184 lines
5.7 KiB
Python
184 lines
5.7 KiB
Python
"""Sparse matrix norms.
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"""
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from __future__ import division, print_function, absolute_import
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import numpy as np
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from scipy.sparse import issparse
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from numpy.core import Inf, sqrt, abs
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__all__ = ['norm']
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def _sparse_frobenius_norm(x):
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if np.issubdtype(x.dtype, np.complexfloating):
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sqnorm = abs(x).power(2).sum()
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else:
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sqnorm = x.power(2).sum()
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return sqrt(sqnorm)
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def norm(x, ord=None, axis=None):
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"""
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Norm of a sparse matrix
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This function is able to return one of seven different matrix norms,
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depending on the value of the ``ord`` parameter.
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Parameters
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----------
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x : a sparse matrix
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Input sparse matrix.
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ord : {non-zero int, inf, -inf, 'fro'}, optional
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Order of the norm (see table under ``Notes``). inf means numpy's
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`inf` object.
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axis : {int, 2-tuple of ints, None}, optional
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If `axis` is an integer, it specifies the axis of `x` along which to
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compute the vector norms. If `axis` is a 2-tuple, it specifies the
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axes that hold 2-D matrices, and the matrix norms of these matrices
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are computed. If `axis` is None then either a vector norm (when `x`
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is 1-D) or a matrix norm (when `x` is 2-D) is returned.
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Returns
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-------
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n : float or ndarray
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Notes
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-----
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Some of the ord are not implemented because some associated functions like,
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_multi_svd_norm, are not yet available for sparse matrix.
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This docstring is modified based on numpy.linalg.norm.
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https://github.com/numpy/numpy/blob/master/numpy/linalg/linalg.py
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The following norms can be calculated:
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===== ============================
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ord norm for sparse matrices
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===== ============================
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None Frobenius norm
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'fro' Frobenius norm
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inf max(sum(abs(x), axis=1))
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-inf min(sum(abs(x), axis=1))
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0 abs(x).sum(axis=axis)
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1 max(sum(abs(x), axis=0))
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-1 min(sum(abs(x), axis=0))
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2 Not implemented
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-2 Not implemented
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other Not implemented
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===== ============================
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The Frobenius norm is given by [1]_:
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:math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
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References
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----------
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.. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
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Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
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Examples
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--------
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>>> from scipy.sparse import *
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>>> import numpy as np
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>>> from scipy.sparse.linalg import norm
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>>> a = np.arange(9) - 4
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>>> a
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array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
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>>> b = a.reshape((3, 3))
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>>> b
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array([[-4, -3, -2],
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[-1, 0, 1],
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[ 2, 3, 4]])
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>>> b = csr_matrix(b)
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>>> norm(b)
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7.745966692414834
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>>> norm(b, 'fro')
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7.745966692414834
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>>> norm(b, np.inf)
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9
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>>> norm(b, -np.inf)
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2
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>>> norm(b, 1)
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7
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>>> norm(b, -1)
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6
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"""
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if not issparse(x):
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raise TypeError("input is not sparse. use numpy.linalg.norm")
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# Check the default case first and handle it immediately.
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if axis is None and ord in (None, 'fro', 'f'):
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return _sparse_frobenius_norm(x)
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# Some norms require functions that are not implemented for all types.
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x = x.tocsr()
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if axis is None:
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axis = (0, 1)
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elif not isinstance(axis, tuple):
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msg = "'axis' must be None, an integer or a tuple of integers"
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try:
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int_axis = int(axis)
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except TypeError:
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raise TypeError(msg)
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if axis != int_axis:
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raise TypeError(msg)
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axis = (int_axis,)
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nd = 2
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if len(axis) == 2:
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row_axis, col_axis = axis
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if not (-nd <= row_axis < nd and -nd <= col_axis < nd):
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raise ValueError('Invalid axis %r for an array with shape %r' %
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(axis, x.shape))
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if row_axis % nd == col_axis % nd:
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raise ValueError('Duplicate axes given.')
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if ord == 2:
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raise NotImplementedError
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#return _multi_svd_norm(x, row_axis, col_axis, amax)
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elif ord == -2:
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raise NotImplementedError
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#return _multi_svd_norm(x, row_axis, col_axis, amin)
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elif ord == 1:
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return abs(x).sum(axis=row_axis).max(axis=col_axis)[0,0]
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elif ord == Inf:
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return abs(x).sum(axis=col_axis).max(axis=row_axis)[0,0]
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elif ord == -1:
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return abs(x).sum(axis=row_axis).min(axis=col_axis)[0,0]
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elif ord == -Inf:
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return abs(x).sum(axis=col_axis).min(axis=row_axis)[0,0]
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elif ord in (None, 'f', 'fro'):
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# The axis order does not matter for this norm.
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return _sparse_frobenius_norm(x)
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else:
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raise ValueError("Invalid norm order for matrices.")
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elif len(axis) == 1:
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a, = axis
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if not (-nd <= a < nd):
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raise ValueError('Invalid axis %r for an array with shape %r' %
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(axis, x.shape))
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if ord == Inf:
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M = abs(x).max(axis=a)
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elif ord == -Inf:
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M = abs(x).min(axis=a)
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elif ord == 0:
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# Zero norm
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M = (x != 0).sum(axis=a)
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elif ord == 1:
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# special case for speedup
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M = abs(x).sum(axis=a)
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elif ord in (2, None):
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M = sqrt(abs(x).power(2).sum(axis=a))
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else:
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try:
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ord + 1
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except TypeError:
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raise ValueError('Invalid norm order for vectors.')
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M = np.power(abs(x).power(ord).sum(axis=a), 1 / ord)
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return M.A.ravel()
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else:
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raise ValueError("Improper number of dimensions to norm.")
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