cvlab_toolbox

This is the repository of CVLAB toolbox

Usage

• Scikit-learn API

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)
# fit
model.fit(X_train, y_train)
# predict
pred = model.predict(X_test)

print(accuracy_score(pred, y_test))

Install

• pip

pip install -U git+https://github.com/ComputerVisionLaboratory/cvlab_toolbox

Coding styles

• Follow PEP8 as much as possible
  • English
  • 日本語
• Write a description as docstring

  def PCA(X, whiten = False):
    '''
      apply PCA
      components, explained_variance = PCA(X)
  
      Parameters
      ----------
      X: ndarray, shape (n_samples, n_features)
        matrix of input vectors
  
      whiten: boolean
        if it is True, the data is treated as whitened
        on each dimensions (average is 0 and variance is 1)
  
      Returns
      -------
      components: ndarray, shape (n_features, n_features)
        the normalized component vectors
  
      explained_variance: ndarray, shape (n_features)
        the variance of each vectors
    '''
  
    ...

Contribution rules

1. Make a pull request
2. Ask some lab members to review the code
3. when all agreements are taken, ask any admin member to merge it