def LML(self, params=None, *kw_args):
"""
calculate LML
"""
if params is not None:
self.setParams(params)
self._update_cache()
start = TIME.time()
#1. const term
lml = self.N * self.P * SP.log(2 * SP.pi)
#2. logdet term
lml += SP.sum(SP.log(self.cache['Sc2'])) * self.N + SP.log(
self.cache['s']).sum()
lml += 2 * SP.log(SP.diag(self.cache['cholB'])).sum()
#3. quatratic term
lml += (self.cache['LY'] * self.cache['DLY']).sum()
lml -= SP.dot(self.cache['z'], self.cache['Biz'])
lml *= 0.5
smartSum(self.time, 'lml', TIME.time() - start)
smartSum(self.count, 'lml', 1)
return lml
def LML(self,params=None,*kw_args):
"""
calculate LML
"""
if params is not None:
self.setParams(params)
self._update_cache()
start = TIME.time()
#1. const term
lml = self.N*self.P*SP.log(2*SP.pi)
#2. logdet term
lml += SP.sum(SP.log(self.cache['s']))
lml += self.S*SP.sum(SP.log(self.cache['Se']))
lml += self.rank*SP.sum(SP.log(self.cache['Sg']))
lml += self.N*SP.sum(SP.log(self.cache['Sn']))
#3. quatratic term
lml += SP.sum(self.cache['LZ']*self.cache['LZ'])
lml -= SP.sum(self.cache['WLZ']*self.cache['DWLZ'])
#4. reml term
if self.F is not None:
lml += 2*SP.log(SP.diag(self.cache['Areml_chol'])).sum()
lml *= 0.5
smartSum(self.time,'lml',TIME.time()-start)
smartSum(self.count,'lml',1)
return lml
def LML(self,params=None,*kw_args):
"""
calculate LML
"""
if params is not None:
self.setParams(params)
self._update_cache()
start = TIME.time()
#1. const term
lml = self.N*self.P*SP.log(2*SP.pi)
#2. logdet term
lml += SP.sum(SP.log(self.cache['Sc2']))*self.N + SP.log(self.cache['s']).sum()
lml += 2*SP.log(SP.diag(self.cache['cholB'])).sum()
#3. quatratic term
lml += (self.cache['LY']*self.cache['DLY']).sum()
lml -= SP.dot(self.cache['z'],self.cache['Biz'])
lml *= 0.5
smartSum(self.time,'lml',TIME.time()-start)
smartSum(self.count,'lml',1)
return lml
def _update_cache(self):
"""
Update cache
"""
cov_params_have_changed = self.Cg.params_have_changed or self.Cn.params_have_changed
if self.XX_has_changed:
start = TIME.time()
""" Row SVD Bg + Noise """
self.cache['Srstar'],Urstar = LA.eigh(self.XX)
self.cache['Lr'] = Urstar.T
self.mean.setRowRotation(Lr=self.cache['Lr'])
smartSum(self.time,'cache_XXchanged',TIME.time()-start)
smartSum(self.count,'cache_XXchanged',1)
if cov_params_have_changed:
start = TIME.time()
""" Col SVD Bg + Noise """
S2,U2 = LA.eigh(self.Cn.K()+self.offset*SP.eye(self.P))
self.cache['Sc2'] = S2
US2 = SP.dot(U2,SP.diag(SP.sqrt(S2)))
USi2 = SP.dot(U2,SP.diag(SP.sqrt(1./S2)))
Cstar = SP.dot(USi2.T,SP.dot(self.Cg.K(),USi2))
self.cache['Scstar'],Ucstar = LA.eigh(Cstar)
self.cache['Lc'] = SP.dot(Ucstar.T,USi2.T)
""" pheno """
self.mean.setColRotation(self.cache['Lc'])
if cov_params_have_changed or self.XX_has_changed:
""" S """
self.cache['s'] = SP.kron(self.cache['Scstar'],self.cache['Srstar'])+1
self.cache['d'] = 1./self.cache['s']
self.cache['D'] = SP.reshape(self.cache['d'],(self.N,self.P), order='F')
""" pheno """
self.cache['LY'] = self.mean.evaluate()
self.cache['DLY'] = self.cache['D']*self.cache['LY']
smartSum(self.time,'cache_colSVDpRot',TIME.time()-start)
smartSum(self.count,'cache_colSVDpRot',1)
self.XX_has_changed = False
self.Cg.params_have_changed = False
self.Cn.params_have_changed = False
def _update_cache(self):
"""
Update cache
"""
cov_params_have_changed = self.Cg.params_have_changed or self.Cn.params_have_changed
if self.XX_has_changed:
start = TIME.time()
""" Row SVD Bg + Noise """
self.cache['Srstar'], Urstar = LA.eigh(self.XX)
self.cache['Lr'] = Urstar.T
self.mean.setRowRotation(Lr=self.cache['Lr'])
smartSum(self.time, 'cache_XXchanged', TIME.time() - start)
smartSum(self.count, 'cache_XXchanged', 1)
if cov_params_have_changed:
start = TIME.time()
""" Col SVD Bg + Noise """
S2, U2 = LA.eigh(self.Cn.K() + self.offset * SP.eye(self.P))
self.cache['Sc2'] = S2
US2 = SP.dot(U2, SP.diag(SP.sqrt(S2)))
USi2 = SP.dot(U2, SP.diag(SP.sqrt(1. / S2)))
Cstar = SP.dot(USi2.T, SP.dot(self.Cg.K(), USi2))
self.cache['Scstar'], Ucstar = LA.eigh(Cstar)
self.cache['Lc'] = SP.dot(Ucstar.T, USi2.T)
""" pheno """
self.mean.setColRotation(self.cache['Lc'])
if cov_params_have_changed or self.XX_has_changed:
""" S """
self.cache['s'] = SP.kron(self.cache['Scstar'],
self.cache['Srstar']) + 1
self.cache['d'] = 1. / self.cache['s']
self.cache['D'] = SP.reshape(self.cache['d'], (self.N, self.P),
order='F')
""" pheno """
self.cache['LY'] = self.mean.evaluate()
self.cache['DLY'] = self.cache['D'] * self.cache['LY']
smartSum(self.time, 'cache_colSVDpRot', TIME.time() - start)
smartSum(self.count, 'cache_colSVDpRot', 1)
self.XX_has_changed = False
self.Cg.params_have_changed = False
self.Cn.params_have_changed = False
def _LMLgrad_covar(self,covar,**kw_args):
"""
calculates LMLgrad for covariance parameters
"""
# precompute some stuff
if covar=='Cg':
LRLdiag = self.cache['Srstar']
n_params = self.Cg.getNumberParams()
elif covar=='Cn':
LRLdiag = SP.ones(self.N)
n_params = self.Cn.getNumberParams()
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
start = TIME.time()
if covar=='Cr': C = self.Cr.Kgrad_param(i)
elif covar=='Cg': C = self.Cg.Kgrad_param(i)
elif covar=='Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'],SP.dot(C,self.cache['Lc'].T))
#1. der of log det
start = TIME.time()
kronDiag = SP.kron(LCL.diagonal(),LRLdiag)
RV[i] = SP.dot(self.cache['d'],kronDiag)
smartSum(self.time,'lmlgrad_trace',TIME.time()-start)
smartSum(self.count,'lmlgrad_trace',1)
#2. der of quad form
start = TIME.time()
KDLY = LRLdiag[:,SP.newaxis]*SP.dot(self.cache['DLY'],LCL.T)
RV[i] -= (self.cache['DLY']*KDLY).sum()
smartSum(self.time,'lmlgrad_quadform',TIME.time()-start)
smartSum(self.count,'lmlgrad_quadform',1)
RV[i] *= 0.5
return RV
def _LMLgrad_covar(self, covar, **kw_args):
"""
calculates LMLgrad for covariance parameters
"""
# precompute some stuff
if covar == 'Cg':
LRLdiag = self.cache['Srstar']
n_params = self.Cg.getNumberParams()
elif covar == 'Cn':
LRLdiag = SP.ones(self.N)
n_params = self.Cn.getNumberParams()
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
start = TIME.time()
if covar == 'Cr': C = self.Cr.Kgrad_param(i)
elif covar == 'Cg': C = self.Cg.Kgrad_param(i)
elif covar == 'Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'], SP.dot(C, self.cache['Lc'].T))
#1. der of log det
start = TIME.time()
kronDiag = SP.kron(LCL.diagonal(), LRLdiag)
RV[i] = SP.dot(self.cache['d'], kronDiag)
smartSum(self.time, 'lmlgrad_trace', TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace', 1)
#2. der of quad form
start = TIME.time()
KDLY = LRLdiag[:, SP.newaxis] * SP.dot(self.cache['DLY'], LCL.T)
RV[i] -= (self.cache['DLY'] * KDLY).sum()
smartSum(self.time, 'lmlgrad_quadform', TIME.time() - start)
smartSum(self.count, 'lmlgrad_quadform', 1)
RV[i] *= 0.5
return RV
def _LMLgrad_covar(self, covar, **kw_args):
"""
calculates LMLgrad for covariance parameters
"""
start = TIME.time()
# precompute some stuff
if covar == 'Cr': n_params = self.Cr.getNumberParams()
elif covar == 'Cg': n_params = self.Cg.getNumberParams()
elif covar == 'Cn': n_params = self.Cn.getNumberParams()
KDW = SP.zeros_like(self.cache['DW'])
if covar == 'Cr':
#_KDWt = NP.einsum('ij,ilk->jlk',self.cache['LXr'],self.cache['DWt'])
#_KDWt = NP.einsum('ij,jlk->ilk',self.cache['LXr'],_KDWt)
_KDWt = NP.tensordot(self.cache['LXr'],
self.cache['DWt'],
axes=(0, 0))
_KDWt = NP.tensordot(self.cache['LXr'], _KDWt, axes=(1, 0))
_KDLYpDLXBiz = SP.dot(self.cache['LXr'].T,
self.cache['DLYpDLXBiz'])
_KDLYpDLXBiz = SP.dot(self.cache['LXr'], _KDLYpDLXBiz)
LRLdiag = (self.cache['LXr']**2).sum(1)
elif covar == 'Cg':
_KDWt = self.cache['Srstar'][:, SP.newaxis,
SP.newaxis] * self.cache['DWt']
_KDLYpDLXBiz = self.cache['Srstar'][:, SP.newaxis] * self.cache[
'DLYpDLXBiz']
LRLdiag = self.cache['Srstar']
else:
_KDWt = self.cache['DWt']
_KDLYpDLXBiz = self.cache['DLYpDLXBiz']
LRLdiag = SP.ones(self.N)
smartSum(self.time, 'lmlgrad_trace2_rKDW_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace2_rKDW_%s' % covar, 1)
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
if covar == 'Cr': C = self.Cr.Kgrad_param(i)
elif covar == 'Cg': C = self.Cg.Kgrad_param(i)
elif covar == 'Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'], SP.dot(C, self.cache['Lc'].T))
#1. der of log det
start = TIME.time()
kronDiag = SP.kron(LCL.diagonal(), LRLdiag)
RV[i] = SP.dot(self.cache['d'], kronDiag)
smartSum(self.time, 'lmlgrad_trace1_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace1_%s' % covar, 1)
start = TIME.time()
#KDWt = NP.einsum('ijk,jl->ilk',_KDWt,LCL)
KDWt = NP.tensordot(_KDWt, LCL, axes=(1, 0))
smartSum(self.time, 'lmlgrad_trace2_cKDW_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace2_cKDW_%s' % covar, 1)
start = TIME.time()
#DKDWt = NP.einsum('ij,ijk->ijk',self.cache['D'],KDWt)
#WDKDWt = NP.einsum('ijk,jl->ilk',DKDWt, self.cache['LAc'])
#WDKDWt = NP.einsum('ij,ilk->jlk',self.cache['LXr'],WDKDWt)
DKDWt = self.cache['D'][:, SP.newaxis, :] * KDWt
WDKDWt = NP.tensordot(DKDWt, self.cache['LAc'], axes=(2, 0))
WDKDWt = NP.tensordot(self.cache['LXr'], WDKDWt, axes=(0, 0))
WDKDWt = NP.transpose(WDKDWt, (0, 2, 1))
WDKDW = WDKDWt.reshape((self.rank * self.S, self.rank * self.S),
order='F')
smartSum(self.time, 'lmlgrad_trace2_WDKDW_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace2_WDKDW_%s' % covar, 1)
RV[i] -= (WDKDW * self.cache['Bi']).sum()
#2. der of quad form
start = TIME.time()
KDLYpDLXBiz = SP.dot(_KDLYpDLXBiz, LCL.T)
RV[i] -= (self.cache['DLYpDLXBiz'] * KDLYpDLXBiz).sum()
smartSum(self.time, 'lmlgrad_quadForm_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_quadForm_%s' % covar, 1)
RV[i] *= 0.5
return RV
def _update_cache(self):
"""
Update cache
"""
cov_params_have_changed = self.Cr.params_have_changed or self.Cg.params_have_changed or self.Cn.params_have_changed
if self.XX_has_changed:
start = TIME.time()
""" Row SVD Bg + Noise """
self.cache['Srstar'], Urstar = LA.eigh(self.XX)
self.cache['Lr'] = Urstar.T
self.mean.setRowRotation(Lr=self.cache['Lr'])
smartSum(self.time, 'cache_XXchanged', TIME.time() - start)
smartSum(self.count, 'cache_XXchanged', 1)
if self.Xr_has_changed or self.XX_has_changed:
start = TIME.time()
""" rotate Xr and XrXr """
self.cache['LXr'] = SP.dot(self.cache['Lr'], self.Xr)
smartSum(self.time, 'cache_Xrchanged', TIME.time() - start)
smartSum(self.count, 'cache_Xrchanged', 1)
if cov_params_have_changed:
start = TIME.time()
""" Col SVD Bg + Noise """
S2, U2 = LA.eigh(self.Cn.K() + self.offset * SP.eye(self.P))
self.cache['Sc2'] = S2
US2 = SP.dot(U2, SP.diag(SP.sqrt(S2)))
USi2 = SP.dot(U2, SP.diag(SP.sqrt(1. / S2)))
Cstar = SP.dot(USi2.T, SP.dot(self.Cg.K(), USi2))
self.cache['Scstar'], Ucstar = LA.eigh(Cstar)
self.cache['Lc'] = SP.dot(Ucstar.T, USi2.T)
""" pheno """
self.mean.setColRotation(self.cache['Lc'])
""" region part """
self.cache['A'] = SP.reshape(self.Cr.getParams(),
(self.P, self.rank),
order='F')
self.cache['LAc'] = SP.dot(self.cache['Lc'], self.cache['A'])
if cov_params_have_changed or self.XX_has_changed:
""" S """
self.cache['s'] = SP.kron(self.cache['Scstar'],
self.cache['Srstar']) + 1
self.cache['d'] = 1. / self.cache['s']
self.cache['D'] = SP.reshape(self.cache['d'], (self.N, self.P),
order='F')
""" pheno """
self.cache['LY'] = self.mean.evaluate()
self.cache['DLY'] = self.cache['D'] * self.cache['LY']
smartSum(self.time, 'cache_colSVDpRot', TIME.time() - start)
smartSum(self.count, 'cache_colSVDpRot', 1)
if cov_params_have_changed or self.XX_has_changed or self.Xr_has_changed:
""" calculate B = I + kron(LcA,LrXr).T*D*kron(kron(LcA,LrXr)) """
start = TIME.time()
W = SP.kron(self.cache['LAc'], self.cache['LXr'])
self.cache['DW'] = W * self.cache['d'][:, SP.newaxis]
self.cache['DWt'] = self.cache['DW'].reshape(
(self.N, self.P, self.rank * self.S), order='F')
#B = NP.einsum('ijk,jl->ilk',self.cache['DWt'],self.cache['LAc'])
#B = NP.einsum('ji,jlk->ilk',self.cache['LXr'],B)
B = SP.tensordot(self.cache['DWt'], self.cache['LAc'], axes=(1, 0))
B = NP.transpose(B, (0, 2, 1))
B = SP.tensordot(self.cache['LXr'], B, axes=(0, 0))
B = B.reshape((self.rank * self.S, self.rank * self.S), order='F')
B += SP.eye(self.rank * self.S)
smartSum(self.time, 'cache_calcB', TIME.time() - start)
smartSum(self.count, 'cache_calcB', 1)
""" invert B """
start = TIME.time()
self.cache['cholB'] = LA.cholesky(B).T
self.cache['Bi'] = LA.cho_solve((self.cache['cholB'], True),
SP.eye(self.S * self.rank))
smartSum(self.time, 'cache_invB', TIME.time() - start)
smartSum(self.count, 'cache_invB', 1)
""" pheno """
start = TIME.time()
Z = SP.dot(self.cache['LXr'].T,
SP.dot(self.cache['DLY'], self.cache['LAc']))
self.cache['z'] = SP.reshape(Z, (self.S * self.rank), order='F')
self.cache['Biz'] = LA.cho_solve((self.cache['cholB'], True),
self.cache['z'])
BiZ = SP.reshape(self.cache['Biz'], (self.S, self.rank), order='F')
self.cache['DLYpDLXBiz'] = SP.dot(self.cache['LXr'],
SP.dot(BiZ, self.cache['LAc'].T))
self.cache['DLYpDLXBiz'] *= -self.cache['D']
self.cache['DLYpDLXBiz'] += self.cache['DLY']
smartSum(self.time, 'cache_phenoCalc', TIME.time() - start)
smartSum(self.count, 'cache_phenoCalc', 1)
self.XX_has_changed = False
self.Xr_has_changed = False
self.Y_has_changed = False
self.Cr.params_have_changed = False
self.Cg.params_have_changed = False
self.Cn.params_have_changed = False
def _LMLgrad_covar(self,covar,**kw_args):
"""
calculates LMLgrad for covariance parameters
"""
start = TIME.time()
# precompute some stuff
if covar=='Cr': n_params = self.Cr.getNumberParams()
elif covar=='Cg': n_params = self.Cg.getNumberParams()
elif covar=='Cn': n_params = self.Cn.getNumberParams()
KDW = SP.zeros_like(self.cache['DW'])
if covar=='Cr':
#_KDWt = NP.einsum('ij,ilk->jlk',self.cache['LXr'],self.cache['DWt'])
#_KDWt = NP.einsum('ij,jlk->ilk',self.cache['LXr'],_KDWt)
_KDWt = NP.tensordot(self.cache['LXr'],self.cache['DWt'],axes=(0,0))
_KDWt = NP.tensordot(self.cache['LXr'],_KDWt,axes=(1,0))
_KDLYpDLXBiz = SP.dot(self.cache['LXr'].T,self.cache['DLYpDLXBiz'])
_KDLYpDLXBiz = SP.dot(self.cache['LXr'],_KDLYpDLXBiz)
LRLdiag = (self.cache['LXr']**2).sum(1)
elif covar=='Cg':
_KDWt = self.cache['Srstar'][:,SP.newaxis,SP.newaxis]*self.cache['DWt']
_KDLYpDLXBiz = self.cache['Srstar'][:,SP.newaxis]*self.cache['DLYpDLXBiz']
LRLdiag = self.cache['Srstar']
else:
_KDWt = self.cache['DWt']
_KDLYpDLXBiz = self.cache['DLYpDLXBiz']
LRLdiag = SP.ones(self.N)
smartSum(self.time,'lmlgrad_trace2_rKDW_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_trace2_rKDW_%s'%covar,1)
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
if covar=='Cr': C = self.Cr.Kgrad_param(i)
elif covar=='Cg': C = self.Cg.Kgrad_param(i)
elif covar=='Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'],SP.dot(C,self.cache['Lc'].T))
#1. der of log det
start = TIME.time()
kronDiag = SP.kron(LCL.diagonal(),LRLdiag)
RV[i] = SP.dot(self.cache['d'],kronDiag)
smartSum(self.time,'lmlgrad_trace1_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_trace1_%s'%covar,1)
start = TIME.time()
#KDWt = NP.einsum('ijk,jl->ilk',_KDWt,LCL)
KDWt = NP.tensordot(_KDWt,LCL,axes=(1,0))
smartSum(self.time,'lmlgrad_trace2_cKDW_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_trace2_cKDW_%s'%covar,1)
start = TIME.time()
#DKDWt = NP.einsum('ij,ijk->ijk',self.cache['D'],KDWt)
#WDKDWt = NP.einsum('ijk,jl->ilk',DKDWt, self.cache['LAc'])
#WDKDWt = NP.einsum('ij,ilk->jlk',self.cache['LXr'],WDKDWt)
DKDWt = self.cache['D'][:,SP.newaxis,:]*KDWt
WDKDWt = NP.tensordot(DKDWt,self.cache['LAc'],axes=(2,0))
WDKDWt = NP.tensordot(self.cache['LXr'],WDKDWt,axes=(0,0))
WDKDWt = NP.transpose(WDKDWt,(0,2,1))
WDKDW = WDKDWt.reshape((self.rank*self.S,self.rank*self.S),order='F')
smartSum(self.time,'lmlgrad_trace2_WDKDW_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_trace2_WDKDW_%s'%covar,1)
RV[i] -= (WDKDW*self.cache['Bi']).sum()
#2. der of quad form
start = TIME.time()
KDLYpDLXBiz = SP.dot(_KDLYpDLXBiz,LCL.T)
RV[i] -= (self.cache['DLYpDLXBiz']*KDLYpDLXBiz).sum()
smartSum(self.time,'lmlgrad_quadForm_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_quadForm_%s'%covar,1)
RV[i] *= 0.5
return RV
def _update_cache(self):
"""
Update cache
"""
cov_params_have_changed = self.Cr.params_have_changed or self.Cg.params_have_changed or self.Cn.params_have_changed
if self.XX_has_changed:
start = TIME.time()
""" Row SVD Bg + Noise """
self.cache['Srstar'],Urstar = LA.eigh(self.XX)
self.cache['Lr'] = Urstar.T
self.mean.setRowRotation(Lr=self.cache['Lr'])
smartSum(self.time,'cache_XXchanged',TIME.time()-start)
smartSum(self.count,'cache_XXchanged',1)
if self.Xr_has_changed or self.XX_has_changed:
start = TIME.time()
""" rotate Xr and XrXr """
self.cache['LXr'] = SP.dot(self.cache['Lr'],self.Xr)
smartSum(self.time,'cache_Xrchanged',TIME.time()-start)
smartSum(self.count,'cache_Xrchanged',1)
if cov_params_have_changed:
start = TIME.time()
""" Col SVD Bg + Noise """
S2,U2 = LA.eigh(self.Cn.K()+self.offset*SP.eye(self.P))
self.cache['Sc2'] = S2
US2 = SP.dot(U2,SP.diag(SP.sqrt(S2)))
USi2 = SP.dot(U2,SP.diag(SP.sqrt(1./S2)))
Cstar = SP.dot(USi2.T,SP.dot(self.Cg.K(),USi2))
self.cache['Scstar'],Ucstar = LA.eigh(Cstar)
self.cache['Lc'] = SP.dot(Ucstar.T,USi2.T)
""" pheno """
self.mean.setColRotation(self.cache['Lc'])
""" region part """
self.cache['A'] = SP.reshape(self.Cr.getParams(),(self.P,self.rank),order='F')
self.cache['LAc'] = SP.dot(self.cache['Lc'],self.cache['A'])
if cov_params_have_changed or self.XX_has_changed:
""" S """
self.cache['s'] = SP.kron(self.cache['Scstar'],self.cache['Srstar'])+1
self.cache['d'] = 1./self.cache['s']
self.cache['D'] = SP.reshape(self.cache['d'],(self.N,self.P), order='F')
""" pheno """
self.cache['LY'] = self.mean.evaluate()
self.cache['DLY'] = self.cache['D']*self.cache['LY']
smartSum(self.time,'cache_colSVDpRot',TIME.time()-start)
smartSum(self.count,'cache_colSVDpRot',1)
if cov_params_have_changed or self.XX_has_changed or self.Xr_has_changed:
""" calculate B = I + kron(LcA,LrXr).T*D*kron(kron(LcA,LrXr)) """
start = TIME.time()
W = SP.kron(self.cache['LAc'],self.cache['LXr'])
self.cache['DW'] = W*self.cache['d'][:,SP.newaxis]
self.cache['DWt'] = self.cache['DW'].reshape((self.N,self.P,self.rank*self.S),order='F')
#B = NP.einsum('ijk,jl->ilk',self.cache['DWt'],self.cache['LAc'])
#B = NP.einsum('ji,jlk->ilk',self.cache['LXr'],B)
B = SP.tensordot(self.cache['DWt'],self.cache['LAc'],axes=(1,0))
B = NP.transpose(B, (0, 2, 1))
B = SP.tensordot(self.cache['LXr'],B,axes=(0,0))
B = B.reshape((self.rank*self.S,self.rank*self.S),order='F')
B+= SP.eye(self.rank*self.S)
smartSum(self.time,'cache_calcB',TIME.time()-start)
smartSum(self.count,'cache_calcB',1)
""" invert B """
start = TIME.time()
self.cache['cholB'] = LA.cholesky(B).T
self.cache['Bi'] = LA.cho_solve((self.cache['cholB'],True),SP.eye(self.S*self.rank))
smartSum(self.time,'cache_invB',TIME.time()-start)
smartSum(self.count,'cache_invB',1)
""" pheno """
start = TIME.time()
Z = SP.dot(self.cache['LXr'].T,SP.dot(self.cache['DLY'],self.cache['LAc']))
self.cache['z'] = SP.reshape(Z,(self.S*self.rank), order='F')
self.cache['Biz'] = LA.cho_solve((self.cache['cholB'],True),self.cache['z'])
BiZ = SP.reshape(self.cache['Biz'],(self.S,self.rank), order='F')
self.cache['DLYpDLXBiz'] = SP.dot(self.cache['LXr'],SP.dot(BiZ,self.cache['LAc'].T))
self.cache['DLYpDLXBiz'] *= -self.cache['D']
self.cache['DLYpDLXBiz'] += self.cache['DLY']
smartSum(self.time,'cache_phenoCalc',TIME.time()-start)
smartSum(self.count,'cache_phenoCalc',1)
self.XX_has_changed = False
self.Xr_has_changed = False
self.Y_has_changed = False
self.Cr.params_have_changed = False
self.Cg.params_have_changed = False
self.Cn.params_have_changed = False
def _LMLgrad_covar(self,covar,**kw_args):
"""
calculates LMLgrad for covariance parameters
"""
start = TIME.time()
# precompute some stuff
if covar=='Cr': n_params = self.Cr.getNumberParams()
elif covar=='Cg': n_params = self.Cg.getNumberParams()
elif covar=='Cn': n_params = self.Cn.getNumberParams()
if covar=='Cr':
trR = self.cache['trXrXr']
RY = self.cache['XrXrY']
RLY = self.cache['XrXrLY']
WrRY1 = self.cache['XrXrXrY']
WrRY2 = self.cache['XXrXrY']
WrRLY1 = self.cache['XrXrXrLY']
WrRLY2 = self.cache['XXrXrLY']
XrRXr = self.cache['XrXrXrXr']
XrRX = self.cache['XrXrXrX']
XRX = self.cache['XXrXrX']
elif covar=='Cg':
trR = self.cache['trXX']
RY = self.cache['XXY']
RLY = self.cache['XXLY']
WrRY1 = self.cache['XrXXY']
WrRY2 = self.cache['XXXY']
WrRLY1 = self.cache['XrXXLY']
WrRLY2 = self.cache['XXXLY']
XrRXr = self.cache['XrXXXr']
XrRX = self.cache['XrXXX']
XRX = self.cache['XXXX']
else:
trR = self.N
RY = self.Y
RLY = self.cache['LY']
WrRY1 = self.cache['XrY']
WrRY2 = self.cache['XY']
WrRLY1 = self.cache['XrLY']
WrRLY2 = self.cache['XLY']
XrRXr = self.cache['XrXr']
XrRX = self.cache['XXr'].T
XRX = self.cache['XX']
smartSum(self.time,'lmlgrad_trace2_rKDW_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_trace2_rKDW_%s'%covar,1)
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
if covar=='Cr': C = self.Cr.Kgrad_param(i)
elif covar=='Cg': C = self.Cg.Kgrad_param(i)
elif covar=='Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'],SP.dot(C,self.cache['Lc'].T))
ELCL = SP.dot(self.cache['Estar'].T,LCL)
ELCLE = SP.dot(ELCL,self.cache['Estar'])
ELCLCsh = SP.dot(ELCL,self.cache['CstarH'])
CshLCL = SP.dot(self.cache['CstarH'].T,LCL)
CshLCLCsh = SP.dot(CshLCL,self.cache['CstarH'])
# WCoRW
WCoRW11 = SP.kron(ELCLE,XrRXr)
WCoRW12 = SP.kron(ELCLCsh,XrRX)
WCoRW22 = SP.kron(CshLCLCsh,XRX)
WCoRW = SP.array(SP.bmat([[WCoRW11,WCoRW12],[WCoRW12.T,WCoRW22]]))
# WCoRLY
WCoRLY1 = SP.dot(WrRLY1,ELCL.T)
WCoRLY2 = SP.dot(WrRLY2,CshLCL.T)
WCoRLY = SP.concatenate([SP.reshape(WCoRLY1,(WCoRLY1.size,1),order='F'),
SP.reshape(WCoRLY2,(WCoRLY2.size,1),order='F')])
# CoRLY
CoRLY = SP.dot(RLY,LCL.T)
#1. der of log det
start = TIME.time()
trC = LCL.diagonal().sum()
RV[i] = trC*trR
RV[i]-= SP.sum(self.cache['Bi']*WCoRW)
smartSum(self.time,'lmlgrad_trace2_WDKDW_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_trace2_WDKDW_%s'%covar,1)
#2. der of quad form
start = TIME.time()
RV[i] -= SP.sum(self.cache['LY']*CoRLY)
RV[i] -= SP.sum(self.cache['BiWLY']*SP.dot(WCoRW,self.cache['BiWLY']))
RV[i] += 2*SP.sum(self.cache['BiWLY']*WCoRLY)
smartSum(self.time,'lmlgrad_quadForm_%s'%covar,TIME.time()-start)
smartSum(self.count,'lmlgrad_quadForm_%s'%covar,1)
RV[i] *= 0.5
return RV
def _LMLgrad_covar(self, covar, **kw_args):
"""
calculates LMLgrad for covariance parameters
"""
start = TIME.time()
# precompute some stuff
if covar == 'Cr': n_params = self.Cr.getNumberParams()
elif covar == 'Cg': n_params = self.Cg.getNumberParams()
elif covar == 'Cn': n_params = self.Cn.getNumberParams()
if covar == 'Cr':
trR = self.cache['trXrXr']
RY = self.cache['XrXrY']
RLY = self.cache['XrXrLY']
WrRY1 = self.cache['XrXrXrY']
WrRY2 = self.cache['XXrXrY']
WrRLY1 = self.cache['XrXrXrLY']
WrRLY2 = self.cache['XXrXrLY']
XrRXr = self.cache['XrXrXrXr']
XrRX = self.cache['XrXrXrX']
XRX = self.cache['XXrXrX']
elif covar == 'Cg':
trR = self.cache['trXX']
RY = self.cache['XXY']
RLY = self.cache['XXLY']
WrRY1 = self.cache['XrXXY']
WrRY2 = self.cache['XXXY']
WrRLY1 = self.cache['XrXXLY']
WrRLY2 = self.cache['XXXLY']
XrRXr = self.cache['XrXXXr']
XrRX = self.cache['XrXXX']
XRX = self.cache['XXXX']
else:
trR = self.N
RY = self.Y
RLY = self.cache['LY']
WrRY1 = self.cache['XrY']
WrRY2 = self.cache['XY']
WrRLY1 = self.cache['XrLY']
WrRLY2 = self.cache['XLY']
XrRXr = self.cache['XrXr']
XrRX = self.cache['XXr'].T
XRX = self.cache['XX']
smartSum(self.time, 'lmlgrad_trace2_rKDW_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace2_rKDW_%s' % covar, 1)
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
if covar == 'Cr': C = self.Cr.Kgrad_param(i)
elif covar == 'Cg': C = self.Cg.Kgrad_param(i)
elif covar == 'Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'], SP.dot(C, self.cache['Lc'].T))
ELCL = SP.dot(self.cache['Estar'].T, LCL)
ELCLE = SP.dot(ELCL, self.cache['Estar'])
ELCLCsh = SP.dot(ELCL, self.cache['CstarH'])
CshLCL = SP.dot(self.cache['CstarH'].T, LCL)
CshLCLCsh = SP.dot(CshLCL, self.cache['CstarH'])
# WCoRW
WCoRW11 = SP.kron(ELCLE, XrRXr)
WCoRW12 = SP.kron(ELCLCsh, XrRX)
WCoRW22 = SP.kron(CshLCLCsh, XRX)
WCoRW = SP.array(
SP.bmat([[WCoRW11, WCoRW12], [WCoRW12.T, WCoRW22]]))
# WCoRLY
WCoRLY1 = SP.dot(WrRLY1, ELCL.T)
WCoRLY2 = SP.dot(WrRLY2, CshLCL.T)
WCoRLY = SP.concatenate([
SP.reshape(WCoRLY1, (WCoRLY1.size, 1), order='F'),
SP.reshape(WCoRLY2, (WCoRLY2.size, 1), order='F')
])
# CoRLY
CoRLY = SP.dot(RLY, LCL.T)
#1. der of log det
start = TIME.time()
trC = LCL.diagonal().sum()
RV[i] = trC * trR
RV[i] -= SP.sum(self.cache['Bi'] * WCoRW)
smartSum(self.time, 'lmlgrad_trace2_WDKDW_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_trace2_WDKDW_%s' % covar, 1)
#2. der of quad form
start = TIME.time()
RV[i] -= SP.sum(self.cache['LY'] * CoRLY)
RV[i] -= SP.sum(self.cache['BiWLY'] *
SP.dot(WCoRW, self.cache['BiWLY']))
RV[i] += 2 * SP.sum(self.cache['BiWLY'] * WCoRLY)
smartSum(self.time, 'lmlgrad_quadForm_%s' % covar,
TIME.time() - start)
smartSum(self.count, 'lmlgrad_quadForm_%s' % covar, 1)
RV[i] *= 0.5
return RV
def _LMLgrad_covar(self,covar,**kw_args):
"""
calculates LMLgrad for covariance parameters
"""
# precompute some stuff
if covar=='Cr':
trR = self.cache['trXrXr']
RLZ = self.cache['XrXrLZ']
SrDWLZ = self.cache['SgDWLZ']
WrRLZ = self.cache['WrXrXrLZ']
diagSr = self.cache['Sg']
n_params = self.Cr.getNumberParams()
if self.F is not None:
SrDWLY = self.cache['SgDWLY']
WrRLY = self.cache['WrXrXrLY']
SrDWLV_t = self.cache['SgDWLV_t']
WrRLF = self.cache['WrXrXrLrF']
FRF = self.cache['FLrXrXrLrF']
FRLrY = self.cache['FXrXrLrY']
elif covar=='Cn':
trR = self.N
RLZ = self.cache['LZ']
SrDWLZ = self.cache['DWLZ']
WrRLZ = self.cache['WrLZ']
diagSr = SP.ones(self.S)
n_params = self.Cn.getNumberParams()
if self.F is not None:
SrDWLY = self.cache['DWLY']
WrRLY = self.cache['WrLY']
SrDWLV = self.cache['DWLV']
WrRLF = self.cache['WrLrF']
SrDWLV_t = self.cache['DWLV_t']
FRF = self.cache['FF']
FRLrY = self.cache['FY']
# fill gradient vector
RV = SP.zeros(n_params)
for i in range(n_params):
#0. calc LCL
start = TIME.time()
if covar=='Cr': C = self.Cr.Kgrad_param(i)
elif covar=='Cn': C = self.Cn.Kgrad_param(i)
LCL = SP.dot(self.cache['Lc'],SP.dot(C,self.cache['Lc'].T))
LLCLL = SP.dot(self.cache['Lc'].T,SP.dot(LCL,self.cache['Lc']))
LCLW = SP.dot(LCL,self.cache['Wc'].T)
WLCLW = SP.dot(self.cache['Wc'],LCLW)
CoRLZ = SP.dot(RLZ,LCL.T)
CoSrDWLZ = SP.dot(SrDWLZ,WLCLW.T)
WCoRLZ = SP.dot(WrRLZ,LCLW)
if self.F is not None:
WcCLcA = SP.dot(SP.dot(self.cache['Wc'],LCL),self.cache['LcA'])
CoSrDWLY = SP.dot(SrDWLY,WLCLW.T)
DCoSrDWLY = self.cache['D']*CoSrDWLY
WCoRLY = SP.dot(WrRLY,LCLW)
DWCoRLY = self.cache['D']*WCoRLY
#0a. grad of Areml
if 1:
Areml_grad = SP.dot(SP.kron(WcCLcA,WrRLF).T,self.cache['DWLV'])
else:
Areml_grad = SP.tensordot(SP.tensordot(WrRLF,self.cache['DWLV_t'],axes=(0,0)),WcCLcA,axes=(1,0))
# and then resize...
Areml_grad+= Areml_grad.T
Areml_grad-= SP.kron(LLCLL,FRF) #TODO: think about LLCLL
CoSrDWLV_t = SP.tensordot(SrDWLV_t,WLCLW,axes=(1,1))
Areml_grad-= SP.tensordot(self.cache['DWLV_t'],CoSrDWLV_t,axes=([0,1],[0,2]))
#0b. grad of beta
B_grad1 = -SP.dot(FRLrY,LLCLL)
B_grad1-= SP.dot(SP.dot(self.cache['WrLrF'].T,DCoSrDWLY),self.cache['WcLcA'])
B_grad1+= SP.dot(SP.dot(WrRLF.T,self.cache['DWLY']),WcCLcA)
B_grad1+= SP.dot(SP.dot(self.cache['WrLrF'].T,DWCoRLY),self.cache['WcLcA'])
b_grad = SP.reshape(B_grad1,(self.K*self.P,1),order='F')
b_grad-= SP.dot(Areml_grad,self.cache['b'])
b_grad = SP.dot(self.cache['Areml_inv'],b_grad)
#1. der of log det
start = TIME.time()
trC = LCL.diagonal().sum()
RV[i] = trC*trR
RV[i]-= SP.dot(self.cache['d'],SP.kron(WLCLW.diagonal(),diagSr))
smartSum(self.time,'lmlgrad_trace',TIME.time()-start)
smartSum(self.count,'lmlgrad_trace',1)
#2. der of quad form
start = TIME.time()
RV[i]-= SP.sum(self.cache['LZ']*CoRLZ)
RV[i]-= SP.sum(self.cache['DWLZ']*CoSrDWLZ)
RV[i]+= 2*SP.sum(self.cache['DWLZ']*WCoRLZ)
if self.F is not None:
RV[i]-= 2*SP.dot(self.cache['vecVKiZ'].T,b_grad)
smartSum(self.time,'lmlgrad_quadform',TIME.time()-start)
smartSum(self.count,'lmlgrad_quadform',1)
if self.F is not None:
#3. reml term
RV[i] += (self.cache['Areml_inv']*Areml_grad).sum()
RV[i] *= 0.5
return RV
def _update_cache(self):
"""
Update cache
"""
cov_params_have_changed = self.Cr.params_have_changed or self.Cn.params_have_changed
if self.Xr_has_changed:
start = TIME.time()
""" Row SVD on small matrix """
Ug,Sgh,Vg = NLA.svd(self.Xr,full_matrices=0)
I = Sgh<self.tol
if I.any():
warnings.warn('Xr has dependent columns, dimensionality reduced')
Sgh = Sgh[~I]
Ug = Ug[:,~I]
Vg = SP.eye(Sgh.shape[0])
Xr = Ug*Sgh[SP.newaxis,:]
self.set_Xr(Xr)
self.cache['Sg'] = Sgh**2
self.cache['Wr'] = Ug.T
self.cache['Vg'] = Vg
self.cache['trXrXr'] = self.cache['Sg'].sum()
if cov_params_have_changed:
start = TIME.time()
""" Col SVD on big matrix """
self.cache['Sn'],Un = LA.eigh(self.Cn.K()+self.offset*SP.eye(self.P))
self.cache['Lc'] = (self.cache['Sn']**(-0.5))[:,SP.newaxis]*Un.T
E = SP.reshape(self.Cr.getParams(),(self.P,self.rank),order='F')
Estar = SP.dot(self.cache['Lc'],E)
Ue,Seh,Ve = NLA.svd(Estar,full_matrices=0)
self.cache['Se'] = Seh**2
self.cache['Wc'] = Ue.T
if cov_params_have_changed or self.Xr_has_changed:
""" S """
self.cache['s'] = SP.kron(1./self.cache['Se'],1./self.cache['Sg'])+1
self.cache['d'] = 1./self.cache['s']
self.cache['D'] = SP.reshape(self.cache['d'],(self.S,self.rank), order='F')
if self.Xr_has_changed or self.Y_has_changed:
""" phenos transf """
self.cache['WrLrY'] = SP.dot(self.cache['Wr'],self.Y)
XrLrY = SP.dot(self.Xr.T,self.Y)
self.cache['XrXrLrY'] = SP.dot(self.Xr,XrLrY)
self.cache['WrXrXrLrY'] = (self.cache['Sg']**0.5)[:,SP.newaxis]*SP.dot(self.cache['Vg'],XrLrY)
if (self.Xr_has_changed or self.F_has_changed) and self.F is not None:
""" F transf """
self.cache['FF'] = SP.dot(self.F.T,self.F)
self.cache['WrLrF'] = SP.dot(self.cache['Wr'],self.F)
XrLrF = SP.dot(self.Xr.T,self.F)
self.cache['XrXrLrF'] = SP.dot(self.Xr,XrLrF)
self.cache['FLrXrXrLrF'] = SP.dot(self.F.T,self.cache['XrXrLrF'])
self.cache['WrXrXrLrF'] = (self.cache['Sg']**0.5)[:,SP.newaxis]*SP.dot(self.cache['Vg'],XrLrF)
if (self.F_has_changed or self.Y_has_changed) and self.F is not None:
self.cache['FY'] = SP.dot(self.F.T,self.Y)
if (self.Xr_has_changed or self.F_has_changed or self.Y_has_changed) and self.F is not None:
self.cache['FXrXrLrY'] = SP.dot(self.F.T,self.cache['XrXrLrY'])
if cov_params_have_changed or self.Y_has_changed:
""" phenos transf """
self.cache['LY'] = SP.dot(self.Y,self.cache['Lc'].T)
self.cache['WrLY'] = SP.dot(self.cache['WrLrY'],self.cache['Lc'].T)
self.cache['WLY'] = SP.dot(self.cache['WrLY'],self.cache['Wc'].T)
self.cache['XrXrLY'] = SP.dot(self.cache['XrXrLrY'],self.cache['Lc'].T)
self.cache['WrXrXrLY'] = SP.dot(self.cache['WrXrXrLrY'],self.cache['Lc'].T)
if cov_params_have_changed and self.F is not None:
""" A transf """
# A for now is just I
self.cache['LcA'] = self.cache['Lc']
self.cache['Cni'] = SP.dot(self.cache['Lc'].T,self.cache['Lc'])
self.cache['LcALcA'] = self.cache['Cni']
self.cache['WcLcA'] = SP.dot(self.cache['Wc'],self.cache['LcA'])
if cov_params_have_changed or self.Xr_has_changed or self.Y_has_changed:
self.cache['DWLY'] = self.cache['D']*self.cache['WLY']
self.cache['SgDWLY'] = self.cache['Sg'][:,SP.newaxis]*self.cache['DWLY']
smartSum(self.time,'cache_colSVDpRot',TIME.time()-start)
smartSum(self.count,'cache_colSVDpRot',1)
if (cov_params_have_changed or self.Xr_has_changed or self.F_has_changed) and self.F is not None:
self.cache['WLV'] = SP.kron(self.cache['WcLcA'],self.cache['WrLrF'])
self.cache['DWLV'] = self.cache['d'][:,SP.newaxis]*self.cache['WLV']
self.cache['DWLV_t'] = SP.reshape(self.cache['DWLV'],(self.S,self.rank,self.P*self.K),order='F')
self.cache['SgDWLV_t'] = self.cache['Sg'][:,SP.newaxis,SP.newaxis]*self.cache['DWLV_t']
self.cache['Areml'] = SP.kron(self.cache['LcALcA'],self.cache['FF'])
self.cache['Areml']-= SP.dot(self.cache['WLV'].T,self.cache['DWLV'])
self.cache['Areml_chol'] = LA.cholesky(self.cache['Areml']).T
# TODO: handle pseudo inverses
self.cache['Areml_inv'] = LA.cho_solve((self.cache['Areml_chol'],True),SP.eye(self.K*self.P))
if (cov_params_have_changed or self.Xr_has_changed or self.Y_has_changed or self.F_has_changed) and self.F is not None:
VKiY = SP.dot(self.cache['FY'],self.cache['Cni'])
#TODO: have not controlled factorization in the following line
VKiY-= SP.dot(SP.dot(self.cache['WrLrF'].T,self.cache['DWLY']),self.cache['WcLcA'])
self.cache['b'] = SP.dot(self.cache['Areml_inv'],SP.reshape(VKiY,(VKiY.size,1),order='F'))
self.cache['B'] = SP.reshape(self.cache['b'],(self.K,self.P), order='F')
self.cache['BLc'] = SP.dot(self.cache['B'],self.cache['Lc'].T)
self.cache['BLcWc'] = SP.dot(self.cache['BLc'],self.cache['Wc'].T)
self.cache['Z'] = self.Y-SP.dot(self.F,self.cache['B'])
self.cache['FZ'] = self.cache['FY']-SP.dot(self.cache['FF'],self.cache['B'])
self.cache['LZ'] = self.cache['LY']-SP.dot(self.F,self.cache['BLc'])
self.cache['WrLZ'] = self.cache['WrLY']-SP.dot(self.cache['WrLrF'],self.cache['BLc'])
self.cache['WLZ'] = self.cache['WLY']-SP.dot(self.cache['WrLrF'],self.cache['BLcWc'])
self.cache['DWLZ'] = self.cache['D']*self.cache['WLZ']
self.cache['SgDWLZ'] = self.cache['Sg'][:,SP.newaxis]*self.cache['DWLZ']
self.cache['XrXrLZ'] = self.cache['XrXrLY']-SP.dot(self.cache['XrXrLrF'],self.cache['BLc'])
self.cache['WrXrXrLZ'] = self.cache['WrXrXrLY']-SP.dot(self.cache['WrXrXrLrF'],self.cache['BLc'])
VKiZ = SP.dot(self.cache['FZ'],self.cache['Cni'])
VKiZ-= SP.dot(self.cache['WrLrF'].T,SP.dot(self.cache['DWLZ'],self.cache['WcLcA']))
self.cache['vecVKiZ'] = SP.reshape(VKiZ,(self.K*self.P,1),order='F')
if self.F is None:
""" Then Z=Y """
self.cache['LZ'] = self.cache['LY']
self.cache['WLZ'] = self.cache['WLY']
self.cache['DWLZ'] = self.cache['DWLY']
self.cache['XrXrLZ'] = self.cache['XrXrLY']
self.cache['SgDWLZ'] = self.cache['SgDWLY']
self.cache['WrXrXrLZ'] = self.cache['WrXrXrLY']
self.cache['WrLZ'] = self.cache['WrLY']
self.Y_has_changed = False
self.F_has_changed = False
self.Xr_has_changed = False
self.Cr.params_have_changed = False
self.Cn.params_have_changed = False