the directed acyclic graph specifying conditional dependencies
the array of tables recording conditional probabilities
the number of classes
the names for all classes
Given a continuous data vector 'z', classify it returning the class number (0, .
Given a continuous data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. The vector 'z' id first converted to an integer valued vector by rounding.
the data vector to classify
Given an integer-valued data vector 'z', classify it returning the class number (0, .
Given an integer-valued data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.
the data vector to classify
Compute the Conditional Probability (CP) of 'x_i' given its parents' values.
Compute the Conditional Probability (CP) of 'x_i' given its parents' values.
the ith variable (whose conditional probability is sought)
the values of x_i's parents and x_i
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Compute the Joint Probability (JP) of vector 'x' ('z' concat outcome).
Compute the Joint Probability (JP) of vector 'x' ('z' concat outcome). as the product of each of its element's conditional probability.
the vector of variables
Train the classifier, i.
Train the classifier, i.e., ...
The
BayesNetworkclass implements a Bayesian Network Classifier. It classifies a data vector 'z' by determining which of 'k' classes has the highest Joint Probability of 'z' and the outcome (i.e., one of the 'k' classes) of occurring. The Joint Probability calculation is factored into multiple calculations of Conditional Probability. Conditional dependencies are specified using a Directed Acyclic Graph (DAG). Nodes are conditionally dependent on their parents only. Conditional probability are recorded in tables. Training is achieved by ...