Lambda-ml 0.1.1
A small machine learning library aimed at providing simple, concise implementations of machine learning techniques and utilities.
Installation
To install, add the following dependency to your project or build file:
[lambda-ml "0.1.1"]
Namespaces
lambda-ml.clustering.hierarchical
Hierarchical agglomerative clustering.
Public variables and functions:
lambda-ml.decision-tree
Decision tree learning using the Classification and Regression Trees (CART) algorithm.
lambda-ml.distance
Functions that compute measures of distance between values.
lambda-ml.factorization
Unsupervised learning with non-negative matrix factorization.
Public variables and functions:
lambda-ml.metrics
Functions that compute measures of cost or gain.
lambda-ml.nearest-neighbors
Classification and regression using the k-nearest neighbors algorithm.
lambda-ml.neural-network
Multilayer perceptron neural network learning using backpropagation.
Public variables and functions:
- add-neural-network-layer
- back-propagate
- bias
- compute-gradients
- cross-entropy-cost
- cross-entropy-output-error
- drop-bias
- epsilon
- feed-forward
- feed-forward-batch
- gradient-descent
- gradient-descent-step
- init-parameters
- make-neural-network
- neural-network-cost
- neural-network-fit
- neural-network-predict
- numeric-gradients
- print-neural-network
- quadratic-cost
- quadratic-output-error
lambda-ml.random-forest
Random forest classification and regression learning.
Public variables and functions:
lambda-ml.regression
Generalized linear model learning for two of the more popular techniques, linear regression and logistic regression.