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common.py
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common.py
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import, print_function, division
import time
from six.moves import range
import numpy as np
from .normalized_distance import all_pairs_normalized_distances
def knn_initialize(X, missing_mask, verbose=False):
"""
Fill X with NaN values if necessary, construct the n_samples x n_samples
distance matrix and set the self-distance of each row to infinity.
"""
X_row_major = X.copy("C")
if missing_mask.sum() != np.isnan(X_row_major).sum():
# if the missing values have already been zero-filled need
# to put NaN's back in the data matrix for the distances function
X_row_major[missing_mask] = np.nan
D = all_pairs_normalized_distances(X_row_major, verbose=verbose)
# set diagonal of distance matrix to infinity since we don't want
# points considering themselves as neighbors
np.fill_diagonal(D, np.inf)
return X_row_major, D