Código
import seaborn as sb
import numpy as np
from sklearn.mixture import GaussianMixtureEn este post veremos un modelos de cluster
Como ya es costumbre primero importamos las librerias necesarias
Importemos los datos
Código
iris=sb.load_dataset('iris')
X_iris=iris.drop('species',axis=1)
y_iris=iris['species']Definimos el modelo
Código
Gauss=GaussianMixture(n_components=3,
covariance_type='full')Ajustamos el modelo
Código
Gauss.fit(X_iris)GaussianMixture(n_components=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GaussianMixture(n_components=3)
Determinamos las etiquetas
Código
y_gmm=Gauss.predict(X_iris)graficamente
Código
iris.head()| sepal_length | sepal_width | petal_length | petal_width | species | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
Código
iris['cluster']=y_gmm
sb.lmplot(data=iris,x='sepal_length',y='petal_length',hue='species',col='cluster',fit_reg=False)