Advertisement
Guest User

Untitled

a guest
Jun 25th, 2019
78
0
Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 0.39 KB | None | 0 0
  1. N_comp=60
  2. from sklearn.decomposition import PCA
  3. pca = PCA(n_components = N_comp)
  4. X_pca=pca.fit_transform(X_scale) #lower dimension data
  5. eigenvalues=pca.components_
  6.  
  7. N_elements=10
  8. PC1=abs(eigenvalues[1,:])
  9. PC1.sort(axis=0)
  10. PC1=PC1[::-1]
  11. PC1=PC1[0:N_elements]
  12. PC1
  13.  
  14. plt.bar(range(N_elements), PC1, alpha=0.3, align='center')
  15. plt.title('Contributions of variables to PC1')
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement