Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/sparse/__init__.py

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+"""
+=====================================
+Sparse matrices (:mod:`scipy.sparse`)
+=====================================
+
+.. currentmodule:: scipy.sparse
+
+SciPy 2-D sparse matrix package for numeric data.
+
+Contents
+========
+
+Sparse matrix classes
+---------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   bsr_matrix - Block Sparse Row matrix
+   coo_matrix - A sparse matrix in COOrdinate format
+   csc_matrix - Compressed Sparse Column matrix
+   csr_matrix - Compressed Sparse Row matrix
+   dia_matrix - Sparse matrix with DIAgonal storage
+   dok_matrix - Dictionary Of Keys based sparse matrix
+   lil_matrix - Row-based list of lists sparse matrix
+   spmatrix - Sparse matrix base class
+
+Functions
+---------
+
+Building sparse matrices:
+
+.. autosummary::
+   :toctree: generated/
+
+   eye - Sparse MxN matrix whose k-th diagonal is all ones
+   identity - Identity matrix in sparse format
+   kron - kronecker product of two sparse matrices
+   kronsum - kronecker sum of sparse matrices
+   diags - Return a sparse matrix from diagonals
+   spdiags - Return a sparse matrix from diagonals
+   block_diag - Build a block diagonal sparse matrix
+   tril - Lower triangular portion of a matrix in sparse format
+   triu - Upper triangular portion of a matrix in sparse format
+   bmat - Build a sparse matrix from sparse sub-blocks
+   hstack - Stack sparse matrices horizontally (column wise)
+   vstack - Stack sparse matrices vertically (row wise)
+   rand - Random values in a given shape
+   random - Random values in a given shape
+
+Save and load sparse matrices:
+
+.. autosummary::
+   :toctree: generated/
+
+   save_npz - Save a sparse matrix to a file using ``.npz`` format.
+   load_npz - Load a sparse matrix from a file using ``.npz`` format.
+
+Sparse matrix tools:
+
+.. autosummary::
+   :toctree: generated/
+
+   find
+
+Identifying sparse matrices:
+
+.. autosummary::
+   :toctree: generated/
+
+   issparse
+   isspmatrix
+   isspmatrix_csc
+   isspmatrix_csr
+   isspmatrix_bsr
+   isspmatrix_lil
+   isspmatrix_dok
+   isspmatrix_coo
+   isspmatrix_dia
+
+Submodules
+----------
+
+.. autosummary::
+
+   csgraph - Compressed sparse graph routines
+   linalg - sparse linear algebra routines
+
+Exceptions
+----------
+
+.. autosummary::
+   :toctree: generated/
+
+   SparseEfficiencyWarning
+   SparseWarning
+
+
+Usage information
+=================
+
+There are seven available sparse matrix types:
+
+    1. csc_matrix: Compressed Sparse Column format
+    2. csr_matrix: Compressed Sparse Row format
+    3. bsr_matrix: Block Sparse Row format
+    4. lil_matrix: List of Lists format
+    5. dok_matrix: Dictionary of Keys format
+    6. coo_matrix: COOrdinate format (aka IJV, triplet format)
+    7. dia_matrix: DIAgonal format
+
+To construct a matrix efficiently, use either dok_matrix or lil_matrix.
+The lil_matrix class supports basic slicing and fancy indexing with a
+similar syntax to NumPy arrays. As illustrated below, the COO format
+may also be used to efficiently construct matrices. Despite their
+similarity to NumPy arrays, it is **strongly discouraged** to use NumPy
+functions directly on these matrices because NumPy may not properly convert
+them for computations, leading to unexpected (and incorrect) results. If you
+do want to apply a NumPy function to these matrices, first check if SciPy has
+its own implementation for the given sparse matrix class, or **convert the
+sparse matrix to a NumPy array** (e.g., using the `toarray()` method of the
+class) first before applying the method.
+
+To perform manipulations such as multiplication or inversion, first
+convert the matrix to either CSC or CSR format. The lil_matrix format is
+row-based, so conversion to CSR is efficient, whereas conversion to CSC
+is less so.
+
+All conversions among the CSR, CSC, and COO formats are efficient,
+linear-time operations.
+
+Matrix vector product
+---------------------
+To do a vector product between a sparse matrix and a vector simply use
+the matrix `dot` method, as described in its docstring:
+
+>>> import numpy as np
+>>> from scipy.sparse import csr_matrix
+>>> A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
+>>> v = np.array([1, 0, -1])
+>>> A.dot(v)
+array([ 1, -3, -1], dtype=int64)
+
+.. warning:: As of NumPy 1.7, `np.dot` is not aware of sparse matrices,
+  therefore using it will result on unexpected results or errors.
+  The corresponding dense array should be obtained first instead:
+
+  >>> np.dot(A.toarray(), v)
+  array([ 1, -3, -1], dtype=int64)
+
+  but then all the performance advantages would be lost.
+
+The CSR format is specially suitable for fast matrix vector products.
+
+Example 1
+---------
+Construct a 1000x1000 lil_matrix and add some values to it:
+
+>>> from scipy.sparse import lil_matrix
+>>> from scipy.sparse.linalg import spsolve
+>>> from numpy.linalg import solve, norm
+>>> from numpy.random import rand
+
+>>> A = lil_matrix((1000, 1000))
+>>> A[0, :100] = rand(100)
+>>> A[1, 100:200] = A[0, :100]
+>>> A.setdiag(rand(1000))
+
+Now convert it to CSR format and solve A x = b for x:
+
+>>> A = A.tocsr()
+>>> b = rand(1000)
+>>> x = spsolve(A, b)
+
+Convert it to a dense matrix and solve, and check that the result
+is the same:
+
+>>> x_ = solve(A.toarray(), b)
+
+Now we can compute norm of the error with:
+
+>>> err = norm(x-x_)
+>>> err < 1e-10
+True
+
+It should be small :)
+
+
+Example 2
+---------
+
+Construct a matrix in COO format:
+
+>>> from scipy import sparse
+>>> from numpy import array
+>>> I = array([0,3,1,0])
+>>> J = array([0,3,1,2])
+>>> V = array([4,5,7,9])
+>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
+
+Notice that the indices do not need to be sorted.
+
+Duplicate (i,j) entries are summed when converting to CSR or CSC.
+
+>>> I = array([0,0,1,3,1,0,0])
+>>> J = array([0,2,1,3,1,0,0])
+>>> V = array([1,1,1,1,1,1,1])
+>>> B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()
+
+This is useful for constructing finite-element stiffness and mass matrices.
+
+Further details
+---------------
+
+CSR column indices are not necessarily sorted. Likewise for CSC row
+indices. Use the .sorted_indices() and .sort_indices() methods when
+sorted indices are required (e.g., when passing data to other libraries).
+
+"""
+
+# Original code by Travis Oliphant.
+# Modified and extended by Ed Schofield, Robert Cimrman,
+# Nathan Bell, and Jake Vanderplas.
+
+import warnings as _warnings
+
+from .base import *
+from .csr import *
+from .csc import *
+from .lil import *
+from .dok import *
+from .coo import *
+from .dia import *
+from .bsr import *
+from .construct import *
+from .extract import *
+from ._matrix_io import *
+
+# For backward compatibility with v0.19.
+from . import csgraph
+
+__all__ = [s for s in dir() if not s.startswith('_')]
+
+# Filter PendingDeprecationWarning for np.matrix introduced with numpy 1.15
+_warnings.filterwarnings('ignore', message='the matrix subclass is not the recommended way')
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester