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create_similarityMatrix_cls_part_cub.py
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create_similarityMatrix_cls_part_cub.py
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import numpy as np
from scipy._lib._util import _asarray_validated
from scipy.stats import entropy
def compute_ClsSimilarity_underPart(class_attributes, part_type):
ClsSimilarity_underPart = np.zeros((200, 200))
for i_p in range(part_type):
part_attributes = softmax(class_attributes[i_p], axis=1)
for i in range(200):
for j in range(i+1, 200):
ClsSimilarity_underPart[i, j] = ClsSimilarity_underPart[i, j] + Dominik2003IT(part_attributes[i], part_attributes[j])
ClsSimilarity_underPart[j, i] = ClsSimilarity_underPart[i, j]
return ClsSimilarity_underPart / part_type
def Dominik2003IT(distribution1, distribution2):
term1 = distribution1 * np.log((2.0 * distribution1) / (distribution1 + distribution2))
term2 = distribution2 * np.log((2.0 * distribution2) / (distribution1 + distribution2))
return np.sum(term1 + term2)
def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
a = _asarray_validated(a, check_finite=False)
if b is not None:
a, b = np.broadcast_arrays(a, b)
if np.any(b == 0):
a = a + 0. # promote to at least float
a[b == 0] = -np.inf
a_max = np.amax(a, axis=axis, keepdims=True)
if a_max.ndim > 0:
a_max[~np.isfinite(a_max)] = 0
elif not np.isfinite(a_max):
a_max = 0
if b is not None:
b = np.asarray(b)
tmp = b * np.exp(a - a_max)
else:
tmp = np.exp(a - a_max)
# suppress warnings about log of zero
with np.errstate(divide='ignore'):
s = np.sum(tmp, axis=axis, keepdims=keepdims)
if return_sign:
sgn = np.sign(s)
s *= sgn # /= makes more sense but we need zero -> zero
out = np.log(s)
if not keepdims:
a_max = np.squeeze(a_max, axis=axis)
out += a_max
if return_sign:
return out, sgn
else:
return out
def softmax(x, axis=None):
# compute in log space for numerical stability
return np.exp(x - logsumexp(x, axis=axis, keepdims=True))
flist = './cub200/CUB_200_2011/attributes/class_attribute_labels_continuous.txt'
part_num = 15
class_num = 200
attributes = np.zeros((200, 312))
class_index = 0
with open(flist, 'r') as rf:
for line in rf.readlines():
attributes_per_class = line.strip().split()
attributes[class_index, :] = np.float32(attributes_per_class)
class_index = class_index + 1
similarityMatrix_cls_part = np.zeros((class_num, class_num, part_num))
for i_part in range(part_num):
if i_part == 0:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 59 - 1:73], attributes[:, 237 - 1: 240]], 2)
if i_part == 1:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 1 - 1:9], attributes[:, 150 - 1: 152], attributes[:, 279 - 1:293]], 3)
if i_part == 2:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 198 - 1:212], attributes[:, 245 - 1: 248]], 2)
if i_part == 3:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 55 - 1:58], attributes[:, 106 - 1: 120]], 2)
if i_part == 4:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 95 - 1:105], attributes[:, 153 - 1: 167], attributes[:, 294 - 1: 308]], 3)
if i_part == 5:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 95 - 1:105], attributes[:, 153 - 1: 167]], 2)
if i_part == 6:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 136 - 1:149], attributes[:, 95 - 1: 105]], 2)
if i_part == 7:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 264 - 1:278]], 1)
if i_part == 8:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 10 - 1:24], attributes[:, 213 - 1: 217], attributes[:, 309 - 1: 312]], 3)
if i_part == 9:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 183 - 1:197]], 1)
if i_part == 10:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 136 - 1:149], attributes[:, 95 - 1: 105]], 2)
if i_part == 11:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 264 - 1:278]], 1)
if i_part == 12:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 10 - 1:24], attributes[:, 213 - 1: 217], attributes[:, 309 - 1: 312]], 3)
if i_part == 13:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 74 - 1:79], attributes[:, 80 - 1: 94], attributes[:, 168 - 1: 182], attributes[:, 241 - 1: 244]], 4)
if i_part == 14:
similarityMatrix_cls_part[:, :, i_part] = compute_ClsSimilarity_underPart([attributes[:, 95 - 1:105], attributes[:, 121 - 1: 135]], 2)
np.save('./cub200/CUB_200_2011/attributes/similarityMatrix_cls_part.npy', similarityMatrix_cls_part)
similarityMatrix_cls_part = np.load('./cub200/CUB_200_2011/attributes/similarityMatrix_cls_part.npy')
np.save('./cub200/CUB_200_2011/attributes/Dominik2003IT_similarityMatrix_cls_part.npy', similarityMatrix_cls_part)
similarityMatrix_cls_part = np.load('./cub200/CUB_200_2011/attributes/Dominik2003IT_similarityMatrix_cls_part.npy')
# threshold and remain most similar parts between each class pair
threshold = np.sort(similarityMatrix_cls_part.flatten())[int(0.2*class_num*class_num*part_num)]
similarityMatrix_cls_part_copy = np.copy(similarityMatrix_cls_part)
similarityMatrix_cls_part_copy[similarityMatrix_cls_part <= threshold] = 1
similarityMatrix_cls_part_copy[similarityMatrix_cls_part > threshold] = 0
similarityMatrix_cls_part_copy = similarityMatrix_cls_part_copy.astype(int)
com_extracted_attributes = np.zeros((class_num, class_num), dtype=object)
for i in range(class_num):
for j in range(i+1, class_num):
part_idx = np.argwhere(similarityMatrix_cls_part_copy[i, j, :] == 1).flatten()
part_idx = part_idx.tolist()
com_extracted_attributes[i, j] = part_idx
com_extracted_attributes[j, i] = part_idx
np.save('./cub200/Dominik2003IT_com_extracted_attributes_02.npy', com_extracted_attributes)