# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...lib.sparse import SparseNDArray
from ...lib.sparse.core import get_array_module, get_sparse_module, naked
from ...serialization.serializables import AnyField, KeyField, StringField
from ..array_utils import create_array
from ..utils import get_order
from .array import tensor
from .core import TensorLike, TensorNoInput
class TensorFull(TensorNoInput):
_op_type_ = OperandDef.TENSOR_FULL
_fill_value = AnyField("fill_value")
_order = StringField("order")
def __init__(self, fill_value=None, dtype=None, order=None, **kw):
if dtype is not None:
dtype = np.dtype(dtype)
if fill_value is not None:
fill_value = dtype.type(fill_value)
elif fill_value is not None:
dtype = np.array(fill_value).dtype
super().__init__(_fill_value=fill_value, dtype=dtype, _order=order, **kw)
@property
def fill_value(self):
return self._fill_value
@property
def order(self):
return self._order
@classmethod
def execute(cls, ctx, op):
chunk = op.outputs[0]
ctx[chunk.key] = create_array(op)(
"full", chunk.shape, op.fill_value, dtype=op.dtype, order=op.order
)
[docs]def full(shape, fill_value, dtype=None, chunk_size=None, gpu=None, order="C"):
"""
Return a new tensor of given shape and type, filled with `fill_value`.
Parameters
----------
shape : int or sequence of ints
Shape of the new tensor, e.g., ``(2, 3)`` or ``2``.
fill_value : scalar
Fill value.
dtype : data-type, optional
The desired data-type for the tensor The default, `None`, means
`np.array(fill_value).dtype`.
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
Returns
-------
out : Tensor
Tensor of `fill_value` with the given shape, dtype, and order.
See Also
--------
zeros_like : Return a tensor of zeros with shape and type of input.
ones_like : Return a tensor of ones with shape and type of input.
empty_like : Return an empty tensor with shape and type of input.
full_like : Fill a tensor with shape and type of input.
zeros : Return a new tensor setting values to zero.
ones : Return a new tensor setting values to one.
empty : Return a new uninitialized tensor.
Examples
--------
>>> import mars.tensor as mt
>>> mt.full((2, 2), mt.inf).execute()
array([[ inf, inf],
[ inf, inf]])
>>> mt.full((2, 2), 10).execute()
array([[10, 10],
[10, 10]])
"""
v = np.asarray(fill_value)
if len(v.shape) > 0:
from ..base import broadcast_to
return broadcast_to(
tensor(v, dtype=dtype, chunk_size=chunk_size, gpu=gpu, order=order), shape
)
tensor_order = get_order(
order,
None,
available_options="CF",
err_msg="only 'C' or 'F' order is permitted",
)
op = TensorFull(fill_value, dtype=dtype, gpu=gpu, order=order)
return op(shape, chunk_size=chunk_size, order=tensor_order)
class TensorFullLike(TensorLike):
_op_type_ = OperandDef.TENSOR_FULL_LIKE
_input = KeyField("input")
_fill_value = AnyField("fill_value")
_order = StringField("order")
def __init__(
self, fill_value=None, dtype=None, gpu=None, sparse=False, order=None, **kw
):
if dtype is not None:
dtype = np.dtype(dtype)
if fill_value is not None:
fill_value = dtype.type(fill_value)
elif fill_value is not None:
dtype = np.array(fill_value).dtype
super().__init__(
_fill_value=fill_value,
_order=order,
dtype=dtype,
gpu=gpu,
sparse=sparse,
**kw
)
@property
def fill_value(self):
return self._fill_value
@property
def order(self):
return self._order
@classmethod
def execute(cls, ctx, op):
chunk = op.outputs[0]
if op.issparse():
in_data = naked(ctx[op.inputs[0].key])
xps = get_sparse_module(in_data)
xp = get_array_module(in_data)
ctx[chunk.key] = SparseNDArray(
xps.csr_matrix(
(
xp.full_like(in_data.data, op.fill_value, dtype=op.dtype),
in_data.indices,
in_data.indptr,
),
shape=in_data.shape,
)
)
else:
ctx[chunk.key] = create_array(op)(
"full_like",
ctx[op.inputs[0].key],
op.fill_value,
dtype=op.dtype,
order=op.order,
)
[docs]def full_like(a, fill_value, dtype=None, gpu=None, order="K"):
"""
Return a full tensor with the same shape and type as a given tensor.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned tensor.
fill_value : scalar
Fill value.
dtype : data-type, optional
Overrides the data type of the result.
gpu : bool, optional
Allocate the tensor on GPU if True, None as default
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
Returns
-------
out : Tensor
Tensor of `fill_value` with the same shape and type as `a`.
See Also
--------
empty_like : Return an empty tensor with shape and type of input.
ones_like : Return a tensor of ones with shape and type of input.
zeros_like : Return a tensor of zeros with shape and type of input.
full : Return a new tensor of given shape filled with value.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.arange(6, dtype=int)
>>> mt.full_like(x, 1).execute()
array([1, 1, 1, 1, 1, 1])
>>> mt.full_like(x, 0.1).execute()
array([0, 0, 0, 0, 0, 0])
>>> mt.full_like(x, 0.1, dtype=mt.double).execute()
array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> mt.full_like(x, mt.nan, dtype=mt.double).execute()
array([ nan, nan, nan, nan, nan, nan])
>>> y = mt.arange(6, dtype=mt.double)
>>> mt.full_like(y, 0.1).execute()
array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
"""
a = tensor(a)
tensor_order = get_order(order, a.order)
if dtype is None:
dtype = a.dtype
gpu = a.op.gpu if gpu is None else gpu
op = TensorFullLike(
fill_value=fill_value, dtype=dtype, gpu=gpu, sparse=a.issparse()
)
return op(a, order=tensor_order)