def setUp(self):
with open("resources/matrix-ebc-paper-sparse.tsv", "r") as f:
data = []
for line in f:
sl = line.split("\t")
if len(sl) < 5: # headers
continue
data.append([sl[0], sl[2], float(sl[4])])
self.matrix = ebc2d.get_matrix_from_data(data)
def testEbc2dOnSparseMatrix(self):
with open("resources/matrix-itcc-paper-orig.tsv", "r") as f:
data = [l.split('\t') for l in f]
m = ebc2d.get_matrix_from_data(data)
# run without assigned clusters
ebc = EBC2D(m, [3, 2], 10, 1e-10, 0.01)
cXY, objective, it = ebc.run(verbose=False)
print "--> ebc2d"
print "objective: ", objective
print "iterations: ", it
# run with assigned clusters
ebc = EBC2D(m, [3, 2], 10, 1e-10, 0.01)
cXY, objective, it = ebc.run(assigned_clusters=[[2, 0, 1, 1, 2, 2], [0, 0, 1, 0, 1, 1]], verbose=False)
indices = [range(N_d) for N_d in ebc.pXY.shape]
index_list = self.cartesian(indices)
approx_distribution = {}
qX_xhat = [ebc.qX_xhat, ebc.qY_yhat]
for location in index_list:
q = 1.0
c_location = []
for i in range(len(location)):
c_i = cXY[i][location[i]]
c_location.append(c_i)
q *= qX_xhat[i][location[i]]
q *= ebc.qXhatYhat[c_location[0], c_location[1]]
approx_distribution[tuple(location)] = q
self.assertAlmostEquals(approx_distribution[(0, 0)], 0.054)
self.assertAlmostEquals(approx_distribution[(0, 1)], 0.054)
self.assertAlmostEquals(approx_distribution[(0, 2)], 0.042)
self.assertAlmostEquals(approx_distribution[(0, 3)], 0.0)
self.assertAlmostEquals(approx_distribution[(0, 4)], 0.0)
self.assertAlmostEquals(approx_distribution[(0, 5)], 0.0)
self.assertAlmostEquals(approx_distribution[(1, 0)], 0.054)
self.assertAlmostEquals(approx_distribution[(1, 1)], 0.054)
self.assertAlmostEquals(approx_distribution[(1, 2)], 0.042)
self.assertAlmostEquals(approx_distribution[(1, 3)], 0.0)
self.assertAlmostEquals(approx_distribution[(1, 4)], 0.0)
self.assertAlmostEquals(approx_distribution[(1, 5)], 0.0)
self.assertAlmostEquals(approx_distribution[(2, 0)], 0.0)
self.assertAlmostEquals(approx_distribution[(2, 1)], 0.0)
self.assertAlmostEquals(approx_distribution[(2, 2)], 0.0)
self.assertAlmostEquals(approx_distribution[(2, 3)], 0.042)
self.assertAlmostEquals(approx_distribution[(2, 4)], 0.054)
self.assertAlmostEquals(approx_distribution[(2, 5)], 0.054)
self.assertAlmostEquals(approx_distribution[(3, 0)], 0.0)
self.assertAlmostEquals(approx_distribution[(3, 1)], 0.0)
self.assertAlmostEquals(approx_distribution[(3, 2)], 0.0)
self.assertAlmostEquals(approx_distribution[(3, 3)], 0.042)
self.assertAlmostEquals(approx_distribution[(3, 4)], 0.054)
self.assertAlmostEquals(approx_distribution[(3, 5)], 0.054)
self.assertAlmostEquals(approx_distribution[(4, 0)], 0.036)
self.assertAlmostEquals(approx_distribution[(4, 1)], 0.036)
self.assertAlmostEquals(approx_distribution[(4, 2)], 0.028)
self.assertAlmostEquals(approx_distribution[(4, 3)], 0.028)
self.assertAlmostEquals(approx_distribution[(4, 4)], 0.036)
self.assertAlmostEquals(approx_distribution[(4, 5)], 0.036)
self.assertAlmostEquals(approx_distribution[(5, 0)], 0.036)
self.assertAlmostEquals(approx_distribution[(5, 1)], 0.036)
self.assertAlmostEquals(approx_distribution[(5, 2)], 0.028)
self.assertAlmostEquals(approx_distribution[(5, 3)], 0.028)
self.assertAlmostEquals(approx_distribution[(5, 4)], 0.036)
self.assertAlmostEquals(approx_distribution[(5, 5)], 0.036)