Source code for mars.tensor.random.rand

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, TensorSimpleRandomData

class TensorRand(TensorSimpleRandomData, TensorRandomOperandMixin):
    _op_type_ = OperandDef.RAND_RAND
    _func_name = "rand"

    def __call__(self, chunk_size=None):
        return self.new_tensor(None, None, raw_chunk_size=chunk_size)

[docs]def rand(random_state, *dn, **kw): """ Random values in a given shape. Create a tensor of the given shape and populate it with random samples from a uniform distributionc over ``[0, 1)``. Parameters ---------- d0, d1, ..., dn : int, optional The dimensions of the returned tensor, should all be positive. If no argument is given a single Python float is returned. Returns ------- out : Tensor, shape ``(d0, d1, ..., dn)`` Random values. See Also -------- random Notes ----- This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to mt.random.random_sample . Examples -------- >>> import mars.tensor as mt >>> mt.random.rand(3, 2).execute() array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random """ if len(dn) == 1 and isinstance(dn[0], (tuple, list)): raise TypeError("'tuple' object cannot be interpreted as an integer") if "dtype" not in kw: kw["dtype"] = np.dtype("f8") chunk_size = kw.pop("chunk_size", None) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorRand(seed=seed, size=dn, **kw) for key in op.extra_params: if not key.startswith("_"): raise ValueError(f"rand got unexpected key arguments {key}") return op(chunk_size=chunk_size)