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Commit 3dfa76a3 authored by Davis King's avatar Davis King
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Added svd_fast()

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dlib/matrix/matrix_la.h
View file @ 3dfa76a3
...@@ -815,6 +815,224 @@ convergence: ...@@ -815,6 +815,224 @@ convergence:
#endif #endif
} }
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <typename T>
void find_matrix_range (
const matrix<T>& A,
unsigned long l,
matrix<T>& Q,
unsigned long q = 0
)
/*!
requires
- A.nr() >= l
ensures
- #Q.nr() == A.nr()
- #Q.nc() == l
- #Q == an orthonormal matrix whose range approximates the range of the
matrix A.
- This function implements the randomized subspace iteration defined
in the algorithm 4.4 box of the paper:
Finding Structure with Randomness: Probabilistic Algorithms for
Constructing Approximate Matrix Decompositions by Halko et al.
- q defines the number of extra subspace iterations this algorithm will
perform. Often q == 0 is fine, but performing more iterations can lead to a
more accurate approximation of the range of A if A has slowly decaying
singular values. In these cases, using a q of 1 or 2 is good.
!*/
{
DLIB_ASSERT(A.nr() >= (long)l, "Invalid inputs were given to this function.");
matrix<T> G = matrix_cast<T>(gaussian_randm(A.nc(), l));
matrix<T> Y = A*G;
Q = qr_decomposition<matrix<T> >(Y).get_q();
// Do some extra iterations of the power method to make sure we get Q into the
// span of the most important singular vectors of A.
if (q != 0)
{
matrix<T> Z;
for (unsigned long itr = 0; itr < q; ++itr)
{
Z = trans(A)*Q;
Z = qr_decomposition<matrix<T> >(Z).get_q();
Y = A*Z;
Q = qr_decomposition<matrix<T> >(Y).get_q();
}
}
}
// ----------------------------------------------------------------------------------------
template <typename T>
void svd_fast (
const matrix<T>& A,
matrix<T>& u,
matrix<T,0,1>& w,
matrix<T>& v,
unsigned long l,
unsigned long q = 0
)
{
const unsigned long k = std::min(l, std::min<unsigned long>(A.nr(),A.nc()));
DLIB_ASSERT(l > 0 && A.size() > 0,
"\t void svd_fast()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t l: " << l
<< "\n\t A.size(): " << A.size()
);
matrix<T> Q;
find_matrix_range(A, k, Q, q);
// Compute trans(B) = trans(Q)*A. The reason we store B transposed
// is so that when we take its SVD later using svd3() it doesn't consume
// a whole lot of RAM. That is, we make sure the square matrix coming out
// of svd3() has size lxl rather than the potentially much larger nxn.
matrix<T> B = trans(A)*Q;
svd3(B, v,w,u);
u = Q*u;
}
// ----------------------------------------------------------------------------------------
template <typename sparse_vector_type, typename T>
void find_matrix_range (
const std::vector<sparse_vector_type>& A,
unsigned long l,
matrix<T>& Q,
unsigned long q = 0
)
/*!
requires
- A.size() >= l
ensures
- #Q.nr() == A.size()
- #Q.nc() == l
- #Q == an orthonormal matrix whose range approximates the range of the
matrix A. In this case, we interpret A as a matrix of A.size() rows,
where each row is defined by a sparse vector.
- This function implements the randomized subspace iteration defined
in the algorithm 4.4 box of the paper:
Finding Structure with Randomness: Probabilistic Algorithms for
Constructing Approximate Matrix Decompositions by Halko et al.
- q defines the number of extra subspace iterations this algorithm will
perform. Often q == 0 is fine, but performing more iterations can lead to a
more accurate approximation of the range of A if A has slowly decaying
singular values. In these cases, using a q of 1 or 2 is good.
!*/
{
DLIB_ASSERT(A.size() >= l, "Invalid inputs were given to this function.");
matrix<T> Y(A.size(), l);
// Compute Y = A*gaussian_randm()
for (long r = 0; r < Y.nr(); ++r)
{
for (long c = 0; c < Y.nc(); ++c)
{
Y(r,c) = dot(A[r], gaussian_randm(std::numeric_limits<long>::max(), 1, c));
}
}
Q = qr_decomposition<matrix<T> >(Y).get_q();
// Do some extra iterations of the power method to make sure we get Q into the
// span of the most important singular vectors of A.
if (q != 0)
{
const unsigned long n = max_index_plus_one(A);
matrix<T> Z(n, l);
for (unsigned long itr = 0; itr < q; ++itr)
{
// Compute Z = trans(A)*Q
Z = 0;
for (unsigned long m = 0; m < A.size(); ++m)
{
for (unsigned long r = 0; r < l; ++r)
{
typename sparse_vector_type::const_iterator i;
for (i = A[m].begin(); i != A[m].end(); ++i)
{
const unsigned long c = i->first;
const T val = i->second;
Z(c,r) += Q(m,r)*val;
}
}
}
Z = qr_decomposition<matrix<T> >(Z).get_q();
// Compute Y = A*Z
for (long r = 0; r < Y.nr(); ++r)
{
for (long c = 0; c < Y.nc(); ++c)
{
Y(r,c) = dot(A[r], colm(Z,c));
}
}
Q = qr_decomposition<matrix<T> >(Y).get_q();
}
}
}
// ----------------------------------------------------------------------------------------
template <typename sparse_vector_type, typename T>
void svd_fast (
const std::vector<sparse_vector_type>& A,
matrix<T>& u,
matrix<T,0,1>& w,
matrix<T>& v,
unsigned long l,
unsigned long q = 0
)
{
const long n = max_index_plus_one(A);
const unsigned long k = std::min(l, std::min<unsigned long>(A.size(),n));
DLIB_ASSERT(l > 0 && A.size() > 0 && n > 0,
"\t void svd_fast()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t l: " << l
<< "\n\t n (i.e. max_index_plus_one(A)): " << n
<< "\n\t A.size(): " << A.size()
);
matrix<T> Q;
find_matrix_range(A, k, Q, q);
// Compute trans(B) = trans(Q)*A. The reason we store B transposed
// is so that when we take its SVD later using svd3() it doesn't consume
// a whole lot of RAM. That is, we make sure the square matrix coming out
// of svd3() has size lxl rather than the potentially much larger nxn.
matrix<T> B(n,k);
B = 0;
for (unsigned long m = 0; m < A.size(); ++m)
{
for (unsigned long r = 0; r < k; ++r)
{
typename sparse_vector_type::const_iterator i;
for (i = A[m].begin(); i != A[m].end(); ++i)
{
const unsigned long c = i->first;
const T val = i->second;
B(c,r) += Q(m,r)*val;
}
}
}
svd3(B, v,w,u);
u = Q*u;
}
// ----------------------------------------------------------------------------------------
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
template < template <
......
dlib/matrix/matrix_la_abstract.h
View file @ 3dfa76a3
...@@ -133,6 +133,108 @@ namespace dlib ...@@ -133,6 +133,108 @@ namespace dlib
from LAPACK is used to compute the SVD. from LAPACK is used to compute the SVD.
!*/ !*/
// ----------------------------------------------------------------------------------------
template <
typename T
>
void svd_fast (
const matrix<T>& A,
matrix<T>& u,
matrix<T,0,1>& w,
matrix<T>& v,
unsigned long l,
unsigned long q = 0
);
/*!
requires
- l > 0
- A.size() > 0
(i.e. A can't be an empty matrix)
ensures
- computes the singular value decomposition of A.
- Lets define some constants we use to document the behavior of svd_fast():
- Let m = A.nr()
- Let n = A.nc()
- Let k = min(l, min(m,n))
- Therefore, A represents an m by n matrix and svd_fast() is designed
to find a rank-k representation of it.
- if (the rank of A is <= k) then
- A == #u*diagm(#w)*trans(#v)
- else
- A is approximated by #u*diagm(#w)*trans(#v)
(i.e. In this case A can't be represented with a rank-k matrix, so the
matrix you get by trying to reconstruct A from the output of the SVD is
not exactly the same.)
- trans(#u)*#u == identity matrix
- trans(#v)*#v == identity matrix
- #w == the top k singular values of the matrix A (in no particular order).
- #u.nr() == m
- #u.nc() == k
- #w.nr() == k
- #w.nc() == 1
- #v.nr() == n
- #v.nc() == k
- This function implements the randomized subspace iteration defined in the
algorithm 4.4 and 5.1 boxes of the paper:
Finding Structure with Randomness: Probabilistic Algorithms for
Constructing Approximate Matrix Decompositions by Halko et al.
Therefore, it is very fast and suitable for use with very large matrices.
!*/
// ----------------------------------------------------------------------------------------
template <
typename sparse_vector_type,
typename T
>
void svd_fast (
const std::vector<sparse_vector_type>& A,
matrix<T>& u,
matrix<T,0,1>& w,
matrix<T>& v,
unsigned long l,
unsigned long q = 0
);
/*!
requires
- A contains a set of sparse vectors. See dlib/svm/sparse_vector_abstract.h
for a definition of what constitutes a sparse vector.
- l > 0
- max_index_plus_one(A) > 0
(i.e. A can't be an empty matrix)
ensures
- computes the singular value decomposition of A. In this case, we interpret A
as a matrix of A.size() rows, where each row is defined by a sparse vector.
- Lets define some constants we use to document the behavior of svd_fast():
- Let m = A.size()
- Let n = max_index_plus_one(A)
- Let k = min(l, min(m,n))
- Therefore, A represents an m by n matrix and svd_fast() is designed
to find a rank-k representation of it.
- if (the rank of A is <= k) then
- A == #u*diagm(#w)*trans(#v)
- else
- A is approximated by #u*diagm(#w)*trans(#v)
(i.e. In this case A can't be represented with a rank-k matrix, so the
matrix you get by trying to reconstruct A from the output of the SVD is
not exactly the same.)
- trans(#u)*#u == identity matrix
- trans(#v)*#v == identity matrix
- #w == the top k singular values of the matrix A (in no particular order).
- #u.nr() == m
- #u.nc() == k
- #w.nr() == k
- #w.nc() == 1
- #v.nr() == n
- #v.nc() == k
- This function implements the randomized subspace iteration defined in the
algorithm 4.4 and 5.1 boxes of the paper:
Finding Structure with Randomness: Probabilistic Algorithms for
Constructing Approximate Matrix Decompositions by Halko et al.
Therefore, it is very fast and suitable for use with very large matrices.
!*/
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
const matrix real_eigenvalues ( const matrix real_eigenvalues (
......
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