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This commit is contained in:
vasiloglou
2008-01-17 05:06:56 +00:00
parent 06094c5859
commit 33a995e116
7 changed files with 64 additions and 75 deletions
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fastlib/u/nvasil/kernel_pca/allknn.h
+17 -14
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@@ -115,7 +115,7 @@ class AllkNN {
/////////////////////////////// Constructors /////////////////////////////////////////////
// Add this at the beginning of a class to prevent accidentally calling the copy constructor
FORBID_ACCIDENTAL_COPIES(AllNN);
FORBID_ACCIDENTAL_COPIES(AllkNN);
public:
@@ -171,7 +171,7 @@ class AllkNN {
// Used to find the query node's new upper bound
double query_max_neighbor_distance = -1.0;
ArrayList<std::pair<dstance, index_t> > neighbors;
ArrayList<std::pair<double, index_t> > neighbors;
neighbors.Init(knns_, knns_+leaf_size_);
// node->begin() is the index of the first point in the node,
// node->end is one past the last index
@@ -184,8 +184,8 @@ class AllkNN {
index_t ind = query_index*knns_;
for(index_t i=0; i<knns_; i++) {
neighbors[i]=std::make_pair(neighbor_distances[ind+i],
neighbor_indices[ind+i]);
neighbors[i]=std::make_pair(neighbor_distances_[ind+i],
neighbor_indices_[ind+i]);
}
// We'll do the same for the references
for (index_t reference_index = reference_node->begin();
@@ -206,13 +206,13 @@ class AllkNN {
if (neighbors.size()>knns_) {
std::sort(neighbors.begin(), neighbors.end());
for(index_t i=0; i<knns_; i++) {
neighbor_distances[ind+i] = neighbors[i].first;
neighbor_indices[ind+i] = neighbors[i].second;
neighbor_distances_[ind+i] = neighbors[i].first;
neighbor_indices_[ind+i] = neighbors[i].second;
}
neighbors.Resize(knns_);
// We need to find the upper bound distance for this query node
if (neighbor_distances_[ind+knns-1] > query_max_neighbor_distance) {
query_max_neighbor_distance = neighbor_distances_[nd+knns_-1];
if (neighbor_distances_[ind+knns_-1] > query_max_neighbor_distance) {
query_max_neighbor_distance = neighbor_distances_[ind+knns_-1];
}
}
@@ -344,7 +344,7 @@ class AllkNN {
* local copies of the data.
*/
void Init(const Matrix& queries_in, const Matrix& references_in,
index_t leaf_size_, ndex_t knns) {
index_t leaf_size, index_t knns) {
// track the number of prunes
@@ -422,14 +422,16 @@ class AllkNN {
MinNodeDistSq_(query_tree_, reference_tree_));
// We need to initialize the results list before filling it
results->Init(neighbor_indices_.size());
resulting_neighbors->Init(neighbor_indices_.size());
// We need to map the indices back from how they have
// been permuted
for (index_t i = 0; i < neighbor_indices_.size(); i++) {
(*results)[old_from_new_queries_[i]] =
(*resulting_neighbors)[
old_from_new_queries_[i/knns_]*knns_+ i%knns_] =
old_from_new_references_[neighbor_indices_[i]];
}
distances.Copy(neighbor_distances_.ptr(), neighbor_distances.len());
distances->Copy(neighbor_distances_.ptr(),
neighbor_distances_.length());
} // ComputeNeighbors
@@ -444,10 +446,11 @@ class AllkNN {
// The same code as above
results->Init(neighbor_indices_.size());
for (index_t i = 0; i < neighbor_indices_.size(); i++) {
(*results)[old_from_new_queries_[i]] =
(*results)[old_from_new_queries_[i/knns_]] =
old_from_new_references_[neighbor_indices_[i]];
}
distances.Copy(neighbor_distances_.ptr(), neighbor_distances.len());
distances->Copy(neighbor_distances_.ptr(),
neighbor_distances_.length());
} // ComputeNaive
}; //class AllNN
fastlib/u/nvasil/kernel_pca/build.py
+1 -1
View File
@@ -1,5 +1,5 @@
binrule(name="kptest",
headers=["kernel_pca.h", "kernel_pca_impl.h"],
headers=["kernel_pca.h", "kernel_pca_impl.h", "allknn.h"],
sources=["kernel_pca_test.cc"],
cflags=" -fexceptions",
deplibs=["sparse:sparse", "fastlib:fastlib", "la:la"]
fastlib/u/nvasil/kernel_pca/kernel_pca.h
+8 -11
View File
@@ -25,17 +25,14 @@
#include <unistd.h>
#include "fastlib/fastlib.h"
#include "la/matrix.h"
#include "u/nvasil/tree/binary_kd_tree_mmapmm.h"
#include "u/nvasil/dataset/binary_dataset.h"
#include "sparse/sparse_matrix.h"
#include "allknn.h"
class KernelPCATest;
class KernelPCA {
public:
friend class KernelPCATest;
typedef BinaryKdTreeMMAPMMKnnNode_t Tree_t;
typedef Tree_t::Precision_t Precision_t;
class GaussianKernel {
public:
void set(double bandwidth) {
@@ -50,10 +47,10 @@ class KernelPCA {
~KernelPCA() {
Destruct();
}
void Init(std::string data_file,
std::string index_file);
void Init(std::string data_file, index_t knns,
index_t leaf_size);
void Destruct();
void ComputeNeighborhoods(index_t knn);
void ComputeNeighborhoods();
void LoadAffinityMatrix();
void EstimateBandwidth(double *bandwidth);
static void SaveToTextFile(std::string file,
@@ -69,8 +66,7 @@ class KernelPCA {
Matrix *eigen_vectors,
std::vector<double> *eigen_values);
void ComputeIsomap(index_t num_of_eigenvalues);
void ComputeLLE(index_t knns,
index_t num_of_eigenvalues,
void ComputeLLE(index_t num_of_eigenvalues,
Matrix *eigen_vectors,
std::vector<double> *eigen_values);
template<typename KERNEL>
@@ -79,10 +75,11 @@ class KernelPCA {
void ComputeSpectralRegression(std::string label_file);
private:
Tree_t tree_;
AllkNN allknn_;
index_t knns_;
Matrix data_;
SparseMatrix kernel_matrix_;
SparseMatrix affinity_matrix_;
BinaryDataset<Precision_t> data_;
index_t dimension_;
};
fastlib/u/nvasil/kernel_pca/kernel_pca_impl.h
+36 -47
View File
@@ -17,43 +17,34 @@
*/
void KernelPCA::Init(std::string data_file,
std::string index_file) {
if (index_file.empty()) {
data_.Init(data_file);
} else {
data_.Init(data_file, index_file);
}
dimension_=data_.get_dimension();
tree_.Init(&data_);
mmapmm::MemoryManager<false>::allocator_ =
new mmapmm::MemoryManager<false>();
mmapmm::MemoryManager<false>::allocator_->Init();
void KernelPCA::Init(std::string data_file, index_t knns,
index_t leaf_size) {
data::Load(data_file.c_str(), &data_);
knns_ = knns;
allknn_.Init(data_, data_, leaf_size, knns);
}
void KernelPCA::Destruct() {
tree_.Destruct();
data_.Destruct();
unlink("allnn.txt");
if (mmapmm::MemoryManager<false>::allocator_ != NULL) {
delete mmapmm::MemoryManager<false>::allocator_;
mmapmm::MemoryManager<false>::allocator_=NULL;
}
}
void KernelPCA::ComputeNeighborhoods(index_t knns) {
void KernelPCA::ComputeNeighborhoods() {
NONFATAL("Building tree...\n");
fflush(stdout);
tree_.set_knns(knns);
tree_.BuildDepthFirst();
NONFATAL("Memory usage: %llu\n",
(unsigned long long)Tree_t::Allocator_t::allocator_->get_usage());
NONFATAL("Tree Statistics\n %s\n", tree_.Statistics().c_str());
NONFATAL("Computing all nearest neighbors...\n");
fflush(stdout);
tree_.AllNearestNeighbors(tree_.get_parent(), knns);
NONFATAL("Collecting results....\n");
tree_.CollectKNearestNeighborWithFwriteText("allnn.txt");
ArrayList<index_t> resulting_neighbors;
ArrayList<double> distances;
allknn_.ComputeNeighbors(&resulting_neighbors,
&distances);
FILE *fp=fopen("allnn.txt", "w");
if (fp==NULL) {
FATAL("Unable to open allnn for exporting the results, error %s\n",
strerror(errno));
}
for(index_t i=0; i<resulting_neighbors.size(); i++) {
fprintf(fp, "%lli %lli %lg\n", i / knns_, resulting_neighbors[i],
distances[i]);
}
fclose(fp);
}
template<typename DISTANCEKERNEL>
@@ -64,7 +55,7 @@ void KernelPCA::ComputeGeneralKernelPCA(DISTANCEKERNEL kernel,
kernel_matrix_.Copy(affinity_matrix_);
kernel_matrix_.ApplyFunction(kernel);
Vector temp;
temp.Init(kernel_matrix_.get_dimension());
temp.Init(kernel_matrix_.dimension());
temp.SetAll(1.0);
kernel_matrix_.SetDiagonal(temp);
kernel_matrix_.EndLoading();
@@ -127,48 +118,46 @@ void KernelPCA::EstimateBandwidth(double *bandwidth) {
*bandwidth=mean/count;
}
void KernelPCA::ComputeLLE(index_t knns,
index_t num_of_eigenvalues,
void KernelPCA::ComputeLLE(index_t num_of_eigenvalues,
Matrix *eigen_vectors,
std::vector<double> *eigen_values);
{
FILE *fp=fopen("allnn.txt");
std::vector<double> *eigen_values) {
FILE *fp=fopen("allnn.txt", "r");
if unlikely(fp==NULL) {
FATAL("Unable to open allnn.txt, error %s\n", strerror(errno));
}
uint64 p1, p2;
double dist;
double mean=0;
uint64 last_point=numeric_limits<uint64>::max();
Vector point;
point.Init(dimension_);
Matriix neighbors;
neighbor_vals.Init(dimension_, knns);
Matrix cov(neighbors);
Matrix neighbor_vals;
neighbor_vals.Init(dimension_, knns_);
Matrix cov(neighbor_vals);
Vector ones;
ones.Init(dimension_);
ones.SetAll(1);
Vector weights;
index_t neighbors[knns];
index_t neighbors[knns_];
index_t i;
kernel_matrix_.Init(data_.get_num_of_points(),
data_.get_num_of_point());
kernel_matrix_.Init(data_.n_rows(),
data_.n_rows(),
knns_);
while (!feof(fp)) {
fscanf(fp, "%llu %llu %lg", &p1, &p2, &dist);
i=0;
if (p1==last_point) {
memcpy(neighbor_vals.GetColumnPtr(i), data_.At(p2),
memcpy(neighbor_vals.GetColumnPtr(i), data_.GetColumnPtr(p2),
sizeof(double)*dimension_);
neighbors[i]=p2;
la::SubFrom(dimension_, point.ptr(), neighbor_vals.GetColumnPtr(i));
} else {
point.Copy(data_.At());
point.Copy(data_.GetColumnPtr(p1), dimension_);
last_point=p1;
i=0;
la::MulTransBInit(neighbor_vals, neighbor_vals, &cov);
la::SolveInit(cov, ones, &weights);
kernel_matrix_.LoadRow(p1, neighbors,weights.ptr());
weights.Destruct()
kernel_matrix_.LoadRow(p1, knns_, neighbors, weights.ptr());
weights.Destruct();
}
}
kernel_matrix_.Negate();
fastlib/u/nvasil/kernel_pca/kernel_pca_test.cc
+2 -2
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@@ -25,7 +25,7 @@ class KernelPCATest {
public:
void Init() {
engine_ = new KernelPCA();
engine_->Init("test_data_3_1000", "");
engine_->Init("test_data_3_1000.csv", 3, 20);
}
void Destruct() {
delete engine_;
@@ -34,7 +34,7 @@ class KernelPCATest {
Matrix eigen_vectors;
std::vector<double> eigen_values;
Init();
engine_->ComputeNeighborhoods(10);
engine_->ComputeNeighborhoods();
double bandwidth;
engine_->EstimateBandwidth(&bandwidth);
NONFATAL("Estimated bandwidth %lg ...\n", bandwidth);
fastlib/u/nvasil/kernel_pca/test_data_3_1000
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fastlib/u/nvasil/kernel_pca/test_data_3_1000.ind
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