4343scat_pca = axs [0 ].scatter (T_pca [:, 0 ], T_pca [:, 1 ], c = y )
4444scat_pcovc = axs [1 ].scatter (T_pcovc [:, 0 ], T_pcovc [:, 1 ], c = y )
4545fig .colorbar (scat_pca , ax = axs , orientation = "horizontal" )
46+ fig .suptitle ("Multiclass PCovC with One Label" )
4647
4748# %%
4849# Next, let's try a two-label classification problem, with both labels
7677 handles , _ = scat_pca .legend_elements ()
7778 labels = labels_list [i ]
7879 axs [0 , i ].legend (handles , labels )
79- print (labels )
80- print (i )
81- print (handles )
82-
83-
80+
8481axs [0 , 0 ].set_title ("Even/Odd" )
8582axs [0 , 1 ].set_title ("Greater/Less than 5" )
8683axs [0 , 2 ].set_title ("Digit" )
8784
8885axs [0 , 0 ].set_ylabel ("PCA" )
8986axs [1 , 0 ].set_ylabel ("PCovC" )
9087fig .colorbar (scat_pca , ax = axs , orientation = "horizontal" )
88+ fig .suptitle ("Multilabel PCovC with Binary Labels" )
9189# %%
9290# Let's try a more complicated example:
9391
120118 handles , _ = scat_pca .legend_elements ()
121119 labels = labels_list [i ]
122120 axs [0 , i ].legend (handles , labels )
123- print (labels )
124- print (i )
125- print (handles )
126-
127121
128122axs [0 , 0 ].set_title ("Even/Odd" )
129123axs [0 , 1 ].set_title ("Number of Holes" )
132126axs [0 , 0 ].set_ylabel ("PCA" )
133127axs [1 , 0 ].set_ylabel ("PCovC" )
134128fig .colorbar (scat_pca , ax = axs , orientation = "horizontal" )
129+ fig .suptitle ("Multiclass-Multilabel PCovC" )
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