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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, biom development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
from __future__ import division
import numpy as np
cimport numpy as cnp
def _subsample(arr, n):
"""Subsample non-zero values of a sparse array
Parameters
----------
arr : {csr_matrix, csc_matrix}
A 1xM sparse vector
n : int
Number of items to subsample from `arr`
Returns
-------
ndarray
Subsampled data
Notes
-----
This code was adapted from scikit-bio (`skbio.math._subsample`)
"""
cdef:
cnp.int64_t counts_sum
cnp.ndarray[cnp.float64_t, ndim=1] data = arr.data
cnp.ndarray[cnp.float64_t, ndim=1] result
cnp.ndarray[cnp.int32_t, ndim=1] indices = arr.indices
cnp.ndarray[cnp.int32_t, ndim=1] indptr = arr.indptr
cnp.ndarray[cnp.int32_t, ndim=1] permuted, unpacked
cnp.float64_t cnt
Py_ssize_t unpacked_idx, i, j
for i in range(indptr.shape[0] - 1):
start, end = indptr[i], indptr[i+1]
counts_sum = data[start:end].sum()
if counts_sum < n:
data[start:end] = 0
continue
unpacked = np.empty(counts_sum, dtype=np.int32)
unpacked_idx = 0
for i in range(start, end):
cnt = data[i]
for j in range(int(cnt)):
unpacked[unpacked_idx] = i - start
unpacked_idx += 1
permuted = np.random.permutation(unpacked)[:n]
result = np.zeros(end - start, dtype=np.float64)
for idx in range(permuted.shape[0]):
result[permuted[idx]] += 1
data[start:end] = result
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