Classes | |
| class | ExampleCounter |
Public Member Functions | |
| void | addClusterer (String nodeName, Clusterer clusterer) throws Exception |
| CPTLearner (DomainLearner dl) throws Exception | |
| CPTLearner (BeliefNetworkEx bn) | |
| void | learn (Map< String, String > data) throws Exception |
| void | learn (Instances instances) throws Exception |
| void | learn (ResultSet rs) throws Exception |
| void | setUniformDefault (boolean value) |
Protected Member Functions | |
| void | end_learning () |
Protected Attributes | |
| Clusterer[] | clusterers |
| ExampleCounter[] | counters |
| boolean | uniformDefault |
Static Package Functions | |
| [static initializer] | |
Static Package Attributes | |
| static final Logger | logger = Logger.getLogger(CPTLearner.class) |
Private Member Functions | |
| void | init () |
learns the conditional probability tables for all nodes in a Bayesian network when given a set of examples. CPTs are learnt by initializing all the table values to zero and incrementing individual values whenever a corresponding example is passed. In the end, probablities are obtained by means of normalization.
Definition at line 26 of file bayesnets/learning/CPTLearner.java.
| edu::tum::cs::bayesnets::learning::CPTLearner::CPTLearner | ( | BeliefNetworkEx | bn | ) | [inline] |
constructs a CPTLearner object from a BeliefNetworkEx object
| bn |
Definition at line 54 of file bayesnets/learning/CPTLearner.java.
| edu::tum::cs::bayesnets::learning::CPTLearner::CPTLearner | ( | DomainLearner | dl | ) | throws Exception [inline] |
constructs a CPTLearner object from a DomainLearner. If you consecutively want to learn domains and CPTs, you should make use of this constructor, because it relieves you of the burden of having to pass the clusterers that categorize instances for certain domains manually (duplicate domains are taken into consideration, i.e. clusterers will be reused appropriately).
| dl | the domain learner |
| Exception |
Definition at line 76 of file bayesnets/learning/CPTLearner.java.
| edu::tum::cs::bayesnets::learning::CPTLearner::[static initializer] | ( | ) | [inline, static, package] |
| void edu::tum::cs::bayesnets::learning::CPTLearner::addClusterer | ( | String | nodeName, | |
| Clusterer | clusterer | |||
| ) | throws Exception [inline] |
learns all the examples in a fipm.data.QueryResult (otherwise analogous to learn(ResultSet))
| res | the query result containing the data for a set of examples |
| Exception | tells the CPTLearner to use a clusterer to categorize instances (i.e. example outcomes) for a certain node. |
| nodeName | the name of the node | |
| clusterer | the clusterer to use for categorization |
| Exception | if the name of the node is invalid |
Definition at line 397 of file bayesnets/learning/CPTLearner.java.
| void edu::tum::cs::bayesnets::learning::CPTLearner::end_learning | ( | ) | [inline, protected, virtual] |
normalizes the CPTs (is called by finish and should not be called)
Implements edu::tum::cs::bayesnets::learning::Learner.
Definition at line 409 of file bayesnets/learning/CPTLearner.java.
| void edu::tum::cs::bayesnets::learning::CPTLearner::init | ( | ) | [inline, private] |
initializes the array of clusterers (initially an array of null references) and the array of example counters (one for each node)
Definition at line 99 of file bayesnets/learning/CPTLearner.java.
| void edu::tum::cs::bayesnets::learning::CPTLearner::learn | ( | Map< String, String > | data | ) | throws Exception [inline] |
learns an example from a Map<String,String>. This is the only learning method without using BeliefNetworkEx#getAttributeNameForNode(String).
| data | a Map containing the data for one example. The names of all the random variables (nodes) in the network must be found in the set of keys of the hash map. |
| Exception | if required keys are missing from the HashMap |
Definition at line 301 of file bayesnets/learning/CPTLearner.java.
| void edu::tum::cs::bayesnets::learning::CPTLearner::learn | ( | Instances | instances | ) | throws Exception [inline] |
learns all the examples in the instances. Each instance in the instances represents one example. All the random variables (nodes) in the network need to be found in each instance as columns that are named accordingly, i.e. for each random variable, there must be an attribute with a matching name in the instance.
| instances | the instances |
| Exception | if the result set is empty | |
| SQLException | particularly if there is no matching column for one of the node names |
Definition at line 197 of file bayesnets/learning/CPTLearner.java.
| void edu::tum::cs::bayesnets::learning::CPTLearner::learn | ( | ResultSet | rs | ) | throws Exception [inline, virtual] |
learns all the examples in the result set. Each row in the result set represents one example. All the random variables (nodes) in the network need to be found in each result row as columns that are named accordingly, i.e. for each random variable, there must be a column with a matching name in the result set.
| rs | the result set |
| Exception | if the result set is empty | |
| SQLException | particularly if there is no matching column for one of the node names |
Implements edu::tum::cs::bayesnets::learning::Learner.
Definition at line 117 of file bayesnets/learning/CPTLearner.java.
| void edu::tum::cs::bayesnets::learning::CPTLearner::setUniformDefault | ( | boolean | value | ) | [inline] |
controls how to finalize a column of the CPT when there were no examples (i.e. all of the column's entries are zero); By default, the zeros are kept
| value | If true, use a uniform distribution for such columns; otherwise leave the column as it was (all zeros) |
Definition at line 63 of file bayesnets/learning/CPTLearner.java.
Clusterer [] edu::tum::cs::bayesnets::learning::CPTLearner::clusterers [protected] |
an array of clusterers - one for each node; for nodes that do not use clustering to determine the index of the domain, the entry is null
Definition at line 43 of file bayesnets/learning/CPTLearner.java.
an array of example counter objects - one for each node in the network
Definition at line 38 of file bayesnets/learning/CPTLearner.java.
final Logger edu::tum::cs::bayesnets::learning::CPTLearner::logger = Logger.getLogger(CPTLearner.class) [static, package] |
The logger for this class.
Definition at line 30 of file bayesnets/learning/CPTLearner.java.
boolean edu::tum::cs::bayesnets::learning::CPTLearner::uniformDefault [protected] |
controls how to finalize a column of the CPT for which there were no examples (i.e. all of the column entries are 0); If true, assume a uniform distribution, otherwise keep the zeros.
Definition at line 48 of file bayesnets/learning/CPTLearner.java.
