365 lines
11 KiB
Python
365 lines
11 KiB
Python
"""Compressed Sparse Column matrix format"""
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__docformat__ = "restructuredtext en"
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__all__ = ['csc_array', 'csc_matrix', 'isspmatrix_csc']
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import numpy as np
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from ._matrix import spmatrix
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from ._base import _spbase, sparray
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from ._sparsetools import csc_tocsr, expandptr
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from ._sputils import upcast
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from ._compressed import _cs_matrix
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class _csc_base(_cs_matrix):
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_format = 'csc'
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def transpose(self, axes=None, copy=False):
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if axes is not None and axes != (1, 0):
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raise ValueError("Sparse arrays/matrices do not support "
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"an 'axes' parameter because swapping "
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"dimensions is the only logical permutation.")
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M, N = self.shape
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return self._csr_container((self.data, self.indices,
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self.indptr), (N, M), copy=copy)
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transpose.__doc__ = _spbase.transpose.__doc__
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def __iter__(self):
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yield from self.tocsr()
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def tocsc(self, copy=False):
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if copy:
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return self.copy()
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else:
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return self
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tocsc.__doc__ = _spbase.tocsc.__doc__
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def tocsr(self, copy=False):
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M,N = self.shape
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idx_dtype = self._get_index_dtype((self.indptr, self.indices),
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maxval=max(self.nnz, N))
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indptr = np.empty(M + 1, dtype=idx_dtype)
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indices = np.empty(self.nnz, dtype=idx_dtype)
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data = np.empty(self.nnz, dtype=upcast(self.dtype))
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csc_tocsr(M, N,
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self.indptr.astype(idx_dtype),
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self.indices.astype(idx_dtype),
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self.data,
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indptr,
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indices,
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data)
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A = self._csr_container(
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(data, indices, indptr),
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shape=self.shape, copy=False
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)
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A.has_sorted_indices = True
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return A
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tocsr.__doc__ = _spbase.tocsr.__doc__
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def nonzero(self):
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# CSC can't use _cs_matrix's .nonzero method because it
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# returns the indices sorted for self transposed.
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# Get row and col indices, from _cs_matrix.tocoo
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major_dim, minor_dim = self._swap(self.shape)
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minor_indices = self.indices
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major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype)
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expandptr(major_dim, self.indptr, major_indices)
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row, col = self._swap((major_indices, minor_indices))
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# Remove explicit zeros
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nz_mask = self.data != 0
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row = row[nz_mask]
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col = col[nz_mask]
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# Sort them to be in C-style order
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ind = np.argsort(row, kind='mergesort')
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row = row[ind]
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col = col[ind]
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return row, col
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nonzero.__doc__ = _cs_matrix.nonzero.__doc__
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def _getrow(self, i):
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"""Returns a copy of row i of the matrix, as a (1 x n)
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CSR matrix (row vector).
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"""
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M, N = self.shape
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i = int(i)
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if i < 0:
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i += M
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if i < 0 or i >= M:
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raise IndexError('index (%d) out of range' % i)
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return self._get_submatrix(minor=i).tocsr()
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def _getcol(self, i):
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"""Returns a copy of column i of the matrix, as a (m x 1)
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CSC matrix (column vector).
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"""
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M, N = self.shape
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i = int(i)
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if i < 0:
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i += N
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if i < 0 or i >= N:
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raise IndexError('index (%d) out of range' % i)
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return self._get_submatrix(major=i, copy=True)
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def _get_intXarray(self, row, col):
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return self._major_index_fancy(col)._get_submatrix(minor=row)
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def _get_intXslice(self, row, col):
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if col.step in (1, None):
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return self._get_submatrix(major=col, minor=row, copy=True)
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return self._major_slice(col)._get_submatrix(minor=row)
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def _get_sliceXint(self, row, col):
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if row.step in (1, None):
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return self._get_submatrix(major=col, minor=row, copy=True)
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return self._get_submatrix(major=col)._minor_slice(row)
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def _get_sliceXarray(self, row, col):
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return self._major_index_fancy(col)._minor_slice(row)
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def _get_arrayXint(self, row, col):
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return self._get_submatrix(major=col)._minor_index_fancy(row)
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def _get_arrayXslice(self, row, col):
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return self._major_slice(col)._minor_index_fancy(row)
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# these functions are used by the parent class (_cs_matrix)
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# to remove redundancy between csc_array and csr_matrix
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@staticmethod
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def _swap(x):
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"""swap the members of x if this is a column-oriented matrix
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"""
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return x[1], x[0]
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def isspmatrix_csc(x):
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"""Is `x` of csc_matrix type?
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Parameters
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----------
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x
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object to check for being a csc matrix
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Returns
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-------
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bool
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True if `x` is a csc matrix, False otherwise
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Examples
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--------
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>>> from scipy.sparse import csc_array, csc_matrix, coo_matrix, isspmatrix_csc
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>>> isspmatrix_csc(csc_matrix([[5]]))
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True
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>>> isspmatrix_csc(csc_array([[5]]))
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False
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>>> isspmatrix_csc(coo_matrix([[5]]))
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False
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"""
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return isinstance(x, csc_matrix)
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# This namespace class separates array from matrix with isinstance
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class csc_array(_csc_base, sparray):
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"""
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Compressed Sparse Column array.
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This can be instantiated in several ways:
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csc_array(D)
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where D is a 2-D ndarray
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csc_array(S)
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with another sparse array or matrix S (equivalent to S.tocsc())
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csc_array((M, N), [dtype])
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to construct an empty array with shape (M, N)
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dtype is optional, defaulting to dtype='d'.
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csc_array((data, (row_ind, col_ind)), [shape=(M, N)])
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where ``data``, ``row_ind`` and ``col_ind`` satisfy the
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relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
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csc_array((data, indices, indptr), [shape=(M, N)])
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is the standard CSC representation where the row indices for
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column i are stored in ``indices[indptr[i]:indptr[i+1]]``
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and their corresponding values are stored in
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``data[indptr[i]:indptr[i+1]]``. If the shape parameter is
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not supplied, the array dimensions are inferred from
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the index arrays.
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Attributes
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----------
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dtype : dtype
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Data type of the array
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shape : 2-tuple
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Shape of the array
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ndim : int
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Number of dimensions (this is always 2)
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nnz
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size
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data
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CSC format data array of the array
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indices
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CSC format index array of the array
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indptr
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CSC format index pointer array of the array
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has_sorted_indices
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has_canonical_format
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T
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Notes
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-----
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Sparse arrays can be used in arithmetic operations: they support
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addition, subtraction, multiplication, division, and matrix power.
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Advantages of the CSC format
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- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
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- efficient column slicing
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- fast matrix vector products (CSR, BSR may be faster)
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Disadvantages of the CSC format
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- slow row slicing operations (consider CSR)
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- changes to the sparsity structure are expensive (consider LIL or DOK)
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Canonical format
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- Within each column, indices are sorted by row.
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- There are no duplicate entries.
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.sparse import csc_array
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>>> csc_array((3, 4), dtype=np.int8).toarray()
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array([[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]], dtype=int8)
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>>> row = np.array([0, 2, 2, 0, 1, 2])
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>>> col = np.array([0, 0, 1, 2, 2, 2])
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>>> data = np.array([1, 2, 3, 4, 5, 6])
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>>> csc_array((data, (row, col)), shape=(3, 3)).toarray()
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array([[1, 0, 4],
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[0, 0, 5],
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[2, 3, 6]])
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>>> indptr = np.array([0, 2, 3, 6])
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>>> indices = np.array([0, 2, 2, 0, 1, 2])
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>>> data = np.array([1, 2, 3, 4, 5, 6])
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>>> csc_array((data, indices, indptr), shape=(3, 3)).toarray()
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array([[1, 0, 4],
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[0, 0, 5],
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[2, 3, 6]])
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"""
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class csc_matrix(spmatrix, _csc_base):
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"""
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Compressed Sparse Column matrix.
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This can be instantiated in several ways:
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csc_matrix(D)
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where D is a 2-D ndarray
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csc_matrix(S)
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with another sparse array or matrix S (equivalent to S.tocsc())
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csc_matrix((M, N), [dtype])
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to construct an empty matrix with shape (M, N)
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dtype is optional, defaulting to dtype='d'.
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csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
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where ``data``, ``row_ind`` and ``col_ind`` satisfy the
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relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
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csc_matrix((data, indices, indptr), [shape=(M, N)])
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is the standard CSC representation where the row indices for
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column i are stored in ``indices[indptr[i]:indptr[i+1]]``
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and their corresponding values are stored in
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``data[indptr[i]:indptr[i+1]]``. If the shape parameter is
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not supplied, the matrix dimensions are inferred from
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the index arrays.
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Attributes
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----------
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dtype : dtype
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Data type of the matrix
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shape : 2-tuple
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Shape of the matrix
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ndim : int
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Number of dimensions (this is always 2)
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nnz
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size
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data
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CSC format data array of the matrix
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indices
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CSC format index array of the matrix
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indptr
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CSC format index pointer array of the matrix
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has_sorted_indices
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has_canonical_format
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T
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Notes
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-----
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Sparse matrices can be used in arithmetic operations: they support
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addition, subtraction, multiplication, division, and matrix power.
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Advantages of the CSC format
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- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
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- efficient column slicing
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- fast matrix vector products (CSR, BSR may be faster)
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Disadvantages of the CSC format
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- slow row slicing operations (consider CSR)
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- changes to the sparsity structure are expensive (consider LIL or DOK)
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Canonical format
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- Within each column, indices are sorted by row.
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- There are no duplicate entries.
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.sparse import csc_matrix
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>>> csc_matrix((3, 4), dtype=np.int8).toarray()
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array([[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]], dtype=int8)
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>>> row = np.array([0, 2, 2, 0, 1, 2])
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>>> col = np.array([0, 0, 1, 2, 2, 2])
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>>> data = np.array([1, 2, 3, 4, 5, 6])
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>>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray()
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array([[1, 0, 4],
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[0, 0, 5],
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[2, 3, 6]])
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>>> indptr = np.array([0, 2, 3, 6])
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>>> indices = np.array([0, 2, 2, 0, 1, 2])
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>>> data = np.array([1, 2, 3, 4, 5, 6])
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>>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray()
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array([[1, 0, 4],
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[0, 0, 5],
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[2, 3, 6]])
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"""
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