diff options
Diffstat (limited to 'resources/libraries/python/PLRsearch/stat_trackers.py')
| -rw-r--r-- | resources/libraries/python/PLRsearch/stat_trackers.py | 56 |
1 files changed, 38 insertions, 18 deletions
diff --git a/resources/libraries/python/PLRsearch/stat_trackers.py b/resources/libraries/python/PLRsearch/stat_trackers.py index e0b21dc3a9..d19eebedb7 100644 --- a/resources/libraries/python/PLRsearch/stat_trackers.py +++ b/resources/libraries/python/PLRsearch/stat_trackers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021 Cisco and/or its affiliates. +# Copyright (c) 2024 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -64,8 +64,10 @@ class ScalarStatTracker: :returns: Expression constructing an equivalent instance. :rtype: str """ - return f"ScalarStatTracker(log_sum_weight={self.log_sum_weight!r}," \ + return ( + f"ScalarStatTracker(log_sum_weight={self.log_sum_weight!r}," f"average={self.average!r},log_variance={self.log_variance!r})" + ) def copy(self): """Return new ScalarStatTracker instance with the same state as self. @@ -110,7 +112,8 @@ class ScalarStatTracker: if absolute_shift > 0.0: log_square_shift = 2 * math.log(absolute_shift) log_variance = log_plus( - log_variance, log_square_shift + log_sample_ratio) + log_variance, log_square_shift + log_sample_ratio + ) if log_variance is not None: log_variance += old_log_sum_weight - new_log_sum_weight self.log_sum_weight = new_log_sum_weight @@ -133,10 +136,17 @@ class ScalarDualStatTracker(ScalarStatTracker): One typical use is for Monte Carlo integrator to decide whether the partial sums so far are reliable enough. """ + def __init__( - self, log_sum_weight=None, average=0.0, log_variance=None, - log_sum_secondary_weight=None, secondary_average=0.0, - log_secondary_variance=None, max_log_weight=None): + self, + log_sum_weight=None, + average=0.0, + log_variance=None, + log_sum_secondary_weight=None, + secondary_average=0.0, + log_secondary_variance=None, + max_log_weight=None, + ): """Initialize new tracker instance, empty by default. :param log_sum_weight: Natural logarithm of sum of weights @@ -177,12 +187,14 @@ class ScalarDualStatTracker(ScalarStatTracker): :rtype: str """ sec = self.secondary - return f"ScalarDualStatTracker(log_sum_weight={self.log_sum_weight!r},"\ - f"average={self.average!r},log_variance={self.log_variance!r}," \ - f"log_sum_secondary_weight={sec.log_sum_weight!r}," \ - f"secondary_average={sec.average!r}," \ - f"log_secondary_variance={sec.log_variance!r}," \ + return ( + f"ScalarDualStatTracker(log_sum_weight={self.log_sum_weight!r}," + f"average={self.average!r},log_variance={self.log_variance!r}," + f"log_sum_secondary_weight={sec.log_sum_weight!r}," + f"secondary_average={sec.average!r}," + f"log_secondary_variance={sec.log_variance!r}," f"max_log_weight={self.max_log_weight!r})" + ) def add(self, scalar_value, log_weight=0.0): """Return updated both stats after addition of another sample. @@ -242,8 +254,12 @@ class VectorStatTracker: """ def __init__( - self, dimension=2, log_sum_weight=None, averages=None, - covariance_matrix=None): + self, + dimension=2, + log_sum_weight=None, + averages=None, + covariance_matrix=None, + ): """Initialize new tracker instance, two-dimensional empty by default. If any of latter two arguments is None, it means @@ -272,10 +288,12 @@ class VectorStatTracker: :returns: Expression constructing an equivalent instance. :rtype: str """ - return f"VectorStatTracker(dimension={self.dimension!r}," \ - f"log_sum_weight={self.log_sum_weight!r}," \ - f"averages={self.averages!r}," \ + return ( + f"VectorStatTracker(dimension={self.dimension!r}," + f"log_sum_weight={self.log_sum_weight!r}," + f"averages={self.averages!r}," f"covariance_matrix={self.covariance_matrix!r})" + ) def copy(self): """Return new instance with the same state as self. @@ -287,8 +305,10 @@ class VectorStatTracker: :rtype: VectorStatTracker """ return VectorStatTracker( - self.dimension, self.log_sum_weight, self.averages[:], - copy.deepcopy(self.covariance_matrix) + self.dimension, + self.log_sum_weight, + self.averages[:], + copy.deepcopy(self.covariance_matrix), ) def reset(self): |