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import numpy as np

from struct import pack, unpack_from

NO_DEFAULT = object()

class SparseVector:

def __init__(self, value, dimensions=NO_DEFAULT, /):

if value.__class__.__module__.startswith('scipy.sparse.'):

if dimensions is not NO_DEFAULT:

raise ValueError('extra argument')

self._from_sparse(value)

elif isinstance(value, dict):

if dimensions is NO_DEFAULT:

raise ValueError('missing dimensions')

self._from_dict(value, dimensions)

else:

if dimensions is not NO_DEFAULT:

raise ValueError('extra argument')

self._from_dense(value)

def __repr__(self):

elements = dict(zip(self._indices, self._values))

return f'SparseVector({elements}, {self._dim})'

def __eq__(self, other):

if isinstance(other, self.__class__):

return self.dimensions() == other.dimensions() and self.indices() == other.indices() and self.values() == other.values()

return False

def dimensions(self):

return self._dim

def indices(self):

return self._indices

def values(self):

return self._values

def to_coo(self):

from scipy.sparse import coo_array

coords = ([0] * len(self._indices), self._indices)

return coo_array((self._values, coords), shape=(1, self._dim))

def to_list(self):

vec = [0.0] * self._dim

for i, v in zip(self._indices, self._values):

vec[i] = v

return vec

def to_numpy(self):

vec = np.repeat(0.0, self._dim).astype(np.float32)

for i, v in zip(self._indices, self._values):

vec[i] = v

return vec

def to_text(self):

return '{' + ','.join([f'{int(i) + 1}:{float(v)}' for i, v in zip(self._indices, self._values)]) + '}/' + str(int(self._dim))

def to_binary(self):

nnz = len(self._indices)

return pack(f'>iii{nnz}i{nnz}f', self._dim, nnz, 0, *self._indices, *self._values)

def _from_dict(self, d, dim):

elements = [(i, v) for i, v in d.items() if v != 0]

elements.sort()

self._dim = int(dim)

self._indices = [int(v[0]) for v in elements]

self._values = [float(v[1]) for v in elements]

def _from_sparse(self, value):

value = value.tocoo()

if value.ndim == 1:

self._dim = value.shape[0]

elif value.ndim == 2 and value.shape[0] == 1:

self._dim = value.shape[1]

else:

raise ValueError('expected ndim to be 1')

if hasattr(value, 'coords'):

# scipy 1.13+

self._indices = value.coords[-1].tolist()

else:

self._indices = value.col.tolist()

self._values = value.data.tolist()

def _from_dense(self, value):

self._dim = len(value)

self._indices = [i for i, v in enumerate(value) if v != 0]

self._values = [float(value[i]) for i in self._indices]

@classmethod

def from_text(cls, value):

elements, dim = value.split('/', 2)

indices = []

values = []

# split on empty string returns single element list

if len(elements) > 2:

for e in elements[1:-1].split(','):

i, v = e.split(':', 2)

indices.append(int(i) - 1)

values.append(float(v))

return cls._from_parts(int(dim), indices, values)

@classmethod

def from_binary(cls, value):

dim, nnz, unused = unpack_from('>iii', value)

indices = unpack_from(f'>{nnz}i', value, 12)

values = unpack_from(f'>{nnz}f', value, 12 + nnz * 4)

return cls._from_parts(int(dim), list(indices), list(values))

@classmethod

def _from_parts(cls, dim, indices, values):

vec = cls.__new__(cls)

vec._dim = dim

vec._indices = indices

vec._values = values

return vec

@classmethod

def _to_db(cls, value, dim=None):

if value is None:

return value

if not isinstance(value, cls):

value = cls(value)

if dim is not None and value.dimensions() != dim:

raise ValueError('expected %d dimensions, not %d' % (dim, value.dimensions()))

return value.to_text()

@classmethod

def _to_db_binary(cls, value):

if value is None:

return value

if not isinstance(value, cls):

value = cls(value)

return value.to_binary()

@classmethod

def _from_db(cls, value):

if value is None or isinstance(value, cls):

return value

return cls.from_text(value)

@classmethod

def _from_db_binary(cls, value):

if value is None or isinstance(value, cls):

return value

return cls.from_binary(value)