import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
from .soft_dtw_fast import _jacobian_product_sq_euc
class SquaredEuclidean(object):
def __init__(self, X, Y):
"""
Parameters
----------
X: array, shape = [m, d]
First time series.
Y: array, shape = [n, d]
Second time series.
"""
self.X = X.astype(np.float64)
self.Y = Y.astype(np.float64)
def compute(self):
"""
Compute distance matrix.
Returns
-------
D: array, shape = [m, n]
Distance matrix.
"""
return euclidean_distances(self.X, self.Y, squared=True)
def jacobian_product(self, E):
"""
Compute the product between the Jacobian
(a linear map from m x d to m x n) and a matrix E.
Parameters
----------
E: array, shape = [m, n]
Second time series.
Returns
-------
G: array, shape = [m, d]
Product with Jacobian
([m x d, m x n] * [m x n] = [m x d]).
"""
G = np.zeros_like(self.X)
_jacobian_product_sq_euc(self.X, self.Y, E, G)
return G
