(C) Copyright 2021, Written by Guanglei Yu, 114456486@qq.com, part of code is from Supervised latent Dirichlet allocation for classification.
This is a C++ implementation of Hierarchical sLDA for classification.
A preprocessed 4-class hospital discharge records(HDRs) dataset from the First Affiliated Hospital of Xinjiang Medical University, China.
[1] Chong Wang, David M. Blei and Li Fei-Fei. Simultaneous image classification and annotation. In CVPR, 2009.
Note that this code requires the Gnu Scientific Library, http://www.gnu.org/software/gsl/
TABLE OF CONTENTS
A. COMPILING
B. ESTIMATION
C. INFERENCE
A. COMPILING
Type "make" in a shell. Make sure the GSL is installed.
1) download GSL: wget http://mirrors.ustc.edu.cn/gnu/gsl/gsl-1.9.tar.gz
B. ESTIMATION
Estimate the model by executing:
slda [est] [data] [data-fature] [label] [settings] [alpha] [k] [seeded/random/model_path] [directory]
The saved models are in two files:
<iteration>.model is the model saved in the binary format, which is easy and
fast to use for inference.
<iteration>.model.txt is the model saved in the text format, which is
convenient for printing topics or analysis using python.
The variational posterior Dirichlets are in:
<iteration>.gamma
Data format
(1) [data] is a file where each line is of the form:
[M] [term_1]:[count] [term_2]:[count] ... [term_K]:[count]
where [M] is the number of unique feature-value pair in the document, and the [count] associated with each feature-value pair is how many times that term appeared in the document.
(2) [data-feature] is a file where each line is of the form: [N] [term_1]:[count] [term_2]:[count] ... [term_K]:[count] where [N] is the number of unique feature in the document, and the [count] associated with each feature is how many times that the corresponding feature-value pairs appeared in the document, which is equal to the sum of same feature's feature-value pairs.
(3) [label] is a file where each line is the corresponding label for [data]. The labels must be 0, 1, ..., C-1, if we have C classes.
C. INFERENCE
To perform inference on a different set of data (in the same format as for estimation), execute:
slda [inf] [data] [data-fature] [label] [settings] [model] [directory]
where [model] is the binary file from the estimation.
The target labels and predictive labels are in:
inf-labels.dat
The variational posterior Dirichlets are in:
inf-gamma.dat
Software provided as is under Gnu License.
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