Cupy Implementation

!pip install --upgrade NNSOM

Requirement already satisfied: NNSOM in /usr/local/lib/python3.10/dist-packages (1.6.8)
Collecting NNSOM
  Downloading nnsom-1.6.9-py3-none-any.whl (32 kB)
Installing collected packages: NNSOM
  Attempting uninstall: NNSOM
    Found existing installation: NNSOM 1.6.8
    Uninstalling NNSOM-1.6.8:
      Successfully uninstalled NNSOM-1.6.8
Successfully installed NNSOM-1.6.9

from NNSOM.plots import SOMPlots

You are using GPU acceleration with Cupy

# Data Prep.
# Generate a random DataFrame with 10 features and 15000 samples
import numpy as np
np.random.seed(42)  # For reproducibility
data = np.random.rand(30000, 100)

Rows = 8
Dimensions = (Rows, Rows)

som = SOMPlots(Dimensions)
som.init_w(data)
som.train(data)

Beginning Initialization
Current Time = 17:58:28

/usr/local/lib/python3.10/dist-packages/NNSOM/som_gpu.py:110: UserWarning: Without normalization function: SOM training may be inefficient if you are not normalized.
  x = self.normalize(x, norm_func)

Ending Initialization
Current Time = 17:58:33
Beginning Training
Current Time = 17:58:33

/usr/local/lib/python3.10/dist-packages/NNSOM/som_gpu.py:213: UserWarning: Without normalization function: SOM training may be inefficient if you are not normalized.
  x = self.normalize(x, norm_func)

50
Current Time = 17:58:37
100
Current Time = 17:58:37
150
Current Time = 17:58:38
200
Current Time = 17:58:38
Ending Training
Current Time = 17:58:38

clust, dist, mdist, clustSizes = som.cluster_data(data)

/usr/local/lib/python3.10/dist-packages/NNSOM/som_gpu.py:324: UserWarning: Without normalization function: SOM training may be inefficient if you are not normalized.
  x = self.normalize(x, self.norm_func)

data_dict = {
    "data": data,
    "clust": clust,
}

import matplotlib.pyplot as plt
%matplotlib inline

fig, ax, patches, text = som.plot('hit_hist', data_dict)
plt.show()

fig, ax, patches = som.plot('neuron_dist')
plt.show()