/data/users/matthijs/github_faiss/faiss/Index.h

/**
 * Copyright (c) 2015-present, Facebook, Inc.
 * All rights reserved.
 *
 * This source code is licensed under the BSD+Patents license found in the
 * LICENSE file in the root directory of this source tree.
 */

// Copyright 2004-present Facebook. All Rights Reserved
// -*- c++ -*-

#ifndef FAISS_INDEX_H
#define FAISS_INDEX_H


#include <cstdio>
#include <typeinfo>
#include <string>
#include <sstream>


/**
 * @namespace faiss
 *
 * Throughout the library, vectors are provided as float * pointers.
 * Most algorithms can be optimized when several vectors are processed
 * (added/searched) together in a batch. In this case, they are passed
 * in as a matrix. When n vectors of size d are provided as float * x,
 * component j of vector i is
 *
 *   x[ i * d + j ]
 *
 * where 0 <= i < n and 0 <= j < d. In other words, matrices are
 * always compact. When specifying the size of the matrix, we call it
 * an n*d matrix, which implies a row-major storage.
 */


namespace faiss {


/// Some algorithms support both an inner product vetsion and a L2 search version.
enum MetricType {
    METRIC_INNER_PRODUCT = 0,
    METRIC_L2 = 1,
};


/// Forward declarations see AuxIndexStructures.h
struct IDSelector;
struct RangeSearchResult;

/** Abstract structure for an index
 *
 * Supports adding vertices and searching them.
 *
 * Currently only asymmetric queries are supported:
 * database-to-database queries are not implemented.
 */
struct Index {

    typedef long idx_t;    ///< all indices are this type

    int d;                 ///< vector dimension
    idx_t ntotal;          ///< total nb of indexed vectors
    bool verbose;          ///< verbosity level

    /// set if the Index does not require training, or if training is done already
    bool is_trained;

    /// type of metric this index uses for search
    MetricType metric_type;

    explicit Index (idx_t d = 0, MetricType metric = METRIC_INNER_PRODUCT):
                    d(d),
                    ntotal(0),
                    verbose(false),
                    is_trained(true),
                    metric_type (metric) {}

    virtual ~Index () {  }


    /** Perform training on a representative set of vectors
     *
     * @param n      nb of training vectors
     * @param x      training vecors, size n * d
     */
    virtual void train(idx_t /*n*/, const float* /*x*/) {
      // does nothing by default
    }

    /** Add n vectors of dimension d to the index.
     *
     * Vectors are implicitly assigned labels ntotal .. ntotal + n - 1
     * This function slices the input vectors in chuncks smaller than
     * blocksize_add and calls add_core.
     * @param x      input matrix, size n * d
     */
    virtual void add (idx_t n, const float *x) = 0;

    /** Same as add, but stores xids instead of sequential ids.
     *
     * The default implementation fails with an assertion, as it is
     * not supported by all indexes.
     *
     * @param xids if non-null, ids to store for the vectors (size n)
     */
    virtual void add_with_ids (idx_t n, const float * x, const long *xids);

    /** query n vectors of dimension d to the index.
     *
     * return at most k vectors. If there are not enough results for a
     * query, the result array is padded with -1s.
     *
     * @param x           input vectors to search, size n * d
     * @param labels      output labels of the NNs, size n*k
     * @param distances   output pairwise distances, size n*k
     */
    virtual void search (idx_t n, const float *x, idx_t k,
                         float *distances, idx_t *labels) const = 0;

    /** query n vectors of dimension d to the index.
     *
     * return all vectors with distance < radius. Note that many
     * indexes do not implement the range_search (only the k-NN search
     * is mandatory).
     *
     * @param x           input vectors to search, size n * d
     * @param radius      search radius
     * @param result      result table
     */
    virtual void range_search (idx_t n, const float *x, float radius,
                               RangeSearchResult *result) const;

    /** return the indexes of the k vectors closest to the query x.
     *
     * This function is identical as search but only return labels of neighbors.
     * @param x           input vectors to search, size n * d
     * @param labels      output labels of the NNs, size n*k
     */
    void assign (idx_t n, const float * x, idx_t * labels, idx_t k = 1);

    /// removes all elements from the database.
    virtual void reset() = 0;

    /** removes IDs from the index. Not supported by all indexes
     */
    virtual long remove_ids (const IDSelector & sel);

    /** Reconstruct a stored vector (or an approximation if lossy coding)
     *
     * this function may not be defined for some indexes
     * @param key         id of the vector to reconstruct
     * @param recons      reconstucted vector (size d)
     */
    virtual void reconstruct (idx_t key, float * recons) const;


    /** Reconstruct vectors i0 to i0 + ni - 1
     *
     * this function may not be defined for some indexes
     * @param recons      reconstucted vector (size ni * d)
     */
    virtual void reconstruct_n (idx_t i0, idx_t ni, float *recons) const;


    /** Computes a residual vector after indexing encoding.
     *
     * The residual vector is the difference between a vector and the
     * reconstruction that can be decoded from its representation in
     * the index. The residual can be used for multiple-stage indexing
     * methods, like IndexIVF's methods.
     *
     * @param x           input vector, size d
     * @param residual    output residual vector, size d
     * @param key         encoded index, as returned by search and assign
     */
    void compute_residual (const float * x, float * residual, idx_t key) const;

    /** Display the actual class name and some more info */
    void display () const;



};

}


#endif