Struct faiss::ITQMatrix

struct faiss::ITQMatrix : public faiss::LinearTransform

ITQ implementation from

Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval,

Yunchao Gong, Svetlana Lazebnik, Albert Gordo, Florent Perronnin, PAMI’12.

Public Types

typedef Index::idx_t idx_t

Public Functions

explicit ITQMatrix(int d = 0)
virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors. Does nothing by default.

Parameters
  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void apply_noalloc(idx_t n, const float *x, float *xt) const override

same as apply, but result is pre-allocated

void transform_transpose(idx_t n, const float *y, float *x) const

compute x = A^T * (x - b) is reverse transform if A has orthonormal lines

virtual void reverse_transform(idx_t n, const float *xt, float *x) const override

works only if is_orthonormal

void set_is_orthonormal()

compute A^T * A to set the is_orthonormal flag

void print_if_verbose(const char *name, const std::vector<double> &mat, int n, int d) const
float *apply(idx_t n, const float *x) const

apply the random rotation, return new allocated matrix

Parameters

x – size n * d_in

Returns

size n * d_out

Public Members

int max_iter
int seed
std::vector<double> init_rotation
bool have_bias
bool is_orthonormal

! whether to use the bias term

check if matrix A is orthonormal (enables reverse_transform)

std::vector<float> A

Transformation matrix, size d_out * d_in.

std::vector<float> b

bias vector, size d_out

bool verbose
int d_in
int d_out

! input dimension

bool is_trained

set if the VectorTransform does not require training, or if training is done already