Source code for mxnet.ndarray.contrib

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.

# coding: utf-8
# pylint: disable=wildcard-import, unused-wildcard-import
"""Contrib NDArray API of MXNet."""
import math
from ..context import current_context
from ..random import uniform
try:
    from .gen_contrib import *
except ImportError:
    pass

__all__ = ["rand_zipfian"]

# pylint: disable=line-too-long
[docs]def rand_zipfian(true_classes, num_sampled, range_max, ctx=None): """Draw random samples from an approximately log-uniform or Zipfian distribution. This operation randomly samples *num_sampled* candidates the range of integers [0, range_max). The elements of sampled_candidates are drawn with replacement from the base distribution. The base distribution for this operator is an approximately log-uniform or Zipfian distribution: P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1) This sampler is useful when the true classes approximately follow such a distribution. For example, if the classes represent words in a lexicon sorted in decreasing order of \ frequency. If your classes are not ordered by decreasing frequency, do not use this op. Additionaly, it also returns the number of times each of the \ true classes and the sampled classes is expected to occur. Parameters ---------- true_classes : NDArray A 1-D NDArray of the target classes. num_sampled: int The number of classes to randomly sample. range_max: int The number of possible classes. ctx : Context Device context of output. Default is current context. Overridden by `mu.context` when `mu` is an NDArray. Returns ------- samples: NDArray The sampled candidate classes in 1-D `int64` dtype. expected_count_true: NDArray The expected count for true classes in 1-D `float64` dtype. expected_count_sample: NDArray The expected count for sampled candidates in 1-D `float64` dtype. Examples -------- >>> true_cls = mx.nd.array([3]) >>> samples, exp_count_true, exp_count_sample = mx.nd.contrib.rand_zipfian(true_cls, 4, 5) >>> samples [1 3 3 3] >>> exp_count_true [ 0.12453879] >>> exp_count_sample [ 0.22629439 0.12453879 0.12453879 0.12453879] """ if ctx is None: ctx = current_context() log_range = math.log(range_max + 1) rand = uniform(0, log_range, shape=(num_sampled,), dtype='float64', ctx=ctx) # make sure sampled_classes are in the range of [0, range_max) sampled_classes = (rand.exp() - 1).astype('int64') % range_max true_cls = true_classes.as_in_context(ctx).astype('float64') expected_count_true = ((true_cls + 2.0) / (true_cls + 1.0)).log() / log_range * num_sampled # cast sampled classes to fp64 to avoid interget division sampled_cls_fp64 = sampled_classes.astype('float64') expected_prob_sampled = ((sampled_cls_fp64 + 2.0) / (sampled_cls_fp64 + 1.0)).log() / log_range expected_count_sampled = expected_prob_sampled * num_sampled return sampled_classes, expected_count_true, expected_count_sampled
# pylint: enable=line-too-long