#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 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 ...core import ExecutableTuple from ...serialize import Int64Field, BoolField, AnyField from ...config import options from ..utils import decide_chunk_sizes from .core import TensorNoInput from ..array_utils import create_array class TensorLinspace(TensorNoInput): _op_type_ = OperandDef.TENSOR_LINSPACE _start = AnyField('start') _stop = AnyField('stop') _num = Int64Field('num') _endpoint = BoolField('endpoint') def __init__(self, start=None, stop=None, num=None, endpoint=None, dtype=None, gpu=None, **kw): dtype = np.dtype(np.linspace(0, 1, 1).dtype if dtype is None else dtype) super().__init__(_start=start, _stop=stop, _num=num, _endpoint=endpoint, _dtype=dtype, _gpu=gpu, **kw) def to_chunk_op(self, *args): start, stop, num, endpoint = args op = self.copy().reset_key() op._start = start op._stop = stop op._num = num op._endpoint = endpoint return op @classmethod def tile(cls, op): tensor = op.outputs[0] chunk_length = tensor.extra_params.raw_chunk_size or options.chunk_size chunk_length = decide_chunk_sizes(tensor.shape, chunk_length, tensor.dtype.itemsize) start, stop, num, endpoint = \ tensor.op.start, tensor.op.stop, tensor.op.num, tensor.op.endpoint if num > 1: step = float(stop - start) / (num if not endpoint else num - 1) else: step = 0.0 chunks = [] chunk_start = start nsplit = [] for i, cs in enumerate(chunk_length[0]): chunk_stop = chunk_start + (cs - 1) * step chunk_op = op.to_chunk_op(chunk_start, chunk_stop, cs, True) chunk_shape = (cs,) chunk_idx = (i,) chunk = chunk_op.new_chunk(None, shape=chunk_shape, index=chunk_idx) chunks.append(chunk) nsplit.append(cs) chunk_start = chunk_start + cs * step new_op = op.copy() return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=chunks, nsplits=(tuple(nsplit),)) @property def start(self): return self._start @property def stop(self): return self._stop @property def num(self): return self._num @property def endpoint(self): return self._endpoint @classmethod def execute(cls, ctx, op): ctx[op.outputs[0].key] = create_array(op)( 'linspace', op.start, op.stop, num=op.num, endpoint=op.endpoint, dtype=op.dtype) [docs]def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, gpu=False, chunk_size=None): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. Parameters ---------- start : scalar The starting value of the sequence. stop : scalar The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output tensor. If `dtype` is not given, infer the data type from the other input arguments. gpu : bool, optional Allocate the tensor on GPU if True, False as default chunk_size : int or tuple of int or tuple of ints, optional Desired chunk size on each dimension Returns ------- samples : Tensor There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). logspace : Samples uniformly distributed in log space. Examples -------- >>> import mars.tensor as mt >>> mt.linspace(2.0, 3.0, num=5).execute() array([ 2. , 2.25, 2.5 , 2.75, 3. ]) >>> mt.linspace(2.0, 3.0, num=5, endpoint=False).execute() array([ 2. , 2.2, 2.4, 2.6, 2.8]) >>> mt.linspace(2.0, 3.0, num=5, retstep=True).execute() (array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = mt.zeros(N) >>> x1 = mt.linspace(0, 10, N, endpoint=True) >>> x2 = mt.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1.execute(), y.execute(), 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2.execute(), y.execute() + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ num = int(num) op = TensorLinspace(start, stop, num, endpoint, dtype=dtype, gpu=gpu) shape = (num,) ret = op(shape, chunk_size=chunk_size) if not retstep: return ret if num > 1: step = float(stop - start) / (num if not endpoint else num - 1) else: step = np.nan return ExecutableTuple([ret, step])