Package rdkit :: Package ML :: Package NaiveBayes :: Module ClassificationModel :: Class NaiveBayesClassifier

[hide private]
[frames] | no frames]

# Class NaiveBayesClassifier

source code

    _NaiveBayesClassifier_s can save the following pieces of internal state, accessible via
    standard setter/getter functions:

    1) _Examples_: a list of examples which have been predicted

    2) _TrainingExamples_: List of training examples - the descriptor value of these examples
      are quantized based on info gain using ML/Data/Quantize.py if necessary

    3) _TestExamples_: the list of examples used to test the model

    4) _BadExamples_ : list of examples that were incorrectly classified

    4) _QBoundVals_: Quant bound values for each varaible - a list of lists

    5) _QBounds_ : Number of bounds for each variable

Instance Methods | [hide private]

__init__(self, attrs, nPossibleVals, nQuantBounds, mEstimateVal=-1.0, useSigs=False) Constructor | source code

GetName(self) | source code

SetName(self, name) | source code

NameModel(self, varNames) | source code

GetExamples(self) | source code

SetExamples(self, examples) | source code

GetTrainingExamples(self) | source code

SetTrainingExamples(self, examples) | source code

GetTestExamples(self) | source code

SetTestExamples(self, examples) | source code

SetBadExamples(self, examples) | source code

GetBadExamples(self) | source code

_computeQuantBounds(self) | source code

trainModel(self) We will assume at this point that the training examples have been set | source code

ClassifyExamples(self, examples, appendExamples=0) | source code

GetClassificationDetails(self) returns the probability of the last prediction | source code

ClassifyExample(self, example, appendExamples=0) Classify an example by summing over the conditional probabilities... | source code

Method Details | [hide private]

### trainModel(self)

source code

    We will assume at this point that the training examples have been set

    We have to estmate the conditional probabilities for each of the (binned) descriptor
    component give a outcome (or class). Also the probabilities for each class is estimated

### ClassifyExample(self, example, appendExamples=0)

source code

    Classify an example by summing over the conditional probabilities
    The most likely class is the one with the largest probability