Simple-Implementation-of-ML-Algorithms

My simplest implementations of common ML algorithms from scratch.

  • Most commonly used algorithms including Regressions, Graph Models, Deep Learning, Ensemble Learning, Reinforcement Learning, Optimization Algorithms, Swarm Intelligence, Heuristic Search, and other basic ML algorithms (see below list for more details).
  • For an easy understanding, most of the codes implements only minimal version of the algorithm.
  • The implementation uses only numpy.
  • Most algorithms are within 100 lines of codes.

Example Output

  • Decision boundary visualization for implemented classifiers in decision_boundary_visualization.py supervised_model

  • Optimization process visualization in optimization_visualization.py: optimization

  • digit images from the generative models trained with MNIST dataset:

    • Deep Convolutional Generative Adversarial Network (DC GAN) in generative_adversarial_network.py: dc_gan

    • Variational Autoencoders (VAE) in variational_autoencoder.py: vae

    • Average image by Restricted Boltzmann Machine (RBM) restricted_boltzman_machine.py: rbm

Implemented algorithms:

Regression Models

  • Linear Regression linear_regression.py
    • Matrix solver
    • SGD/Adam solver
    • L1 regularization Lasso
    • L2 regularization Ridge
  • Logistic Regression logistic_regression.py
    • Multi-class prediction
  • Factorization Machines factorization_machines.py
    • Regularization
    • Classification/regression

Tree Models and Ensemble Learning

  • Decision Tree decision_tree.py
    • Classification/regression
    • Different metrics
    • Feature importances
    • Sample weights
  • Random Forest random_forest.py
  • Adaboost adaboost.py
  • Gradient Boosting Decision Tree (GBDT) gradient_boosting_decision_tree.py
    • Shrinkage
    • Line search of multiplier
  • XGBoost xgboost.py
    • XGBoost Regression Tree
    • Shrinkage

Deep Learning

  • Architecture
    • Supervised Model
      • Multilayer Perceptron (MLP) multilayer_perceptron.py / simple_mlp.py
      • Convolutional Neural Network (CNN) convolutional_neural_network.py
        • Convolutional layer with vectorized img2col and col2img
      • Recurrent neural network (RNN) recurrent_neural_network.py
        • Backpropagation through time (BPTT)
      • Long short-term memory (LSTM) long_short_term_memory.py
    • Generative Model
      • Restricted Boltzman Machine (RBM) restricted_boltzman_machine.py
      • Deep Belief Network (DBN) deep_belief_network.py
      • Variational autoencoder (VAE) variational_autoencoder.py
      • Generative Adversarial Network (GAN) generative_adversarial_network.py
        • Vanilla GAN
        • Deep Convolutional GAN (DCGAN)
        • discriminator vs generator
    • Transfer Learning transfer_learning.py
        * CNN on MNIST - freeze convolutional and fine tune dense layers
  • Layers nn_layers.py / simple_cnn_layers.py
    • Feedforward layer (dense)
    • Convolutional layer
    • Max pooling layer
    • Batch normalization layer
    • Softmax layer for classification
    • Activation layer
      • ReLU (Leaky)
      • Tanh (Leaky)
      • Sigmoid
    • Drop out layer (WIP)
  • Training nn_layers.py / multilayer_perceptron.py
    • Mini Batch
    • He initialization
    • Loss functions
      • Mean squared error for regression
      • Cross entropy for classification
      • Log loss for classification
    • L1/L2 Regularization
    • Gradient check

Optimization Algorithms

  • Gradient Descent and variations optimization_visualization.py / multilayer_perceptron.py
    • Stochastic Gradient Descent
    • Gradient Descent with Momentum
    • Nesterov Momentum
    • AdaGrad
    • RMSProp
    • Adam
  • Monte Carlo method for global optimum
    • Simulated annealing optimization_visualization.py

Graph Models

  • Naive Bayes naive_bayes.py
    • Multinomial model
    • Document tokenizer
  • Beyesian Network beyesian_net.py
    • Conditional probability MLE
    • Beyesian inference
  • Hidden Markov Model hidden_markov_model.py
    • Fitting by Baum-Welch
    • Prediction by Viterbi
  • Markov Random Field markov_random_field.py
    • Image Segmentation
    • Simulated Annealing for optimal Energy Function

Instance-based Learning and Kernel Machines

  • k-Nearest Neighbors (kNN) k_nearest_neighbors.py
  • Learning Vector Quantization
  • Support Vector Machine (SVM) support_vector_machine.py
    • Soft boundary
    • SMO algorithm
    • Different heuristics for selecting pairs in SMO

Swarm Intelligence

  • Evolutionary Algorithm (EA) evolutionary_algorithm.py
    • Training a NN model by Genetic Algorithm
    • Selection by Fitness
    • Crossover approaches
    • Mutation rate
  • Ant Colony Optimization (ACO) ant_colony.py
    • Traveling Salesman Problem
  • Monte Carlo tree search monte_carlo_tree_search.py
    • Upper Confidence Bound 1 applied to trees (UCT)
  • Minimax minimax.py
    • Alpha-Beta Pruning

Reinforcement Learning

  • Temporal difference learning temporal_difference.py
    • Tabular Q Learning
  • Deep Q-Network (DQN) deep_q_network.py
    • CNN Target & Policy Net
    • Epsilon-Greedy

Unsupervised Learning

  • Clustering (WIP)
    • k-Means / dbscan / spectrum / hierachical
  • Dimension Reduction (WIP)
    • SOM
    • Principal Component Analysis / Linear Discriminant Analysis / MDS / t-SNE

Feel free to use the code. Please contact me if you have any question: xiecng [at] gmail.com

Simple Implementation Of Ml Algorithms

My simplest implementations of common ML algorithms

Simple Implementation Of Ml Algorithms Info

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🔗 Source Code github.com
🕒 Last Update 6 months ago
🕒 Created 3 years ago
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😎 Author xiecong