Java Code Examples for weka.core.Utils#smOrEq()
The following examples show how to use
weka.core.Utils#smOrEq() .
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Example 1
| Source File: PruneableClassifierTree.java From tsml with GNU General Public License v3.0 | 6 votes |
|
/**
* Prunes a tree.
*
* @throws Exception if tree can't be pruned successfully
*/
public void prune() throws Exception {
if (!m_isLeaf) {
// Prune all subtrees.
for (int i = 0; i < m_sons.length; i++)
son(i).prune();
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsForLeaf(),errorsForTree())) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(localModel().distribution());
}
}
}
Example 2
| Source File: BinC45Split.java From tsml with GNU General Public License v3.0 | 6 votes |
|
/**
* Returns index of subset instance is assigned to.
* Returns -1 if instance is assigned to more than one subset.
*
* @exception Exception if something goes wrong
*/
public final int whichSubset(Instance instance) throws Exception {
if (instance.isMissing(m_attIndex))
return -1;
else{
if (instance.attribute(m_attIndex).isNominal()){
if ((int)m_splitPoint == (int)instance.value(m_attIndex))
return 0;
else
return 1;
}else
if (Utils.smOrEq(instance.value(m_attIndex),m_splitPoint))
return 0;
else
return 1;
}
}
Example 3
| Source File: C45Split.java From tsml with GNU General Public License v3.0 | 6 votes |
|
/**
* Returns index of subset instance is assigned to.
* Returns -1 if instance is assigned to more than one subset.
*
* @exception Exception if something goes wrong
*/
public final int whichSubset(Instance instance)
throws Exception {
if (instance.isMissing(m_attIndex))
return -1;
else{
if (instance.attribute(m_attIndex).isNominal())
return (int)instance.value(m_attIndex);
else
if (Utils.smOrEq(instance.value(m_attIndex),m_splitPoint))
return 0;
else
return 1;
}
}
Example 4
| Source File: ResidualSplit.java From tsml with GNU General Public License v3.0 | 5 votes |
|
public final int whichSubset(Instance instance)
throws Exception {
if (instance.isMissing(m_attIndex))
return -1;
else{
if (instance.attribute(m_attIndex).isNominal())
return (int)instance.value(m_attIndex);
else
if (Utils.smOrEq(instance.value(m_attIndex),m_splitPoint))
return 0;
else
return 1;
}
}
Example 5
| Source File: PruneableDecList.java From tsml with GNU General Public License v3.0 | 5 votes |
|
/**
* Prunes the end of the rule.
*/
protected void pruneEnd() throws Exception {
double errorsLeaf, errorsTree;
errorsTree = errorsForTree();
errorsLeaf = errorsForLeaf();
if (Utils.smOrEq(errorsLeaf,errorsTree)){
m_isLeaf = true;
m_sons = null;
m_localModel = new NoSplit(localModel().distribution());
}
}
Example 6
| Source File: C45PruneableDecList.java From tsml with GNU General Public License v3.0 | 5 votes |
|
/**
* Prunes the end of the rule.
*/
protected void pruneEnd() {
double errorsLeaf, errorsTree;
errorsTree = getEstimatedErrorsForTree();
errorsLeaf = getEstimatedErrorsForLeaf();
if (Utils.smOrEq(errorsLeaf,errorsTree+0.1)) { // +0.1 as in C4.5
m_isLeaf = true;
m_sons = null;
m_localModel = new NoSplit(localModel().distribution());
}
}
Example 7
| Source File: ClassifierDecList.java From tsml with GNU General Public License v3.0 | 5 votes |
|
/**
* Method for choosing a subset to expand.
*/
public final int chooseIndex() {
int minIndex = -1;
double estimated, min = Double.MAX_VALUE;
int i, j;
for (i = 0; i < m_sons.length; i++)
if (son(i) == null) {
if (Utils.sm(localModel().distribution().perBag(i),
(double)m_minNumObj))
estimated = Double.MAX_VALUE;
else{
estimated = 0;
for (j = 0; j < localModel().distribution().numClasses(); j++)
estimated -= m_splitCrit.logFunc(localModel().distribution().
perClassPerBag(i,j));
estimated += m_splitCrit.logFunc(localModel().distribution().
perBag(i));
estimated /= localModel().distribution().perBag(i);
}
if (Utils.smOrEq(estimated,0))
return i;
if (Utils.sm(estimated,min)) {
min = estimated;
minIndex = i;
}
}
return minIndex;
}
Example 8
| Source File: C45PruneableClassifierTreeG.java From tsml with GNU General Public License v3.0 | 4 votes |
|
/**
* Prunes a tree using C4.5's pruning procedure.
*
* @throws Exception if something goes wrong
*/
public void prune() throws Exception {
double errorsLargestBranch;
double errorsLeaf;
double errorsTree;
int indexOfLargestBranch;
C45PruneableClassifierTreeG largestBranch;
int i;
if (!m_isLeaf){
// Prune all subtrees.
for (i=0;i<m_sons.length;i++)
son(i).prune();
// Compute error for largest branch
indexOfLargestBranch = localModel().distribution().maxBag();
if (m_subtreeRaising) {
errorsLargestBranch = son(indexOfLargestBranch).
getEstimatedErrorsForBranch((Instances)m_train);
} else {
errorsLargestBranch = Double.MAX_VALUE;
}
// Compute error if this Tree would be leaf
errorsLeaf =
getEstimatedErrorsForDistribution(localModel().distribution());
// Compute error for the whole subtree
errorsTree = getEstimatedErrors();
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsLeaf,errorsTree+0.1) &&
Utils.smOrEq(errorsLeaf,errorsLargestBranch+0.1)){
// Free son Trees
m_sons = null;
m_isLeaf = true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(localModel().distribution());
return;
}
// Decide if largest branch is better choice
// than whole subtree.
if (Utils.smOrEq(errorsLargestBranch,errorsTree+0.1)){
largestBranch = son(indexOfLargestBranch);
m_sons = largestBranch.m_sons;
m_localModel = largestBranch.localModel();
m_isLeaf = largestBranch.m_isLeaf;
newDistribution(m_train);
prune();
}
}
}
Example 9
| Source File: C45PruneableClassifierTree.java From tsml with GNU General Public License v3.0 | 4 votes |
|
/**
* Prunes a tree using C4.5's pruning procedure.
*
* @throws Exception if something goes wrong
*/
public void prune() throws Exception {
double errorsLargestBranch;
double errorsLeaf;
double errorsTree;
int indexOfLargestBranch;
C45PruneableClassifierTree largestBranch;
int i;
if (!m_isLeaf){
// Prune all subtrees.
for (i=0;i<m_sons.length;i++)
son(i).prune();
// Compute error for largest branch
indexOfLargestBranch = localModel().distribution().maxBag();
if (m_subtreeRaising) {
errorsLargestBranch = son(indexOfLargestBranch).
getEstimatedErrorsForBranch((Instances)m_train);
} else {
errorsLargestBranch = Double.MAX_VALUE;
}
// Compute error if this Tree would be leaf
errorsLeaf =
getEstimatedErrorsForDistribution(localModel().distribution());
// Compute error for the whole subtree
errorsTree = getEstimatedErrors();
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsLeaf,errorsTree+0.1) &&
Utils.smOrEq(errorsLeaf,errorsLargestBranch+0.1)){
// Free son Trees
m_sons = null;
m_isLeaf = true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(localModel().distribution());
return;
}
// Decide if largest branch is better choice
// than whole subtree.
if (Utils.smOrEq(errorsLargestBranch,errorsTree+0.1)){
largestBranch = son(indexOfLargestBranch);
m_sons = largestBranch.m_sons;
m_localModel = largestBranch.localModel();
m_isLeaf = largestBranch.m_isLeaf;
newDistribution(m_train);
prune();
}
}
}
Example 10
| Source File: FTNode.java From tsml with GNU General Public License v3.0 | 4 votes |
|
/**
* Method for prunning a tree using C4.5 pruning procedure.
*
* @exception Exception if something goes wrong
*/
public double prune() throws Exception {
double errorsLeaf;
double errorsTree;
double errorsConstModel;
double treeError=0;
int i;
double probBranch;
// Compute error if this Tree would be leaf without contructor
errorsLeaf = getEstimatedErrorsForDistribution(m_localModel.distribution());
if (m_isLeaf ) {
return errorsLeaf;
} else {
//Computes da error of the constructor model
errorsConstModel = getEtimateConstModel(m_localModel.distribution());
errorsTree=0;
for (i = 0; i < m_sons.length; i++) {
probBranch = m_localModel.distribution().perBag(i) /
m_localModel.distribution().total();
errorsTree += probBranch* m_sons[i].prune();
}
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsLeaf, errorsTree) && Utils.smOrEq(errorsLeaf, errorsConstModel)) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
m_hasConstr=false;
m_leafclass=m_localModel.distribution().maxClass();
// Get NoSplit Model for node.
m_localModel = new NoSplit(m_localModel.distribution());
treeError=errorsLeaf;
}else{
// Decide if Constructor is best choice.
if (Utils.smOrEq(errorsConstModel, errorsTree)) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
m_hasConstr =true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(m_localModel.distribution());
treeError=errorsConstModel;
} else
treeError=errorsTree;
}
}
return treeError;
}
Example 11
| Source File: FTLeavesNode.java From tsml with GNU General Public License v3.0 | 4 votes |
|
/**
* Prunes a tree using C4.5 pruning procedure.
*
* @exception Exception if something goes wrong
*/
public double prune() throws Exception {
double errorsLeaf;
double errorsTree;
double errorsConstModel;
double treeError=0;
int i;
double probBranch;
// Compute error if this Tree would be leaf without contructor
errorsLeaf = getEstimatedErrorsForDistribution(m_localModel.distribution());
if (m_isLeaf ) {
return errorsLeaf;
} else {
//Computes da error of the constructor model
errorsConstModel = getEtimateConstModel(m_localModel.distribution());
errorsTree=0;
for (i = 0; i < m_sons.length; i++) {
probBranch = m_localModel.distribution().perBag(i) /
m_localModel.distribution().total();
errorsTree += probBranch* m_sons[i].prune();
}
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsLeaf, errorsTree) && Utils.smOrEq(errorsLeaf, errorsConstModel)) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
m_hasConstr=false;
m_leafclass=m_localModel.distribution().maxClass();
// Get NoSplit Model for node.
m_localModel = new NoSplit(m_localModel.distribution());
treeError=errorsLeaf;
}else{
// Decide if Constructor is best choice.
if (Utils.smOrEq(errorsConstModel, errorsTree)) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
m_hasConstr =true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(m_localModel.distribution());
treeError=errorsConstModel;
} else
treeError=errorsTree;
}
}
return treeError;
}
Example 12
| Source File: FTInnerNode.java From tsml with GNU General Public License v3.0 | 4 votes |
|
/**
* Prunes a tree using C4.5 pruning procedure.
*
* @exception Exception if something goes wrong
*/
public double prune() throws Exception {
double errorsLeaf;
double errorsTree;
double errorsConstModel;
double treeError=0;
int i;
double probBranch;
// Compute error if this Tree would be leaf without contructor
errorsLeaf = getEstimatedErrorsForDistribution(m_localModel.distribution());
if (m_isLeaf ) {
return errorsLeaf;
} else {
//Computes da error of the constructor model
errorsConstModel = getEtimateConstModel(m_localModel.distribution());
errorsTree=0;
for (i = 0; i < m_sons.length; i++) {
probBranch = m_localModel.distribution().perBag(i) /
m_localModel.distribution().total();
errorsTree += probBranch* m_sons[i].prune();
}
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsLeaf, errorsTree)) {
// Free son Trees
m_sons = null;
m_isLeaf = true;
m_hasConstr=false;
m_leafclass=m_localModel.distribution().maxClass();
// Get NoSplit Model for node.
m_localModel = new NoSplit(m_localModel.distribution());
treeError=errorsLeaf;
} else{
treeError=errorsTree;
}
}
return treeError;
}
Example 13
| Source File: ConjunctiveRule.java From tsml with GNU General Public License v3.0 | 2 votes |
|
/**
* Function used to calculate the weighted mean squared error,
* i.e., sum[x-avg(x)]^2 based on the given elements of the formula:
* meanSquaredError = sum(Wi*Xi^2) - (sum(WiXi))^2/sum(Wi)
*
* @param weightedSq sum(Wi*Xi^2)
* @param weightedValue sum(WiXi)
* @param sum sum of weights
* @return the weighted mean-squared error
*/
protected double wtMeanSqErr(double weightedSq, double weightedValue, double sum){
if(Utils.smOrEq(sum, 1.0E-6))
return 0;
return (weightedSq - (weightedValue * weightedValue) / sum);
}
