def get_nearest_neighbor(self, x_test, k, sample_class):
distances = []
targets_index = []
for i in range(len(sample_class)):
if (sample_class[i][:] != x_test).any():
if self.distance_calculator == 'jaccard':
distance = dis.jaccard(x_test, sample_class[i][:])
elif self.distance_calculator == 'dice':
distance = dis.dice(x_test, sample_class[i][:])
elif self.distance_calculator == 'correlation':
distance = dis.correlation(x_test, sample_class[i][:])
elif self.distance_calculator == 'yule':
distance = dis.yule(x_test, sample_class[i][:])
elif self.distance_calculator == 'russelo-rao':
distance = dis.russellrao(x_test, sample_class[i][:])
elif self.distance_calculator == 'sokal-michener':
distance = dis.sokalmichener(x_test, sample_class[i][:])
elif self.distance_calculator == 'rogers-tanimoto':
distance = dis.rogerstanimoto(x_test, sample_class[i][:])
elif self.distance_calculator == 'kulzinsky':
distance = dis.kulsinski(x_test, sample_class[i][:])
distances.append([distance, i])
# make a list of the k neighbors' targets
distances.sort()
for i in range(k):
targets_index.append(distances[i][1])
return targets_index
def distanciaSokalMichener(self):
'''
Calcula a distancia de SokalMichener.
'''
for i in range(0, 667):
if(i != self.heroi):
self.valor_distancias[i,1] = distance.sokalmichener([self.baseSPoderes_Escolhidos[self.heroi,:]],[self.baseSPoderes_Escolhidos[i,:]])
self.valor_distancias[i,0] = i
def sokalmichener(self, x=None, y=None, w=None):
"""
索卡尔米切纳差异
x = [1, 0, 0]
y = [0, 1, 0]
"""
x = x or self.x
y = y or self.y
w = w or self.w
return distance.sokalmichener(x, y, w)
def hero_sokalmichener_distance(
heros_superpowers,
hero_name='A-Bomb',
features=['Agility', 'Accelerated Healing', 'Lantern Power Ring']):
result = []
# Get the hero to be compared;
main_hero = heros_superpowers.loc[heros_superpowers['hero_names'] ==
hero_name][features]
# Compare using only the given features;
heros_indexed_by_features = heros_superpowers[features]
# For each line in the dataset, with the columns indexed by features, calculate the score and create a new column called 'Score'
for index, row in heros_indexed_by_features.iterrows():
result.append(distance.sokalmichener(main_hero, row))
heros_indexed_by_features.insert(0, "Score", result, True)
return heros_indexed_by_features
def cross_channel_boolean_distance_features(mask):
"""calculates the cross channel distance features
Calculates the distances across channels
Parameters
----------
mask : 3D array, shape (M, N, C)
The input mask with multiple channels.
Returns
-------
features : dict
dictionary including different distances across channels
"""
features = dict()
for ch1 in range(mask.shape[2]):
for ch2 in range(ch1 + 1, mask.shape[2]):
# rehaping the channels to 1D
channel1 = mask[:, :, ch1].ravel()
channel2 = mask[:, :, ch2].ravel()
# creating the suffix name for better readability
suffix = "_Ch" + str(ch1 + 1) + "_Ch" + str(ch2 + 1)
# storing the distance values
features["dice_distance" + suffix] = dist.dice(channel1, channel2)
features["hamming_distance" + suffix] = dist.hamming(
channel1, channel2)
features["jaccard_distance" + suffix] = dist.jaccard(
channel1, channel2)
features["kulsinski_distance" + suffix] = dist.kulsinski(
channel1, channel2)
features["rogerstanimoto_distance" + suffix] = dist.rogerstanimoto(
channel1, channel2)
features["russellrao_distance" + suffix] = dist.russellrao(
channel1, channel2)
features["sokalmichener_distance" + suffix] = dist.sokalmichener(
channel1, channel2)
features["sokalsneath_distance" + suffix] = dist.sokalsneath(
channel1, channel2)
features["yule_distance" + suffix] = dist.yule(channel1, channel2)
return features
def calculate_pss(self,
profile,
ignore=None,
method="pairwise"):
"""
Calculate Profiles Similarity Score.
"""
if len(self) != len(profile):
raise ProfileError("Different profiles' lengths")
prof_1 = self
prof_2 = profile
if ignore:
for i in ignore:
try:
prof_1.profile = list(prof_1.profile)
del prof_1.profile[prof_1.query.index(i)]
prof_1.profile = tuple(prof_1.profile)
except IndexError:
raise ProfileError("Element to ignore not in profile")
try:
prof_2.profile = list(prof_2.profile)
del prof_2.profile[prof_2.query.index(i)]
prof_2.profile = tuple(prof_2.profile)
except IndexError:
raise ProfileError("Element to ignore not in profile")
if method == "pairwise":
return sum(a == b for a, b in zip(prof_1.profile, prof_2.profile))
elif method == "jaccard":
return dist.jaccard(prof_1.profile, prof_2.profile)
elif method == "yule":
return dist.yule(prof_1.profile, prof_2.profile)
elif method == "dice":
return dist.dice(prof_1.profile, prof_2.profile)
elif method == "hamming":
return dist.hamming(prof_1.profile, prof_2.profile)
elif method == "kulsinski":
return dist.kulsinski(prof_1.profile, prof_2.profile)
elif method == "rogerstanimoto":
return dist.rogerstanimoto(prof_1.profile, prof_2.profile)
elif method == "russellrao":
return dist.russellrao(prof_1.profile, prof_2.profile)
elif method == "sokalmichener":
return dist.sokalmichener(prof_1.profile, prof_2.profile)
def sokalmichener(app1SyscallsVector, app2SyscallsVector):
return spDist.sokalmichener(app1SyscallsVector, app2SyscallsVector)
def sokalmichener_(x, y):
try:
return sokalmichener(x, y)
except ZeroDivisionError:
return 0
def exec_similarity(dct, algorithm):
if validate_similarity_algorithms(dct, algorithm):
return {}
if algorithm == 'braycurtis':
return [
answer.update({
algorithm:
braycurtis(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'canberra':
return [
answer.update({
algorithm:
canberra(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'chebyshev':
return [
answer.update({
algorithm:
chebyshev(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'cityblock':
return [
answer.update({
algorithm:
cityblock(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'correlation':
return [
answer.update({
algorithm:
correlation(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'cosine':
return [
answer.update({
algorithm:
cosine(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'euclidean':
return [
answer.update({
algorithm:
euclidean(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'mahalanobis':
return [
answer.update({
algorithm:
mahalanobis(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
#elif algorithm is 'minkowski':
#return [answer.update({algorithm:minkowski(ndarray_dict(dct['tf_idf']), ndarray_dict(answer['tf_idf']))}) for answer in dct['answers']]
elif algorithm == 'seuclidean':
return [
answer.update({
algorithm:
seuclidean(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'sqeuclidean':
return [
answer.update({
algorithm:
sqeuclidean(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'wminkowski':
return [
answer.update({
algorithm:
wminkowski(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'dice':
return [
answer.update({
algorithm:
dice(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'hamming':
return [
answer.update({
algorithm:
hamming(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'jaccard':
return [
answer.update({
algorithm:
jaccard(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'kulsinski':
return [
answer.update({
algorithm:
kulsinski(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'rogerstanimoto':
return [
answer.update({
algorithm:
rogerstanimoto(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'russellrao':
return [
answer.update({
algorithm:
russellrao(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'sokalmichener':
return [
answer.update({
algorithm:
sokalmichener(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'sokalsneath':
return [
answer.update({
algorithm:
sokalsneath(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
elif algorithm == 'yule':
return [
answer.update({
algorithm:
yule(ndarray_dict(dct['tf_idf']),
ndarray_dict(answer['tf_idf']))
}) for answer in dct['answers']
]
def distance(self, vector1, vector2, type_):
"""
Calculate distance between two vectors.
Args:
vector1 (list of int/float/bool): Vector in vector space
vector2 (list of int/float/bool): Vector in vector space
type_ (str): Type of distance calculation. Allowed types are:
* For numeric vectors *
- braycurtis: Computes the Bray-Curtis distance between two arrays.
- canberra: Computes the Canberra distance between two arrays.
- chebyshev: Computes the Chebyshev distance.
- cityblock: Computes the City Block (Manhattan) distance.
- correlation: Computes the correlation distance between two arrays.
- cosine: Computes the Cosine distance between arrays.
- euclidean: Computes the Euclidean distance between two arrays.
- sqeuclidean: Computes the squared Euclidean distance between two arrays.
* For boolean vectors *
- dice: Computes the Dice dissimilarity between two boolean arrays.
- hamming: Computes the Hamming distance between two arrays.
- jaccard: Computes the Jaccard-Needham dissimilarity between two boolean arrays.
- kulsinski: Computes the Kulsinski dissimilarity between two boolean arrays.
- rogerstanimoto: Computes the Rogers-Tanimoto dissimilarity between two boolean arrays.
- russellrao: Computes the Russell-Rao dissimilarity between two boolean arrays.
- sokalmichener: Computes the Sokal-Michener dissimilarity between two boolean arrays.
- sokalsneath: Computes the Sokal-Sneath dissimilarity between two boolean arrays.
- yule: Computes the Yule dissimilarity between two boolean arrays.
Returns:
float: Distance between vectors.
"""
if type_ == "braycurtis":
return distance.braycurtis(vector1, vector2)
elif type_ == "canberra":
return distance.canberra(vector1, vector2)
elif type_ == "chebyshev":
return distance.chebyshev(vector1, vector2)
elif type_ == "cityblock":
return distance.cityblock(vector1, vector2)
elif type_ == "correlation":
return distance.correlation(vector1, vector2)
elif type_ == "cosine":
return distance.cosine(vector1, vector2)
elif type_ == "euclidean":
return distance.euclidean(vector1, vector2)
elif type_ == "sqeuclidean":
return distance.sqeuclidean(vector1, vector2)
elif type_ == "dice":
return distance.dice(vector1, vector2)
elif type_ == "hamming":
return distance.hamming(vector1, vector2)
elif type_ == "jaccard":
return distance.jaccard(vector1, vector2)
elif type_ == "kulsinski":
return distance.kulsinski(vector1, vector2)
elif type_ == "kulsinski":
return distance.kulsinski(vector1, vector2)
elif type_ == "rogerstanimoto":
return distance.rogerstanimoto(vector1, vector2)
elif type_ == "russellrao":
return distance.russellrao(vector1, vector2)
elif type_ == "sokalmichener":
return distance.sokalmichener(vector1, vector2)
elif type_ == "sokalsneath":
return distance.sokalsneath(vector1, vector2)
elif type_ == "yule":
return distance.yule(vector1, vector2)
else:
raise ValueError(
"""Wrong value for type_. Please enter one of supported values.
Type help(distance) to see supported values.""")
def main():
from scipy.spatial import distance
a = np.array([1, 2, 43])
b = np.array([3, 2, 1])
d = Distance()
print('-----------------------------------------------------------------')
print('My braycurtis: {}'.format(d.braycurtis(a, b)))
print('SciPy braycurtis: {}'.format(distance.braycurtis(a, b)))
print('-----------------------------------------------------------------')
print('My canberra: {}'.format(d.canberra(a, b)))
print('SciPy canberra: {}'.format(distance.canberra(a, b)))
print('-----------------------------------------------------------------')
print('My chebyshev: {}'.format(d.chebyshev(a, b)))
print('SciPy chebyshev: {}'.format(distance.chebyshev(a, b)))
print('-----------------------------------------------------------------')
print('My cityblock: {}'.format(d.cityblock(a, b)))
print('SciPy cityblock: {}'.format(distance.cityblock(a, b)))
print('-----------------------------------------------------------------')
print('My correlation: {}'.format(d.correlation(a, b)))
print('SciPy correlation: {}'.format(distance.correlation(a, b)))
print('-----------------------------------------------------------------')
print('My euclidean: {}'.format(d.euclidean(a, b)))
print('SciPy euclidean: {}'.format(distance.euclidean(a, b)))
print('-----------------------------------------------------------------')
print('My hamming: {}'.format(d.hamming(a, b)))
print('SciPy hamming: {}'.format(distance.hamming(a, b)))
print('-----------------------------------------------------------------')
print('My jaccard: {}'.format(d.jaccard(a, b)))
print('SciPy jaccard: {}'.format(distance.jaccard(a, b)))
print('-----------------------------------------------------------------')
print('My manhattan: {}'.format(d.cityblock(a, b)))
print('SciPy manhattan: {}'.format(distance.cityblock(a, b)))
print('-----------------------------------------------------------------')
print('My cosine: {}'.format(d.cosine(a, b)))
print('SciPy cosine: {}'.format(distance.cosine(a, b)))
print('-----------------------------------------------------------------')
print('My dice: {}'.format(d.dice(a, b)))
print('SciPy dice: {}'.format(distance.dice(a, b)))
print('-----------------------------------------------------------------')
print('My kulsinski: {}'.format(d.kulsinski(a, b)))
print('SciPy kulsinski: {}'.format(distance.kulsinski(a, b)))
print('-----------------------------------------------------------------')
iv = np.array([[1, 0.5, 0.5], [0.5, 1, 0.5], [0.5, 0.5, 1]])
print('My mahalanobis: {}'.format(d.mahalanobis(a, b, iv)))
print('SciPy mahalanobis: {}'.format(distance.mahalanobis(a, b, iv)))
print('-----------------------------------------------------------------')
print('My seuclidean: {}'.format(
d.seuclidean(a, b, np.array([0.1, 0.1, 0.1]))))
print('SciPy seuclidean: {}'.format(
distance.seuclidean(a, b, [0.1, 0.1, 0.1])))
print('-----------------------------------------------------------------')
print('My sokalmichener: {}'.format(d.sokalmichener(a, b)))
print('SciPy sokalmichener: {}'.format(distance.sokalmichener(a, b)))
print('-----------------------------------------------------------------')
print('My sokal_sneath: {}'.format(d.sokalsneath(a, b)))
print('SciPy sokal_sneath: {}'.format(distance.sokalsneath(a, b)))
print('-----------------------------------------------------------------')
print('My sqeuclidean: {}'.format(d.sqeuclidean(a, b)))
print('SciPy sqeuclidean: {}'.format(distance.sqeuclidean(a, b)))
print('-----------------------------------------------------------------')
print('My minkowski: {}'.format(d.minkowski(a, b, 2)))
print('SciPy minkowski: {}'.format(distance.minkowski(a, b, 2)))
print('-----------------------------------------------------------------')
print('My rogerstanimoto: {}'.format(d.rogerstanimoto(a, b)))
print('SciPy rogerstanimoto: {}'.format(distance.rogerstanimoto(a, b)))
print('-----------------------------------------------------------------')
print('My russellrao: {}'.format(d.russellrao(a, b)))
print('SciPy russellrao: {}'.format(distance.russellrao(a, b)))
print('-----------------------------------------------------------------')
print('My wminkowski: {}'.format(d.wminkowski(a, b, 2, np.ones(3))))
print('SciPy wminkowski: {}'.format(
distance.wminkowski(a, b, 2, np.ones(3))))
print('-----------------------------------------------------------------')
print('My yule: {}'.format(d.yule(a, b)))
print('SciPy yule: {}'.format(distance.yule(a, b)))
print('-----------------------------------------------------------------')
def sokalmichener_(x, y):
try:
return sokalmichener(x, y)
except ZeroDivisionError:
return 0
#SCADI = pd.read_csv('../csv/SCADI.csv',sep=',')
A = np.array(SCADI.values[:, 2:-1], dtype='int32')
np.set_printoptions(linewidth=100)
# test the metrics
v1 = A[0]
v2 = A[1]
CM = confusion_matrix(v1, v2)
print("my matching distance=%f, hamming distance %f" %
(matching_distance(CM), dist.hamming(v1, v2)))
print("my Jaccard distance=%f, Jaccard distance %f" %
(Jaccard_distance(CM), dist.jaccard(v1, v2)))
print("my Dice distance %f, Dice distance %f" %
(Dice_distance(CM), dist.dice(v1, v2)))
print("my SokalMichener distance %f, SokalMichener distance %f" %
(SokalMichener_distance(CM), dist.sokalmichener(v1, v2)))
#
#
#Build the dissimilary table
DT = build_dissimilarity_table(A)
# build adjacency matrix associated to the threshold
threshold = 0.5
marker = (DT < threshold)
#print("--------------------------");print(marker)
#Close the graphe
marker = transitive_closure(marker)
#print("--------------------------");print(marker)
# build the clusters
marker = build_clusters(marker)
#print("--------------------------");print(marker)