https://github.com/huawei-noah/streamdm-cpp
stream Machine Learning in C++
https://github.com/huawei-noah/streamdm-cpp
Last synced: 6 months ago
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stream Machine Learning in C++
- Host: GitHub
- URL: https://github.com/huawei-noah/streamdm-cpp
- Owner: huawei-noah
- Created: 2015-06-27T08:09:40.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2015-07-02T06:23:02.000Z (over 10 years ago)
- Last Synced: 2025-03-23T23:26:47.503Z (7 months ago)
- Language: C++
- Homepage: http://huawei-noah.github.io/streamDM-Cpp/
- Size: 3.76 MB
- Stars: 72
- Watchers: 15
- Forks: 35
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
streamDM-C++: C++ Stream Data Mining
=================
streamDM in C++ implements extremely fast streaming decision trees in C++ for big data streams. It is a project developed at Huawei Noah's Ark Lab. streamDM in C++ is licensed under Apache Software License v2.0.
The main advantages of streamDM in C++ over other C/C++ data stream libraries are the following:
- Faster than VFML in C and MOA in Java.
- Evaluation and learners are separated, not linked together.
- It contains several methods for learning numeric attributes.
- It is easy to extend and add new methods.
- The adaptive decision tree is more accurate and does not need an expert user to choose optimal parameters to use.
- It contains powerful ensemble methods.
- It is much faster and uses less memory.
## Getting Started
Getting Started
First download and build streamDM in C++:
```
git clone https://github.com/huawei-noah/streamDM-Cpp.git
cd streamDM-Cpp
make
```
Download a dataset:
```
wget "http://downloads.sourceforge.net/project/moa-datastream/Datasets/Classification/covtypeNorm.arff.zip"
unzip covtypeNorm.arff.zip
```
Evaluate the dataset:
```
./streamdm-cpp "EvaluatePrequential -l (HoeffdingTree -l NBAdaptive) -r ArffReader -ds covtypeNorm.arff -e (BasicClassificationEvaluator -f 100000)"
```
## Methods
streamDM in C++ executes tasks. Tasks can be evaluation tasks as "EvaluatePrequential" or "EvaluateHoldOut" and the parameters needed are a learner, a stream reader, and an evaluator.
The methods currently implemented are: Naive Bayes, Logistic Regression, Perceptron, Majority Class, Hoeffding Tree, Hoeffding Adaptive Tree, and Bagging.
The stream readers currently implemented support Arff, C45, and LibSVM formats.