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Final performance tuning on the dense version of the algorithm

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This commit is contained in:
Dongryeol Lee
2008-02-19 05:27:35 +00:00
parent 8ac34d0683
commit d3de9a182f
3 changed files with 13 additions and 13 deletions
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fastlib2/contrib/dongryel/regression/dense_lpr.h
+2 -2
View File
@@ -788,8 +788,8 @@ class DenseLpr {
* for a given query and a reference node pair.
*/
void SqdistAndKernelRanges_(QueryTree *qnode, ReferenceTree *rnode,
DRange &dsqd_range, DRange &kernel_value_range,
Vector *furthest_point_in_qnode);
DRange &dsqd_range,
DRange &kernel_value_range);
/** @brief Resets bounds relevant to the given query point.
*/
fastlib2/contrib/dongryel/regression/dense_lpr_impl.h
+4 -6
View File
@@ -10,9 +10,8 @@
template<typename TKernel, typename TPruneRule>
void DenseLpr<TKernel, TPruneRule>::SqdistAndKernelRanges_
(QueryTree *qnode, ReferenceTree *rnode, DRange &dsqd_range,
DRange &kernel_value_range, Vector *furthest_point_in_qnode) {
DRange &kernel_value_range) {
furthest_point_in_qnode->Init(dimension_);
dsqd_range = qnode->bound().RangeDistanceSq(rnode->bound());
kernel_value_range = kernel_.RangeUnnormOnSq(dsqd_range);
}
@@ -425,9 +424,7 @@ void DenseLpr<TKernel, TPruneRule>::DualtreeLprCanonical_
weight_diagram_numerator_de.Init(row_length_, row_length_);
// Compute distance ranges and kernel ranges first.
Vector furthest_point_in_qnode;
SqdistAndKernelRanges_(qnode, rnode, dsqd_range, kernel_value_range,
&furthest_point_in_qnode);
SqdistAndKernelRanges_(qnode, rnode, dsqd_range, kernel_value_range);
// Try finite difference pruning first
if(TPruneRule::Prunable
@@ -470,7 +467,8 @@ void DenseLpr<TKernel, TPruneRule>::DualtreeLprCanonical_
// moments if the maximum distance between the two nodes is within
// the bandwidth! This if-statement does not apply to the Gaussian
// kernel, so I need to fix in the future!
if(kernel_.bandwidth_sq() >= dsqd_range.hi) {
if(kernel_.bandwidth_sq() >= dsqd_range.hi &&
rnode->count() > dimension_ * dimension_) {
for(index_t j = 0; j < row_length_; j++) {
fastlib2/contrib/dongryel/regression/relative_prune_lpr.h
+7 -5
View File
@@ -88,7 +88,8 @@ class RelativePruneLpr {
(relative_error * new_denominator_norm_l -
qnode->stat().denominator_used_error_) /
(denominator_total_alloc_error - qnode->stat().denominator_n_pruned_);
/*
// Refine the bound using the new info for the weight diagram
// numerator matrix.
la::AddOverwrite(qnode->stat().postponed_weight_diagram_numerator_l_,
@@ -101,6 +102,7 @@ class RelativePruneLpr {
(relative_error * new_weight_diagram_numerator_norm_l -
qnode->stat().weight_diagram_numerator_used_error_) /
(denominator_total_alloc_error - qnode->stat().denominator_n_pruned_);
*/
// this is error per each query/reference pair for a fixed query
// for the numerator and the denominator used for computing the
@@ -109,7 +111,7 @@ class RelativePruneLpr {
// This is the error per each query/reference pair for a fixed
// query for the weight diagram numerator matrix approximation.
double squared_kernel_error = 0.5 * squared_kernel_value_range.width();
//double squared_kernel_error = 0.5 * squared_kernel_value_range.width();
// This is total norm error for each query point for
// approximating the B^T W(q) Y vector.
@@ -123,18 +125,18 @@ class RelativePruneLpr {
(rnode->stat().sum_data_outer_products_error_norm_);
denominator_n_pruned = rnode->stat().sum_data_outer_products_alloc_norm_;
/*
// The total norm error for each query point for approximating
// the B^T W(q)^2 B matrix.
weight_diagram_numerator_used_error = squared_kernel_error *
(rnode->stat().sum_data_outer_products_error_norm_);
*/
// Check pruning condition. Note that this pruning criterion
// does not enforce error directly on the weight diagram
// computation.
return (numerator_used_error <= numerator_allowed_err &&
denominator_used_error <= denominator_allowed_err &&
weight_diagram_numerator_used_error <=
weight_diagram_numerator_allowed_err);
denominator_used_error <= denominator_allowed_err);
}
};