def inverse_transform(self, X):
"""Compute time series corresponding to given 1d-SAX representations.
Parameters
----------
X : array-like of shape (n_ts, sz_sax, 2 * d)
A dataset of SAX series.
Returns
-------
numpy.ndarray of shape (n_ts, sz_original_ts, d)
A dataset of time series corresponding to the provided
representation.
"""
self._is_fitted()
X = check_array(X, allow_nd=True)
X = check_dims(X,
X_fit_dims=(None, None, 2 * self._X_fit_dims_[-1]),
check_n_features_only=True)
X_orig = inv_transform_1d_sax(
X,
breakpoints_avg_middle_=self.breakpoints_avg_middle_,
breakpoints_slope_middle_=self.breakpoints_slope_middle_,
original_size=self._X_fit_dims_[1])
return self._unscale(X_orig)
def inverse_transform(self, X):
"""Compute time series corresponding to given 1d-SAX representations.
Parameters
----------
X : array-like of shape (n_ts, sz_sax, 2 * d)
A dataset of SAX series.
Returns
-------
numpy.ndarray of shape (n_ts, sz_original_ts, d)
A dataset of time series corresponding to the provided representation.
"""
X_ = to_time_series_dataset(X, dtype=numpy.int)
return inv_transform_1d_sax(X_,
breakpoints_avg_middle_=self.breakpoints_avg_middle_,
breakpoints_slope_middle_=self.breakpoints_slope_middle_,
original_size=self.size_fitted_)