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"""Compressed Sparse Column matrix format"""
""" Compressed Sparse Column matrix
This can be instantiated in several ways:
csc_matrix(D) with a dense matrix or rank-2 ndarray D
csc_matrix(S) with another sparse matrix S (equivalent to S.tocsc())
csc_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'.
csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)]) where ``data``, ``row_ind`` and ``col_ind`` satisfy the relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
csc_matrix((data, indices, indptr), [shape=(M, N)]) is the standard CSC representation where the row indices for column i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays.
Attributes ---------- dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) nnz Number of nonzero elements data Data array of the matrix indices CSC format index array indptr CSC format index pointer array has_sorted_indices Whether indices are sorted
Notes -----
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSC format - efficient arithmetic operations CSC + CSC, CSC * CSC, etc. - efficient column slicing - fast matrix vector products (CSR, BSR may be faster)
Disadvantages of the CSC format - slow row slicing operations (consider CSR) - changes to the sparsity structure are expensive (consider LIL or DOK)
Examples --------
>>> import numpy as np >>> from scipy.sparse import csc_matrix >>> csc_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8)
>>> row = np.array([0, 2, 2, 0, 1, 2]) >>> col = np.array([0, 0, 1, 2, 2, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]])
>>> indptr = np.array([0, 2, 3, 6]) >>> indices = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]])
"""
if axes is not None: raise ValueError(("Sparse matrices do not support " "an 'axes' parameter because swapping " "dimensions is the only logical permutation."))
M, N = self.shape
from .csr import csr_matrix return csr_matrix((self.data, self.indices, self.indptr), (N, M), copy=copy)
for r in self.tocsr(): yield r
if copy: return self.copy() else: return self
M,N = self.shape idx_dtype = get_index_dtype((self.indptr, self.indices), maxval=max(self.nnz, N)) indptr = np.empty(M + 1, dtype=idx_dtype) indices = np.empty(self.nnz, dtype=idx_dtype) data = np.empty(self.nnz, dtype=upcast(self.dtype))
csc_tocsr(M, N, self.indptr.astype(idx_dtype), self.indices.astype(idx_dtype), self.data, indptr, indices, data)
from .csr import csr_matrix A = csr_matrix((data, indices, indptr), shape=self.shape, copy=False) A.has_sorted_indices = True return A
# Use CSR to implement fancy indexing.
row, col = self._unpack_index(key) # Things that return submatrices. row or col is a int or slice. if (isinstance(row, slice) or isinstance(col, slice) or isintlike(row) or isintlike(col)): return self.T[col, row].T # Things that return a sequence of values. else: return self.T[col, row]
# CSC can't use _cs_matrix's .nonzero method because it # returns the indices sorted for self transposed.
# Get row and col indices, from _cs_matrix.tocoo major_dim, minor_dim = self._swap(self.shape) minor_indices = self.indices major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype) _sparsetools.expandptr(major_dim, self.indptr, major_indices) row, col = self._swap((major_indices, minor_indices))
# Remove explicit zeros nz_mask = self.data != 0 row = row[nz_mask] col = col[nz_mask]
# Sort them to be in C-style order ind = np.argsort(row, kind='mergesort') row = row[ind] col = col[ind]
return row, col
"""Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector). """ # we convert to CSR to maintain compatibility with old impl. # in spmatrix.getrow() return self._get_submatrix(i, slice(None)).tocsr()
"""Returns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector). """ M, N = self.shape i = int(i) if i < 0: i += N if i < 0 or i >= N: raise IndexError('index (%d) out of range' % i) idx = slice(*self.indptr[i:i+2]) data = self.data[idx].copy() indices = self.indices[idx].copy() indptr = np.array([0, len(indices)], dtype=self.indptr.dtype) return csc_matrix((data, indices, indptr), shape=(M, 1), dtype=self.dtype, copy=False)
# these functions are used by the parent class (_cs_matrix) # to remove redudancy between csc_matrix and csr_matrix """swap the members of x if this is a column-oriented matrix """ return x[1], x[0]
"""Is x of csc_matrix type?
Parameters ---------- x object to check for being a csc matrix
Returns ------- bool True if x is a csc matrix, False otherwise
Examples -------- >>> from scipy.sparse import csc_matrix, isspmatrix_csc >>> isspmatrix_csc(csc_matrix([[5]])) True
>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc >>> isspmatrix_csc(csr_matrix([[5]])) False """ return isinstance(x, csc_matrix) |