ddbourgin / numpy-ml

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Installation For rapid experimentation To use this code as a starting point for ML prototyping / experimentation, just clone the repository, create a new virtualenv, and start hacking: As a package If you don't plan to modify the source, you can also install numpy-ml as a Python package: . The reinforcement learning agents train on environments defined in the OpenAI gym. To install these alongside numpy-ml, you can use . Documentation For more details on the available models, see the project documentation. Available models Click to expand! • **Gaussian mixture model** • EM training • **Hidden Markov model** • Viterbi decoding • Likelihood computation • MLE parameter estimation via Baum-Welch/forward-backward algorithm • **Latent Dirichlet allocation** (topic model) • Standard model with MLE parameter estimation via variational EM • Smoothed model with MAP parameter estimation via MCMC • **Neural networks** • Layers / Layer-wise ops • Add • Flatten • Multiply • Softmax • Fully-connected/Dense • Sparse evolutionary connections • LSTM • Elman-style RNN • Max + average pooling • Dot-product attention • Embedding layer • Restricted Boltzmann machine (w. CD-n training) • 2D deconvolution (w. padding and stride) • 2D convolution (w. padding, dilation, and stride) • 1D convolution (w. padding, dilation, stride, and causality) • Modules • Bidirectional LSTM • ResNet-style residual blocks (identity and convolution) • WaveNet-style residual blocks with dilated causal convolutions • Transformer-style multi-headed scaled dot product attention • Regularizers • Dropout • Normalization • Batch normalization (spatial and temporal) • Layer normalization (spatial and temporal) • Optimizers • SGD w/ momentum • AdaGrad • RMSProp • Adam • Learning Rate Schedulers • Constant • Exponential • Noam/Transformer • Dlib scheduler • Weight Initializers • Glorot/Xavier uniform and normal • He/Kaiming uniform and normal • Standard and truncated normal • Losses • Cross entropy • Squared error • Bernoulli VAE loss • Wasserstein loss with gradient penalty • Noise contrastive estimation loss • Activations • ReLU • Tanh • Affine • Sigmoid • Leaky ReLU • ELU • SELU • GELU • Exponential • Hard Sigmoid • Softplus • Models • Bernoulli variational autoencoder • Wasserstein GAN with gradient penalty • word2vec encoder with skip-gram and CBOW architectures • Utilities • (MATLAB port) • (MATLAB port) • - • - • **Tree-based models** • Decision trees (CART) • [Bagging] Random forests • [Boosting] Gradient-boosted decision trees • **Linear models** • Ridge regression • Logistic regression • Ordinary least squares • Weighted linear regression • Generalized linear model (log, logit, and identity link) • Gaussian naive Bayes classifier • Bayesian linear regression w/ conjugate priors • Unknown mean, known variance (Gaussian prior) • Unknown mean, unknown variance (Normal-Gamma / Normal-Inverse-Wishart prior) • **n-Gram sequence models** • Maximum likelihood scores • Additive/Lidstone smoothing • Simple Good-Turing smoothing • **Multi-armed bandit models** • UCB1 • LinUCB • Epsilon-greedy • Thompson sampling w/ conjugate priors • Beta-Bernoulli sampler • LinUCB • **Reinforcement learning models** • Cross-entropy method agent • First visit on-policy Monte Carlo agent • Weighted incremental importance sampling Monte Carlo agent • Expected SARSA agent • TD-0 Q-learning agent • Dyna-Q / Dyna-Q+ with prioritized sweeping • **Nonparameteric models** • Nadaraya-Watson kernel regression • k-Nearest neighbors classification and regression • Gaussian process regression • **Matrix factorization** • Regularized alternating least-squares • Non-negative matrix factorization • **Preprocessing** • Discrete Fourier transform (1D signals) • Discrete cosine transform (type-II) (1D signals) • Bilinear interpolation (2D signals) • Nearest neighbor interpolation (1D and 2D signals) • Autocorrelation (1D signals) • Signal windowing • Text tokenization • Feature hashing • Feature standardization • One-hot encoding / decoding • Huffman coding / decoding • Byte pair encoding / decoding • Term frequency-inverse document frequency (TF-IDF) encoding • MFCC encoding • **Utilities** • Similarity kernels • Distance metrics • Priority queue • Ball tree • Discrete sampler • Graph processing and generators Contributing Am I missing your favorite model? Is there something that could be cleaner / less confusing? Did I mess something up? Submit a PR! The only requirement is that your models are written with just the Python standard library and NumPy. The SciPy library is also permitted under special circumstances ;) See full contributing guidelines here.