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-rw-r--r--dpd/src/Heuristics.py13
1 files changed, 10 insertions, 3 deletions
diff --git a/dpd/src/Heuristics.py b/dpd/src/Heuristics.py
index a32ccff..f98490d 100644
--- a/dpd/src/Heuristics.py
+++ b/dpd/src/Heuristics.py
@@ -1,26 +1,33 @@
# -*- coding: utf-8 -*-
#
-# DPD Calculation Engine, heuristics we use to tune the parameters
+# DPD Calculation Engine, heuristics we use to tune the parameters.
#
# http://www.opendigitalradio.org
# Licence: The MIT License, see notice at the end of this file
import numpy as np
+
def get_learning_rate(idx_run):
+ """Gradually reduce learning rate from lr_max to lr_min within
+ idx_max steps, then keep the learning rate at lr_min"""
idx_max = 10.0
lr_min = 0.05
lr_max = 0.4
lr_delta = lr_max - lr_min
idx_run = min(idx_run, idx_max)
- learning_rate = lr_max - lr_delta * idx_run/idx_max
+ learning_rate = lr_max - lr_delta * idx_run / idx_max
return learning_rate
+
def get_n_meas(idx_run):
+ """Gradually increase number of measurements used to extract
+ a statistic from n_meas_min to n_meas_max within idx_max steps,
+ then keep number of measurements at n_meas_max"""
idx_max = 10.0
n_meas_min = 10
n_meas_max = 20
n_meas_delta = n_meas_max - n_meas_min
idx_run = min(idx_run, idx_max)
- learning_rate = n_meas_delta * idx_run/idx_max + n_meas_min
+ learning_rate = n_meas_delta * idx_run / idx_max + n_meas_min
return int(np.round(learning_rate))