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ddbourgin / numpy-ml

Machine learning, in numpy
https://numpy-ml.readthedocs.io/
GNU General Public License v3.0
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attention bayesian-inference gaussian-mixture-models gaussian-processes good-turing-smoothing gradient-boosting hidden-markov-models knn lstm machine-learning mfcc neural-networks reinforcement-learning resnet topic-modeling vae wavenet wgan-gp word2vec

readme

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:

$ git clone https://github.com/ddbourgin/numpy-ml.git
$ cd numpy-ml && virtualenv npml && source npml/bin/activate
$ pip3 install -r requirements-dev.txt

As a package

If you don't plan to modify the source, you can also install numpy-ml as a Python package: pip3 install -u numpy_ml.

The reinforcement learning agents train on environments defined in the OpenAI gym. To install these alongside numpy-ml, you can use pip3 install -u 'numpy_ml[rl]'.

Documentation

For more details on the available models, see the project documentation.

Available models

Click to expand! 1. **Gaussian mixture model** - EM training 2. **Hidden Markov model** - Viterbi decoding - Likelihood computation - MLE parameter estimation via Baum-Welch/forward-backward algorithm 3. **Latent Dirichlet allocation** (topic model) - Standard model with MLE parameter estimation via variational EM - Smoothed model with MAP parameter estimation via MCMC 4. **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 - `col2im` (MATLAB port) - `im2col` (MATLAB port) - `conv1D` - `conv2D` - `deconv2D` - `minibatch` 5. **Tree-based models** - Decision trees (CART) - [Bagging] Random forests - [Boosting] Gradient-boosted decision trees 6. **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) 7. **n-Gram sequence models** - Maximum likelihood scores - Additive/Lidstone smoothing - Simple Good-Turing smoothing 8. **Multi-armed bandit models** - UCB1 - LinUCB - Epsilon-greedy - Thompson sampling w/ conjugate priors - Beta-Bernoulli sampler - LinUCB 8. **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 9. **Nonparameteric models** - Nadaraya-Watson kernel regression - k-Nearest neighbors classification and regression - Gaussian process regression 10. **Matrix factorization** - Regularized alternating least-squares - Non-negative matrix factorization 11. **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 12. **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.