""" Sketching-based Matrix Computations """

# Author: Jordi Montes <jomsdev@gmail.com>
# August 28, 2017

import numpy as np

from scipy._lib._util import (check_random_state, rng_integers,
                              _transition_to_rng, _apply_over_batch)

__all__ = ['clarkson_woodruff_transform']


def cwt_matrix(n_rows, n_columns, rng=None):
    r"""
    Generate a matrix S which represents a Clarkson-Woodruff transform.

    Given the desired size of matrix, the method returns a matrix S of size
    (n_rows, n_columns) where each column has all the entries set to 0
    except for one position which has been randomly set to +1 or -1 with
    equal probability.

    Parameters
    ----------
    n_rows : int
        Number of rows of S
    n_columns : int
        Number of columns of S
    rng : `numpy.random.Generator`, optional
        Pseudorandom number generator state. When `rng` is None, a new
        `numpy.random.Generator` is created using entropy from the
        operating system. Types other than `numpy.random.Generator` are
        passed to `numpy.random.default_rng` to instantiate a ``Generator``.

    Returns
    -------
    S : (n_rows, n_columns) csc_array
        The returned matrix has ``n_columns`` nonzero entries.

    Notes
    -----
    Given a matrix A, with probability at least 9/10,
    .. math:: \|SA\| = (1 \pm \epsilon)\|A\|
    Where the error epsilon is related to the size of S.
    """
    # lazy import to prevent to prevent sparse dependency for whole module (gh-23420)
    from scipy.sparse import csc_array
    rng = check_random_state(rng)
    rows = rng_integers(rng, 0, n_rows, n_columns)
    cols = np.arange(n_columns+1)
    signs = rng.choice([1, -1], n_columns)
    S = csc_array((signs, rows, cols), shape=(n_rows, n_columns))
    return S


@_transition_to_rng("seed", position_num=2)
def clarkson_woodruff_transform(input_matrix, sketch_size, rng=None):
    r"""
    Applies a Clarkson-Woodruff Transform/sketch to the input matrix.

    Given an input_matrix ``A`` of size ``(n, d)``, compute a matrix ``A'`` of
    size (sketch_size, d) so that

    .. math:: \|Ax\| \approx \|A'x\|

    with high probability via the Clarkson-Woodruff Transform, otherwise
    known as the CountSketch matrix.

    The documentation is written assuming array arguments are of specified
    "core" shapes. However, array argument(s) of this function may have additional
    "batch" dimensions prepended to the core shape. In this case, the array is treated
    as a batch of lower-dimensional slices; see :ref:`linalg_batch` for details.

    Parameters
    ----------
    input_matrix : array_like, shape (..., n, d)
        Input matrix.
    sketch_size : int
        Number of rows for the sketch.
    rng : `numpy.random.Generator`, optional
        Pseudorandom number generator state. When `rng` is None, a new
        `numpy.random.Generator` is created using entropy from the
        operating system. Types other than `numpy.random.Generator` are
        passed to `numpy.random.default_rng` to instantiate a ``Generator``.

    Returns
    -------
    A' : array_like
        Sketch of the input matrix ``A``, of size ``(sketch_size, d)``.

    Notes
    -----
    To make the statement

    .. math:: \|Ax\| \approx \|A'x\|

    precise, observe the following result which is adapted from the
    proof of Theorem 14 of [2]_ via Markov's Inequality. If we have
    a sketch size ``sketch_size=k`` which is at least

    .. math:: k \geq \frac{2}{\epsilon^2\delta}

    Then for any fixed vector ``x``,

    .. math:: \|Ax\| = (1\pm\epsilon)\|A'x\|

    with probability at least one minus delta.

    This implementation takes advantage of sparsity: computing
    a sketch takes time proportional to ``A.nnz``. Data ``A`` which
    is in ``scipy.sparse.csc_array`` format gives the quickest
    computation time for sparse input.

    >>> import numpy as np
    >>> from scipy import linalg
    >>> from scipy import sparse
    >>> rng = np.random.default_rng()
    >>> *shape, density, sketch_n_rows = 15000, 100, 0.01, 200
    >>> A = sparse.random_array(shape, density=density, format='csc')
    >>> B = sparse.random_array(shape, density=density, format='csr')
    >>> C = sparse.random_array(shape, density=density, format='coo')
    >>> D = rng.standard_normal(shape)
    >>> SA = linalg.clarkson_woodruff_transform(A, sketch_n_rows) # fastest
    >>> SB = linalg.clarkson_woodruff_transform(B, sketch_n_rows) # fast
    >>> SC = linalg.clarkson_woodruff_transform(C, sketch_n_rows) # slower
    >>> SD = linalg.clarkson_woodruff_transform(D, sketch_n_rows) # slowest

    That said, this method does perform well on dense inputs, just slower
    on a relative scale.

    References
    ----------
    .. [1] Kenneth L. Clarkson and David P. Woodruff. Low rank approximation
           and regression in input sparsity time. In STOC, 2013.
    .. [2] David P. Woodruff. Sketching as a tool for numerical linear algebra.
           In Foundations and Trends in Theoretical Computer Science, 2014.

    Examples
    --------
    Create a big dense matrix ``A`` for the example:

    >>> import numpy as np
    >>> from scipy import linalg
    >>> n_rows, n_columns  = 15000, 100
    >>> rng = np.random.default_rng()
    >>> A = rng.standard_normal((n_rows, n_columns))

    Apply the transform to create a new matrix with 200 rows:

    >>> sketch_n_rows = 200
    >>> sketch = linalg.clarkson_woodruff_transform(A, sketch_n_rows, seed=rng)
    >>> sketch.shape
    (200, 100)

    Now with high probability, the true norm is close to the sketched norm
    in absolute value.

    >>> linalg.norm(A)
    1224.2812927123198
    >>> linalg.norm(sketch)
    1226.518328407333

    Similarly, applying our sketch preserves the solution to a linear
    regression of :math:`\min \|Ax - b\|`.

    >>> b = rng.standard_normal(n_rows)
    >>> x = linalg.lstsq(A, b)[0]
    >>> Ab = np.hstack((A, b.reshape(-1, 1)))
    >>> SAb = linalg.clarkson_woodruff_transform(Ab, sketch_n_rows, seed=rng)
    >>> SA, Sb = SAb[:, :-1], SAb[:, -1]
    >>> x_sketched = linalg.lstsq(SA, Sb)[0]

    As with the matrix norm example, ``linalg.norm(A @ x - b)`` is close
    to ``linalg.norm(A @ x_sketched - b)`` with high probability.

    >>> linalg.norm(A @ x - b)
    122.83242365433877
    >>> linalg.norm(A @ x_sketched - b)
    166.58473879945151

    """
    # lazy import to prevent to prevent sparse dependency for whole module (gh-23420)
    from scipy.sparse import issparse
    if issparse(input_matrix) and input_matrix.ndim > 2:
        message = "Batch support for sparse arrays is not available."
        raise NotImplementedError(message)

    S = cwt_matrix(sketch_size, input_matrix.shape[-2], rng=rng)
    if input_matrix.ndim <= 2:
        # transposes are cheap and ensure output class matches input_matrix
        return (input_matrix.T @ S.T).T

    # Despite argument order (required by decorator), this is  S @ input_matrix
    # Can avoid _batch_dot when gh-22153 is resolved.
    return _batch_dot(input_matrix, S)


@_apply_over_batch(('input_matrix', 2))
def _batch_dot(input_matrix, S):
    return S @ input_matrix
