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"""Compressed Sparse Row matrix format""" |
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__docformat__ = "restructuredtext en" |
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__all__ = ['csr_array', 'csr_matrix', 'isspmatrix_csr'] |
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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 (csr_tocsc, csr_tobsr, csr_count_blocks, |
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get_csr_submatrix, csr_sample_values) |
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from ._sputils import upcast |
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from ._compressed import _cs_matrix |
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class _csr_base(_cs_matrix): |
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_format = 'csr' |
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_allow_nd = (1, 2) |
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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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if self.ndim == 1: |
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return self.copy() if copy else self |
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M, N = self.shape |
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return self._csc_container((self.data, self.indices, |
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self.indptr), shape=(N, M), copy=copy) |
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transpose.__doc__ = _spbase.transpose.__doc__ |
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def tolil(self, copy=False): |
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if self.ndim != 2: |
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raise ValueError("Cannot convert a 1d sparse array to lil format") |
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lil = self._lil_container(self.shape, dtype=self.dtype) |
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self.sum_duplicates() |
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ptr,ind,dat = self.indptr,self.indices,self.data |
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rows, data = lil.rows, lil.data |
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for n in range(self.shape[0]): |
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start = ptr[n] |
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end = ptr[n+1] |
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rows[n] = ind[start:end].tolist() |
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data[n] = dat[start:end].tolist() |
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return lil |
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tolil.__doc__ = _spbase.tolil.__doc__ |
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def tocsr(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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tocsr.__doc__ = _spbase.tocsr.__doc__ |
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def tocsc(self, copy=False): |
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if self.ndim != 2: |
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raise ValueError("Cannot convert a 1d sparse array to csc format") |
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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, M)) |
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indptr = np.empty(N + 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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csr_tocsc(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._csc_container((data, indices, indptr), shape=self.shape) |
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A.has_sorted_indices = True |
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return A |
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tocsc.__doc__ = _spbase.tocsc.__doc__ |
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def tobsr(self, blocksize=None, copy=True): |
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if self.ndim != 2: |
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raise ValueError("Cannot convert a 1d sparse array to bsr format") |
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if blocksize is None: |
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from ._spfuncs import estimate_blocksize |
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return self.tobsr(blocksize=estimate_blocksize(self)) |
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elif blocksize == (1,1): |
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arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr) |
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return self._bsr_container(arg1, shape=self.shape, copy=copy) |
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else: |
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R,C = blocksize |
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M,N = self.shape |
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if R < 1 or C < 1 or M % R != 0 or N % C != 0: |
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raise ValueError(f'invalid blocksize {blocksize}') |
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blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices) |
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idx_dtype = self._get_index_dtype((self.indptr, self.indices), |
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maxval=max(N//C, blks)) |
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indptr = np.empty(M//R+1, dtype=idx_dtype) |
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indices = np.empty(blks, dtype=idx_dtype) |
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data = np.zeros((blks,R,C), dtype=self.dtype) |
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csr_tobsr(M, N, R, C, |
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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, indices, data.ravel()) |
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return self._bsr_container( |
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(data, indices, indptr), shape=self.shape |
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) |
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tobsr.__doc__ = _spbase.tobsr.__doc__ |
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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 |
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def __iter__(self): |
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if self.ndim == 1: |
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zero = self.dtype.type(0) |
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u = 0 |
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for v, d in zip(self.indices, self.data): |
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for _ in range(v - u): |
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yield zero |
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yield d |
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u = v + 1 |
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for _ in range(self.shape[0] - u): |
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yield zero |
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return |
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indptr = np.zeros(2, dtype=self.indptr.dtype) |
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shape = self.shape[1:] if isinstance(self, sparray) else (1, self.shape[1]) |
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i0 = 0 |
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for i1 in self.indptr[1:]: |
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indptr[1] = i1 - i0 |
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indices = self.indices[i0:i1] |
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data = self.data[i0:i1] |
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yield self.__class__((data, indices, indptr), shape=shape, copy=True) |
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i0 = i1 |
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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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if self.ndim == 1: |
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if i not in (0, -1): |
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raise IndexError(f'index ({i}) out of range') |
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return self.reshape((1, self.shape[0]), copy=True) |
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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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indptr, indices, data = get_csr_submatrix( |
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M, N, self.indptr, self.indices, self.data, i, i + 1, 0, N) |
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return self.__class__((data, indices, indptr), shape=(1, N), |
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dtype=self.dtype, copy=False) |
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def _getcol(self, i): |
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"""Returns a copy of column i. A (m x 1) sparse array (column vector). |
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""" |
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if self.ndim == 1: |
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raise ValueError("getcol not provided for 1d arrays. Use indexing A[j]") |
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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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indptr, indices, data = get_csr_submatrix( |
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M, N, self.indptr, self.indices, self.data, 0, M, i, i + 1) |
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return self.__class__((data, indices, indptr), shape=(M, 1), |
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dtype=self.dtype, copy=False) |
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def _get_int(self, idx): |
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spot = np.flatnonzero(self.indices == idx) |
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if spot.size: |
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return self.data[spot[0]] |
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return self.data.dtype.type(0) |
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def _get_slice(self, idx): |
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if idx == slice(None): |
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return self.copy() |
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if idx.step in (1, None): |
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ret = self._get_submatrix(0, idx, copy=True) |
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return ret.reshape(ret.shape[-1]) |
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return self._minor_slice(idx) |
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def _get_array(self, idx): |
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idx_dtype = self._get_index_dtype(self.indices) |
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idx = np.asarray(idx, dtype=idx_dtype) |
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if idx.size == 0: |
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return self.__class__([], dtype=self.dtype) |
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M, N = 1, self.shape[0] |
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row = np.zeros_like(idx, dtype=idx_dtype) |
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col = np.asarray(idx, dtype=idx_dtype) |
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val = np.empty(row.size, dtype=self.dtype) |
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csr_sample_values(M, N, self.indptr, self.indices, self.data, |
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row.size, row, col, val) |
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new_shape = col.shape if col.shape[0] > 1 else (col.shape[0],) |
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return self.__class__(val.reshape(new_shape)) |
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def _get_intXarray(self, row, col): |
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return self._getrow(row)._minor_index_fancy(col) |
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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(row, col, copy=True) |
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M, N = self.shape |
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start, stop, stride = col.indices(N) |
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ii, jj = self.indptr[row:row+2] |
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row_indices = self.indices[ii:jj] |
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row_data = self.data[ii:jj] |
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if stride > 0: |
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ind = (row_indices >= start) & (row_indices < stop) |
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else: |
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ind = (row_indices <= start) & (row_indices > stop) |
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if abs(stride) > 1: |
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ind &= (row_indices - start) % stride == 0 |
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row_indices = (row_indices[ind] - start) // stride |
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row_data = row_data[ind] |
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row_indptr = np.array([0, len(row_indices)]) |
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if stride < 0: |
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row_data = row_data[::-1] |
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row_indices = abs(row_indices[::-1]) |
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shape = (1, max(0, int(np.ceil(float(stop - start) / stride)))) |
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return self.__class__((row_data, row_indices, row_indptr), shape=shape, |
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dtype=self.dtype, copy=False) |
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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(row, col, copy=True) |
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return self._major_slice(row)._get_submatrix(minor=col) |
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def _get_sliceXarray(self, row, col): |
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return self._major_slice(row)._minor_index_fancy(col) |
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def _get_arrayXint(self, row, col): |
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res = self._major_index_fancy(row)._get_submatrix(minor=col) |
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if row.ndim > 1: |
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return res.reshape(row.shape) |
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return res |
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def _get_arrayXslice(self, row, col): |
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if col.step not in (1, None): |
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col = np.arange(*col.indices(self.shape[1])) |
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return self._get_arrayXarray(row, col) |
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return self._major_index_fancy(row)._get_submatrix(minor=col) |
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def _set_int(self, idx, x): |
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self._set_many(0, idx, x) |
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def _set_array(self, idx, x): |
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x = np.broadcast_to(x, idx.shape) |
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self._set_many(np.zeros_like(idx), idx, x) |
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def isspmatrix_csr(x): |
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"""Is `x` of csr_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 csr matrix |
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Returns |
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------- |
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bool |
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True if `x` is a csr matrix, False otherwise |
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Examples |
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-------- |
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>>> from scipy.sparse import csr_array, csr_matrix, coo_matrix, isspmatrix_csr |
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>>> isspmatrix_csr(csr_matrix([[5]])) |
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True |
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>>> isspmatrix_csr(csr_array([[5]])) |
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False |
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>>> isspmatrix_csr(coo_matrix([[5]])) |
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False |
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""" |
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return isinstance(x, csr_matrix) |
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class csr_array(_csr_base, sparray): |
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""" |
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Compressed Sparse Row array. |
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This can be instantiated in several ways: |
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csr_array(D) |
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where D is a 2-D ndarray |
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csr_array(S) |
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with another sparse array or matrix S (equivalent to S.tocsr()) |
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csr_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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csr_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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csr_array((data, indices, indptr), [shape=(M, N)]) |
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is the standard CSR representation where the column indices for |
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row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their |
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corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. |
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If the shape parameter is not supplied, the array dimensions |
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are inferred from 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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CSR format data array of the array |
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indices |
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CSR format index array of the array |
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indptr |
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CSR 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 CSR format |
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- efficient arithmetic operations CSR + CSR, CSR * CSR, etc. |
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- efficient row slicing |
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- fast matrix vector products |
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Disadvantages of the CSR format |
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- slow column slicing operations (consider CSC) |
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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 row, indices are sorted by column. |
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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 csr_array |
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>>> csr_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, 0, 1, 2, 2, 2]) |
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>>> col = 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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>>> csr_array((data, (row, col)), shape=(3, 3)).toarray() |
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array([[1, 0, 2], |
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[0, 0, 3], |
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[4, 5, 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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>>> csr_array((data, indices, indptr), shape=(3, 3)).toarray() |
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array([[1, 0, 2], |
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[0, 0, 3], |
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[4, 5, 6]]) |
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Duplicate entries are summed together: |
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>>> row = np.array([0, 1, 2, 0]) |
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>>> col = np.array([0, 1, 1, 0]) |
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>>> data = np.array([1, 2, 4, 8]) |
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>>> csr_array((data, (row, col)), shape=(3, 3)).toarray() |
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array([[9, 0, 0], |
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[0, 2, 0], |
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[0, 4, 0]]) |
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As an example of how to construct a CSR array incrementally, |
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the following snippet builds a term-document array from texts: |
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>>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] |
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>>> indptr = [0] |
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>>> indices = [] |
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>>> data = [] |
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>>> vocabulary = {} |
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>>> for d in docs: |
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... for term in d: |
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... index = vocabulary.setdefault(term, len(vocabulary)) |
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... indices.append(index) |
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... data.append(1) |
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... indptr.append(len(indices)) |
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... |
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>>> csr_array((data, indices, indptr), dtype=int).toarray() |
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array([[2, 1, 0, 0], |
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[0, 1, 1, 1]]) |
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""" |
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class csr_matrix(spmatrix, _csr_base): |
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""" |
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Compressed Sparse Row matrix. |
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This can be instantiated in several ways: |
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csr_matrix(D) |
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where D is a 2-D ndarray |
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csr_matrix(S) |
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with another sparse array or matrix S (equivalent to S.tocsr()) |
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csr_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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csr_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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csr_matrix((data, indices, indptr), [shape=(M, N)]) |
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is the standard CSR representation where the column indices for |
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row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their |
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corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. |
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If the shape parameter is not supplied, the matrix dimensions |
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are inferred from 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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CSR format data array of the matrix |
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indices |
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CSR format index array of the matrix |
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indptr |
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CSR 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 CSR format |
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- efficient arithmetic operations CSR + CSR, CSR * CSR, etc. |
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- efficient row slicing |
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- fast matrix vector products |
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Disadvantages of the CSR format |
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- slow column slicing operations (consider CSC) |
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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 row, indices are sorted by column. |
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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 csr_matrix |
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>>> csr_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, 0, 1, 2, 2, 2]) |
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>>> col = 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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>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray() |
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array([[1, 0, 2], |
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[0, 0, 3], |
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[4, 5, 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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>>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray() |
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array([[1, 0, 2], |
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[0, 0, 3], |
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[4, 5, 6]]) |
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Duplicate entries are summed together: |
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>>> row = np.array([0, 1, 2, 0]) |
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>>> col = np.array([0, 1, 1, 0]) |
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>>> data = np.array([1, 2, 4, 8]) |
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>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray() |
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array([[9, 0, 0], |
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[0, 2, 0], |
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[0, 4, 0]]) |
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As an example of how to construct a CSR matrix incrementally, |
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the following snippet builds a term-document matrix from texts: |
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>>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] |
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>>> indptr = [0] |
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>>> indices = [] |
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>>> data = [] |
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>>> vocabulary = {} |
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>>> for d in docs: |
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... for term in d: |
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... index = vocabulary.setdefault(term, len(vocabulary)) |
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... indices.append(index) |
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... data.append(1) |
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... indptr.append(len(indices)) |
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... |
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>>> csr_matrix((data, indices, indptr), dtype=int).toarray() |
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array([[2, 1, 0, 0], |
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[0, 1, 1, 1]]) |
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""" |
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