nothing@0: #formal concept analysis
nothing@0: 
nothing@0: import rdflib
nothing@0: from rdflib import plugin, OWL, URIRef
nothing@0: from rdflib.graph import Graph
nothing@0: from rdflib.namespace import Namespace
nothing@0: 
nothing@0: execfile('/Users/alo/MusicOntology/features/fca/writeHTML.py')
nothing@0: 
nothing@0: plugin.register(
nothing@0:     'sparql', rdflib.query.Processor,
nothing@0:     'rdfextras.sparql.processor', 'Processor')
nothing@0: plugin.register(
nothing@0:     'sparql', rdflib.query.Result,
nothing@0:     'rdfextras.sparql.query', 'SPARQLQueryResult')
nothing@0: 
nothing@0: graph = Graph()
nothing@0: graph.parse('/Users/alo/MusicOntology/features/featuresCatalogue.rdf')
nothing@0: 
nothing@0: # conceptual scaling of many-valued feature attributes: complexity - low, medium, high 
nothing@0: # must be transformed into one-valued context: complexity-low, complexity-medium, complexity-high
nothing@0: atmatrix = []
nothing@0: 
nothing@0: #which attributes/categories to include
nothing@0: cat = ["appdomain", "complexity", "computation", "dimensions", "domain", "level", "temporalscale", "computedIn", "tag"]
nothing@0: catdict = {}
nothing@0: 
nothing@0: for name in cat:
nothing@0:     catdict[name] = []
nothing@0: 
nothing@0: atcols = []
nothing@0: atrows = []
nothing@0: 
nothing@0: #traverse all items of type OWL.Class and then for each class the attributes in the dictionary and enumerate all the possible values
nothing@0: for s, p, o in graph.triples((None, None, OWL.Class)):
nothing@0:     row = s.split('/')[-1]
nothing@0:     if atrows.count(row) == 0:
nothing@0:         atrows.append(row)
nothing@0:     for su, pr, ob in graph.triples((s, None, None)):
nothing@0:         col = pr.split('/')[-1]
nothing@0:         if cat.count(col) != 0:
nothing@0:             if catdict[col].count(ob) == 0:
nothing@0:                 atcols.append(col+"-"+ob)
nothing@0:                 catdict[col].append(ob)
nothing@0:                 
nothing@0: 
nothing@0: atrows.sort()
nothing@0: atcols.sort()
nothing@0: 
nothing@0: #construct the matrix    
nothing@0: for i in range(len(atrows)):
nothing@0:     atrow = []
nothing@0:     for j in range(len(atcols)):
nothing@0:         atrow.append(0)
nothing@0:     atmatrix.append(atrow)
nothing@0: 
nothing@0: 
nothing@0: for ns, value in graph.namespaces():
nothing@0:     if ns == 'local':
nothing@0:         local = value
nothing@0: 
nothing@0: index = 0
nothing@0: for feature in atrows:
nothing@0:     for s, p, o in graph.triples((URIRef(local+feature), None, None)):
nothing@0:         col = p.split('/')[-1]
nothing@0:         if cat.count(col) != 0:
nothing@0:             atmatrix[index][atcols.index(col+"-"+o)] = 1
nothing@0:     index += 1
nothing@0: 
nothing@0: #writeHTML('atmatrix.html', atmatrix, atcols, atrows)
nothing@0: #writeWikiTable('fcamatrixWiki.txt', atmatrix, atcols, atrows)
nothing@0: 
nothing@0: #writeCXT('atmatrix.cxt', atmatrix, atcols, atrows)
nothing@0: writeFIMI('atmatrix.fimi', atmatrix, atcols, atrows)
nothing@0: 
nothing@0: