
}