rdkit.ML

Package rdkit :: Package ML |
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# Package ML

source code

    module containing machine learning code

Submodules | [hide private]
rdkit.ML.AnalyzeComposite: command line utility to report on the contributions of descriptors to tree-based composite models rdkit.ML.BuildComposite: command line utility for building composite models rdkit.ML.Cluster rdkit.ML.Cluster.Butina: Implementation of the clustering algorithm published in:... rdkit.ML.Cluster.ClusterUtils: utility functions for clustering rdkit.ML.Cluster.ClusterVis: Cluster tree visualization using Sping rdkit.ML.Cluster.Clustering rdkit.ML.Cluster.Clusters: contains the Cluster class for representing hierarchical cluster trees rdkit.ML.Cluster.Murtagh: Interface to the C++ Murtagh hierarchic clustering code rdkit.ML.Cluster.Resemblance: code for dealing with resemblance (metric) matrices rdkit.ML.Cluster.Standardize: contains code for standardization of data matrices for clustering rdkit.ML.Composite rdkit.ML.Composite.AdjustComposite: functionality to allow adjusting composite model contents rdkit.ML.Composite.BayesComposite: code for dealing with Bayesian composite models rdkit.ML.Composite.Composite: code for dealing with composite models rdkit.ML.CompositeRun: contains a class to store parameters for and results from... rdkit.ML.Data rdkit.ML.Data.DataUtils: Utilities for data manipulation rdkit.ML.Data.FindQuantBounds rdkit.ML.Data.MLData: classes to be used to help work with data sets rdkit.ML.Data.Quantize: Automatic search for quantization bounds rdkit.ML.Data.SplitData rdkit.ML.Data.Stats: various statistical operations on data rdkit.ML.Data.Transforms rdkit.ML.Data.cQuantize rdkit.ML.DecTree: Here we're implementing the Decision Tree stuff found in Chapter 3 of Tom Mitchell's Machine Learning Book. rdkit.ML.DecTree.BuildQuantTree: rdkit.ML.DecTree.BuildSigTree: rdkit.ML.DecTree.CrossValidate: handles doing cross validation with decision trees rdkit.ML.DecTree.DecTree: Defines the class _DecTreeNode_, used to represent decision trees rdkit.ML.DecTree.Forest: code for dealing with forests (collections) of decision trees rdkit.ML.DecTree.ID3: ID3 Decision Trees rdkit.ML.DecTree.PruneTree: Contains functionality for doing tree pruning rdkit.ML.DecTree.QuantTree: Defines the class _QuantTreeNode_, used to represent decision trees with automatic quantization bounds rdkit.ML.DecTree.SigTree: Defines the class SigTreeNode, used to represent trees that use signatures (bit vectors) to represent data. rdkit.ML.DecTree.Tree: Implements a class used to represent N-ary trees rdkit.ML.DecTree.TreeUtils: Utilities for working with trees rdkit.ML.DecTree.TreeVis: functionality for drawing trees on sping canvases rdkit.ML.Descriptors rdkit.ML.Descriptors.CompoundDescriptors: descriptor calculator for compounds defined by a composition alone... rdkit.ML.Descriptors.Descriptors: Various bits and pieces for calculating descriptors rdkit.ML.Descriptors.MoleculeDescriptors: Various bits and pieces for calculating Molecular descriptors rdkit.ML.Descriptors.Parser: The "parser" for compound descriptors. rdkit.ML.EnrichPlot: Command line tool to construct an enrichment plot from saved composite models rdkit.ML.GrowComposite: command line utility for growing composite models rdkit.ML.InfoTheory: Information Theory functionality rdkit.ML.InfoTheory.BitClusterer rdkit.ML.InfoTheory.BitRank: Functionality for ranking bits using info gains rdkit.ML.InfoTheory.entropy: Informational Entropy functions rdkit.ML.InfoTheory.rdInfoTheory: Module containing bunch of functions for information metrics and a ranker to rank bits rdkit.ML.KNN: rdkit.ML.KNN.CrossValidate: handles doing cross validation with k-nearest neighbors model rdkit.ML.KNN.DistFunctions rdkit.ML.KNN.KNNClassificationModel: Define the class _KNNClassificationModel_, used to represent a k-nearest neighbhors classification model rdkit.ML.KNN.KNNModel: Define the class _KNNModel_, used to represent a k-nearest neighbhors model rdkit.ML.KNN.KNNRegressionModel: Define the class _KNNRegressionModel_, used to represent a k-nearest neighbhors regression model rdkit.ML.MLUtils rdkit.ML.MLUtils.VoteImg: functionality for generating an image showing the results of a composite model voting on a data set rdkit.ML.MatOps: Matrix operations which may or may not come in handy some day rdkit.ML.ModelPackage rdkit.ML.ModelPackage.PackageUtils rdkit.ML.ModelPackage.Packager rdkit.ML.NaiveBayes: rdkit.ML.NaiveBayes.ClassificationModel: Defines Naive Baysean classification model... rdkit.ML.NaiveBayes.CrossValidate: handles doing cross validation with naive bayes models... rdkit.ML.Neural rdkit.ML.Neural.ActFuncs: Activation functions for neural network nodes rdkit.ML.Neural.CrossValidate: handles doing cross validation with neural nets rdkit.ML.Neural.NetNode: Contains the class _NetNode_ which is used to represent nodes in neural nets rdkit.ML.Neural.Network: Contains the class _Network_ which is used to represent neural nets rdkit.ML.Neural.Trainers: Training algorithms for feed-forward neural nets rdkit.ML.SLT rdkit.ML.SLT.Risk: code for calculating empirical risk rdkit.ML.Scoring rdkit.ML.Scoring.Scoring: $Id$ rdkit.ML.ScreenComposite: command line utility for screening composite models rdkit.ML.files: Generic file manipulation stuff

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Imports: Cluster, Data, InfoTheory