caffe2op-stump

Overview

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.

Operators

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.

Naming

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.