KmeansFitPredict
K-Means — Lloyd's algorithm with k-means++ initialisation. / K-Means — algorithme de Lloyd avec initialisation k-means++.
⚡ Rust-native
✓ sklearn parity
Quick start — Python
import seraplot as sp, numpy as np
from sklearn.datasets import make_blobs
X, _ = make_blobs(n_samples=500, centers=4)
result = sp.KMeans(k=4, max_iter=300).fit_predict(X)
print(result)
EN — Drop-in replacement:
FR — Remplacement direct : même API que sklearn, changez l'import.
sp.KmeansFitPredict has the same API as sklearn.FR — Remplacement direct : même API que sklearn, changez l'import.
API Reference
JSON function name
ml_kmeans_fit_predict — aliases: kmeans_fit_predict, ml_kmeans
Python class
sp.KmeansFitPredict(k=3, max_iter=300, n_init=10)
Constructor Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
k | int | 3 | Number of clusters. |
max_iter | int | 300 | Maximum iterations. |
n_init | int | 10 | Number of random restarts. |
Returns
JSON with labels, inertia.
Algorithm
$$J = \sum_{k=1}^{K}\sum_{i \in C_k}|x_i - \mu_k|_2^2$$
Example
import seraplot as sp, numpy as np
from sklearn.datasets import make_blobs
X, _ = make_blobs(n_samples=500, centers=4)
result = sp.KMeans(k=4, max_iter=300).fit_predict(X)
print(result)
Référence API
Nom de fonction JSON
ml_kmeans_fit_predict — alias : kmeans_fit_predict, ml_kmeans
Classe Python
sp.KmeansFitPredict(k=3, max_iter=300, n_init=10)
Paramètres du constructeur
| Paramètre | Type | Défaut | Description |
|---|---|---|---|
k | int | 3 | Nombre de clusters. |
max_iter | int | 300 | Nombre maximum d'itérations. |
n_init | int | 10 | Nombre de redémarrages aléatoires. |
Retourne
JSON avec labels, inertia.
Algorithme
$$J = \sum_{k=1}^{K}\sum_{i \in C_k}|x_i - \mu_k|_2^2$$
Exemple
import seraplot as sp, numpy as np
from sklearn.datasets import make_blobs
X, _ = make_blobs(n_samples=500, centers=4)
result = sp.KMeans(k=4, max_iter=300).fit_predict(X)
print(result)