v0.3.3 (#18)

* Centrality
* Run Black formatter

fcapsy/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.3.2"
+__version__ = "0.3.3"
 __author__ = "Tomáš Mikula"
 __email__ = "[email protected]"
-__license__ = 'MIT license'
+__license__ = "MIT license"

fcapsy/basic_level.py

@@ -132,7 +132,9 @@ def basic_level_avg(
         0.108
     """
 
-    return _basic_level(concept, cohesion, similarity, statistics.mean, statistics.mean, theta)
+    return _basic_level(
+        concept, cohesion, similarity, statistics.mean, statistics.mean, theta
+    )
 
 
 def basic_level_min(

fcapsy/centrality.py

@@ -0,0 +1,53 @@
+__all__ = ["centrality"]
+
+
+def centrality(item: str, concept: "concepts.lattices.Concept") -> float:
+    """Calculates centrality of object/attribute in the given concept.
+    The object/attribute does not has to be from the extent/intent.
+
+    Args:
+        item (str): object or attribute name
+        concept (concepts.lattices.Concept)
+
+    Returns:
+        float: centrality
+
+    Example:
+        >>> from concepts import Context
+        >>> context = Context.fromstring('''
+        ...          |2 legs |nests  |flies  |raptor |
+        ... sparrow  |   X   |   X   |   X   |       |
+        ... lark     |   X   |   X   |   X   |       |
+        ... penguin  |   X   |       |       |       |
+        ... chicken  |   X   |   X   |   X   |       |
+        ... vulture  |   X   |       |   X   |   X   |
+        ... ''')
+        >>> birds = context.lattice.supremum
+        >>> birds.extent
+        ('sparrow', 'lark', 'penguin', 'chicken', 'vulture')
+        >>> centrality("raptor", birds)
+        0.2
+        >>> centrality("flies", birds)
+        0.8
+        >>> centrality("2 legs", birds)
+        1.0
+    """
+    context = concept.lattice._context
+
+    try:
+        instances_with_item = context.intension([item], raw=True)
+        Vector = type(concept._intent)
+        concept_core = concept._intent
+    except KeyError:
+        instances_with_item = context.extension([item], raw=True)
+        Vector = type(concept._extent)
+        concept_core = concept._extent
+
+    occurence_in_concept = Vector.fromint(instances_with_item & concept_core).count()
+
+    if not instances_with_item.count():
+        return 0.0
+
+    return (occurence_in_concept / instances_with_item.count()) * (
+        occurence_in_concept / concept._extent.count()
+    )

setup.py

@@ -21,9 +21,9 @@
     long_description=pathlib.Path("README.md").read_text(encoding="utf-8"),
     long_description_content_type="text/markdown",
     classifiers=[
-        'Development Status :: 3 - Alpha',
+        "Development Status :: 3 - Alpha",
         "Intended Audience :: Science/Research",
-        'Intended Audience :: Developers',
+        "Intended Audience :: Developers",
         "License :: OSI Approved :: MIT License",
         "Natural Language :: English",
         "Programming Language :: Python :: 3",