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@ -1,11 +1,56 @@
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#[derive(Debug, Clone)] |
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use std::{fmt, sync::Arc}; |
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use parking_lot::Mutex; |
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use serde_json; |
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use serde::{Serialize, Deserialize}; |
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use linfa::prelude::*; |
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use linfa; |
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use linfa_svm::{error::Result, Svm}; |
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use ndarray::{Array, ArrayView, Axis}; |
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#[derive(Clone)] |
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pub struct LearningResults { |
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// model: Vec<f64>,
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model: Arc<Mutex<Svm<f64, bool>>>, |
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patterns: Vec<Vec<f64>>, |
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anti_patterns: Vec<Vec<f64>>, |
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} |
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const CORR_THRESHOLD: f64 = 0.95; |
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// impl Clone for LearningResults {
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// fn clone(&self) -> Self {
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// // TODO: it's a hack
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// // https://github.com/rust-ml/linfa/issues/174
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// let model_str = serde_json::to_string(&self.model).unwrap();
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// let model = serde_json::from_str(&model_str).unwrap();
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// return LearningResults {
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// model,
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// patterns: self.patterns.clone(),
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// anti_patterns: self.anti_patterns.clone()
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// };
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// }
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// }
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impl fmt::Debug for LearningResults { |
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { |
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f.debug_struct("Point") |
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.field("{:?}", &self.patterns) |
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.field("{:?}", &self.anti_patterns) |
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.finish() |
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} |
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} |
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const FEATURES_SIZE: usize = 6; |
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type Features = [f64; FEATURES_SIZE]; |
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const SCORE_THRESHOLD: f64 = 0.95; |
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#[derive(Clone)] |
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pub struct PatternDetector { |
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@ -34,21 +79,65 @@ impl PatternDetector {
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let mut patterns = Vec::<Vec<f64>>::new(); |
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let mut anti_patterns = Vec::<Vec<f64>>::new(); |
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// TODO: implement actual learning
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let mut records = Array::zeros((0, FEATURES_SIZE)); |
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let mut targets_raw = Vec::<bool>::new(); |
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for r in reads { |
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let xs: Vec<f64> = r.iter().map(|e| e.1).map(nan_to_zero).collect(); |
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let fs = PatternDetector::get_features(&xs); |
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records.push_row(ArrayView::from(&fs)).unwrap(); |
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targets_raw.push(true); |
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patterns.push(xs); |
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} |
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for r in anti_reads { |
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let xs: Vec<f64> = r.iter().map(|e| e.1).map(nan_to_zero).collect(); |
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let fs = PatternDetector::get_features(&xs); |
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records.push_row(ArrayView::from(&fs)).unwrap(); |
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targets_raw.push(false); |
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anti_patterns.push(xs); |
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} |
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let targets = Array::from_vec(targets_raw); |
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// println!("{:?}", records);
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// println!("{:?}", targets);
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let train = linfa::Dataset::new(records, targets); |
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// The 'view' describes what set of data is drawn
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// let v = ContinuousView::new()
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// .add(s1)
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// // .add(s2)
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// .x_range(-500., 100.)
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// .y_range(-200., 600.)
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// .x_label("Some varying variable")
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// .y_label("The response of something");
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// Page::single(&v).save("scatter.svg").unwrap();
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// let model = stat.iter().map(|(c, v)| v / *c as f64).collect();
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let model = Svm::<_, bool>::params() |
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.pos_neg_weights(50000., 5000.) |
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.gaussian_kernel(80.0) |
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.fit(&train).unwrap(); |
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// let prediction = model.predict(Array::from_vec(vec![
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// 715.3122807017543, 761.1228070175438, 745.0, 56.135764727158595, 0.0, 0.0
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// ]));
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// println!("pridiction: {}", prediction );
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LearningResults { |
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model: Arc::new(Mutex::new(model)), |
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patterns, |
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anti_patterns, |
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} |
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@ -57,12 +146,9 @@ impl PatternDetector {
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// TODO: get iterator instead of vector
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pub fn detect(&self, ts: &Vec<(u64, f64)>) -> Vec<(u64, u64)> { |
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let mut results = Vec::new(); |
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// let mut i = 0;
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let pt = &self.learning_results.patterns; |
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let apt = &self.learning_results.anti_patterns; |
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for i in 0..ts.len() { |
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@ -97,7 +183,21 @@ impl PatternDetector {
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} |
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} |
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if pattern_match_score > anti_pattern_match_score && pattern_match_score >= CORR_THRESHOLD { |
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{
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let mut backet = Vec::<f64>::new(); |
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for j in 0..pattern_match_len { |
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backet.push(nan_to_zero(ts[i + j].1)); |
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} |
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let fs = PatternDetector::get_features(&backet); |
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let detected = self.learning_results.model.lock().predict(Array::from_vec(fs.to_vec())); |
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if detected { |
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pattern_match_score += 0.1; |
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} else { |
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anti_pattern_match_score += 0.1; |
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} |
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} |
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if pattern_match_score > anti_pattern_match_score && pattern_match_score >= SCORE_THRESHOLD { |
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results.push((ts[i].0, ts[i + pattern_match_len - 1].0)); |
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} |
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} |
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@ -148,4 +248,39 @@ impl PatternDetector {
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return result; |
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} |
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fn get_features(xs: &Vec<f64>) -> Features { |
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let mut min = f64::MAX; |
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let mut max = f64::MIN; |
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let mut sum = 0f64; |
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for x in xs { |
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min = min.min(*x); |
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max = max.max(*x); |
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sum += x; |
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} |
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let mean = sum / xs.len() as f64; |
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sum = 0f64; |
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for x in xs { |
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sum += (x - mean) * (x - mean); |
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} |
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let sd = sum.sqrt(); |
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// TODO: add autocorrelation
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// TODO: add FFT
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return [ |
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min, |
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max,
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mean, |
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sd, |
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0f64,0f64, |
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//0f64,0f64,0f64, 0f64
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]; |
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} |
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} |
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Alexey Velikiy
3 years ago