You need Python >= 3.6, the latest version of skmultiflow installed as well as the Incremental PCA, which you can find below.

• execute python pca_streams.py inside the experiments folder to reproduce the PCA experiments
• execute python rp_streams.py inside the experiments folder to reproduce the Random Projection experiments

• execute python pca_nasdaq_skipgram.py inside the experiments folder to reproduce the PCA experiments via Skipgram embedding
• execute python pca_nasdaq_tf-idf.py inside the experiments folder to reproduce the PCA experiments via tf-idf encoding
• execute python rp_nasdaq.py inside the experiments folder to reproduce the Random Projection experiments for Skipgram as well as tf-idf

• C++11 compiler, Python3, Eigen3, Pybind11, Numpy

• Note: Tested on macOS Mojave and Ubuntu 19.0.4 LTS.

• Install libraries

  brew install python3

  brew install eigen

  brew install pybind11

  pip3 install numpy

• Build with cmake

  cmake .

  make

• This generates a shared library, "inc_pca_cpp.xxxx.so" (e.g., inc_pca_cpp.cpython-37m-darwin.so).

• Install the modules with pip3.

  pip3 install .

• Install libraries

  sudo apt update

  sudo apt install libeigen3-dev

  sudo apt install python3-pip python3-dev

  pip3 install pybind11

  pip3 install numpy

• Compile with:

  c++ -O3 -Wall -mtune=native -march=native -shared -std=c++11 -I/usr/include/eigen3/ -fPIC `python3 -m pybind11 --includes` inc_pca.cpp inc_pca_wrap.cpp -o inc_pca_cpp`python3-config --extension-suffix`

• This generates a shared library, "inc_pca_cpp.xxxx.so" (e.g., inc_pca_cpp.cpython-37m-x86_64-linux-gnu.so).

• Install the modules with pip3.

  pip3 install .

• Incremental PCA for visualizing streaming multidimensional data from:
  An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
  Takanori Fujiwara, Jia-Kai Chou, Shilpika, Panpan Xu, Liu Ren, and Kwan-Liu Ma
  IEEE Transactions on Visualization and Computer Graphics and IEEE VIS 2019 (InfoVis). DOI: 10.1109/TVCG.2019.2934433