Description
in sklearn/cluster/k_means_.py, line 1559:
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batch_inertia, centers_squared_diff = _mini_batch_step( |
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X_valid, sample_weight_valid, |
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x_squared_norms[validation_indices], cluster_centers, |
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weight_sums, old_center_buffer, False, distances=None, |
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verbose=self.verbose) |
Are variables batch_inertia and centers_squared_diff actually used before being reassigned in line 1589?
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for iteration_idx in range(n_iter): |
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# Sample a minibatch from the full dataset |
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minibatch_indices = random_state.randint( |
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0, n_samples, self.batch_size) |
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# Perform the actual update step on the minibatch data |
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batch_inertia, centers_squared_diff = _mini_batch_step( |
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X[minibatch_indices], sample_weight[minibatch_indices], |
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x_squared_norms[minibatch_indices], |
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self.cluster_centers_, self.counts_, |
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old_center_buffer, tol > 0.0, distances=distances, |
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# Here we randomly choose whether to perform |
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# random reassignment: the choice is done as a function |
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# of the iteration index, and the minimum number of |
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# counts, in order to force this reassignment to happen |
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# every once in a while |
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random_reassign=((iteration_idx + 1) |
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% (10 + int(self.counts_.min())) == 0), |
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random_state=random_state, |
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reassignment_ratio=self.reassignment_ratio, |
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verbose=self.verbose) |
I presume that if n_iter is 0, then the for loop at line 1583 will not be run, which makes the assignment useful. But for k-means is n_iter ever set to 0?
Note
I found this through lgtm.com, as recommended by #12167
Versions
0.20