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Refined function contract a little.

This commit is contained in:
Davis King 2012-04-29 18:05:17 -04:00
parent 84523a056f
commit e5ef72c4aa
1 changed files with 20 additions and 7 deletions
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27
dlib/svm/structural_svm_potts_problem.h
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@ -20,30 +20,43 @@ namespace dlib
// ----------------------------------------------------------------------------------------
template <typename graph_type>
bool is_potts_problem (
template <
typename graph_type
>
bool is_potts_learning_problem (
const dlib::array<graph_type>& samples,
const std::vector<std::vector<node_label> >& labels
)
/*!
requires
- graph_type is an implementation of dlib/graph/graph_kernel_abstract.h
- graph_type::edge_type is either a dlib::matrix capable of containing
column vectors or is some kind of sparse vector type.
- graph_type::type and graph_type::edge_type are either dlib::matrix types
capable of containing column vectors or some kind of sparse vector type.
ensures
- returns true if all of the following are true and false otherwise:
- Note that a potts learning problem is a task to learn a binary classifier which
predicts the correct label for each node in the provided graphs. Additionally,
we have information in the form of graph edges between nodes where edges are
present when we believe the linked nodes are likely to have the same label.
Therefore, part of a potts learning problem is to learn to score each edge in
terms of how strongly the edge should enforce labeling consistency between
its two nodes. Thus, to be a valid potts problem, samples should contain
example graphs of connected nodes while labels should indicate the desired
label of each node. The precise requirements for a valid potts learning
problem are listed below.
- This function returns true if all of the following are true and false otherwise:
- is_learning_problem(samples, labels) == true
- All the vectors stored on the edges of each graph in samples
contain only values which are >= 0.
- graph_type::type and graph_type::edge_type either both represent
dlib::matrix column vectors or are both sparse vectors.
- for all valid i:
- graph_contains_length_one_cycle(samples[i]) == false
- samples[i].number_of_nodes() == labels[i].size()
(i.e. Every graph node gets its own label)
- if (graph_type::edge_type is a dlib::matrix) then
- All the nodes must contain vectors with the same number of dimensions.
- All the edges must contain vectors with the same number of dimensions.
(However, edge vectors may differ in dimension from node vectors though.)
(However, edge vectors may differ in dimension from node vectors.)
- All vectors have non-zero size. That is, they have more than 0 dimensions.
!*/
{
@ -108,7 +121,7 @@ namespace dlib
labels(labels_)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_potts_problem(samples, labels) == true,
DLIB_ASSERT(is_potts_learning_problem(samples, labels) == true,
"\t structural_svm_potts_problem::structural_svm_potts_problem()"
<< "\n\t invalid inputs were given to this function");