Add attribute 'lr' (#90)

* Update elastic_net.py

* add lr as attribute to lasso.py

* add lr as attribute to ridge.py

* refactor w_bound=0. for weights elastic_net.py

deactivated w_bound for weights elastic_net.py

* Update lasso.py

* deactivated w_bound for weights ridge.py

ngclearn/modules/regression/elastic_net.py

@@ -73,12 +73,14 @@ def __init__(self, key, name, sys_dim, dict_dim, batch_size, weight_fill=0.05, l
         self.weight_fill = weight_fill
         self.threshold = threshold
         self.name = name
+        self.lr = lr
         feature_dim = dict_dim
 
         with Context(self.name) as self.circuit:
-            self.W = HebbianSynapse("W", shape=(feature_dim, sys_dim), eta=lr,
+            self.W = HebbianSynapse("W", shape=(feature_dim, sys_dim), eta=self.lr,
                                    sign_value=-1, weight_init=dist.constant(weight_fill),
-                                   prior=('elastic_net', (lmbda, l1_ratio)), optim_type=optim_type, key=subkeys[0])
+                                   prior=('elastic_net', (lmbda, l1_ratio)), w_bound=0.,
+                                   optim_type=optim_type, key=subkeys[0])
             self.err = GaussianErrorCell("err", n_units=sys_dim)
 
             # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ngclearn/modules/regression/lasso.py

@@ -76,13 +76,14 @@ def __init__(self, key, name, sys_dim, dict_dim, batch_size, weight_fill=0.05, l
         self.weight_fill = weight_fill
         self.threshold = threshold
         self.name = name
+        self.lr = lr
         feature_dim = dict_dim
 
         with Context(self.name) as self.circuit:
-            self.W = HebbianSynapse("W", shape=(feature_dim, sys_dim), eta=lr,
+            self.W = HebbianSynapse("W", shape=(feature_dim, sys_dim), eta=self.lr,
                                    sign_value=-1, weight_init=dist.constant(weight_fill),
-                                   prior=('lasso', lasso_lmbda),
-                                    optim_type=optim_type, key=subkeys[0])
+                                   prior=('lasso', lasso_lmbda), w_bound=0.,
+                                   optim_type=optim_type, key=subkeys[0])
             self.err = GaussianErrorCell("err", n_units=sys_dim)
             # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
             self.W.batch_size = batch_size

ngclearn/modules/regression/ridge.py

@@ -71,12 +71,14 @@ def __init__(self, key, name, sys_dim, dict_dim, batch_size, weight_fill=0.05, l
         self.weight_fill = weight_fill
         self.threshold = threshold
         self.name = name
+        self.lr = lr
         feature_dim = dict_dim
 
         with Context(self.name) as self.circuit:
-            self.W = HebbianSynapse("W", shape=(feature_dim, sys_dim), eta=lr,
+            self.W = HebbianSynapse("W", shape=(feature_dim, sys_dim), eta=self.lr,
                                    sign_value=-1, weight_init=dist.constant(weight_fill),
-                                   prior=('ridge', ridge_lmbda), optim_type=optim_type, key=subkeys[0])
+                                   prior=('ridge', ridge_lmbda), w_bound=0.,
+                                   optim_type=optim_type, key=subkeys[0])
             self.err = GaussianErrorCell("err", n_units=sys_dim)
 
             # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~