def __get_pruning_matrix(self, k: int, T: float, recompute=False) -> np.ndarray:
"""
Compute the Pruning Matrix for the given dataset in self.dataset_path. with k coefficients and T threshold.
If the pruning matrix has been computed previously it is returned without recomputation, unless
recompute is set to True
"""
if self.pruning_matrix is not None and recompute is False:
return self.pruning_matrix
pmatrix = PruningMatrix(self.norm_ds_path)
self.pruning_matrix = pmatrix.compute_pruning_matrix(k, T)
return self.pruning_matrix
def test_Caching(testfiles):
logging.basicConfig(level=logging.DEBUG)
name = testfiles["h5100"]
# name = "/home/george/msc/workspaces/PyCharmWorkspace/TimeSeriesCorrelation/test_resources/h5100.db"
pm = PruningMatrix(name)
pm.compute_pruning_matrix(1, 0.7, disable_store=True)
c = Caching(pm.pruning_matrix, name, 20)
c.calculate_batches()
for b in c.batches:
print(b)
assert True
def __get_pruning_matrix(self,
k: int,
T: float,
recompute=False) -> np.ndarray:
"""
Compute the Pruning Matrix for the given dataset in self.dataset_path. with k coefficients and T threshold.
If the pruning matrix has been computed previously it is returned without recomputation, unless
recompute is set to True
"""
if self.pruning_matrix is not None and recompute is False:
return self.pruning_matrix
pmatrix = PruningMatrix(self.norm_ds_path)
self.pruning_matrix = pmatrix.compute_pruning_matrix(k, T)
return self.pruning_matrix
