Working through new sampling

docs/source/samplers.rst

@@ -3,6 +3,14 @@ Samplers
 ========
 
 
+High Level Interface
+====================
+
+.. autoclass:: psdr.Sampler
+	:members:
+
+.. autoclass:: psdr.SequentialMaximinSampler
+
 
 Low Level Functions
 ===================

psdr/sample.py

@@ -213,6 +213,12 @@ def seq_maximin_sample(domain, Xhat, L = None, Nsamp = int(1e3), X0 = None):
 	return Xcan[i]
 
 
+def multiobj_seq_maximin_sample(domain, Xhat, Ls, Nsamp = int(1e3), X0 = None):
+	r""" A multi-objective sequential maximin sampling 
+	"""
+	pass
+
+
 def fill_distance_estimate(domain, Xhat, L = None, Nsamp = int(1e4), X0 = None ):
 	r""" Estimate the fill distance of the points Xhat in the domain
 
@@ -346,7 +352,7 @@ def fX(self):
 			return None
 
 class SequentialMaximinSampler(Sampler):
-	r""" Sequential maximin sampling
+	r""" Sequential maximin sampling with a fixed metric
 
 	Given a distance metric provided by :math:`\mathbf{L}`,
 	construct a sequence of samples :math:`\widehat{\mathbf{x}}_i`

psdr/subspace.py

@@ -215,6 +215,11 @@ def _fix_subspace_signs_samps(self, U, X, fX):
 	def _fix_subspace_signs_grads(self, U, grads):
 		self._U = U.dot(np.diag(np.sign(np.mean(grads.dot(U), axis = 0))))
 
+
+	def approximate_lipschitz(self, X = None, fX = None, grads = None,  dim = None):
+		r""" Approximate the Lipschitz matrix on the low-dimensional subspace
+		"""
+		pass
 
 
 class ActiveSubspace(SubspaceBasedDimensionReduction):