本文整理汇总了Python中scipy.kron函数的典型用法代码示例。如果您正苦于以下问题：Python kron函数的具体用法？Python kron怎么用？Python kron使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了kron函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: K
def K(self):
if self.dim > _MAX_DIM:
raise TooExpensiveOperationError(msg_too_expensive_dim(my_name(),
_MAX_DIM))
rv = sp.kron(self.Cg.K(), self.R) + sp.kron(self.Cn.K(), sp.eye(self.dim_r))
return rv
开发者ID:jeffhsu3，项目名称:limix，代码行数:7，代码来源:cov2kronSum.py
示例2: _LMLgrad_s
def _LMLgrad_s(self,hyperparams,debugging=False):
"""
evaluate gradients with respect to covariance matrix Sigma
"""
try:
KV = self.get_covariances(hyperparams, debugging=debugging)
except LA.LinAlgError:
LG.error('linalg exception in _LMLgrad_x_sigma')
return {'X_s':SP.zeros(hyperparams['X_s'].shape)}
Si = 1./KV['Stilde_os']
SS = SP.dot(unravel(Si,self.n,self.t).T,KV['Stilde_o'])
USU = SP.dot(KV['USi_c'],KV['Utilde_s'])
Yhat = unravel(Si * ravel(KV['UYtildeU_os']),self.n,self.t)
RV = {}
if 'X_s' in hyperparams:
USUY = SP.dot(USU,Yhat.T)
USUYSYUSU = SP.dot(USUY,(KV['Stilde_o']*USUY).T)
LMLgrad = SP.zeros((self.t,self.covar_s.n_dimensions))
LMLgrad_det = SP.zeros((self.t,self.covar_s.n_dimensions))
LMLgrad_quad = SP.zeros((self.t,self.covar_s.n_dimensions))
for d in xrange(self.covar_s.n_dimensions):
Kd_grad = self.covar_s.Kgrad_x(hyperparams['covar_s'],d)
UsU = SP.dot(Kd_grad.T,USU)*USU
LMLgrad_det[:,d] = SP.dot(UsU,SS.T)
# calculate gradient of squared form
LMLgrad_quad[:,d] = -(USUYSYUSU*Kd_grad).sum(0)
LMLgrad = LMLgrad_det + LMLgrad_quad
RV['X_s'] = LMLgrad
if debugging:
_LMLgrad = SP.zeros((self.t,self.covar_s.n_dimensions))
for t in xrange(self.t):
for d in xrange(self.covar_s.n_dimensions):
Kgrad_x = self.covar_s.Kgrad_x(hyperparams['covar_s'],d,t)
Kgrad_x = SP.kron(Kgrad_x,KV['K_o'])
_LMLgrad[t,d] = 0.5*(KV['W']*Kgrad_x).sum()
assert SP.allclose(LMLgrad,_LMLgrad,rtol=1E-3,atol=1E-2), 'ouch, something is wrong: %.2f'%LA.norm(LMLgrad-_LMLgrad)
if 'covar_s' in hyperparams:
theta = SP.zeros(len(hyperparams['covar_s']))
for i in range(len(theta)):
Kgrad_s = self.covar_s.Kgrad_theta(hyperparams['covar_s'],i)
UdKU = SP.dot(USU.T, SP.dot(Kgrad_s, USU))
SYUdKU = SP.dot(UdKU,KV['Stilde_o'] * Yhat.T)
LMLgrad_det = SP.sum(Si*SP.kron(SP.diag(UdKU),KV['Stilde_o']))
LMLgrad_quad = -(Yhat.T*SYUdKU).sum()
LMLgrad = 0.5*(LMLgrad_det + LMLgrad_quad)
theta[i] = LMLgrad
if debugging:
Kd = SP.kron(Kgrad_s, KV['K_o'])
_LMLgrad = 0.5 * (KV['W']*Kd).sum()
assert SP.allclose(LMLgrad,_LMLgrad,rtol=1E-3,atol=1E-2), 'ouch, something is wrong: %.2f'%LA.norm(LMLgrad-_LMLgrad)
RV['covar_s'] = theta
return RV
开发者ID:PMBio，项目名称:pygp_kronsum，代码行数:60，代码来源:gp_kronsum.py
示例3: _LMLgrad_covar_debug
def _LMLgrad_covar_debug(self,covar):
assert self.N*self.P<2000, 'gp2kronSum:: N*P>=2000'
y = SP.reshape(self.Y,(self.N*self.P), order='F')
K = SP.kron(self.Cg.K(),self.XX)
K += SP.kron(self.Cn.K()+self.offset*SP.eye(self.P),SP.eye(self.N))
cholK = LA.cholesky(K).T
Ki = LA.cho_solve((cholK,True),SP.eye(y.shape[0]))
Kiy = LA.cho_solve((cholK,True),y)
if covar=='Cr': n_params = self.Cr.getNumberParams()
elif covar=='Cg': n_params = self.Cg.getNumberParams()
elif covar=='Cn': n_params = self.Cn.getNumberParams()
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc grad_i
if covar=='Cg':
C = self.Cg.Kgrad_param(i)
Kgrad = SP.kron(C,self.XX)
elif covar=='Cn':
C = self.Cn.Kgrad_param(i)
Kgrad = SP.kron(C,SP.eye(self.N))
#1. der of log det
RV[i] = 0.5*(Ki*Kgrad).sum()
#2. der of quad form
RV[i] -= 0.5*(Kiy*SP.dot(Kgrad,Kiy)).sum()
return RV
开发者ID:PMBio，项目名称:mtSet，代码行数:35，代码来源:gp2kronSum.py
示例4: _update_indicator
def _update_indicator(self,K,L):
""" update the indicator """
_update = {'term': self.n_terms*SP.ones((K,L)).T.ravel(),
'row': SP.kron(SP.arange(K)[:,SP.newaxis],SP.ones((1,L))).T.ravel(),
'col': SP.kron(SP.ones((K,1)),SP.arange(L)[SP.newaxis,:]).T.ravel()}
for key in _update.keys():
self.indicator[key] = SP.concatenate([self.indicator[key],_update[key]])
开发者ID:PMBio，项目名称:mtSet，代码行数:7，代码来源:mean.py
示例5: varMLE
def varMLE(self):
""" calculate inverse of fisher information """
self._update_cache()
Sr = {}
Sr['Cg'] = self.cache['Srstar']
Sr['Cn'] = SP.ones(self.N)
n_params = self.Cg.getNumberParams()+self.Cn.getNumberParams()
fisher = SP.zeros((n_params,n_params))
header = SP.zeros((n_params,n_params),dtype='|S10')
C1 = SP.zeros((self.P,self.P))
C2 = SP.zeros((self.P,self.P))
idx1 = 0
for key1 in ['Cg','Cn']:
for key1_p1 in range(self.P):
for key1_p2 in range(key1_p1,self.P):
C1[key1_p1,key1_p2] = C1[key1_p2,key1_p1] = 1
LCL1 = SP.dot(self.cache['Lc'],SP.dot(C1,self.cache['Lc'].T))
CSr1 = SP.kron(Sr[key1][:,SP.newaxis,SP.newaxis],LCL1[SP.newaxis,:])
DCSr1 = self.cache['D'][:,:,SP.newaxis]*CSr1
idx2 = 0
for key2 in ['Cg','Cn']:
for key2_p1 in range(self.P):
for key2_p2 in range(key2_p1,self.P):
C2[key2_p1,key2_p2] = C2[key2_p2,key2_p1] = 1
LCL2 = SP.dot(self.cache['Lc'],SP.dot(C2,self.cache['Lc'].T))
CSr2 = SP.kron(Sr[key2][:,SP.newaxis,SP.newaxis],LCL2[SP.newaxis,:])
DCSr2 = self.cache['D'][:,:,SP.newaxis]*CSr2
fisher[idx1,idx2] = 0.5*(DCSr1*DCSr2).sum()
header[idx1,idx2] = '%s%d%d_%s%d%d'%(key1,key1_p1,key1_p1,key2,key2_p1,key2_p2)
C2[key2_p1,key2_p2] = C2[key2_p2,key2_p1] = 0
idx2+=1
C1[key1_p1,key1_p2] = C1[key1_p2,key1_p1] = 0
idx1+=1
RV = LA.inv(fisher)
return RV,header
开发者ID:PMBio，项目名称:mtSet，代码行数:35，代码来源:gp2kronSum.py
示例6: LMLdebug
def LMLdebug(self):
"""
LML function for debug
"""
assert self.N*self.P<5000, 'gp2kronSum:: N*P>=5000'
y = SP.reshape(self.Y,(self.N*self.P), order='F')
V = SP.kron(SP.eye(self.P),self.F)
XX = SP.dot(self.Xr,self.Xr.T)
K = SP.kron(self.Cr.K(),XX)
K += SP.kron(self.Cn.K()+self.offset*SP.eye(self.P),SP.eye(self.N))
# inverse of K
cholK = LA.cholesky(K)
Ki = LA.cho_solve((cholK,False),SP.eye(self.N*self.P))
# Areml and inverse
Areml = SP.dot(V.T,SP.dot(Ki,V))
cholAreml = LA.cholesky(Areml)
Areml_i = LA.cho_solve((cholAreml,False),SP.eye(self.K*self.P))
# effect sizes and z
b = SP.dot(Areml_i,SP.dot(V.T,SP.dot(Ki,y)))
z = y-SP.dot(V,b)
Kiz = SP.dot(Ki,z)
# lml
lml = y.shape[0]*SP.log(2*SP.pi)
lml += 2*SP.log(SP.diag(cholK)).sum()
lml += 2*SP.log(SP.diag(cholAreml)).sum()
lml += SP.dot(z,Kiz)
lml *= 0.5
return lml
开发者ID:PMBio，项目名称:mtSet，代码行数:35，代码来源:gp2kronSumLR.py
示例7: diag_Ctilde_o_Sr
def diag_Ctilde_o_Sr(self, i):
if i < self.Cg.getNumberParams():
r = sp.kron(sp.diag(self.LcGradCgLc(i)), self.Sr())
else:
_i = i - self.Cg.getNumberParams()
r = sp.kron(sp.diag(self.LcGradCnLc(_i)), sp.ones(self.dim_r))
return r
开发者ID:jeffhsu3，项目名称:limix，代码行数:7，代码来源:cov2kronSum.py
示例8: sqrtm3
def sqrtm3(X):
M = sp.copy(X)
m, fb, fe = block_structure(M)
n = M.shape[0]
for i in range(0,m):
M[fb[i]:fe[i],fb[i]:fe[i]] = twobytworoot(M[fb[i]:fe[i],fb[i]:fe[i]])
#print M
for j in range(1,m):
for i in range(0,m-j):
#print M[fb[i]:fe[i],fb[JJ]:fe[JJ]]
JJ = i+j
Tnoto = M[fb[i]:fe[i],fb[JJ]:fe[JJ]] #dopo togliere il copy
#print "Tnot: "
#print Tnoto
for k in range(i+1,JJ):
Tnoto -= (M[fb[i]:fe[i],fb[k]:fe[k]]).dot(M[fb[k]:fe[k],fb[JJ]:fe[JJ]])
#print M[fb[i]:fe[i],fb[k]:fe[k]]
#print M[fb[k]:fe[k],fb[JJ]:fe[JJ]]
if((M[fb[i]:fe[i],fb[JJ]:fe[JJ]]).shape==(1,1)):
#print "forma 1"
#print M[fb[i]:fe[i],fb[JJ]:fe[JJ]] # Uij
#print M[fb[i]:fe[i],fb[i]:fe[i]] # Uii
#print M[fb[JJ]:fe[JJ],fb[JJ]:fe[JJ]] # Ujj
M[fb[i]:fe[i],fb[JJ]:fe[JJ]] = Tnoto/(M[fb[i]:fe[i],fb[i]:fe[i]] + M[fb[JJ]:fe[JJ],fb[JJ]:fe[JJ]])
else:
Uii = M[fb[i]:fe[i],fb[i]:fe[i]]
Ujj = M[fb[JJ]:fe[JJ],fb[JJ]:fe[JJ]]
shapeUii = Uii.shape[0]
shapeUjj = Ujj.shape[0]
"""
print "------------"
print Tnoto
print Tnoto.shape
print sp.kron(sp.eye(shapeUjj),Uii)
print sp.kron(Ujj.T,sp.eye(shapeUii))
print Tnoto
"""
#M[fb[i]:fe[i],fb[JJ]:fe[JJ]] = sp.linalg.solve_sylvester(Uii, Ujj, Tnoto)
"""
x, scale, info = dtrsyl(Uii, Ujj, Tnoto
if (scale==1.0):
= x
else:
M[fb[i]:fe[i],fb[JJ]:fe[JJ]] = x*scale
print "scale!=0"
"""
Tnoto = Tnoto.reshape((shapeUii*shapeUjj),1,order="F")
M[fb[i]:fe[i],fb[JJ]:fe[JJ]] = \
linalg.solve(sp.kron(sp.eye(shapeUjj),Uii) +
sp.kron(Ujj.T,sp.eye(shapeUii)),
Tnoto).reshape(shapeUii,shapeUjj,order="F")
return M
开发者ID:sn1p3r46，项目名称:Tiro，代码行数:60，代码来源:sqrtm3.py
示例9: AlphaBetaCoeffs_old
def AlphaBetaCoeffs_old(n, a, b):
" Construct the alpha and beta coefficient matrices. "
Z = sp.matrix([[1,0],[0,-1]])
I = sp.identity(2)
alpha = sp.zeros((2**n,2**n))
beta = sp.zeros((2**n,2**n))
m1 = []
m2 = []
for i in range(0,n):
for m in range(0,n-1): m1.append(I)
m1.insert(i, Z)
temp1 = m1[0]
m1.pop(0)
while (len(m1) != 0):
temp1 = sp.kron(temp1, m1[0])
m1.pop(0)
alpha += temp1*a[i]
for j in range(i+1, n):
for m in range(0, n-2): m2.append(I)
m2.insert(i, Z)
m2.insert(j, Z)
temp2 = m2[0]
m2.pop(0)
while (len(m2) != 0):
temp2 = sp.kron(temp2, m2[0])
m2.pop(0)
beta += (temp2)*b[i,j]
return [alpha, beta]
开发者ID:Roger-luo，项目名称:AdiaQC，代码行数:29，代码来源:initialize.py
示例10: _LMLgrad_covar
def _LMLgrad_covar(self,hyperparams,debugging=False):
"""
evaluates the gradient of the log marginal likelihood with respect to the
hyperparameters of the covariance function
"""
try:
KV = self.get_covariances(hyperparams,debugging=debugging)
except LA.LinAlgError:
LG.error('linalg exception in _LMLgrad_covar')
return {'covar_r':SP.zeros(len(hyperparams['covar_r'])),'covar_c':SP.zeros(len(hyperparams['covar_c'])),'covar_r':SP.zeros(len(hyperparams['covar_r']))}
except ValueError:
LG.error('value error in _LMLgrad_covar')
return {'covar_r':SP.zeros(len(hyperparams['covar_r'])),'covar_c':SP.zeros(len(hyperparams['covar_c'])),'covar_r':SP.zeros(len(hyperparams['covar_r']))}
RV = {}
Si = unravel(1./KV['S'],self.n,self.t)
if 'covar_r' in hyperparams:
theta = SP.zeros(len(hyperparams['covar_r']))
for i in range(len(theta)):
Kgrad_r = self.covar_r.Kgrad_theta(hyperparams['covar_r'],i)
d=(KV['U_r']*SP.dot(Kgrad_r,KV['U_r'])).sum(0)
LMLgrad_det = SP.dot(d,SP.dot(Si,KV['S_c']))
UdKU = SP.dot(KV['U_r'].T,SP.dot(Kgrad_r,KV['U_r']))
SYUdKU = SP.dot(UdKU,(KV['Ytilde']*SP.tile(KV['S_c'][SP.newaxis,:],(self.n,1))))
LMLgrad_quad = - (KV['Ytilde']*SYUdKU).sum()
LMLgrad = 0.5*(LMLgrad_det + LMLgrad_quad)
theta[i] = LMLgrad
if debugging:
Kd = SP.kron(KV['K_c'], Kgrad_r)
_LMLgrad = 0.5 * (KV['W']*Kd).sum()
assert SP.allclose(LMLgrad,_LMLgrad), 'ouch, gradient is wrong for covar_r'
RV['covar_r'] = theta
if 'covar_c' in hyperparams:
theta = SP.zeros(len(hyperparams['covar_c']))
for i in range(len(theta)):
Kgrad_c = self.covar_c.Kgrad_theta(hyperparams['covar_c'],i)
d=(KV['U_c']*SP.dot(Kgrad_c,KV['U_c'])).sum(0)
LMLgrad_det = SP.dot(KV['S_r'],SP.dot(Si,d))
UdKU = SP.dot(KV['U_c'].T,SP.dot(Kgrad_c,KV['U_c']))
SYUdKU = SP.dot((KV['Ytilde']*SP.tile(KV['S_r'][:,SP.newaxis],(1,self.t))),UdKU.T)
LMLgrad_quad = -SP.sum(KV['Ytilde']*SYUdKU)
LMLgrad = 0.5*(LMLgrad_det + LMLgrad_quad)
theta[i] = LMLgrad
if debugging:
Kd = SP.kron(Kgrad_c, KV['K_r'])
_LMLgrad = 0.5 * (KV['W']*Kd).sum()
assert SP.allclose(LMLgrad,_LMLgrad), 'ouch, gradient is wrong for covar_c'
RV['covar_c'] = theta
return RV
开发者ID:PMBio，项目名称:pygp_kronsum，代码行数:58，代码来源:gp_kronprod.py
示例11: get_linds
def get_linds(N, eps):
#Lindblad operators must have same range as Hamiltonian terms. In this case they are nearest-neighbour.
Sp1 = (sp.kron(Sp, sp.eye(2))).reshape(2, 2, 2, 2)
Sm2 = (sp.kron(sp.eye(2), Sm)).reshape(2, 2, 2, 2)
L1 = (1, sp.sqrt(eps) * Sp1)
L2 = (N-1, sp.sqrt(eps) * Sm2)
return [L1, L2]
开发者ID:mlewe，项目名称:evoMPS，代码行数:9，代码来源:XXZ_edge_driven.py
示例12: _fit
def _fit(self, type, vc=False):
#2. init
if type=='null':
self.gp[type].covar.Cg.setCovariance(0.5*self.covY)
self.gp[type].covar.Cn.setCovariance(0.5*self.covY)
elif type=='full':
Cg0_K = self.gp['null'].covar.Cg.K()
Cn0_K = self.gp['null'].covar.Cn.K()
self.gp[type].covar.Cr.setCovariance((Cn0_K+Cg0_K)/3.)
self.gp[type].covar.Cg.setCovariance(2.*Cg0_K/3.)
self.gp[type].covar.Cn.setCovariance(2.*Cn0_K/3.)
elif type=='block':
Crf_K = self.gp['full'].covar.Cr.K()
Cnf_K = self.gp['full'].covar.Cn.K()
self.gp[type].covar.Cr.scale = sp.mean(Crf_K)
self.gp[type].covar.Cn.setCovariance(Cnf_K)
elif type=='rank1':
Crf_K = self.gp['full'].covar.Cr.K()
Cnf_K = self.gp['full'].covar.Cn.K()
self.gp[type].covar.Cr.setCovariance(Crf_K)
self.gp[type].covar.Cn.setCovariance(Cnf_K)
else:
print('poppo')
self.gp[type].optimize(factr=self.factr, verbose=False)
RV = {'Cg': self.gp[type].covar.Cg.K(),
'Cn': self.gp[type].covar.Cn.K(),
'LML': sp.array([self.gp[type].LML()]),
'LMLgrad': sp.array([sp.mean((self.gp[type].LML_grad()['covar'])**2)])}
if type=='null': RV['Cr'] = sp.zeros(RV['Cg'].shape)
else: RV['Cr'] = self.gp[type].covar.Cr.K()
if vc:
# tr(P CoR) = tr(C)tr(R) - tr(Ones C) tr(Ones R) / float(NP)
# = tr(C)tr(R) - C.sum() * R.sum() / float(NP)
trRr = (self.Xr**2).sum()
var_r = sp.trace(RV['Cr'])*trRr / float(self.Y.size-1)
var_g = sp.trace(RV['Cg'])*self.trRg / float(self.Y.size-1)
var_n = sp.trace(RV['Cn'])*self.Y.shape[0]
var_n-= RV['Cn'].sum() / float(RV['Cn'].shape[0])
var_n/= float(self.Y.size-1)
RV['var'] = sp.array([var_r, var_g, var_n])
if 0 and self.Y.size<5000:
pdb.set_trace()
Kr = sp.kron(RV['Cr'], sp.dot(self.Xr, self.Xr.T))
Kn = sp.kron(RV['Cn'], sp.eye(self.Y.shape[0]))
_var_r = sp.trace(Kr-Kr.mean(0)) / float(self.Y.size-1)
_var_n = sp.trace(Kn-Kn.mean(0)) / float(self.Y.size-1)
_var = sp.array([_var_r, var_g, _var_n])
print(((_var-RV['var'])**2).mean())
if type=='full':
# calculate within region vcs
Cr_block = sp.mean(RV['Cr']) * sp.ones(RV['Cr'].shape)
Cr_rank1 = lowrank_approx(RV['Cr'], rank=1)
var_block = sp.trace(Cr_block)*trRr / float(self.Y.size-1)
var_rank1 = sp.trace(Cr_rank1)*trRr / float(self.Y.size-1)
RV['var_r'] = sp.array([var_block, var_rank1-var_block, var_r-var_rank1])
return RV
开发者ID:PMBio，项目名称:limix，代码行数:56，代码来源:mvSetFull.py
示例13: distanz
def distanz(x, y=None):
r"""
Calculate the distanz between two colon vectors
Parameters
----------
x : ndarray
First colon vector
y : ndarray
Second colon vector
Returns
-------
d : ndarray
Distance between x and y
Examples
--------
>>> import numpy as np
>>> from pygsp import utils
>>> x = np.random.rand(16)
>>> y = np.random.rand(16)
>>> distanz = utils.distanz(x, y)
"""
try:
x.shape[1]
except IndexError:
x = x.reshape(1, x.shape[0])
if y is None:
y = x
else:
try:
y.shape[1]
except IndexError:
y = y.reshape(1, y.shape[0])
rx, cx = x.shape
ry, cy = y.shape
# Size verification
if rx != ry:
raise("The sizes of x and y do not fit")
xx = (x*x).sum(axis=0)
yy = (y*y).sum(axis=0)
xy = np.dot(x.T, y)
d = abs(sp.kron(sp.ones((cy, 1)), xx).T +
sp.kron(sp.ones((cx, 1)), yy) - 2*xy)
return np.sqrt(d)
开发者ID:wangg12，项目名称:pygsp，代码行数:54，代码来源:utils.py
示例14: test_inv
def test_inv(self):
C = self.C
L = sp.kron(C.Lc(), sp.eye(C.dim_r))
W = sp.kron(C.Wc(), C.Wr())
WdW = sp.dot(W.T, C.d()[:, sp.newaxis] * W)
I_WdW = sp.eye(C.dim_c * C.dim_r) - WdW
inv1 = sp.dot(L.T, sp.dot(I_WdW, L))
inv2 = C.inv()
np.testing.assert_array_almost_equal(inv1, inv2)
开发者ID:PMBio，项目名称:limix，代码行数:11，代码来源:test_cov2kronSumLR.py
示例15: diag_Ctilde_o_Sr
def diag_Ctilde_o_Sr(self, i):
np_r = self.Cr.getNumberParams()
np_g = self.Cg.getNumberParams()
if i < np_r:
r = sp.kron(sp.diag(self.LcGradCrLc(i)), self.diagWrWr())
elif i < (np_r + np_g):
_i = i - np_r
r = sp.kron(sp.diag(self.LcGradCgLc(_i)), self.Sr())
else:
_i = i - np_r - np_g
r = sp.kron(sp.diag(self.LcGradCnLc(_i)), sp.ones(self.dim_r))
return r
开发者ID:BioinformaticsArchive，项目名称:limix，代码行数:12，代码来源:cov3kronSumLR.py
示例16: get_full_op
def get_full_op(op):
fop = sp.zeros((2**N, 2**N), dtype=sp.complex128)
for n in range(1, min(len(op), N + 1)):
if op[n] is None:
continue
n_sites = len(op[n].shape) / 2
opn = op[n].reshape(2**n_sites, 2**n_sites)
fop_n = sp.kron(sp.eye(2**(n - 1)), sp.kron(opn, sp.eye(2**(N - n - n_sites + 1))))
assert fop.shape == fop_n.shape
fop += fop_n
return fop
开发者ID:mlewe，项目名称:evoMPS，代码行数:13，代码来源:XXZ_edge_driven.py
示例17: IsingHamiltonian_old
def IsingHamiltonian_old(n, h, J, g):
### Construct Hamiltonian ###
Z = sp.matrix([[1,0],[0,-1]])
X = sp.matrix([[0,1],[1,0]])
I = sp.identity(2)
alpha = sp.zeros((2**n,2**n))
beta = sp.zeros((2**n,2**n))
delta = sp.zeros((2**n,2**n))
matrices = []
# Calculate alpha
for i in range(0,n):
for m in range(0,n-1):
matrices.append(I)
matrices.insert(i, Z)
temp = matrices[0]
matrices.pop(0)
while (len(matrices) != 0):
temp = sp.kron(temp, matrices[0])
matrices.pop(0)
alpha = alpha + temp*h[i]
temp = 0
# Calculate beta
for i in range(0,n):
for j in range(0,n):
if (i != j):
for m in range(0,n-2):
matrices.append(I)
matrices.insert(i, Z)
matrices.insert(j, Z)
temp = matrices[0]
matrices.pop(0)
while (len(matrices) != 0):
temp = sp.kron(temp, matrices[0])
matrices.pop(0)
beta = beta + temp*J[i,j]
beta = beta + g*sp.identity(2**n)
temp = 0
# Calculate delta
for i in range(0,n) :
for m in range(0,n-1):
matrices.append(I)
matrices.insert(i, X)
temp = matrices[0]
matrices.pop(0)
while (len(matrices) != 0):
temp = sp.kron(temp, matrices[0])
matrices.pop(0)
delta += temp
return [alpha, beta, delta]
开发者ID:Roger-luo，项目名称:AdiaQC，代码行数:49，代码来源:initialize.py
示例18: LW
def LW(self):
R = sp.zeros((self.mean.Y.size, self.mean.n_covs))
istart = 0
for ti in range(self.mean.n_terms):
iend = istart + self.mean.F[ti].shape[1] * self.mean.A[ti].shape[0]
R[:, istart:iend] = sp.kron(self.ALc()[ti].T, self.LrF()[ti])
return R
开发者ID:jeffhsu3，项目名称:limix，代码行数:7，代码来源:gp2kronSum.py
示例19: _LML_covar
def _LML_covar(self, hyperparams):
#calculate marginal likelihood of kronecker GP
#1. get covariance structures needed:
try:
KV = self.get_covariances(hyperparams)
except linalg.LinAlgError:
LG.error("exception caught (%s)" % (str(hyperparams)))
return 1E6
#2. build lml
LML = 0
LMLc = 0.5* self.nd * SP.log(2.0 * SP.pi)
#constant part of negative lml
#quadratic form
Si = KV['Si']
LMLq = 0.5 * SP.dot(KV['y_rot'].ravel(),KV['YSi'].ravel() )
#determinant stuff
LMLd = -0.5 * SP.log(Si).sum()
if VERBOSE:
print "costly verbose debugging on"
K = SP.kron(KV['Kr'],KV['Kc']) + SP.diag(KV['Knoise'])
Ki = SP.linalg.inv(K)
LMLq_ = 0.5* SP.dot(SP.dot(self.y.ravel(),Ki),self.y.ravel())
LMLd_ = 0.5* 2 * SP.log(SP.linalg.cholesky(K).diagonal()).sum()
check_dist(LMLq,LMLq_)
check_dist(LMLd,LMLd_)
return LMLc+LMLq+LMLd
开发者ID:MMesbahU，项目名称:limix，代码行数:31，代码来源:kronecker_gplvm.py
示例20: WcCtildeLcA_o_WrRF
def WcCtildeLcA_o_WrRF(self, i):
if i < self.covar.Cr.getNumberParams():
self.WrRF = self.WrGGF()
else:
self.WrRF = self.WrF()
WcCtildeLcA = sp.dot(self.covar.Wc(), self.CtildeLcA(i))
return sp.kron(WcCtildeLcA, self.WrRF)
开发者ID:mennowitteveen，项目名称:limix，代码行数:7，代码来源:gp2kronSumLR.py
注：本文中的scipy.kron函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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