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haversine_distances — scikit-learn 1.8.0 ...
data must be 2. \[D(x, y) = 2\arcsin[\sqrt{\sin^2((x_{lat} - y_{lat})...y_{lat}) / 2) + \cos(x_{lat})\cos(y_{lat})\ sin^2((x_{lon} -...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html -
Robust vs Empirical covariance estimate —...
subplot ( 2 , 1 , 1 ) lw = 2 plt . errorbar ( range_n_outliers...font_prop ) plt . subplot ( 2 , 1 , 2 ) x_size = range_n_outliers...scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html -
unique_labels — scikit-learn 1.8.0 docume...
2 , 3 , 4 ], [ 2 , 2 , 3 , 4 ]) array([1, 2, 3, 4]) >>>...unique_labels ([ 1 , 2 , 10 ], [ 5 , 11 ]) array([ 1, 2, 5, 10, 11])...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.unique_labels.html -
Label Propagation digits: Active learning ̵...
0 24 0] [ 0 0 0 0 2 1 0 2 2 27]] Iteration 2 __________ Label...0 0 0 0 0 25 0] [ 0 0 0 0 2 1 0 2 2 27]] Iteration 1 __________...scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learni... -
inplace_swap_column — scikit-learn 1.8.0 ...
2 , 2 ]) >>> data = np . array ([ 8 , 2 , 5 ])...>>> indptr = np . array ([ 0 , 2 , 3 , 3 , 3 ]) >>> indices...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html -
inplace_row_scale — scikit-learn 1.8.0 do...
2 , 5 , 6 ]) >>> scale = np . array ([ 2 , 3 ,...>>> indptr = np . array ([ 0 , 2 , 3 , 4 , 5 ]) >>> indices...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_row_scale.html -
hinge_loss — scikit-learn 1.8.0 documenta...
>>> pred_decision array([-2.18, 2.36, 0.09]) >>> hinge_loss...], [ 2 ], [ 3 ]]) >>> Y = np . array ([ 0 , 1 , 2 , 3...scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html -
StratifiedGroupKFold — scikit-learn 1.8.0...
index=[ 0 1 2 3 15 16] group=[1 1 2 2 8 8] Fold 2: Train: index=[...index=[ 0 1 2 3 7 8 9 10 11 15 16] group=[1 1 2 2 4 5 5 5 5 8...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html -
shuffle — scikit-learn 1.8.0 documentation
[2., 1.], [1., 0.]]) >>> y array([2, 1, 0]) >>>...= np . array ([[ 1. , 0. ], [ 2. , 1. ], [ 0. , 0. ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html -
manhattan_distances — scikit-learn 1.8.0 ...
2 ], [ 3 , 4 ]], [[ 1 , 2 ], [ 0 , 3 ]]) array([[0., 2.],...manhattan_distances ([[ 3 ]], [[ 2 ]]) array([[1.]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html