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set_title ( "First three PCA directions" ) ax. scatter ( X_reduced, X_reduced, X_reduced, c = y, cmap = plt. add_subplot ( 111, projection = "3d", elev =- 150, azim = 110 ) X_reduced = PCA ( n_components = 3 ). figure ( 1, figsize = ( 8, 6 )) ax = fig. yticks (()) # To getter a better understanding of interaction of the dimensions # plot the first three PCA dimensions fig = plt. data # we only take the first two features. # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt # unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 from sklearn import datasets from composition import PCA # import some data to play with iris = datasets.
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