61 lines
No EOL
2.5 KiB
Markdown
61 lines
No EOL
2.5 KiB
Markdown
# lyagushka
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(Russian лягушка: frog)
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Cluster and Gap Analysis Tool inspired by Fatum Project's 'Zhaba' algorithm (Russian 'жаба': toad) that finds attractor clusters in lists of integers.
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This Rust command-line tool analyzes a dataset of integers to identify clusters of closely grouped points and significant gaps between these clusters. It calculates z-scores for each cluster or gap to measure their statistical significance relative to the dataset's mean distance. The analysis results, including clusters, gaps, and their z-scores, are output as a JSON string.
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## Features
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- **Cluster Identification**: Identifies groups of points that are closely spaced together based on a customizable threshold.
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- **Gap Detection**: Detects significant gaps between clusters, providing insights into the dataset's distribution.
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- **Z-Score Calculation**: Calculates z-scores for both clusters and gaps, offering a statistical measure of their deviation from the mean distance.
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- **Flexible Input**: Accepts input data either from a file specified as a command-line argument or piped directly into stdin.
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- **JSON Output**: Outputs the analysis results in a readable JSON format, making it easy to interpret or use in further processing.
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## Usage
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### From a File
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To analyze a dataset from a file, provide the filename as an argument along with two additional parameters: the factor for adjusting clustering and gap detection thresholds, and the minimum cluster size.
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```sh
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cargo run -- filename.txt 0.5 2
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```
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### From Stdin
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Alternatively, you can pipe a list of integers into the tool, followed by the factor and minimum cluster size.
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```sh
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echo "1\n2\n10\n20" | cargo run -- 0.5 2
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```
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#### Parameters
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* `filename.txt` (optional): A file containing a newline-separated list of integers to analyze. If not provided, the program expects input from stdin.
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* `factor`: A floating-point value used to fine-tune the sensitivity of cluster and gap detection. Lower values result in tighter clusters and wider gaps, while higher values do the opposite.
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* `min_cluster_size`: An integer specifying the minimum number of contiguous points required to be considered a cluster.
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### Output
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The tool outputs a JSON string that includes details about the identified clusters and gaps, along with their respective z-scores. Here's an example of the JSON output format:
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```json
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[
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{
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"span_length": 1.0,
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"num_elements": 2,
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"centroid": 1.5,
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"z_score": -1.23
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},
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{
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"span_length": 8.0,
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"num_elements": 0,
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"centroid": 6.0,
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"z_score": 2.45
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}
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]
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``` |