Python scipy.spatial.distance.braycurtis() Examples
The following are 6
code examples of scipy.spatial.distance.braycurtis().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
scipy.spatial.distance
, or try the search function
.
.
Example #1
| Source File: text.py From textplot with MIT License | 6 votes |
|
def score_braycurtis(self, term1, term2, **kwargs):
"""
Compute a weighting score based on the "City Block" distance between
the kernel density estimates of two terms.
Args:
term1 (str)
term2 (str)
Returns: float
"""
t1_kde = self.kde(term1, **kwargs)
t2_kde = self.kde(term2, **kwargs)
return 1-distance.braycurtis(t1_kde, t2_kde)
Example #2
| Source File: make_handcrafted_33_features.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #3
| Source File: train_predict_trees_batch2.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #4
| Source File: train_predict_trees_batch1.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #5
| Source File: train_predict_trees_batch3.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #6
| Source File: utils.py From teneto with GNU General Public License v3.0 | 5 votes |
|
def get_distance_function(requested_metric):
"""
This function returns a specified distance function.
Paramters
---------
requested_metric: str
Distance function. Can be any function in: https://docs.scipy.org/doc/scipy/reference/spatial.distance.html.
Returns
-------
requested_metric : distance function
"""
distance_options = {
'braycurtis': distance.braycurtis,
'canberra': distance.canberra,
'chebyshev': distance.chebyshev,
'cityblock': distance.cityblock,
'correlation': distance.correlation,
'cosine': distance.cosine,
'euclidean': distance.euclidean,
'sqeuclidean': distance.sqeuclidean,
'dice': distance.dice,
'hamming': distance.hamming,
'jaccard': distance.jaccard,
'kulsinski': distance.kulsinski,
'matching': distance.matching,
'rogerstanimoto': distance.rogerstanimoto,
'russellrao': distance.russellrao,
'sokalmichener': distance.sokalmichener,
'sokalsneath': distance.sokalsneath,
'yule': distance.yule,
}
if requested_metric in distance_options:
return distance_options[requested_metric]
else:
raise ValueError('Distance function cannot be found.')
