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DeprecationWarning: Substituded all (\!) flapack occ. with lapack (scipy said so)
This commit is contained in:
Max Zwiessele
parent
32034872af
commit
a53690ab7f
15 changed files with 101 additions and 96 deletions
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@ -95,7 +95,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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return x
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def _clipped(self, x):
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return x # np.clip(x, -1e300, 1e300)
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return np.clip(x, -1e300, 1e300)
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def _set_params(self, x, save_old=True, save_count=0):
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# try:
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@ -11,8 +11,8 @@ from sparse_GP import sparse_GP
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def backsub_both_sides(L,X):
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""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
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tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
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return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
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tmp, _ = linalg.lapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
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return linalg.lapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
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class FITC(sparse_GP):
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@ -36,7 +36,7 @@ class FITC(sparse_GP):
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#factor Kmm
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self.Lm = jitchol(self.Kmm)
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self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
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self.Lmi, info = linalg.lapack.dtrtrs(self.Lm, np.eye(self.M), lower=1)
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Lmipsi1 = np.dot(self.Lmi,self.psi1)
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self.Qnn = np.dot(Lmipsi1.T,Lmipsi1).copy()
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self.Diag0 = self.psi0 - np.diag(self.Qnn)
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@ -50,7 +50,7 @@ class FITC(sparse_GP):
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if self.likelihood.is_heteroscedastic:
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assert self.likelihood.D == 1
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tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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tmp, _ = linalg.lapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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# factor B
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@ -59,8 +59,8 @@ class FITC(sparse_GP):
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self.LBi = chol_inv(self.LB)
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self.psi1V = np.dot(self.psi1, self.V_star)
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Lmi_psi1V, info = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(Lmi_psi1V), lower=1, trans=0)
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Lmi_psi1V, info = linalg.lapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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self._LBi_Lmi_psi1V, _ = linalg.lapack.dtrtrs(self.LB, np.asfortranarray(Lmi_psi1V), lower=1, trans=0)
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Kmmipsi1 = np.dot(self.Lmi.T,Lmipsi1)
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b_psi1_Ki = self.beta_star * Kmmipsi1.T
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@ -190,7 +190,7 @@ class FITC(sparse_GP):
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self.P = Iplus_Dprod_i[:,None] * self.psi1.T
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self.RPT0 = np.dot(self.Lmi,self.psi1)
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self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
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self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
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self.R, info = linalg.dtrtrs(self.L, self.Lmi, lower=1)
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self.RPT = np.dot(self.R,self.P.T)
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self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
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self.w = self.Diag * self.likelihood.v_tilde
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@ -212,7 +212,7 @@ class FITC(sparse_GP):
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# = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
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# = I - V.T * V
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U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
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V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
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V, info = linalg.dtrtrs(U, self.RPT0.T, lower=1)
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C = np.eye(self.M) - np.dot(V.T,V)
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mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
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#self.C = C
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@ -74,13 +74,13 @@ class GP(model):
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# the gradient of the likelihood wrt the covariance matrix
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if self.likelihood.YYT is None:
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#alpha = np.dot(self.Ki, self.likelihood.Y)
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alpha,_ = linalg.lapack.flapack.dpotrs(self.L, self.likelihood.Y,lower=1)
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alpha, _ = linalg.lapack.dpotrs(self.L, self.likelihood.Y, lower=1)
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self.dL_dK = 0.5 * (tdot(alpha) - self.D * self.Ki)
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else:
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#tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
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tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
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tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
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tmp, _ = linalg.lapack.dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
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tmp, _ = linalg.lapack.dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
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self.dL_dK = 0.5 * (tmp - self.D * self.Ki)
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def _get_params(self):
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@ -104,7 +104,7 @@ class GP(model):
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Computes the model fit using YYT if it's available
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"""
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if self.likelihood.YYT is None:
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.L, np.asfortranarray(self.likelihood.Y), lower=1)
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tmp, _ = linalg.lapack.dtrtrs(self.L, np.asfortranarray(self.likelihood.Y), lower=1)
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return -0.5 * np.sum(np.square(tmp))
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#return -0.5 * np.sum(np.square(np.dot(self.Li, self.likelihood.Y)))
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else:
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@ -136,7 +136,7 @@ class GP(model):
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"""
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Kx = self.kern.K(_Xnew,self.X,which_parts=which_parts).T
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#KiKx = np.dot(self.Ki, Kx)
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KiKx, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(Kx), lower=1)
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KiKx, _ = linalg.lapack.dpotrs(self.L, np.asfortranarray(Kx), lower=1)
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mu = np.dot(KiKx.T, self.likelihood.Y)
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if full_cov:
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Kxx = self.kern.K(_Xnew, which_parts=which_parts)
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@ -11,8 +11,8 @@ from sparse_GP import sparse_GP
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def backsub_both_sides(L,X):
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""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
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tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
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return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
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tmp, _ = linalg.lapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
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return linalg.lapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
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class generalized_FITC(sparse_GP):
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@ -82,7 +82,7 @@ class generalized_FITC(sparse_GP):
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if self.likelihood.is_heteroscedastic:
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# Compute generalized FITC's diagonal term of the covariance
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self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
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self.Lmi, info = linalg.lapack.dtrtrs(self.Lm, np.eye(self.M), lower=1)
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Lmipsi1 = np.dot(self.Lmi,self.psi1)
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self.Qnn = np.dot(Lmipsi1.T,Lmipsi1)
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#self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
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@ -95,7 +95,7 @@ class generalized_FITC(sparse_GP):
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self.P = Iplus_Dprod_i[:,None] * self.psi1.T
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self.RPT0 = np.dot(self.Lmi,self.psi1)
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self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
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self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
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self.R, info = linalg.dtrtrs(self.L, self.Lmi, lower=1)
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self.RPT = np.dot(self.R,self.P.T)
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self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
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self.w = self.Diag * self.likelihood.v_tilde
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@ -182,7 +182,7 @@ class generalized_FITC(sparse_GP):
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# = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
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# = I - V.T * V
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U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
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V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
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V, info = linalg.dtrtrs(U, self.RPT0.T, lower=1)
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C = np.eye(self.M) - np.dot(V.T,V)
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mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
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#self.C = C
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@ -65,7 +65,7 @@ class MRD(model):
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self._init = True
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X = self._init_X(initx, likelihood_or_Y_list)
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Z = self._init_Z(initz, X)
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self.bgplvms = [Bayesian_GPLVM(l, Q=Q, kernel=k, X=X, Z=Z, M=self.M, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
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self.bgplvms = [Bayesian_GPLVM(l, Q=Q, kernel=k, X=X, Z=Z, M=self.M, _debug=_debug, ** kw) for l, k in zip(likelihood_or_Y_list, kernels)]
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del self._init
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self.gref = self.bgplvms[0]
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@ -143,9 +143,9 @@ class MRD(model):
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# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.Q)] for n in range(self.N)], [])
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n1 = self.gref._get_param_names()
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n1var = n1[:self.NQ * 2 + self.MQ]
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map_names = lambda ns, name: map(lambda x: "{1}_{0}".format(*x),
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map_names = lambda ns, cd48_name: map(lambda x: "{1}_{0}".format(*x),
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itertools.izip(ns,
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itertools.repeat(name)))
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itertools.repeat(cd48_name)))
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return list(itertools.chain(n1var, *(map_names(\
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sparse_GP._get_param_names(g)[self.MQ:], n) \
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for g, n in zip(self.bgplvms, self.names))))
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@ -213,12 +213,12 @@ class MRD(model):
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dLdmuS = numpy.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
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dldzt1 = reduce(lambda a, b: a + b, (sparse_GP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
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return numpy.hstack((dLdmuS,
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return self.gref._clipped(numpy.hstack((dLdmuS,
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dldzt1,
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numpy.hstack([numpy.hstack([g.dL_dtheta(),
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g.likelihood._gradients(\
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partial=g.partial_for_likelihood)]) \
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for g in self.bgplvms])))
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for g in self.bgplvms]))))
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def _init_X(self, init='PCA', likelihood_list=None):
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if likelihood_list is None:
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@ -80,7 +80,7 @@ class sparse_GP(GP):
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
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else:
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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tmp, _ = linalg.lapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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@ -92,10 +92,10 @@ class sparse_GP(GP):
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self.psi1V = np.dot(self.psi1, self.likelihood.V)
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# back substutue C into psi1V
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tmp, info1 = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(tmp), lower=1, trans=0)
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tmp, info2 = linalg.lapack.flapack.dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, info3 = linalg.lapack.flapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
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tmp, info1 = linalg.lapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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self._LBi_Lmi_psi1V, _ = linalg.lapack.dtrtrs(self.LB, np.asfortranarray(tmp), lower=1, trans=0)
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tmp, info2 = linalg.lapack.dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, info3 = linalg.lapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
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# Compute dL_dKmm
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tmp = tdot(self._LBi_Lmi_psi1V)
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@ -220,7 +220,7 @@ class sparse_GP(GP):
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def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
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"""Internal helper function for making predictions, does not account for normalization"""
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Bi, _ = linalg.lapack.flapack.dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
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Bi, _ = linalg.lapack.dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
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symmetrify(Bi)
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Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
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