def __init__(self, X=None, y=None, kernel="se", kernel_params=(1, 1,), kernel_priors = None, kernel_extra = None, mean="constant"):
self.kernel_name = kernel
self.kernel_params = kernel_params
self.kernel_priors = kernel_priors
self.kernel = kernels.setup_kernel(kernel, kernel_params, kernel_extra, kernel_priors)
self.X = X
self.n = X.shape[0]
if mean == "zero":
self.mu = 0
self.y = y
if mean == "constant":
self.mu = np.mean(y)
self.y = y - self.mu
if mean == "linear":
raise RuntimeError("linear mean not yet implemented...")
self.mean = mean
self.K = None
self.L = None
self.alpha = None
self.Kinv = None
self.posdef = True
self.tree = KDTree(X)
self.alpha = self._treeCG()
print "trained CG, found alpha", self.alpha
self._invert_kernel_matrix()
print "true alpha is", self.alpha
def __init__(self, X=None, y=None, kernel="se", kernel_params=(1, 1,), kernel_priors = None, kernel_extra = None, K=None, inv=True, mean="constant", ignore_pos_def_errors=False, fname=None):
if fname is not None:
self.load_trained_model(fname)
else:
self.kernel_name = kernel
self.kernel_params = kernel_params
self.kernel_priors = kernel_priors
self.kernel = kernels.setup_kernel(kernel, kernel_params, kernel_extra, kernel_priors)
self.X = X
self.n = X.shape[0]
if mean == "zero":
self.mu = 0
self.y = y
if mean == "constant":
self.mu = np.mean(y)
self.y = y - self.mu
if mean == "linear":
raise RuntimeError("linear mean not yet implemented...")
self.mean = mean
# train model
if K is None:
self.K = self.kernel(X, X)
else:
self.K = K
self.L = None
self.alpha = None
self.Kinv = None
self.posdef = True
try:
self.L = scipy.linalg.cholesky(self.K, lower=True)
self.alpha = scipy.linalg.cho_solve((self.L, True), self.y)
except np.linalg.linalg.LinAlgError:
if ignore_pos_def_errors:
self.posdef=False
return
else:
# print traceback.format_exc(limit=100)
# u,v = np.linalg.eig(self.K)
# print self.K, u
print "raising"
# import pdb
# pdb.set_trace()
# sys.exit(1)
raise
if inv:
self._invert_kernel_matrix()
def load_trained_model(self, filename):
npzfile = np.load(filename)
self.X = npzfile['X']
self.y = npzfile['y']
self.mu = npzfile['mu'][0]
self.mean = npzfile['mname'][0]
self.kernel_name = npzfile['kernel_name'][0]
self.kernel_params = npzfile['kernel_params']
self.alpha = npzfile['alpha']
self.Kinv = npzfile['Kinv']
self.L = npzfile['L']
self.K = npzfile['K']
self.n = self.X.shape[0]
self.kernel = kernels.setup_kernel(kernel_name, kernel_params)