ddbourgin/numpy-ml

Machine learning, in numpy

Python

16,323

318 commits

updated Oct 29, 2023

numpy-ml.readthedocs.io

See the code

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!

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
  - col2im (MATLAB port)
  - im2col (MATLAB port)
  - conv1D
  - conv2D
  - deconv2D
  - minibatch
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.

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

Project health

Overall

2.3/10

Maintained

0/10

Code review

2/10

OpenSSF Scorecard

PyPI

numpy-ml

Latest release

0.1.2 · Jun 20, 2020

Releases this year

Significant stargazers

(top 24 of 32)

Han Xiao

4,232 followers · starred Oct 2021

HUSEIN ZOLKEPLI

1,904 followers · starred Jul 2019

Haoran MO

280 followers · starred Jul 2019

Jiahao Li

453 followers · starred Oct 2022

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ddbourgin/numpy-ml

Machine learning, in numpy

Python

16,323

318 commits

updated Oct 29, 2023

numpy-ml.readthedocs.io

See the code

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!

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
  - col2im (MATLAB port)
  - im2col (MATLAB port)
  - conv1D
  - conv2D
  - deconv2D
  - minibatch
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

See full contributing guidelines here.

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

Project health

Overall

2.3/10

Maintained

0/10

Code review

2/10

OpenSSF Scorecard

PyPI

numpy-ml

Latest release

0.1.2 · Jun 20, 2020

Releases this year

Significant stargazers

(top 24 of 32)

Han Xiao

4,232 followers · starred Oct 2021

HUSEIN ZOLKEPLI

1,904 followers · starred Jul 2019

Haoran MO

280 followers · starred Jul 2019

Jiahao Li

453 followers · starred Oct 2022

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aurimas13/Machine-Learning-Goodness

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286

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NLP-LOVE/ML-NLP

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