Change fixed learning rate and number of measurements to heuristic

1 files changed, 9 insertions, 5 deletions

diff --git a/dpd/main.py b/dpd/main.py
index d71fd2d..d2b7297 100755
--- a/dpd/main.py
+++ b/dpd/main.py
@@ -13,6 +13,7 @@ predistortion module of ODR-DabMod."""
 import datetime
 import os
 import matplotlib
+
 matplotlib.use('GTKAgg')
 
 import logging
@@ -49,6 +50,7 @@ from src.Const import Const
 from src.MER import MER
 from src.Measure_Shoulders import Measure_Shoulders
 import argparse
+import src.Heuristics as Heur
 
 parser = argparse.ArgumentParser(
     description="DPD Computation Engine for ODR-DabMod")
@@ -189,7 +191,8 @@ while i < num_iter:
             # Extract usable data from measurement
             tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned)
 
-            if extStat.n_meas >= c.n_meas:
+            n_meas = Heur.get_n_meas(i)
+            if extStat.n_meas >= n_meas:  # Use as many measurements nr of runs
                 state = 'model'
             else:
                 state = 'measure'
@@ -197,7 +200,8 @@ while i < num_iter:
         # Model
         elif state == 'model':
             # Calculate new model parameters and delete old measurements
-            model.train(tx, rx, phase_diff)
+            lr = Heur.get_learning_rate(i)
+            model.train(tx, rx, phase_diff, lr=lr)
             dpddata = model.get_dpd_data()
             extStat = ExtractStatistic.ExtractStatistic(c)
             state = 'adapt'
@@ -215,9 +219,9 @@ while i < num_iter:
 
                 # Collect logging data
                 off = SA.calc_offset(txframe_aligned)
-                tx_mer = MER.calc_mer(txframe_aligned[off:off+c.T_U], debug_name='TX')
-                rx_mer = MER.calc_mer(rxframe_aligned[off:off+c.T_U], debug_name='RX')
-                mse = np.mean(np.abs((txframe_aligned - rxframe_aligned)**2))
+                tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U], debug_name='TX')
+                rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug_name='RX')
+                mse = np.mean(np.abs((txframe_aligned - rxframe_aligned) ** 2))
                 tx_gain = adapt.get_txgain()
                 rx_gain = adapt.get_rxgain()
                 digital_gain = adapt.get_digital_gain()