From dd46ed939e56a4e56c3dcec60cce1b93c8786a4a Mon Sep 17 00:00:00 2001
From: andreas128 <Andreas>
Date: Sun, 10 Sep 2017 19:28:45 +0200
Subject: Use ridge regression

---
 dpd/main.py      |  2 +-
 dpd/src/Model.py | 14 +++++++++-----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/dpd/main.py b/dpd/main.py
index 03520f5..5e67c90 100755
--- a/dpd/main.py
+++ b/dpd/main.py
@@ -64,7 +64,7 @@ parser.add_argument('--samplerate', default='8192000',
 parser.add_argument('--coefs', default='poly.coef',
                     help='File with DPD coefficients, which will be read by ODR-DabMod',
                     required=False)
-parser.add_argument('--txgain', default=74,
+parser.add_argument('--txgain', default=71,
                     help='TX Gain',
                     required=False,
                     type=int)
diff --git a/dpd/src/Model.py b/dpd/src/Model.py
index 4ccf0be..827027a 100644
--- a/dpd/src/Model.py
+++ b/dpd/src/Model.py
@@ -13,7 +13,7 @@ logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFil
 
 import numpy as np
 import matplotlib.pyplot as plt
-
+from sklearn import linear_model
 
 class Model:
     """Calculates new coefficients using the measurement and the old
@@ -25,8 +25,8 @@ class Model:
                  MER,
                  coefs_am,
                  coefs_pm,
-                 learning_rate_am=0.1,
-                 learning_rate_pm=0.1,
+                 learning_rate_am=1.,
+                 learning_rate_pm=1.,
                  plot=False):
         self.c = c
         self.SA = SA
@@ -124,7 +124,9 @@ class Model:
         self.mses_am.append(mse)
         self.errs_am.append(np.mean(err**2))
 
-        a_delta = np.linalg.lstsq(rx_A, err)[0]
+        reg = linear_model.Ridge(alpha=0.00001)
+        reg.fit(rx_A, err)
+        a_delta = reg.coef_
         new_coefs_am = self.coefs_am - self.learning_rate_am * a_delta
         new_coefs_am = new_coefs_am * (self.coefs_am[0] / new_coefs_am[0])
         return new_coefs_am
@@ -143,7 +145,9 @@ class Model:
         err_phase = phase_diff_est - phase_diff_choice
         self.errs_pm.append(np.mean(np.abs(err_phase ** 2)))
 
-        p_delta = np.linalg.lstsq(phase_A, err_phase)[0]
+        reg = linear_model.Ridge(alpha=0.00001)
+        reg.fit(phase_A, err_phase)
+        p_delta = reg.coef_
         new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta
 
         return new_coefs_pm, phase_diff_choice
-- 
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