def generateRandomGraph(self, egoFileName, alterFileName, infoProb, graph):
"""
Generate numVertices vertices according to the distributions of egos and
alters found in egoFileName, and alterFileName. Augment with an additional
indicator variable with probabability infoProb. Also, use graph structure
as given by graph.
"""
Parameter.checkFloat(infoProb, 0.0, 1.0)
#Learn the distribution of the egos and alters
eCsvReader = EgoCsvReader()
self.egoQuestionIds = eCsvReader.getEgoQuestionIds()
(X1, _) = eCsvReader.readFile(egoFileName, self.egoQuestionIds)
alterQuestionIds = eCsvReader.getAlterQuestionIds()
(X2, _) = eCsvReader.readFile(alterFileName, alterQuestionIds)
X = numpy.r_[X1, X2]
egoGenerator = EgoGenerator()
(mu, sigmaSq) = Util.computeMeanVar(X)
vList = egoGenerator.generateIndicatorVertices2(graph.getNumVertices(), mu, sigmaSq, infoProb, X.min(0), X.max(0))
graph.setVertexList(vList)
self.graph = graph
return self.graph
def testGenerateIndicatorVertices(self):
egoGenerator = EgoGenerator()
numVertices = 500000
means = numpy.array([1, 10])
vars = numpy.array([[5, 1], [1, 2]])
p = 0.1
vList = egoGenerator.generateIndicatorVertices(numVertices, means, vars, p)
X = numpy.zeros((numVertices, means.shape[0]+1))
for i in range(0, numVertices):
X[i, :] = vList.getVertex(i)
(means2, vars2) = Util.computeMeanVar(X)
self.assertTrue((X.astype(numpy.int32) == X).all())
self.assertAlmostEquals(numpy.linalg.norm(means2[0:2] - means), 0, places=1)
self.assertAlmostEquals(numpy.linalg.norm(vars2[0:2][:,0:2] - vars), 0, places=0)
self.assertAlmostEquals(p, means2[2],places=2)
#Try non-symmetric variance matrix
vars = numpy.array([[5, 1], [8, 2]])
self.assertRaises(ValueError, egoGenerator.generateIndicatorVertices, numVertices, means, vars, p)
def testGenerateIndicatorVertices2(self):
egoGenerator = EgoGenerator()
numVertices = 500000
means = numpy.array([1, 10])
vars = numpy.array([[5, 1], [1, 2]])
mins = numpy.array([-1000, -1000])
maxs = numpy.array([1000, 1000])
p = 0.1
vList = egoGenerator.generateIndicatorVertices2(numVertices, means, vars, p, mins, maxs)
X = numpy.zeros((numVertices, means.shape[0]+1))
for i in range(0, numVertices):
X[i, :] = vList.getVertex(i)
(means2, vars2) = Util.computeMeanVar(X)
self.assertTrue((X.astype(numpy.int32) == X).all())
self.assertAlmostEquals(numpy.linalg.norm(means2[0:2] - means), 0, places=1)
self.assertAlmostEquals(numpy.linalg.norm(vars2[0:2][:,0:2] - vars), 0, places=0)
self.assertAlmostEquals(p, means2[2],places=2)
self.assertTrue((X[:, 0:2].min(0) >= mins).all())
self.assertTrue((X[:, 0:2].max(0) <= maxs).all())
#Try non-symmetric variance matrix
vars = numpy.array([[5, 1], [8, 2]])
self.assertRaises(ValueError, egoGenerator.generateIndicatorVertices2, numVertices, means, vars, p, mins, maxs)
#Test min > max
vars = numpy.array([[5, 1], [1, 2]])
mins = numpy.array([-2, 6])
maxs = numpy.array([10, 5])
self.assertRaises(ValueError, egoGenerator.generateIndicatorVertices2, numVertices, means, vars, p, mins, maxs)
#Test min == max
numVertices = 1000
vars = numpy.array([[5, 1], [1, 2]])
mins = numpy.array([-2, 5])
maxs = numpy.array([10, 5])
vList = egoGenerator.generateIndicatorVertices2(numVertices, means, vars, p, mins, maxs)
for i in range(0, numVertices):
self.assertTrue(vList.getVertex(i)[1] == 5)
#Try a new example with small range of min and max - check the mean and var
numVertices = 500000
means = numpy.array([1, 10])
vars = numpy.array([[2, 0], [0, 2]])
mins = numpy.array([-3, 6])
maxs = numpy.array([5, 14])
p = 0.1
vList = egoGenerator.generateIndicatorVertices2(numVertices, means, vars, p, mins, maxs)
X = numpy.zeros((numVertices, means.shape[0]+1))
for i in range(0, numVertices):
X[i, :] = vList.getVertex(i)
(means2, vars2) = Util.computeMeanVar(X)
self.assertAlmostEquals(numpy.linalg.norm(means2[0:2] - means), 0, places=1)
self.assertAlmostEquals(numpy.linalg.norm(vars2[0:2][:,0:2] - vars), 0, places=0)
self.assertAlmostEquals(p, means2[2],places=2)
logging.debug((X[:, 0:2].min(0)))
logging.debug((X[:, 0:2].max(0)))
self.assertTrue((X[:, 0:2].min(0) >= mins).all())
self.assertTrue((X[:, 0:2].max(0) <= maxs).all())
