sxjscience commented on a change in pull request #9747: Add
contrib.rand_log_uniform
URL: https://github.com/apache/incubator-mxnet/pull/9747#discussion_r167741923
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File path: python/mxnet/ndarray/contrib.py
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@@ -18,9 +18,76 @@
# 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__ = []
+__all__ = ["rand_log_uniform"]
+
+def rand_log_uniform(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
+ -------
+ list of NDArrays
+ A 1-D `int64` `NDArray` for sampled candidate classes, a 1-D `float64`
`NDArray` for \
+ the expected count for true classes, and a 1-D `float64` `NDArray` for
the \
+ expected count for sampled classes.
+
+ Examples
+ --------
+ >>> true_cls = mx.nd.array([3])
+ >>> samples, exp_count_true, exp_count_sample =
mx.nd.contrib.rand_log_uniform(true_cls, 4, 5)
+ >>> samples
+ [1 3 3 3]
+ <NDArray 4 @cpu(0)>
+ >>> exp_count_true
+ [ 0.12453879]
+ <NDArray 1 @cpu(0)>
+ >>> exp_count_sample
+ [ 0.22629439 0.12453879 0.12453879 0.12453879]
Review comment:
I feel it's suspicious at first glance because the exp_count of 1 is larger
than the exp_count of 3. However, the sampling result show that 3 is much more
often then 1. We need to sample multiple times and test if the empirical
expectation matches the true expectation.
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