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🍋Lemon🍋

Basic Machine Learning / Deep Learning Library

Implemented with numpy and scipy in python codes.

Also includes a simple version of autogradable Tensor.

For more information, please refer to my blog.

Requirements

  • python==3.6
  • numpy==1.17.0
  • scipy==1.2.1
  • torch==1.3.0

Structure

.
├── LICENSE
├── README.md
├── graph
│   ├── __init__.py
│   ├── _conditional_random_field.py
│   └── _hidden_markov.py
├── nn
│   ├── __init__.py
│   ├── _activation.py
│   ├── _base.py
│   ├── _criterion.py
│   ├── _fully_connect.py
│   └── autograd
│       ├── __init__.py
│       └── tensor.py
├── supervised
│   ├── __init__.py
│   ├── _base.py
│   ├── bayes
│   │   ├── __init__.py
│   │   └── _bayes.py
│   ├── knn
│   │   ├── __init__.py
│   │   └── _k_nearest.py
│   ├── linear
│   │   ├── __init__.py
│   │   ├── _base.py
│   │   ├── _linear_regression.py
│   │   ├── _logistic_regression.py
│   │   ├── _multi_classifier.py
│   │   ├── _perceptron.py
│   │   ├── _regularization.py
│   │   └── _support_vector_machine.py
│   └── tree
│       ├── __init__.py
│       ├── _cart.py
│       ├── _id3.py
│       └── ensemble
│           ├── __init__.py
│           ├── _adaptive_boosting.py
│           └── _random_forest.py
├── test
│   ├── nn_models
│   │   └── fcnn.py
│   ├── test_graph.py
│   └── test_supervised.py
├── unsupervised
│   ├── __init__.py
│   ├── clustering
│   │   ├── __init__.py
│   │   ├── _base.py
│   │   ├── _kmeans.py
│   │   └── _spectral.py
│   └── decomposition
│       ├── __init__.py
│       ├── _base.py
│       └── _pca.py
└── utils
    ├── __init__.py
    ├── _batch.py
    ├── _cross_validate.py
    ├── _make_data.py
    └── _scaling.py

Timeline

  • 2019.6.12
    • Linear Regression
    • Logistic Regression
    • Perceptron
    • utils.scaling / batch / cross_validate
  • 6.13
    • Support Vector Machine
    • K-Nearest-Neighbor
    • test script
  • 6.15
    • Bayes
  • 6.16
    • K-Means
  • 6.19
    • Spectral
    • Principle Component Analysis
  • 6.24
    • Decision Tree(ID3)
  • 7.2
    • Multi-classifier
    • Regularization
  • 7.13
    • Activation
    • Criterion
    • Fully Connected Layer
    • Fully Connected Neural Network Model
  • 8.17-8.20
    • Improve project structure
    • Decision Tree(CART)
    • Random Forest
    • Adaboost
  • 8.23
    • Hidden Markov Model
  • 11.6
    • Conditional Random Field Model(Based on Torch)
    • Autograd Tensor