Change fixed learning rate and number of measurements to heuristic

5 files changed, 48 insertions, 8 deletions

diff --git a/dpd/src/Const.py b/dpd/src/Const.py
index 2f9e151..bf46796 100644
--- a/dpd/src/Const.py
+++ b/dpd/src/Const.py
@@ -58,7 +58,7 @@ class Const:
         self.RAGC_rx_median_target = self.TAGC_tx_median_target
 
         # Constants for Model
-        self.MDL_plot = False
+        self.MDL_plot = True
 
         # Constants for MER
         self.MER_plot = False
diff --git a/dpd/src/Heuristics.py b/dpd/src/Heuristics.py
new file mode 100644
index 0000000..b6ec37f
--- /dev/null
+++ b/dpd/src/Heuristics.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+#
+# 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):
+    idx_max = 10.0
+    lr_min = 0.05
+    lr_max = 1
+    lr_delta = lr_max - lr_min
+    idx_run = min(idx_run, idx_max)
+    learning_rate = lr_max - lr_delta * idx_run/idx_max
+    return learning_rate
+
+def get_n_meas(idx_run):
+    idx_max = 10.0
+    n_meas_min = 5
+    n_meas_max = 50
+    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
+    return int(np.round(learning_rate))
+
+
diff --git a/dpd/src/Model_AM.py b/dpd/src/Model_AM.py
index 4b88e08..c6f7903 100644
--- a/dpd/src/Model_AM.py
+++ b/dpd/src/Model_AM.py
@@ -17,12 +17,12 @@ import matplotlib.pyplot as plt
 
 def check_input_get_next_coefs(tx_dpd, rx_received):
     is_float32 = lambda x: (isinstance(x, np.ndarray) and
-                        x.dtype == np.float32 and
-                        x.flags.contiguous)
+                            x.dtype == np.float32 and
+                            x.flags.contiguous)
     assert is_float32(tx_dpd), \
-           "tx_dpd is not float32 but {}".format(tx_dpd[0].dtype)
+        "tx_dpd is not float32 but {}".format(tx_dpd[0].dtype)
     assert is_float32(rx_received), \
-            "rx_received is not float32 but {}".format(tx_dpd[0].dtype)
+        "rx_received is not float32 but {}".format(tx_dpd[0].dtype)
 
 
 class Model_AM:
@@ -31,7 +31,7 @@ class Model_AM:
 
     def __init__(self,
                  c,
-                 learning_rate_am=0.1,
+                 learning_rate_am=1,
                  plot=False):
         self.c = c
 
@@ -66,6 +66,8 @@ class Model_AM:
             ax.set_title("Model_AM")
             ax.set_xlabel("TX Amplitude")
             ax.set_ylabel("RX Amplitude")
+            xlim = ax.get_xlim()
+            ax.set_xlim(max(xlim[0], -1), min(xlim[1], 2))
             ax.legend(loc=4)
 
             fig.tight_layout()
@@ -87,6 +89,9 @@ class Model_AM:
         check_input_get_next_coefs(tx_dpd, rx_received)
 
         coefs_am_new = self.fit_poly(tx_dpd, rx_received)
+        coefs_am_new = coefs_am + \
+                       self.learning_rate_am * (coefs_am_new - coefs_am)
+
         self._plot(tx_dpd, rx_received, coefs_am, coefs_am_new)
 
         return coefs_am_new
diff --git a/dpd/src/Model_PM.py b/dpd/src/Model_PM.py
index 75fb055..e0fcb55 100644
--- a/dpd/src/Model_PM.py
+++ b/dpd/src/Model_PM.py
@@ -34,7 +34,7 @@ class Model_PM:
 
     def __init__(self,
                  c,
-                 learning_rate_pm=0.1,
+                 learning_rate_pm=1,
                  plot=False):
         self.c = c
 
@@ -97,6 +97,8 @@ class Model_PM:
         check_input_get_next_coefs(tx_dpd, phase_diff)
 
         coefs_pm_new = self.fit_poly(tx_dpd, phase_diff)
+
+        coefs_pm_new = coefs_pm + self.learning_rate_pm * (coefs_pm_new - coefs_pm)
         self._plot(tx_dpd, phase_diff, coefs_pm, coefs_pm_new)
 
         return coefs_pm_new
diff --git a/dpd/src/Model_Poly.py b/dpd/src/Model_Poly.py
index 44e0483..16881ad 100644
--- a/dpd/src/Model_Poly.py
+++ b/dpd/src/Model_Poly.py
@@ -62,14 +62,19 @@ class Poly:
         self.coefs_pm = np.zeros(5, dtype=np.float32)
         return self.coefs_am, self.coefs_pm
 
-    def train(self, tx_abs, rx_abs, phase_diff):
+    def train(self, tx_abs, rx_abs, phase_diff, lr=None):
         """
         :type tx_abs: np.ndarray
         :type rx_abs: np.ndarray
         :type phase_diff: np.ndarray
+        :type lr: float
         """
         _check_input_get_next_coefs(tx_abs, rx_abs, phase_diff)
 
+        if not lr is None:
+            self.model_am.learning_rate_am = lr
+            self.model_pm.learning_rate_pm = lr
+
         coefs_am_new = self.model_am.get_next_coefs(tx_abs, rx_abs, self.coefs_am)
         coefs_pm_new = self.model_pm.get_next_coefs(tx_abs, phase_diff, self.coefs_pm)