Change fixed learning rate and number of measurements to heuristic

1 files changed, 10 insertions, 5 deletions

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