Source code for mars.tensor.datasource.ones

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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#
# 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
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import numpy as np

from ... import opcodes as OperandDef
from ...serialize import KeyField, StringField
from ...lib.sparse.core import get_sparse_module, get_array_module, naked
from ...lib.sparse import SparseNDArray
from ..array_utils import create_array, convert_order
from ..utils import get_order
from .core import TensorNoInput, TensorLike
from .array import tensor


class TensorOnes(TensorNoInput):
    _op_type_ = OperandDef.TENSOR_ONES

    _order = StringField('order')

    def __init__(self, dtype=None, order=None, **kw):
        dtype = np.dtype(dtype or 'f8')
        super().__init__(dtype=dtype, _order=order, **kw)

    @property
    def order(self):
        return self._order

    @classmethod
    def execute(cls, ctx, op):
        chunk = op.outputs[0]
        try:
            ctx[chunk.key] = create_array(op)('ones', chunk.shape,
                                              dtype=op.dtype, order=op.order)
        except TypeError:  # in case that cp.ones does not have arg ``order``
            x = create_array(op)('ones', chunk.shape, dtype=op.dtype)
            ctx[chunk.key] = convert_order(x, op.order)


[docs]def ones(shape, dtype=None, chunk_size=None, gpu=False, order='C'): """ Return a new tensor of given shape and type, filled with ones. Parameters ---------- shape : int or sequence of ints Shape of the new tensor, e.g., ``(2, 3)`` or ``2``. dtype : data-type, optional The desired data-type for the tensor, e.g., `mt.int8`. Default is `mt.float64`. 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, default: C Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Returns ------- out : Tensor Tensor of ones with the given shape, dtype, and order. See Also -------- zeros, ones_like Examples -------- >>> import mars.tensor as mt >>> mt.ones(5).execute() array([ 1., 1., 1., 1., 1.]) >>> mt.ones((5,), dtype=int).execute() array([1, 1, 1, 1, 1]) >>> mt.ones((2, 1)).execute() array([[ 1.], [ 1.]]) >>> s = (2,2) >>> mt.ones(s).execute() array([[ 1., 1.], [ 1., 1.]]) """ tensor_order = get_order(order, None, available_options='CF', err_msg="only 'C' or 'F' order is permitted") op = TensorOnes(dtype=dtype, gpu=gpu, order=order) return op(shape, chunk_size=chunk_size, order=tensor_order)
class TensorOnesLike(TensorLike): _op_type_ = OperandDef.TENSOR_ONES_LIKE _input = KeyField('input') def __init__(self, dtype=None, sparse=False, **kw): dtype = np.dtype(dtype) if dtype is not None else None super().__init__(dtype=dtype, sparse=sparse, **kw) @classmethod def execute_sparse(cls, ctx, op): chunk = op.outputs[0] in_data = naked(ctx[op.input.key]) xps = get_sparse_module(in_data) xp = get_array_module(in_data) ctx[chunk.key] = SparseNDArray(xps.csr_matrix( (xp.ones_like(in_data.data, dtype=chunk.op.dtype), in_data.indices, in_data.indptr), shape=in_data.shape )) @classmethod def execute(cls, ctx, op): if op.sparse: cls.execute_sparse(ctx, op) else: ctx[op.outputs[0].key] = create_array(op)( 'ones_like', ctx[op.inputs[0].key], dtype=op.dtype)
[docs]def ones_like(a, dtype=None, gpu=None, order='K'): """ Return a tensor of ones 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. 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 ones with the same shape and type as `a`. See Also -------- zeros_like : Return a tensor of zeros with shape and type of input. empty_like : Return a empty 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 >>> x = mt.arange(6) >>> x = x.reshape((2, 3)) >>> x.execute() array([[0, 1, 2], [3, 4, 5]]) >>> mt.ones_like(x).execute() array([[1, 1, 1], [1, 1, 1]]) >>> y = mt.arange(3, dtype=float) >>> y.execute() array([ 0., 1., 2.]) >>> mt.ones_like(y).execute() array([ 1., 1., 1.]) """ a = tensor(a) tensor_order = get_order(order, a.order) gpu = a.op.gpu if gpu is None else gpu op = TensorOnesLike(dtype=dtype, gpu=gpu, sparse=a.issparse(), order=order) return op(a, order=tensor_order)