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ml.lib

Contributors

People who contribute to this project.

Objects

  • ml.adaboost ml.adaboost performs classification using Adaptive Boosting, training a model from labeled feature vectors added with add and mapping input feature vectors to output values with map.
  • ml.ann ml.ann implements an artificial neural network for classification or regression, trained from vectors supplied with add.
  • ml.dtree ml.dtree implements decision-tree classification, training a model from labeled feature vectors added with add and generating results for new vectors with map.
  • ml.dtw ml.dtw uses Dynamic Time Warping to train a model on labeled time-series feature vectors and map input vectors to classification output.
  • ml.gmm ml.gmm is a Gaussian Mixture Model classifier that trains on labeled feature vectors and maps input feature vectors to classification output.
  • ml.hmmc ml.hmmc implements a continuous Hidden Markov Model for classifying input feature-vector time series.
  • ml.knn ml.knn implements K-nearest-neighbour classification, mapping an input feature vector to output value(s).
  • ml.linreg ml.linreg implements linear regression, training a model from labeled feature vectors added with add and generating output values for an input feature vector with map.
  • ml.logreg ml.logreg implements logistic regression for classifying feature vectors.
  • ml.mindist ml.mindist implements minimum-distance classification, training a model from labeled feature vectors added as lists whose first value is the class ID.
  • ml.minmax ml.minmax detects minimum and maximum peaks in a list of floats, outputting two lists of peak values preceded by their positions in the input list.
  • ml.randforest ml.randforest implements Random Forest classification, training a model from labeled feature vectors and mapping input feature vectors to output values.
  • ml.softmax ml.softmax is a softmax classifier that trains on labeled feature vectors added with add and classifies an input feature vector using map.
  • ml.svm ml.svm implements a support vector machine for classifying feature vectors.
  • ml.zerox ml.zerox detects zero crossings in a stream of input values.