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from collections import OrderedDict
import khandy
import numpy as np
def convert_feature_dict_to_array(feature_dict):
one_feature = khandy.get_dict_first_item(feature_dict)[1]
num_features = sum([len(item) for item in feature_dict.values()])
key_list = []
start_index = 0
feature_array = np.empty((num_features, one_feature.shape[-1]), one_feature.dtype)
for key, value in feature_dict.items():
feature_array[start_index: start_index + len(value)]= value
key_list += [key] * len(value)
start_index += len(value)
return key_list, feature_array
def convert_feature_array_to_dict(key_list, feature_array):
assert len(key_list) == len(feature_array)
feature_dict = OrderedDict()
for key, feat in zip(key_list, feature_array):
feature_dict.setdefault(key, []).append(feat)
for label in feature_dict.keys():
feature_dict[label] = np.vstack(feature_dict[label])
return feature_dict
def pairwise_distances(x, y, squared=True):
"""Compute pairwise (squared) Euclidean distances.
References:
[2016 CVPR] Deep Metric Learning via Lifted Structured Feature Embedding
`euclidean_distances` from sklearn
"""
assert isinstance(x, np.ndarray) and x.ndim == 2
assert isinstance(y, np.ndarray) and y.ndim == 2
assert x.shape[1] == y.shape[1]
x_square = np.expand_dims(np.einsum('ij,ij->i', x, x), axis=1)
if x is y:
y_square = x_square.T
else:
y_square = np.expand_dims(np.einsum('ij,ij->i', y, y), axis=0)
distances = np.dot(x, y.T)
# use inplace operation to accelerate
distances *= -2
distances += x_square
distances += y_square
# result maybe less than 0 due to floating point rounding errors.
np.maximum(distances, 0, distances)
if x is y:
# Ensure that distances between vectors and themselves are set to 0.0.
# This may not be the case due to floating point rounding errors.
distances.flat[::distances.shape[0] + 1] = 0.0
if not squared:
np.sqrt(distances, distances)
return distances
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