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/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
// -*- c++ -*-
#ifndef FAISS_VECTOR_TRANSFORM_H
#define FAISS_VECTOR_TRANSFORM_H
/** Defines a few objects that apply transformations to a set of
* vectors Often these are pre-processing steps.
*/
#include <vector>
#include <stdint.h>
#include <faiss/Index.h>
namespace faiss {
/** Any transformation applied on a set of vectors */
struct VectorTransform {
typedef Index::idx_t idx_t;
int d_in; ///! input dimension
int d_out; ///! output dimension
explicit VectorTransform (int d_in = 0, int d_out = 0):
d_in(d_in), d_out(d_out), is_trained(true)
{}
/// set if the VectorTransform does not require training, or if
/// training is done already
bool is_trained;
/** Perform training on a representative set of vectors. Does
* nothing by default.
*
* @param n nb of training vectors
* @param x training vecors, size n * d
*/
virtual void train (idx_t n, const float *x);
/** apply the random roation, return new allocated matrix
* @param x size n * d_in
* @return size n * d_out
*/
float *apply (idx_t n, const float * x) const;
/// same as apply, but result is pre-allocated
virtual void apply_noalloc (idx_t n, const float * x,
float *xt) const = 0;
/// reverse transformation. May not be implemented or may return
/// approximate result
virtual void reverse_transform (idx_t n, const float * xt,
float *x) const;
virtual ~VectorTransform () {}
};
/** Generic linear transformation, with bias term applied on output
* y = A * x + b
*/
struct LinearTransform: VectorTransform {
bool have_bias; ///! whether to use the bias term
/// check if matrix A is orthonormal (enables reverse_transform)
bool is_orthonormal;
/// Transformation matrix, size d_out * d_in
std::vector<float> A;
/// bias vector, size d_out
std::vector<float> b;
/// both d_in > d_out and d_out < d_in are supported
explicit LinearTransform (int d_in = 0, int d_out = 0,
bool have_bias = false);
/// same as apply, but result is pre-allocated
void apply_noalloc(idx_t n, const float* x, float* xt) const override;
/// compute x = A^T * (x - b)
/// is reverse transform if A has orthonormal lines
void transform_transpose (idx_t n, const float * y,
float *x) const;
/// works only if is_orthonormal
void reverse_transform (idx_t n, const float * xt,
float *x) const override;
/// compute A^T * A to set the is_orthonormal flag
void set_is_orthonormal ();
bool verbose;
void print_if_verbose (const char*name, const std::vector<double> &mat,
int n, int d) const;
~LinearTransform() override {}
};
/// Randomly rotate a set of vectors
struct RandomRotationMatrix: LinearTransform {
/// both d_in > d_out and d_out < d_in are supported
RandomRotationMatrix (int d_in, int d_out):
LinearTransform(d_in, d_out, false) {}
/// must be called before the transform is used
void init(int seed);
// intializes with an arbitrary seed
void train(idx_t n, const float* x) override;
RandomRotationMatrix () {}
};
/** Applies a principal component analysis on a set of vectors,
* with optionally whitening and random rotation. */
struct PCAMatrix: LinearTransform {
/** after transformation the components are multiplied by
* eigenvalues^eigen_power
*
* =0: no whitening
* =-0.5: full whitening
*/
float eigen_power;
/// random rotation after PCA
bool random_rotation;
/// ratio between # training vectors and dimension
size_t max_points_per_d;
/// try to distribute output eigenvectors in this many bins
int balanced_bins;
/// Mean, size d_in
std::vector<float> mean;
/// eigenvalues of covariance matrix (= squared singular values)
std::vector<float> eigenvalues;
/// PCA matrix, size d_in * d_in
std::vector<float> PCAMat;
// the final matrix is computed after random rotation and/or whitening
explicit PCAMatrix (int d_in = 0, int d_out = 0,
float eigen_power = 0, bool random_rotation = false);
/// train on n vectors. If n < d_in then the eigenvector matrix
/// will be completed with 0s
void train(idx_t n, const float* x) override;
/// copy pre-trained PCA matrix
void copy_from (const PCAMatrix & other);
/// called after mean, PCAMat and eigenvalues are computed
void prepare_Ab();
};
} // namespace faiss
#endif
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