remove custom scipy rand

myscipy_rand/__init__.py

-__all__ = ['myscipy_rand']
-
-from pkg_resources import get_distribution
-__version__ = get_distribution('spams').version
-
-from .myscipy_rand import *

myscipy_rand/myscipy_rand.py

-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import scipy.sparse as ssp
-
-
-def rand(m, n, density=0.01, format="coo", dtype=None):
-    """Generate a sparse matrix of the given shape and density with uniformely
-    distributed values.
-
-    Parameters
-    ----------
-    m, n: int
-        shape of the matrix
-    density: real
-        density of the generated matrix: density equal to one means a full
-        matrix, density of 0 means a matrix with no non-zero items.
-    format: str
-        sparse matrix format.
-    dtype: dtype
-        type of the returned matrix values.
-
-    Notes
-    -----
-    Only float types are supported for now.
-    """
-    if density < 0 or density > 1:
-        raise ValueError("density expected to be 0 <= density <= 1")
-    if dtype and not dtype in [np.float32, np.float64, np.longdouble]:
-        raise NotImplementedError("type %s not supported" % dtype)
-
-    mn = m * n
-
-    # XXX: sparse uses intc instead of intp...
-    tp = np.intp
-    if mn > np.iinfo(tp).max:
-        msg = """\
-Trying to generate a random sparse matrix such as the product of dimensions is
-greater than %d - this is not supported on this machine
-"""
-        raise ValueError(msg % np.iinfo(tp).max)
-
-    # Number of non zero values
-    k = int(density * m * n)
-
-    # Generate a few more values than k so that we can get unique values
-    # afterwards.
-    # XXX: one could be smarter here
-    mlow = 5
-    fac = 1.02
-    gk = min(k + mlow, fac * k)
-
-    def _gen_unique_rand(_gk):
-        id = np.random.rand(_gk)
-        return np.unique(np.floor(id * mn))[:k]
-
-    id = _gen_unique_rand(gk)
-    while id.size < k:
-        gk *= 1.05
-        id = _gen_unique_rand(gk)
-
-    j = np.floor(id * 1. / m).astype(tp)
-    i = (id - j * m).astype(tp)
-    vals = np.random.rand(k).astype(dtype)
-    return ssp.coo_matrix((vals, (i, j)), shape=(m, n)).asformat(format)