[docs] Update documentation examples.

pyclustering/cluster/agglomerative.py

@@ -60,8 +60,6 @@ class agglomerative:
     @brief Class represents agglomerative algorithm for cluster analysis.
     @details Agglomerative algorithm considers each data point (object) as a separate cluster at the beggining and
               step by step finds the best pair of clusters for merge until required amount of clusters is obtained.
-            
-            CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
     
     Example of agglomerative algorithm where centroid link is used:
     @code

pyclustering/cluster/birch.py

@@ -36,19 +36,29 @@ class birch:
     """!
     @brief Class represents clustering algorithm BIRCH.
     
-    Example:
+    Example how to extract clusters from 'OldFaithful' sample using BIRCH algorithm:
     @code
-        # sample for cluster analysis (represented by list)
-        sample = read_sample(path_to_sample);
-        
-        # create object of birch that uses CCORE for processing
-        birch_instance = birch(sample, 2, 5, 5, 0.05, measurement_type.CENTROID_EUCLIDIAN_DISTANCE, 200, True);
-        
-        # cluster analysis
-        birch_instance.process();
-        
-        # obtain results of clustering
-        clusters = birch_instance.get_clusters();
+        from pyclustering.cluster.birch import birch, measurement_type
+        from pyclustering.cluster import cluster_visualizer
+        from pyclustering.utils import read_sample
+        from pyclustering.samples.definitions import FAMOUS_SAMPLES
+
+        # Sample for cluster analysis (represented by list)
+        sample = read_sample(FAMOUS_SAMPLES.SAMPLE_OLD_FAITHFUL)
+
+        # Create BIRCH algorithm
+        birch_instance = birch(sample, 2)
+
+        # Cluster analysis
+        birch_instance.process()
+
+        # Obtain results of clustering
+        clusters = birch_instance.get_clusters()
+
+        # Visualize allocated clusters
+        visualizer = cluster_visualizer()
+        visualizer.append_clusters(clusters, sample)
+        visualizer.show()
     @endcode
     
     """
@@ -69,31 +79,6 @@ def __init__(self, data, number_clusters, branching_factor = 5, max_node_entries
         
         @remark Despite eight arguments only the first two is mandatory, others can be ommitted. In this case default values are used for instance creation.
         
-        Example how to extract clusters from 'OldFaithful' sample using BIRCH algorithm:
-        @code
-            from pyclustering.cluster.birch import birch, measurement_type
-            from pyclustering.cluster import cluster_visualizer
-            from pyclustering.utils import read_sample
-            from pyclustering.samples.definitions import FAMOUS_SAMPLES
-
-            # Sample for cluster analysis (represented by list)
-            sample = read_sample(FAMOUS_SAMPLES.SAMPLE_OLD_FAITHFUL)
-
-            # Create BIRCH algorithm
-            birch_instance = birch(sample, 2)
-
-            # Cluster analysis
-            birch_instance.process()
-
-            # Obtain results of clustering
-            clusters = birch_instance.get_clusters()
-
-            # Visualize allocated clusters
-            visualizer = cluster_visualizer()
-            visualizer.append_clusters(clusters, sample)
-            visualizer.show()
-        @endcode
-        
         """
 
         self.__pointer_data = data;

pyclustering/cluster/hsyncnet.py

@@ -40,8 +40,6 @@ class hsyncnet(syncnet):
     """!
     @brief Class represents clustering algorithm HSyncNet. HSyncNet is bio-inspired algorithm that is based on oscillatory network that uses modified Kuramoto model.
     
-    @details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
-    
     Example:
     @code
         from pyclustering.cluster.hsyncnet import hsyncnet

pyclustering/cluster/kmeans.py

@@ -272,9 +272,7 @@ def frame_generation(index_iteration):
 class kmeans:
     """!
     @brief Class represents K-Means clustering algorithm.
-    @details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
-    
-    CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.
+    @details CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.
     
     K-Means clustering results depend on initial centers. Algorithm K-Means++ can used for initialization 
     initial centers from module 'pyclustering.cluster.center_initializer'.

pyclustering/cluster/kmedians.py

@@ -42,8 +42,6 @@ class kmedians:
     @brief Class represents clustering algorithm K-Medians.
     @details The algorithm is less sensitive to outliers than K-Means. Medians are calculated instead of centroids.
     
-             CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
-    
     Example:
     @code
         from pyclustering.cluster.kmedians import kmedians

pyclustering/cluster/optics.py

@@ -288,8 +288,6 @@ class optics:
              Clustering-ordering information contains information about internal structures of data set in terms of density and proper connectivity radius can be obtained
              for allocation required amount of clusters using this diagram. In case of usage additional input parameter 'amount of clusters' connectivity radius should be
              bigger than real - because it will be calculated by the algorithms if requested amount of clusters is not allocated.
-             
-             CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
 
     @image html optics_example_clustering.png "Scheme how does OPTICS works. At the beginning only one cluster is allocated, but two is requested. At the second step OPTICS calculates connectivity radius using cluster-ordering and performs final cluster allocation."
 

pyclustering/cluster/rock.py

@@ -37,7 +37,6 @@
 class rock:
     """!
     @brief Class represents clustering algorithm ROCK.
-    @details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
 
     Example:
     @code

pyclustering/cluster/somsc.py

@@ -39,8 +39,6 @@ class somsc:
     @brief Class represents simple clustering algorithm based on self-organized feature map. 
     @details This algorithm uses amount of clusters that should be allocated as a size of SOM map. Captured objects by neurons are clusters.
              Algorithm is able to process data with Gaussian distribution that has spherical forms.
-             
-             CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
     
     Example:
     @code

pyclustering/cluster/syncnet.py

@@ -166,8 +166,6 @@ class syncnet(sync_network):
     @brief Class represents clustering algorithm SyncNet. 
     @details SyncNet is bio-inspired algorithm that is based on oscillatory network that uses modified Kuramoto model. Each attribute of a data object
              is considered as a phase oscillator.
-             
-             CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
     
     Example:
     @code

pyclustering/cluster/xmeans.py

@@ -82,8 +82,6 @@ class xmeans:
              and then dynamically increases them. X-means uses specified splitting criterion to control 
              the process of splitting clusters. Method K-Means++ can be used for calculation of initial centers.
              
-             CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
-             
              CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.
     
     Here example how to perform cluster analysis using X-Means algorithm: