本文整理匯總了Python中numpy.bmat方法的典型用法代碼示例。如果您正苦於以下問題：Python numpy.bmat方法的具體用法？Python numpy.bmat怎麽用？Python numpy.bmat使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy的用法示例。
在下文中一共展示了numpy.bmat方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: diagonalize
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def diagonalize(a, b, nroots=4):
a_aa, a_ab, a_bb = a
b_aa, b_ab, b_bb = b
nocc_a, nvir_a, nocc_b, nvir_b = a_ab.shape
a_aa = a_aa.reshape((nocc_a*nvir_a,nocc_a*nvir_a))
a_ab = a_ab.reshape((nocc_a*nvir_a,nocc_b*nvir_b))
a_bb = a_bb.reshape((nocc_b*nvir_b,nocc_b*nvir_b))
b_aa = b_aa.reshape((nocc_a*nvir_a,nocc_a*nvir_a))
b_ab = b_ab.reshape((nocc_a*nvir_a,nocc_b*nvir_b))
b_bb = b_bb.reshape((nocc_b*nvir_b,nocc_b*nvir_b))
a = numpy.bmat([[ a_aa , a_ab],
[ a_ab.T, a_bb]])
b = numpy.bmat([[ b_aa , b_ab],
[ b_ab.T, b_bb]])
e = numpy.linalg.eig(numpy.bmat([[a , b ],
[-b.conj(),-a.conj()]]))[0]
lowest_e = numpy.sort(e[e.real > 0].real)[:nroots]
return lowest_e 示例2: diagonalize
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def diagonalize(a, b, nroots=4):
a_aa, a_ab, a_bb = a
b_aa, b_ab, b_bb = b
nocc_a, nvir_a, nocc_b, nvir_b = a_ab.shape
a_aa = a_aa.reshape((nocc_a*nvir_a,nocc_a*nvir_a))
a_ab = a_ab.reshape((nocc_a*nvir_a,nocc_b*nvir_b))
a_bb = a_bb.reshape((nocc_b*nvir_b,nocc_b*nvir_b))
b_aa = b_aa.reshape((nocc_a*nvir_a,nocc_a*nvir_a))
b_ab = b_ab.reshape((nocc_a*nvir_a,nocc_b*nvir_b))
b_bb = b_bb.reshape((nocc_b*nvir_b,nocc_b*nvir_b))
a = numpy.bmat([[ a_aa , a_ab],
[ a_ab.T, a_bb]])
b = numpy.bmat([[ b_aa , b_ab],
[ b_ab.T, b_bb]])
e = numpy.linalg.eig(numpy.bmat([[a , b ],
[-b.conj(),-a.conj()]]))[0]
lowest_e = numpy.sort(e[e.real > 0].real)[:nroots]
lowest_e = lowest_e[lowest_e > 1e-3]
return lowest_e 示例3: test_basic
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def test_basic(self):
A = np.array([[1, 2], [3, 4]])
mA = matrix(A)
assert_(np.all(mA.A == A))
B = bmat("A,A;A,A")
C = bmat([[A, A], [A, A]])
D = np.array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
assert_(np.all(B.A == D))
assert_(np.all(C.A == D))
E = np.array([[5, 6], [7, 8]])
AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
assert_(np.all(bmat([A, E]) == AEresult))
vec = np.arange(5)
mvec = matrix(vec)
assert_(mvec.shape == (1, 5)) 示例4: test_bmat_nondefault_str
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def test_bmat_nondefault_str(self):
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
Aresult = np.array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
mixresult = np.array([[1, 2, 5, 6],
[3, 4, 7, 8],
[5, 6, 1, 2],
[7, 8, 3, 4]])
assert_(np.all(bmat("A,A;A,A") == Aresult))
assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
assert_(
np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
assert_(np.all(b2 == mixresult)) 示例5: test_np
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def test_np():
npr.seed(0)
nx, nineq, neq = 4, 6, 7
Q = npr.randn(nx, nx)
G = npr.randn(nineq, nx)
A = npr.randn(neq, nx)
D = np.diag(npr.rand(nineq))
K_ = np.bmat((
(Q, np.zeros((nx, nineq)), G.T, A.T),
(np.zeros((nineq, nx)), D, np.eye(nineq), np.zeros((nineq, neq))),
(G, np.eye(nineq), np.zeros((nineq, nineq + neq))),
(A, np.zeros((neq, nineq + nineq + neq)))
))
K = block((
(Q, 0, G.T, A.T),
(0, D, 'I', 0),
(G, 'I', 0, 0),
(A, 0, 0, 0)
))
assert np.allclose(K_, K) 示例6: cov_params_default
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def cov_params_default(self): # p.296 (7.2.21)
# Sigma_co described on p. 287
beta = self.beta
if self.det_coef_coint.size > 0:
beta = vstack((beta, self.det_coef_coint))
dt = self.deterministic
num_det = ("co" in dt) + ("lo" in dt)
num_det += (self.seasons-1) if self.seasons else 0
if self.exog is not None:
num_det += self.exog.shape[1]
b_id = scipy.linalg.block_diag(beta,
np.identity(self.neqs * (self.k_ar-1) +
num_det))
y_lag1 = self._y_lag1
b_y = beta.T.dot(y_lag1)
omega11 = b_y.dot(b_y.T)
omega12 = b_y.dot(self._delta_x.T)
omega21 = omega12.T
omega22 = self._delta_x.dot(self._delta_x.T)
omega = np.bmat([[omega11, omega12],
[omega21, omega22]]).A
mat1 = b_id.dot(inv(omega)).dot(b_id.T)
return np.kron(mat1, self.sigma_u) 示例7: test_basic
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def test_basic(self):
A = array([[1, 2], [3, 4]])
mA = matrix(A)
assert_(all(mA.A == A))
B = bmat("A,A;A,A")
C = bmat([[A, A], [A, A]])
D = array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
assert_(all(B.A == D))
assert_(all(C.A == D))
E = array([[5, 6], [7, 8]])
AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
assert_(all(bmat([A, E]) == AEresult))
vec = arange(5)
mvec = matrix(vec)
assert_(mvec.shape == (1, 5)) 示例8: test_bmat_nondefault_str
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def test_bmat_nondefault_str(self):
A = array([[1, 2], [3, 4]])
B = array([[5, 6], [7, 8]])
Aresult = array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
Bresult = array([[5, 6, 5, 6],
[7, 8, 7, 8],
[5, 6, 5, 6],
[7, 8, 7, 8]])
mixresult = array([[1, 2, 5, 6],
[3, 4, 7, 8],
[5, 6, 1, 2],
[7, 8, 3, 4]])
assert_(all(bmat("A,A;A,A") == Aresult))
assert_(all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
assert_(all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
assert_(all(b2 == mixresult)) 示例9: circumcenter
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def circumcenter(self, tri):
"""Compute circumcenter and circumradius of a triangle in 2D.
Uses an extension of the method described here:
http://www.ics.uci.edu/~eppstein/junkyard/circumcenter.html
"""
pts = np.asarray([self.coords[v] for v in tri])
pts2 = np.dot(pts, pts.T)
A = np.bmat([[2 * pts2, [[1],
[1],
[1]]],
[[[1, 1, 1, 0]]]])
b = np.hstack((np.sum(pts * pts, axis=1), [1]))
x = np.linalg.solve(A, b)
bary_coords = x[:-1]
center = np.dot(bary_coords, pts)
# radius = np.linalg.norm(pts[0] - center) # euclidean distance
radius = np.sum(np.square(pts[0] - center)) # squared distance
return (center, radius) 示例10: Circumcenter
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bmat [as 別名]
def Circumcenter(self, tri):
"""Compute Circumcenter and circumradius of a triangle in 2D.
Uses an extension of the method described here:
http://www.ics.uci.edu/~eppstein/junkyard/circumcenter.html
"""
pts = np.asarray([self.coords[v] for v in tri])
pts2 = np.dot(pts, pts.T)
A = np.bmat([[2 * pts2, [[1],
[1],
[1]]],
[[[1, 1, 1, 0]]]])
b = np.hstack((np.sum(pts * pts, axis=1), [1]))
x = np.linalg.solve(A, b)
bary_coords = x[:-1]
center = np.dot(bary_coords, pts)
# radius = np.linalg.norm(pts[0] - center) # euclidean distance
radius = np.sum(np.square(pts[0] - center)) # squared distance
return (center, radius)