dimensionality-reductions-jmsv 0.2.0

Package with the PCA, SVD and t-SNE methods for dimensionality reduction. It also contains the clustering algorithms K-Means and K-Medoids.

What is it?

dimensionality_reductions_jmsv is a Python package that provides three methods (PCA, SVD, t-SNE) to apply dimensionality reduction to any dataset. Aslo provides two methods (KMeans y KMedoids) to clustering.

Installing the package

1. Requests is available on PyPI:

   pip install dimensionality_reductions_jmsv
   

2. Try your first dimensionality reduction with PCA

   from dimensionality_reductions_jmsv.decomposition import PCA
   import numpy as np
   
   X = (np.random.rand(10, 10) * 10).astype(int)
   pca = PCA(n_components=2)
   X_pca = pca.fit_transform(X)
   print("Original Matrix:", '\n', X, '\n')
   print("Apply dimensionality reduction with PCA to Original Matrix:", '\n', X_pca)
   

3. Try your first KMeans cluster

   from dimensionality_reductions_jmsv.cluster import KMeans
   from sklearn.datasets import make_blobs
   import matplotlib.pyplot as plt
   
   X, y = make_blobs(n_samples=500, n_features=2, centers=4, cluster_std=1, center_box=(-10.0, 10.0), shuffle=True,
                     random_state=1, )
   k = KMeans(n_clusters=4, init_method='kmeans++', random_state=32, n_init=10)
   m = k.fit_transform(X)
   
   plt.scatter(X[:, 0], X[:, 1], c=k._assign_clusters(X))
   plt.title('Cluster KMeans')
   plt.show();