code stringlengths 3 6.57k |
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covfunc.getCovMatrix(x=x[nz,:], mode='train') |
np.linalg.cholesky( (np.eye(nz) |
np.dot(sW,sW.T) |
jitchol( (np.eye(len(nz) |
np.dot(sW,sW.T) |
np.all( np.tril(L,-1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
list(range(nact,min(nact+nperbatch,ns) |
covfunc.getCovMatrix(z=xs[ids,:], mode='self_test') |
isinstance(covfunc, FITCOfKernel) |
covfunc.getCovMatrix(x=x, z=xs[ids,:], mode='cross') |
covfunc.getCovMatrix(x=x[nz,:], z=xs[ids,:], mode='cross') |
meanfunc.getMean(xs[ids,:]) |
alphas (usually 1; more in case of sampling) |
np.tile(ms,(1,N) |
np.dot(Ks.T,alpha[nz]) |
np.reshape(old_div(Fmu.sum(axis=1) |
len(ids) |
parameters (alpha,sW,L) |
np.linalg.solve(L.T,np.tile(sW,(1,len(ids) |
np.array([(V*V) |
sum(axis=0) |
np.array([(Ks*np.dot(L,Ks) |
sum(axis=0) |
np.maximum(fs2[ids],0) |
np.tile(fs2[ids],(1,N) |
likfunc.evaluate(None,Fmu[:],Fs2[:],None,None,3) |
likfunc.evaluate(np.tile(ys[ids],(1,N) |
np.reshape( old_div(np.reshape(Lp,(np.prod(Lp.shape) |
sum(axis=1) |
len(ids) |
np.reshape( old_div(np.reshape(Ymu,(np.prod(Ymu.shape) |
sum(axis=1) |
len(ids) |
np.reshape( old_div(np.reshape(Ys2,(np.prod(Ys2.shape) |
sum(axis=1) |
len(ids) |
predict_with_posterior(self, post, xs, ys=None) |
points (given by xs) |
provided.
(i.e. you already have the posterior and thus don't need the fitting phase.) |
means(ym) |
variances(ys2) |
means(fm) |
variances(fs2) |
probabilities(lp) |
property. (e.g. model.ym) |
target(optional) |
np.reshape(xs, (xs.shape[0],1) |
np.reshape(ys, (ys.shape[0],1) |
deepcopy(post) |
list(range(len(alpha[:,0]) |
len(L) |
covfunc.getCovMatrix(x=x[nz,:], mode='train') |
np.linalg.cholesky( (np.eye(nz) |
np.dot(sW,sW.T) |
jitchol( (np.eye(len(nz) |
np.dot(sW,sW.T) |
np.all( np.tril(L,-1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
np.zeros((ns,1) |
list(range(nact,min(nact+nperbatch,ns) |
covfunc.getCovMatrix(z=xs[id,:], mode='self_test') |
covfunc.getCovMatrix(x=x[nz,:], z=xs[id,:], mode='cross') |
meanfunc.getMean(xs[id,:]) |
alphas (usually 1; more in case of sampling) |
np.tile(ms,(1,N) |
np.dot(Ks.T,alpha[nz]) |
np.reshape(old_div(Fmu.sum(axis=1) |
len(id) |
parameters (alpha,sW,L) |
np.linalg.solve(L.T,np.tile(sW,(1,len(id) |
np.array([(V*V) |
sum(axis=0) |
np.array([(Ks*np.dot(L,Ks) |
sum(axis=0) |
np.maximum(fs2[id],0) |
np.tile(fs2[id],(1,N) |
likfunc.evaluate(None,Fmu[:],Fs2[:],None,None,3) |
likfunc.evaluate(np.tile(ys[id],(1,N) |
np.reshape( old_div(np.reshape(Lp,(np.prod(Lp.shape) |
sum(axis=1) |
len(id) |
np.reshape( old_div(np.reshape(Ymu,(np.prod(Ymu.shape) |
sum(axis=1) |
len(id) |
np.reshape( old_div(np.reshape(Ys2,(np.prod(Ys2.shape) |
sum(axis=1) |
len(id) |
GPR(GP) |
__init__(self) |
super(GPR, self) |
__init__() |
mean.Zero() |
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