imports statements in docs fixed

docs/source/examples/add_ml_models_to_nodes.rst

@@ -7,7 +7,7 @@ First, lets import BAMT modules and required machine learning modules.
 
 .. code-block:: python
 
-    from bamt.networks.hybrid_bn import HybridBN
+    import bamt.networks as networks
     import bamt.Preprocessors as pp
 
     import pandas as pd
@@ -66,7 +66,7 @@ Next, we initialize Bayesian Network object and add nodes to it.
 
 .. code-block:: python
 
-    bn = HybridBN(has_logit=True, use_mixture=True)
+    bn = networks.HybridBN(has_logit=True, use_mixture=True)
     bn.add_nodes(info)
 
 After adding nodes we can perform structure learning.

docs/source/examples/learn_params_vis.rst

@@ -5,7 +5,7 @@ Used imports:
 
 .. code-block:: python
 
-    from bamt.networks.hybrid_bn import HybridBN
+    import bamt.networks as networks
     import bamt.Preprocessors as pp
 
     import pandas as pd
@@ -44,7 +44,7 @@ Preprocessing data, encode categorical features and discretize numerical feature
     p = pp.Preprocessor([('encoder', encoder), ('discretizer', discretizer)])
     discretized_data, est = p.apply(data)
 
-    bn = HybridBN(has_logit=True, use_mixture=True) # init BN
+    bn = networks.HybridBN(has_logit=True, use_mixture=True) # init BN
     info = p.info
     info
     bn.add_nodes(info)

docs/source/examples/learn_sampling_predict.rst

@@ -5,7 +5,7 @@ Used imports:
 
 .. code-block:: python
 
-    from bamt.networks.hybrid_bn import HybridBN
+    import bamt.networks as networks
     import bamt.Preprocessors as pp
 
     import pandas as pd

docs/source/examples/learn_save.rst

@@ -8,7 +8,7 @@ Used imports:
     from bamt.preprocessors import Preprocessor
     import pandas as pd
     from sklearn import preprocessing as pp
-    from bamt.networks.hybrid_bn import HybridBN
+    from bamt.networks import HybridBN
 
 
 Let's start with data loading and preprocessing: 

docs/source/examples/read_structure_and_params.rst

@@ -8,7 +8,7 @@ Used imports:
     from bamt.preprocessors import Preprocessor
     import pandas as pd
     from sklearn import preprocessing as pp
-    from bamt.networks.hybrid_bn import HybridBN
+    from bamt.networks import HybridBN
     import json
 
 You can read the pre-trained structure and distribution parameters from a file.

docs/source/examples/read_structure_param_learning.rst

@@ -8,7 +8,7 @@ Used imports:
     from bamt.preprocessors import Preprocessor
     import pandas as pd
     from sklearn import preprocessing as pp
-    from bamt.networks.hybrid_bn import HybridBN
+    from bamt.networks import HybridBN
     import json
 
 There are two options for loading a BN structure. The first is to read it directly from a JSON file:

docs/source/models/bayesiannetworks/base_network.rst

@@ -27,7 +27,7 @@ For Example:
 
 .. code-block:: python
 
-    from bamt.networks.discrete_bn import DiscreteBN
+    import bamt.networks as networks
     import bamt.preprocessors as pp
     import pandas as pd
 
@@ -38,7 +38,7 @@ For Example:
     p = pp.Preprocessor([('encoder', encoder), ('discretizer', discretizer)])
     discretized_data, est = p.apply(asia)
 
-    bn = DiscreteBN()
+    bn = networks.DiscreteBN()
     info = p.info
     info
 

docs/source/models/bayesiannetworks/composite_bn.rst

@@ -13,9 +13,9 @@ To initialize a ``CompositeBN`` object, you can use the following code:
 
 .. code-block:: python
 
-    from  bamt.networks.composite_bn import CompositeBN
+    import bamt.networks as networks
 
-    bn = CompositeBN()
+    bn = networks.CompositeBN()
 
 
 Data Preprocessing

docs/source/models/bayesiannetworks/continuous_bn.rst

@@ -13,9 +13,9 @@ To initialize a ``ContinuousBN`` object, you can use the following code:
 
 .. code-block:: python
 
-    from bamt.networks.continuous_bn import ContinuousBN
+    import bamt.networks as networks
     
-    bn = ContinuousBN(use_mixture=True)
+    bn = networks.ContinuousBN(use_mixture=True)
 
 ContinuousBN has an additional parameter ``use_mixture``. 
 It is used to determine whether to use mixuters of Gaussian distributions  to represent the conditional distribution of continuous variables.

docs/source/models/bayesiannetworks/discrete_bn.rst

@@ -13,9 +13,9 @@ To initialize a ``DiscreteBN`` object, you can use the following code:
 
 .. code-block:: python
 
-    from bamt.networks.discrete_bn import DiscreteBN
+    import bamt.networks as networks
 
-    bn = DiscreteBN()
+    bn = networks.DiscreteBN()
 
 Data Preprocessing
 ~~~~~~~~~~~~~~~~~~

docs/source/models/bayesiannetworks/hybrid_bn.rst

@@ -13,9 +13,9 @@ To initialize a ``HybridBN`` object, you can use the following code:
 
 .. code-block:: python
 
-    from  bamt.networks.hybrid_bn import HybridBN
+    import bamt.networks as networks
     
-    bn = HybridBN(has_logit=True, use_mixture=True)
+    bn = networks.HybridBN(has_logit=True, use_mixture=True)
 
 HybridBN has two additional parameters ``has_logit`` and ``use_mixture``.
 ``has_logit`` is used to determine whether to use logit nodes. Logit nodes use machine learning algorithms to represent variable.

docs/source/models/bayesiannetworks/large_bn_algs.rst

@@ -50,7 +50,7 @@ Then perform structure learning as usual, but use ``ps`` as ``white_list``:
 
 .. code-block:: python
 
-    bn = Nets.ContinuousBN()
+    bn = networks.ContinuousBN()
 
     bn.add_nodes(descriptor=info)