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- # -*- coding: utf-8 -*-
- """
- Created on Tue May 28 11:53:41 2019
- @author: lancernik
- """
- #
- from sklearn.datasets.samples_generator import make_blobs
- from sklearn.cluster import SpectralClustering
- import numpy as np
- from sklearn.model_selection import train_test_split
- from sklearn.decomposition import PCA
- import pandas as pd
- X,y= make_blobs(n_samples=50, centers=5,random_state=0,n_features=15)
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
- pca = PCA(n_components=2)
- principalComponents = pca.fit_transform(X)
- principalDf = pd.DataFrame(data = principalComponents
- , columns = ['principal component 1', 'principal component 2'])
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