neural-python 0.0.7

NeuralPy is a Python library for Artificial Neural Networks. You can run and test different Neural Network algorithms.

Installation

$ pip install neural-python

Links

• Documentation
• Issues
• Tutorials
• Available algorithms

Dependence

• Python 2.7, 3.3, 3.4
• NumPy >= 1.9.0
• SciPy >= 0.14.0
• Matplotlib >= 1.4.0

Next steps

• Bug fixing and version stabilization
• Speeding up algorithms
• Adding more algorithms

Library support

• Radial Basis Functions Networks (RBFN)
• Backpropagation and different optimization for it
• Neural Network Ensembles
• Associative and Autoasociative Memory
• Competitive Networks
• Step update algorithms for backpropagation
• Weight control algorithms for backpropagation
• Basic Linear Networks

Algorithms

• Backpropagation
  • Classic Gradient Descent
  • Mini-batch Gradient Descent
  • Conjugate Gradient
    • Fletcher-Reeves
    • Polak-Ribiere
    • Hestenes-Stiefel
    • Conjugate Descent
    • Liu-Storey
    • Dai-Yuan
  • quasi-Newton
    • BFGS
    • DFP
    • PSB
    • SR1
  • Levenberg-Marquardt
  • Hessian diagonal
  • Momentum
  • RPROP
  • iRPROP+
  • QuickProp
• Weight update rules
  • Weight Decay
  • Weight Elimination
• Learning rate update rules
  • Adaptive Learning Rate
  • Error difference Update
  • Linear search by Golden Search or Brent
  • Wolfe line search
  • Search than converge
  • Simple Step Minimization
• Ensembles
  • Mixture of Experts
  • Dynamically Averaged Network (DAN)
• Radial Basis Functions Networks (RBFN)
  • Generalized Regression Neural Network (GRNN)
  • Probabilistic Neural Network (PNN)
  • Radial basis function K-means
• Autoasociative Memory
  • Discrete BAM Network
  • CMAC Network
  • Discrete Hopfield Network
• Competitive Networks
  • Adaptive Resonance Theory (ART1) Network
  • Self-Organizing Feature Map (SOFM or SOM)
• Linear networks
  • Perceptron
  • LMS Network
  • Modified Relaxation Network
• Associative
  • OJA
  • Kohonen
  • Instar
  • Hebb

Tests

$ pip install tox
$ tox