Welcome to PySubspace’s documentation!¶
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This is the repository of CVLAB toolbox, which contains various subspace methods for classification.
All of the code is from the Computer Vision Laboratory (CVLAB), Graduate school of Systems and Information Engineering, University of Tsukuba (web). Please check the github repo for individual credits.
We are always looking for motivated students to help us with our efforts. If you would like to join our lab, please contact with Prof. Fukui via e-mail.
Our laboratory serves as one of the machine learning units in the Center for Artificial Intelligence Research (C-AIR).
Our laboratory also is involved with the Empowerment Informatics Program for Leading Graduate Schools. If you are interested in this program, please feel free to contact Prof. Fukui or Prof. Iizuka with any questions.
Installation¶
Below is the command to install with pip.
pip install -U git+https://github.com/ComputerVisionLaboratory/cvlab_toolbox
We use a Scikit-learn API so it should be pretty easy to get your code up and running. Here’s an example that should work copy&paste.
import numpy as np
from numpy.random import randint, rand
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from cvt.models import KernelMSM
dim = 100
n_class = 4
n_train, n_test = 20, 5
# input data X is *list* of vector sets (list of 2d-arrays)
X_train = [rand(randint(10, 20), dim) for i in range(n_train)]
X_test = [rand(randint(10, 20), dim) for i in range(n_test)]
# labels y is 1d-array
y_train = randint(0, n_class, n_train)
y_test = randint(0, n_class, n_test)
model = KernelMSM(n_subdims=3, sigma=0.01)
model.fit(X_train, y_train)
pred = model.predict(X_test)
print(accuracy_score(pred, y_test))
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