# How to use the tslearn.metrics.SquaredEuclidean function in tslearn

## To help you get started, we’ve selected a few tslearn examples, based on popular ways it is used in public projects.

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rtavenar / tslearn / tslearn / metrics.py View on Github
``````...          [1., 2., 2.1, 3.2],
...          gamma=0.01)  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
0.089...

--------
cdist_soft_dtw : Cross similarity matrix between time series datasets

References
----------
.. [1] M. Cuturi, M. Blondel "Soft-DTW: a Differentiable Loss Function for
Time-Series," ICML 2017.
"""
if gamma == 0.:
return dtw(ts1, ts2)
return SoftDTW(SquaredEuclidean(ts1[:ts_size(ts1)], ts2[:ts_size(ts2)]),
gamma=gamma).compute()``````
rtavenar / tslearn / tslearn / deprecated.py View on Github
``````def _func(self, Z):
# Compute objective value and grad at Z.

Z = Z.reshape(self.barycenter_.shape)

G = numpy.zeros_like(Z)

obj = 0

for i in range(len(self._X_fit)):
D = SquaredEuclidean(Z, to_time_series(self._X_fit[i],
remove_nans=True))
sdtw = SoftDTW(D, gamma=self.gamma)
value = sdtw.compute()
G_tmp = D.jacobian_product(E)
G += self.weights[i] * G_tmp
obj += self.weights[i] * value

return obj, G.ravel()``````
rtavenar / tslearn / tslearn / barycenters.py View on Github
``````def _softdtw_func(Z, X, weights, barycenter, gamma):
# Compute objective value and grad at Z.

Z = Z.reshape(barycenter.shape)
G = numpy.zeros_like(Z)
obj = 0

for i in range(len(X)):
D = SquaredEuclidean(Z, X[i])
sdtw = SoftDTW(D, gamma=gamma)
value = sdtw.compute()
G_tmp = D.jacobian_product(E)
G += weights[i] * G_tmp
obj += weights[i] * value

return obj, G.ravel()``````

## tslearn

A machine learning toolkit dedicated to time-series data

BSD-2-Clause