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randogoth 2024-04-09 07:33:12 +03:00
parent 148e9fdca3
commit f0b98c1dfa
2 changed files with 128 additions and 149 deletions
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Cargo.lock generated
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@ -36,7 +36,7 @@ checksum = "9c198f91728a82281a64e1f4f9eeb25d82cb32a5de251c6bd1b5154d63a8e7bd"
[[package]]
name = "lyagushka"
version = "0.1.0"
version = "1.0.0"
dependencies = [
"atty",
"serde",

275
src/main.rs
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@ -16,163 +16,142 @@ struct Anomaly {
z_score: Option<f32>,
}
fn anomaly_info(cluster: &[i32]) -> Anomaly {
let num_elements: usize = cluster.len();
let start: i32 = *cluster.first().expect("Cluster has no start");
let end: i32 = *cluster.last().expect("Cluster has no end");
let span_length: i32 = end - start;
let centroid: f32 = start as f32 + span_length as f32 / 2.0;
impl Anomaly {
Anomaly {
elements: cluster.to_vec(),
start,
end,
span_length,
num_elements,
centroid,
z_score: None, // Placeholder for actual Z-score calculation
pub fn new(cluster: &[i32]) -> Self {
let num_elements: usize = cluster.len();
let start: i32 = *cluster.first().expect("Cluster has no start");
let end: i32 = *cluster.last().expect("Cluster has no end");
let span_length: i32 = end - start;
let centroid: f32 = start as f32 + span_length as f32 / 2.0;
Anomaly {
elements: cluster.to_vec(),
start,
end,
span_length,
num_elements,
centroid,
z_score: None,
}
}
}
struct Lyagushka {
dataset: Vec<i32>,
anomalies: Vec<Anomaly>,
}
/// Calculates the densities (clusters) and significant gaps between points in a dataset.
///
/// This function iterates over a dataset of points, identifying clusters based on a distance threshold
/// (calculated from the mean distance between points and adjusted by a given factor) and identifying significant gaps
/// that exceed a certain threshold. Each cluster or significant gap identified is summarized in a `Anomaly` object.
///
/// # Arguments
/// * `dataset`: A slice of `Point` objects representing the dataset to be analyzed.
/// * `factor`: A multiplier used to define the thresholds for clustering and gap identification.
/// A lower factor tightens the cluster threshold and widens the gap threshold, and vice versa.
/// * `min_cluster_size`: The minimum number of points required for a group of points to be considered a cluster.
///
/// # Returns
/// A vector of `Anomaly` objects, each representing either a cluster of points or a significant gap between points.
///
fn scan_anomalies(dataset: &[i32], factor: f32, min_cluster_size: usize) -> Vec<Anomaly> {
impl Lyagushka {
pub fn new(dataset: Vec<i32>) -> Self {
Lyagushka {
dataset,
anomalies: vec![]
}
}
fn scan_anomalies(&mut self, factor: f32, min_cluster_size: usize) {
// Return early if the dataset is too small to form any clusters or gaps.
if dataset.len() < 2 { return Vec::new(); }
// Calculate the mean distance between consecutive points in the dataset.
let mean_distance: f32 = dataset.windows(2)
.map(|w| (w[1] - w[0]) as f32)
.sum::<f32>() / (dataset.len() - 1) as f32;
// Define thresholds for clustering and gap identification based on the mean distance and factor.
let cluster_threshold: f32 = mean_distance / factor;
let gap_threshold: f32 = factor * mean_distance;
let mut results: Vec<Anomaly> = Vec::new(); // Stores the resulting clusters and gaps.
let mut current_cluster: Vec<i32> = Vec::new(); // Temporary storage for points in the current cluster.
// Iterate through pairs of consecutive points to find clusters and significant gaps.
for window in dataset.windows(2) {
let gap_size: f32 = (window[1] - window[0]) as f32;
if gap_size <= cluster_threshold {
// Add points to the current cluster
if current_cluster.is_empty() {
current_cluster.push(window[0]); // Start a new cluster with the first point
}
current_cluster.push(window[1]); // Add the second point to the cluster
} else {
// End the current cluster and start a new gap
if !current_cluster.is_empty() && current_cluster.len() >= min_cluster_size {
results.push(anomaly_info(&current_cluster));
current_cluster.clear();
}
// Record the gap
if gap_size > gap_threshold {
results.push(Anomaly {
elements: Vec::new(), // No elements in a gap
start: window[0],
end: window[1],
span_length: gap_size as i32,
num_elements: 0,
centroid: (window[0] as f32 + window[1] as f32) / 2.0,
z_score: None,
});
// Calculate the mean distance between consecutive points in the dataset.
let mean_distance: f32 = self.dataset.windows(2)
.map(|w| (w[1] - w[0]) as f32)
.sum::<f32>() / (self.dataset.len() - 1) as f32;
// Define thresholds for clustering and gap identification based on the mean distance and factor.
let cluster_threshold: f32 = mean_distance / factor;
let gap_threshold: f32 = factor * mean_distance;
let mut current_cluster: Vec<i32> = Vec::new(); // Temporary storage for points in the current cluster.
// Iterate through pairs of consecutive points to find clusters and significant gaps.
for window in self.dataset.windows(2) {
let gap_size: f32 = (window[1] - window[0]) as f32;
if gap_size <= cluster_threshold {
// Add points to the current cluster
if current_cluster.is_empty() {
current_cluster.push(window[0]); // Start a new cluster with the first point
}
current_cluster.push(window[1]); // Add the second point to the cluster
} else {
// End the current cluster and start a new gap
if !current_cluster.is_empty() && current_cluster.len() >= min_cluster_size {
self.anomalies.push(Anomaly::new(&current_cluster));
current_cluster.clear();
}
// Record the gap
if gap_size > gap_threshold {
self.anomalies.push(Anomaly {
elements: Vec::new(), // No elements in a gap
start: window[0],
end: window[1],
span_length: gap_size as i32,
num_elements: 0,
centroid: (window[0] as f32 + window[1] as f32) / 2.0,
z_score: None,
});
}
}
}
}
// Finalize the last cluster if applicable
if !current_cluster.is_empty() && current_cluster.len() >= min_cluster_size {
results.push(anomaly_info(&current_cluster));
}
results
}
/// Analyzes a dataset of points to identify clusters and significant gaps, calculates z-scores for each,
/// and serializes the results to a JSON string.
///
/// This function takes a vector of `Point` structs, a factor for adjusting clustering and gap detection thresholds,
/// and a minimum cluster size. It performs an analysis to identify clusters of points that are closely grouped
/// together and significant gaps between these clusters. For each cluster or gap, it calculates a z-score that
/// indicates how far the centroid or span length deviates from the mean distance of the dataset. The results
/// of this analysis are then serialized into a JSON string.
///
/// # Arguments
/// * `dataset` - A vector of `Point` structs representing the dataset to be analyzed.
/// * `factor` - A floating-point value used to adjust the sensitivity of cluster and gap detection. Lower values
/// result in tighter clustering and wider gaps, while higher values do the opposite.
/// * `min_cluster_size` - The minimum number of contiguous points required to be considered a cluster.
///
/// # Returns
/// Returns a `String` containing the JSON-serialized analysis results, including clusters and gaps with their z-scores.
///
fn lyagushka(mut dataset: Vec<i32>, factor: f32, min_cluster_size: usize) -> String {
// Sort the vector
dataset.sort_unstable();
// Calculate clusters and gaps from the dataset using predefined criteria.
let mut anomalies: Vec<Anomaly> = scan_anomalies(&dataset, factor, min_cluster_size);
// Calculate the mean density of clusters in the dataset for comparison.
let mean_density: f32 = anomalies.iter()
.filter(|info: &&Anomaly| info.num_elements > 0)
.map(|info: &Anomaly| info.num_elements as f32 / info.span_length as f32)
.sum::<f32>() / anomalies.iter().filter(|info: &&Anomaly| info.num_elements > 0).count() as f32;
// Calculate the standard deviation of cluster densities to evaluate variation.
let variance_density: f32 = anomalies.iter()
.filter(|info: &&Anomaly| info.num_elements > 0)
.map(|info: &Anomaly| info.num_elements as f32 / info.span_length as f32)
.map(|density: f32| (density - mean_density).powi(2))
.sum::<f32>() / anomalies.iter().filter(|info: &&Anomaly| info.num_elements > 0).count() as f32;
let std_dev_density: f32 = variance_density.sqrt();
// Calculate mean span length
let mean_span_length: f32 = anomalies.iter()
.map(|info: &Anomaly| info.span_length as f32)
.sum::<f32>() / anomalies.len() as f32;
// Calculate variance
let variance: f32 = anomalies.iter()
.map(|info: &Anomaly| (info.span_length as f32 - mean_span_length).powi(2))
.sum::<f32>() / anomalies.len() as f32;
// Standard deviation is the square root of variance
let std_dev_span_length: f32 = variance.sqrt();
// Update Z-scores for both clusters and gaps based on their deviation from mean metrics.
for info in anomalies.iter_mut() {
if info.num_elements > 0 {
// Calculate and update Z-score for clusters based on density deviation.
let cluster_density: f32 = info.num_elements as f32 / info.span_length as f32;
info.z_score = Some((cluster_density - mean_density) / std_dev_density);
} else {
// Calculate and update Z-score for gaps based on span length deviation.
info.z_score = Some((info.span_length as f32 / std_dev_span_length) * -1.0);
// Finalize the last cluster if applicable
if !current_cluster.is_empty() && current_cluster.len() >= min_cluster_size {
self.anomalies.push(Anomaly::new(&current_cluster));
}
}
serde_json::to_string_pretty(&anomalies).unwrap_or_else(|_| "Failed to serialize data".to_string())
pub fn search(&mut self, factor: f32, min_cluster_size: usize) -> String {
// Sort the vector
self.dataset.sort_unstable();
// Calculate clusters and gaps from the dataset using predefined criteria.
self.scan_anomalies(factor, min_cluster_size);
// Calculate the mean density of clusters in the dataset for comparison.
let mean_density: f32 = self.anomalies.iter()
.filter(|info: &&Anomaly| info.num_elements > 0)
.map(|info: &Anomaly| info.num_elements as f32 / info.span_length as f32)
.sum::<f32>() / self.anomalies.iter().filter(|info: &&Anomaly| info.num_elements > 0).count() as f32;
// Calculate the standard deviation of cluster densities to evaluate variation.
let variance_density: f32 = self.anomalies.iter()
.filter(|info: &&Anomaly| info.num_elements > 0)
.map(|info: &Anomaly| info.num_elements as f32 / info.span_length as f32)
.map(|density: f32| (density - mean_density).powi(2))
.sum::<f32>() / self.anomalies.iter().filter(|info: &&Anomaly| info.num_elements > 0).count() as f32;
let std_dev_density: f32 = variance_density.sqrt();
// Calculate mean span length
let mean_span_length: f32 = self.anomalies.iter()
.map(|info: &Anomaly| info.span_length as f32)
.sum::<f32>() / self.anomalies.len() as f32;
// Calculate variance
let variance: f32 = self.anomalies.iter()
.map(|info: &Anomaly| (info.span_length as f32 - mean_span_length).powi(2))
.sum::<f32>() / self.anomalies.len() as f32;
// Standard deviation is the square root of variance
let std_dev_span_length: f32 = variance.sqrt();
// Update Z-scores for both clusters and gaps based on their deviation from mean metrics.
for info in self.anomalies.iter_mut() {
if info.num_elements > 0 {
// Calculate and update Z-score for clusters based on density deviation.
let cluster_density: f32 = info.num_elements as f32 / info.span_length as f32;
info.z_score = Some((cluster_density - mean_density) / std_dev_density);
} else {
// Calculate and update Z-score for gaps based on span length deviation.
info.z_score = Some((info.span_length as f32 / std_dev_span_length) * -1.0);
}
}
serde_json::to_string_pretty(&self.anomalies).unwrap_or_else(|_| "Failed to serialize data".to_string())
}
}
/// The entry point for the command-line tool that reads a dataset of integers from either a file or stdin,
@ -230,12 +209,12 @@ fn main() -> io::Result<()> {
.collect()
};
let factor: f32 = args[args.len() - 2].parse().expect("Factor must be a float");
let min_cluster_size: usize = args[args.len() - 1].parse().expect("Min cluster size must be an integer");
// Analysis and output
println!("{}", lyagushka(dataset, factor, min_cluster_size));
let mut zhaba = Lyagushka::new(dataset);
println!("{}", zhaba.search(factor, min_cluster_size));
Ok(())
}