def predict_with_posterior(self, post, xs, ys=None):
'''
Prediction of test points (given by xs) based on training data
of the current model with posterior already provided.
(i.e. you already have the posterior and thus don't need the fitting phase.)
This method will output the following value:\n
predictive output means(ym),\n
predictive output variances(ys2),\n
predictive latent means(fm),\n
predictive latent variances(fs2),\n
log predictive probabilities(lp).\n
Theses values can also be achieved from model's property. (e.g. model.ym)
:param post: struct representation of posterior
:param xs: test input
:param ys: test target(optional)
:return: ym, ys2, fm, fs2, lp
'''
# check the shape of inputs
# transform to correct shape if neccessary
if xs.ndim == 1:
xs = np.reshape(xs, (xs.shape[0], 1))
self.xs = xs
if not ys is None:
if ys.ndim == 1:
ys = np.reshape(ys, (ys.shape[0], 1))
self.ys = ys
meanfunc = self.meanfunc
covfunc = self.covfunc
likfunc = self.likfunc
inffunc = self.inffunc
x = self.x
y = self.y
self.posterior = deepcopy(post)
alpha = post.alpha
L = post.L
sW = post.sW
nz = range(len(alpha[:, 0])) # non-sparse representation
if L == []: # in case L is not provided, we compute it
K = covfunc.getCovMatrix(x=x[nz, :], mode='train')
#L = np.linalg.cholesky( (np.eye(nz) + np.dot(sW,sW.T)*K).T )
L = jitchol((np.eye(len(nz)) + np.dot(sW, sW.T) * K).T)
Ltril = np.all(np.tril(L, -1) == 0) # is L an upper triangular matrix?
ns = xs.shape[0] # number of data points
nperbatch = 1000 # number of data points per mini batch
nact = 0 # number of already processed test data points
ymu = np.zeros((ns, 1))
ys2 = np.zeros((ns, 1))
fmu = np.zeros((ns, 1))
fs2 = np.zeros((ns, 1))
lp = np.zeros((ns, 1))
while nact <= ns - 1: # process minibatches of test cases to save memory
id = range(nact, min(nact + nperbatch,
ns)) # data points to process
kss = covfunc.getCovMatrix(z=xs[id, :],
mode='self_test') # self-variances
Ks = covfunc.getCovMatrix(x=x[nz, :], z=xs[id, :],
mode='cross') # cross-covariances
ms = meanfunc.getMean(xs[id, :])
N = (alpha.shape
)[1] # number of alphas (usually 1; more in case of sampling)
Fmu = np.tile(ms, (1, N)) + np.dot(
Ks.T, alpha[nz]) # conditional mean fs|f
fmu[id] = np.reshape(Fmu.sum(axis=1) / N,
(len(id), 1)) # predictive means
if Ltril: # L is triangular => use Cholesky parameters (alpha,sW,L)
V = np.linalg.solve(L.T, np.tile(sW, (1, len(id))) * Ks)
fs2[id] = kss - np.array([(V * V).sum(axis=0)
]).T # predictive variances
else: # L is not triangular => use alternative parametrization
fs2[id] = kss + np.array([(Ks * np.dot(L, Ks)).sum(axis=0)
]).T # predictive variances
fs2[id] = np.maximum(
fs2[id], 0) # remove numerical noise i.e. negative variances
Fs2 = np.tile(
fs2[id], (1, N)) # we have multiple values in case of sampling
if ys is None:
[Lp, Ymu, Ys2] = likfunc.evaluate(None, Fmu[:], Fs2[:], None,
None, 3)
else:
[Lp, Ymu, Ys2] = likfunc.evaluate(np.tile(ys[id],
(1, N)), Fmu[:],
Fs2[:], None, None, 3)
lp[id] = np.reshape(
np.reshape(Lp, (np.prod(Lp.shape), N)).sum(axis=1) / N,
(len(id), 1)) # log probability; sample averaging
ymu[id] = np.reshape(
np.reshape(Ymu, (np.prod(Ymu.shape), N)).sum(axis=1) / N,
(len(id), 1)) # predictive mean ys|y and ...
ys2[id] = np.reshape(
np.reshape(Ys2, (np.prod(Ys2.shape), N)).sum(axis=1) / N,
(len(id), 1)) # .. variance
nact = id[-1] + 1 # set counter to index of next data point
self.ym = ymu
self.ys2 = ys2
self.lp = lp
self.fm = fmu
self.fs2 = fs2
if ys is None:
return ymu, ys2, fmu, fs2, None
else:
return ymu, ys2, fmu, fs2, lp
def predict_with_posterior(self, post, xs, ys=None):
'''
Prediction of test points (given by xs) based on training data
of the current model with posterior already provided.
(i.e. you already have the posterior and thus don't need the fitting phase.)
This method will output the following value:\n
predictive output means(ym),\n
predictive output variances(ys2),\n
predictive latent means(fm),\n
predictive latent variances(fs2),\n
log predictive probabilities(lp).\n
Theses values can also be achieved from model's property. (e.g. model.ym)
:param post: struct representation of posterior
:param xs: test input
:param ys: test target(optional)
:return: ym, ys2, fm, fs2, lp
'''
# check the shape of inputs
# transform to correct shape if neccessary
if xs.ndim == 1:
xs = np.reshape(xs, (xs.shape[0],1))
self.xs = xs
if not ys is None:
if ys.ndim == 1:
ys = np.reshape(ys, (ys.shape[0],1))
self.ys = ys
meanfunc = self.meanfunc
covfunc = self.covfunc
likfunc = self.likfunc
inffunc = self.inffunc
x = self.x
y = self.y
self.posterior = deepcopy(post)
alpha = post.alpha
L = post.L
sW = post.sW
nz = range(len(alpha[:,0])) # non-sparse representation
if L == []: # in case L is not provided, we compute it
K = covfunc.getCovMatrix(x=x[nz,:], mode='train')
#L = np.linalg.cholesky( (np.eye(nz) + np.dot(sW,sW.T)*K).T )
L = jitchol( (np.eye(len(nz)) + np.dot(sW,sW.T)*K).T )
Ltril = np.all( np.tril(L,-1) == 0 ) # is L an upper triangular matrix?
ns = xs.shape[0] # number of data points
nperbatch = 1000 # number of data points per mini batch
nact = 0 # number of already processed test data points
ymu = np.zeros((ns,1))
ys2 = np.zeros((ns,1))
fmu = np.zeros((ns,1))
fs2 = np.zeros((ns,1))
lp = np.zeros((ns,1))
while nact<=ns-1: # process minibatches of test cases to save memory
id = range(nact,min(nact+nperbatch,ns)) # data points to process
kss = covfunc.getCovMatrix(z=xs[id,:], mode='self_test') # self-variances
Ks = covfunc.getCovMatrix(x=x[nz,:], z=xs[id,:], mode='cross') # cross-covariances
ms = meanfunc.getMean(xs[id,:])
N = (alpha.shape)[1] # number of alphas (usually 1; more in case of sampling)
Fmu = np.tile(ms,(1,N)) + np.dot(Ks.T,alpha[nz]) # conditional mean fs|f
fmu[id] = np.reshape(Fmu.sum(axis=1)/N,(len(id),1)) # predictive means
if Ltril: # L is triangular => use Cholesky parameters (alpha,sW,L)
V = np.linalg.solve(L.T,np.tile(sW,(1,len(id)))*Ks)
fs2[id] = kss - np.array([(V*V).sum(axis=0)]).T # predictive variances
else: # L is not triangular => use alternative parametrization
fs2[id] = kss + np.array([(Ks*np.dot(L,Ks)).sum(axis=0)]).T # predictive variances
fs2[id] = np.maximum(fs2[id],0) # remove numerical noise i.e. negative variances
Fs2 = np.tile(fs2[id],(1,N)) # we have multiple values in case of sampling
if ys is None:
[Lp, Ymu, Ys2] = likfunc.evaluate(None,Fmu[:],Fs2[:],None,None,3)
else:
[Lp, Ymu, Ys2] = likfunc.evaluate(np.tile(ys[id],(1,N)), Fmu[:], Fs2[:],None,None,3)
lp[id] = np.reshape( np.reshape(Lp,(np.prod(Lp.shape),N)).sum(axis=1)/N , (len(id),1) ) # log probability; sample averaging
ymu[id] = np.reshape( np.reshape(Ymu,(np.prod(Ymu.shape),N)).sum(axis=1)/N ,(len(id),1) ) # predictive mean ys|y and ...
ys2[id] = np.reshape( np.reshape(Ys2,(np.prod(Ys2.shape),N)).sum(axis=1)/N , (len(id),1) ) # .. variance
nact = id[-1]+1 # set counter to index of next data point
self.ym = ymu
self.ys2 = ys2
self.lp = lp
self.fm = fmu
self.fs2 = fs2
if ys is None:
return ymu, ys2, fmu, fs2, None
else:
return ymu, ys2, fmu, fs2, lp