def select(self):
QtQ = self.W.gram().dense_matrix()
n = self.domain_shape[0]
err, inv, weights, queries = self._GreedyHierByLv(QtQ,
n,
0,
withRoot=False)
# form matrix from queries and weights
row_list = []
for q, w in zip(queries, weights):
if w > 0:
row = np.zeros(self.domain_shape[0])
row[q[0]:q[1] + 1] = w
row_list.append(row)
mat = np.vstack(row_list)
mat = sparse.csr_matrix(mat) if sparse.issparse(mat) is False else mat
return matrix.EkteloMatrix(mat)
def setUp(self):
self.prng = np.random.RandomState(0)
self.domain = (2,3,4)
I = lambda n: workload.Identity(n)
T = lambda n: workload.Total(n)
P = lambda n: workload.Prefix(n)
R = lambda n: matrix.EkteloMatrix(self.prng.rand(n,n))
W1 = workload.Kronecker([I(2), T(3), P(4)])
W2 = workload.Kronecker([T(2), T(3), I(4)])
W3 = workload.Kronecker([I(2), I(3), T(4)])
self.W = workload.VStack([W1, W2, W3])
# three representations of Identity matrix
self.A1 = I(2*3*4)
self.A2 = workload.Kronecker([I(2),I(3),I(4)])
self.A3 = workload.Marginals.fromtuples(self.domain, {(0,1,2) : 1.0 })
self.A4 = workload.Marginals(self.domain, self.prng.rand(8))
self.A5 = workload.Kronecker([R(2), R(3), R(4)])
def strategy(self):
return matrix.EkteloMatrix(self.ans)
def strategy(self):
tri = np.zeros((self.n, self.n))
tri[self._mask] = self._params
X = np.eye(self.n) + tri + tri.T
A = np.linalg.cholesky(X).T
return matrix.EkteloMatrix(A)
def strategy(self):
B = sparse.csr_matrix(self._params.reshape(self.p, self.n))
I = sparse.eye(self.n, format='csr')
A = sparse.vstack([I, B], format='csr')
return matrix.EkteloMatrix(A / A.sum(axis=0))
def strategy(self):
A = self._params.reshape(self.shape)
return matrix.EkteloMatrix(A)
def select(self):
mat = sparse.vstack(
(self.M_hat, support.complement(self.M_hat, self.grid_size)))
return matrix.EkteloMatrix(mat)