Note

Click here to download the full example code

Merging and hierarchical cluster examples

This section contains several examples on how to merge cluster data, either generated with pyclugen or from other sources. To run the examples we first need to import the clugen() and clumerge() functions:

import numpy as np
from pyclugen import clugen, clumerge

Although it is possible to merge data in any dimension, these examples will focus on merging 2D data. Therefore, we'll use the same plot_examples_2d function used for the 2D examples:

from plot_functions import plot_examples_2d

Merging two data sets generated with clugen()

seed1 = 444
seed2 = 555

e088 = clugen(2, 5, 1000, [1, 1], np.pi / 12, [20, 20], 14, 1.2, 1.5, rng=seed1,
    proj_dist_fn="unif", point_dist_fn="n")
e089 = clugen(2, 3, 1500, [1, 0], 0.05, [20, 20], 0, 0, 4, rng=seed2,
    point_dist_fn="n", cluster_offset = [20, 0])
e090 = clumerge(e088, e089)

plot_examples_2d(
    e088, "e088: data set 1",
    e089, "e089: data set 2",
    e090, "e090: merged data sets")

In the previous example, clusters from individual data sets remain as separate clusters in the merged data set. It's also possible to maintain the original cluster labels by setting the clusters_field parameter to None:

e091 = clumerge(e088, e089, clusters_field=None)

plot_examples_2d(
    e088, "e088: data set 1",
    e089, "e089: data set 2",
    e091, "e091: merged data sets")

Adding noise to a clugen()-generated data set

seed = 333

prng = np.random.default_rng(seed)
e092 = {"points": 120 * prng.random((500, 2)) - 60, "clusters": np.ones(500, dtype=np.int32)}
e093 = clumerge(e092, e090) # clumerge(e092, e088, e089) would also work

plot_examples_2d(
    e090, "e090: original merged data sets",
    e092, "e092: random uniform noise",
    e093, "e093: data sets with noise",
    pmargin=0)

Merging with data not generated with clugen()

Data generated with clugen() can be merged with other data sets, for example data created with one of scikit-learn's generators:

seed = 321

from sklearn.datasets import make_moons

X, y = make_moons(100, noise=0.05, random_state=seed)

e094 = {"points": X, "clusters": y}
e095 = clugen(2, 4, 200, [1, 1], np.pi / 12, [1, 1], 0.1, 0.01, 0.25, rng=seed,
    proj_dist_fn = "unif", point_dist_fn = "n")
e096 = clumerge(e094, e095)

plt = plot_examples_2d(
    e094, "e094: generated w/ make_moons()",
    e095, "e095: generated w/ clugen()",
    e096, "e096: merged data")

We can also hierarchize clusters from different sources:

e097 = {**e094, "hclusters": np.ones(100, dtype=np.int32)}
e098 = {**e095._asdict(), "hclusters": 2 * np.ones(200, np.int32)}
e099 = clumerge(e097, e098, clusters_field="hclusters")

plt = plot_examples_2d(
    e097, "e097: generated w/ make_moons()",
    e098, "e098: generated w/ clugen()",
    e099, "e099: merged data",
    clusters_field="hclusters")

Total running time of the script: ( 0 minutes 3.079 seconds)

Download Python source code: plot_5_mrg_examples.py

Download Jupyter notebook: plot_5_mrg_examples.ipynb

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