Crates.io | caffe2op-stump |
lib.rs | caffe2op-stump |
version | 0.1.5-alpha.0 |
source | src |
created_at | 2023-03-06 05:04:33.026982 |
updated_at | 2023-03-26 07:17:21.026245 |
description | xxx |
homepage | |
repository | https://github.com/kleb6/caffe2-rs |
max_upload_size | |
id | 802125 |
size | 78,097 |
caffe2op-stump
is a Rust crate that defines two
mathematical operators used in digital signal
processing and machine learning computations. The
operators perform a thresholding operation on
a given input tensor and return either the
thresholded values or the indices of the elements
above and below the threshold.
The crate is in the process of being translated from C++ to Rust, so some of the function bodies may still be in the process of translation.
StumpFuncOp
StumpFuncOp
is a thresholding operator that
takes an input tensor of floats and converts each
element into either a high or low value based on
a given threshold.
Specifically, given an input tensor X
and
threshold threshold
, the operator computes an
output tensor Y
of the same shape as X
, where
the element-wise value of Y
at index i
is
defined as:
Y[i] = low_value if X[i] <= threshold
else high_value
StumpFuncIndexOp
StumpFuncIndexOp
is a thresholding operator that
returns the indices of the elements that are above
and below the threshold in the input tensor.
Specifically, given an input tensor X
of floats
and threshold threshold
, the operator computes
two output tensors, Index_Low
and Index_High
,
both of data type int64
, with the same shape as
X
. The element-wise value of Index_Low
at
index i
is equal to the index of the i-th
element of X
if the value of the i-th element is
less than or equal to threshold
. Similarly, the
element-wise value of Index_High
at index i
is
equal to the index of the i-th element of X
if
the value of the i-th element is greater than
threshold
.
The operators are named StumpFuncOp
and
StumpFuncIndexOp
. It's possible that the authors
of the crate chose this name based on the concept
of a decision stump in machine
learning. A decision stump is a simple model used
in binary classification problems, where the goal
is to partition the input space into two regions
based on a threshold applied to a single
feature. The name "stump" comes from the fact that
the model is a simple tree with one decision node
(i.e., a stump).
Alternatively, the name may be related to a specific use case or domain that is not immediately apparent from the code snippets.