pub fn make_blobs(
n_samples: usize,
n_features: usize,
centers: usize,
cluster_std: f64,
randomseed: Option<u64>,
) -> Result<Dataset>Expand description
Generate a random blobs dataset for clustering
Examples found in repository?
examples/data_generators.rs (lines 51-57)
7fn main() -> Result<(), Box<dyn std::error::Error>> {
8 println!("Creating synthetic datasets...\n");
9
10 // Generate classification dataset
11 let n_samples = 100;
12 let n_features = 5;
13
14 let classificationdata = make_classification(
15 n_samples,
16 n_features,
17 3, // 3 classes
18 2, // 2 clusters per class
19 3, // 3 informative features
20 Some(42), // random seed
21 )?;
22
23 // Train-test split
24 let (train, test) = train_test_split(&classificationdata, 0.2, Some(42))?;
25
26 println!("Classification dataset:");
27 println!(" Total samples: {}", classificationdata.n_samples());
28 println!(" Features: {}", classificationdata.n_features());
29 println!(" Training samples: {}", train.n_samples());
30 println!(" Test samples: {}", test.n_samples());
31
32 // Generate regression dataset
33 let regressiondata = make_regression(
34 n_samples,
35 n_features,
36 3, // 3 informative features
37 0.5, // noise level
38 Some(42),
39 )?;
40
41 println!("\nRegression dataset:");
42 println!(" Samples: {}", regressiondata.n_samples());
43 println!(" Features: {}", regressiondata.n_features());
44
45 // Normalize the data (in-place)
46 let mut data_copy = regressiondata.data.clone();
47 normalize(&mut data_copy);
48 println!(" Data normalized successfully");
49
50 // Generate clustering data (blobs)
51 let clusteringdata = make_blobs(
52 n_samples,
53 2, // 2 features for easy visualization
54 4, // 4 clusters
55 0.8, // cluster standard deviation
56 Some(42),
57 )?;
58
59 println!("\nClustering dataset (blobs):");
60 println!(" Samples: {}", clusteringdata.n_samples());
61 println!(" Features: {}", clusteringdata.n_features());
62
63 // Find the number of clusters by finding the max value of target
64 let num_clusters = clusteringdata.target.as_ref().map_or(0, |t| {
65 let mut max_val = -1.0;
66 for &val in t.iter() {
67 if val > max_val {
68 max_val = val;
69 }
70 }
71 (max_val as usize) + 1
72 });
73
74 println!(" Clusters: {num_clusters}");
75
76 // Generate time series data
77 let time_series = make_time_series(
78 100, // 100 time steps
79 3, // 3 features/variables
80 true, // with trend
81 true, // with seasonality
82 0.2, // noise level
83 Some(42),
84 )?;
85
86 println!("\nTime series dataset:");
87 println!(" Time steps: {}", time_series.n_samples());
88 println!(" Features: {}", time_series.n_features());
89
90 Ok(())
91}