def __init__(self, graph_dict, marginal, CPD):
'''
initialize function in pearls ALG, whcih includes initialization of
BN in DAG class, lamda value, lamda message of all nodes, initialization
of pi value of all root nodes.
'''
DAG.__init__(self, graph_dict)
self.E, self.e = [], []
self.value = {}
for key in list(marginal.keys()):
self.value[key] = list(marginal[key].keys()) # this is value set each node can take
self.lamda = dict.fromkeys(graph_dict.keys())
self.pi = dict.fromkeys(graph_dict.keys())
self.lamda_msg = dict.fromkeys(graph_dict.keys())
self.pi_msg = dict.fromkeys(graph_dict.keys())
self.cpd = dict.fromkeys(graph_dict.keys())
for x in self.vertices:
self.lamda[x], self.lamda_msg[x], self.pi[x], self.pi_msg[x], self.cpd[x] = {}, {}, {}, {}, {}
# given info of the graph
self.marginal = marginal
self.CPD = CPD
self.get_root()
# initialize lamda
for x in self.vertices:
for x_value in self.value[x]:
# data strucutre for lamuda(X = x_value) is self.lamda[X][x_value]
self.lamda[x][x_value] = 1
# initialize lamda_msg
for z in self.parents[x]:
self.lamda_msg[z][x] = {}
for z_value in self.value[z]:
# data strucutre for lamuda_X(Z = z_value) is self.lamda_msg[Z][X][z_value]
self.lamda_msg[z][x][z_value] = 1
# initialize pi_msg
for y in self.graph_dict[x]:
self.pi_msg[x][y] = {}
for x_value in self.value[x]:
# data strucutre for pi_Y(Z = z_value) is self.lamda_msg[Z][Y][z_value]
self.pi_msg[x][y][x_value] = 1
# send pi_message
for R in self.root:
for r_value in self.value[R]:
# data strucutre for pi(X = x_value) is self.pi[X][x_value]
self.pi[R][r_value] = self.marginal[R][r_value]
# data strucutre for P(X = x|e) is self.cpd[X][x]
self.cpd[R][r_value] = self.marginal[R][r_value]
for W in self.graph_dict[R]:
self.send_pi_msg(R, W)
def __init__(self, graph_dict, marginal, CPD):
'''
initialize function in pearls ALG, whcih includes initialization of
BN in DAG class, lamda value, lamda message of all nodes, initialization
of pi value of all root nodes.
'''
DAG.__init__(
self, graph_dict)
self.E, self.e = [], []
self.value = [0, 1] # this is value set each node can take
self.lamda = dict.fromkeys(graph_dict.keys())
self.pi = dict.fromkeys(graph_dict.keys())
self.lamda_msg = dict.fromkeys(graph_dict.keys())
self.pi_msg = dict.fromkeys(graph_dict.keys())
self.cpd = dict.fromkeys(graph_dict.keys())
for x in self.vertices:
self.lamda[x], self.lamda_msg[x], self.pi[x], self.pi_msg[x], self.cpd[x] = {}, {}, {}, {}, {}
# given info of the graph
self.marginal = marginal
self.CPD = CPD
self.get_root()
# initialize lamda
for x in self.vertices:
for x_value in self.value:
# data strucutre for lamuda(X = x_value) is self.lamda[X][x_value]
self.lamda[x][x_value] = 1
# initialize lamda_msg
for z in self.parents[x]:
self.lamda_msg[z][x] = {}
for z_value in self.value:
# data strucutre for lamuda_X(Z = z_value) is self.lamda_msg[Z][X][z_value]
self.lamda_msg[z][x][z_value] = 1
# initialize pi_msg
for y in self.graph_dict[x]:
self.pi_msg[x][y] = {}
for x_value in self.value:
# data strucutre for pi_Y(Z = z_value) is self.lamda_msg[Z][Y][z_value]
self.pi_msg[x][y][x_value] = 1
# send pi_message
for R in self.root:
for r_value in self.value:
# data strucutre for pi(X = x_value) is self.pi[X][x_value]
self.pi[R][r_value] = self.marginal[R][r_value]
# data strucutre for P(X = x|e) is self.cpd[X][x]
self.cpd[R][r_value] = self.marginal[R][r_value]
for W in self.graph_dict[R]:
self.send_pi_msg(R, W)
class DependencyResolver(object):
def __init__(self, path, options):
logging.debug("Creating DependencyResolver")
self.graph = DAG()
self.root = path
self.opts = options
def scan(self):
for root, dirs, files in os.walk(self.root):
for name in files:
(base, ext) = os.path.splitext(name)
if ext in ['.css', '.js']:
data = self.scanfile(os.path.join(root, name))
for module in data[ 'module' ]:
self.graph.addNode(
Module(
module=module,
filename=data['filename'],
package=data['package'],
requires=data['requires']
)
)
for module in self.graph.nodes():
self.graph.addEdgesFromNode(module.id(), module.requirements())
def scanfile(self, path):
logging.debug( "Scanning file: %s" % path )
metadata = {
"filename": path,
"package": [],
"module": [],
"requires": []
}
with open( path, 'r' ) as f:
in_comment = False
for line in f:
if in_comment:
if re.search( "\*\/", line ):
in_comment = False
else:
tags = re.search( "@(?P<name>\w+)\s+(?P<value>.+)\s*$", line )
if tags:
(name, value) = (tags.group( 'name' ), re.split( '\s*,\s*', tags.group( 'value' )))
if metadata.has_key( name ):
metadata[ name ].extend(value)
else:
if re.match( "\s*\/\*\*", line ):
in_comment = True
logging.debug( " - Result: %s" % metadata )
return metadata
def __init__(self, path, options):
logging.debug("Creating DependencyResolver")
self.graph = DAG()
self.root = path
self.opts = options
def setUp(self):
self.graph = DAG()
class TestDAG(unittest.TestCase):
def setUp(self):
self.graph = DAG()
#
# Insertion
#
def test_insertion(self):
self.assertEqual( len(self.graph.nodes()), 0 )
self.graph.addNode(MockNode('id1'))
self.assertTrue( self.graph.contains( 'id1' ) )
self.assertRaises(DuplicateInsertionError, lambda: self.graph.addNode(MockNode('id1')))
def test_edgeinsertion(self):
self.graph.addNode(MockNode('id1'))
self.graph.addNode(MockNode('id2'))
self.assertRaises(NodeNotExists, lambda: self.graph.addEdge('id1', 'id3'))
self.assertRaises(NodeNotExists, lambda: self.graph.addEdge('id3', 'id1'))
self.graph.addEdge('id1', 'id2')
self.assertEquals(self.graph.neighbors('id1'), [ 'id2' ])
self.assertEquals(self.graph.neighbors('id2'), [])
#
# Cycle Detection
#
def test_cycledetection_simple(self):
self.graph.addNode(MockNode('id1'))
self.graph.addNode(MockNode('id2'))
self.graph.addEdge('id1','id2')
self.assertRaises(CyclicInsertionError, lambda: self.graph.addEdge('id2','id1'))
def test_cycledetection_complex(self):
prev = None
for x in range( 100 ):
self.graph.addNode(MockNode(x))
if prev is not None:
self.graph.addEdge(prev, x)
prev = x
self.assertRaises(CyclicInsertionError, lambda: self.graph.addEdge(99, 0))
#
# Topological Sort
#
def test_topologicalsort_empty(self):
self.assertEquals( [], self.graph.topologicalSort(self.graph.nodes()))
def test_topologicalsort_one(self):
self.graph.addNode(MockNode('id1'))
self.assertEquals( ['id1'], self.graph.topologicalSort(self.graph.nodes()))
def test_topologicalsort_two(self):
self.graph.addNode(MockNode('id1'))
self.graph.addNode(MockNode('id2'))
self.graph.addEdge('id1','id2')
self.assertEquals( ['id2', 'id1'], self.graph.topologicalSort(self.graph.nodes()))
def test_topologicalsort_many(self):
prev = None
list = []
for x in range( 100 ):
self.graph.addNode(MockNode(x))
if prev is not None:
self.graph.addEdge(prev, x)
prev = x
list.append( x )
list.reverse()
self.assertEquals( list, self.graph.topologicalSort(self.graph.nodes()))
