Learning
It
finds closest and furthest vectors for each label pair.
public void findAllClosestAndFurthest(List<OneVsOtherBox>
labelPairs, SimilaritySettingsBox closest, SimilaritySettingsBox furthest) {
for (int i = 0; i < labelPairs.size(); i++) {
OneVsOtherBox labelPair = labelPairs.get(i);
findClosestAndFurthest(labelPair, closest, furthest);
}
}
Find
closest and furthest uses find with Boolean which sets what to find.
for (int i = 0; i < targets.size(); i++) {
VectorBox
target = targets.get(i);
VectorBox
closestVector = closestOrFurthest(findClosestOrFurthestThis, target, settings, closest);
if (closestVector != null) {
if (closest) {
// if it is not already used
if (closestVector.getClosest() == 0) {
closestOrFurthestVectors.add(closestVector);
It
uses settings which contain the pointers to formulas which measure the
distances to try and solve inseparable problem.
public double euclidean(VectorBox a, VectorBox b) {
return v.euclideanDistance(a, b);
}
public double linear(VectorBox a, VectorBox b) {
return v.dotProduct(a, b);
}
public double RBF(VectorBox a, VectorBox b, double ro) {
double gamma = 1 / (2 * v.square(ro));
return gaussian(a, b, gamma);
}
public double gaussian(VectorBox a, VectorBox b, double gamma) {
Predicting
Various
modifications have been done to prevent high precision because of how data is
sorted and how class labels are counted. Shuffling and hash maps gave the most
reliable results.
For each
label pair, check which class the unknown value might be.
public
List<VectorBox> predict(List<VectorBox> predictData, SettingsBox settings) {
currentSettings = settings;
Collections.shuffle(predictData);
return predict(predictData);
}
public
List<VectorBox> predict(List<VectorBox> data) {
resetDataLabels(data);
for (int i = 0; i < data.size(); i++) {
assignLabels(data.get(i));
}
return data;
}
The
“assignLabels” method iterates through furthest vectors and measures radiuses
in the way it is described in the design section.
public void assignLabels(VectorBox
unknownLabel) {
for (int i = 0; i < labelPairs.size(); i++) {
OneVsOtherBox l = labelPairs.get(i);
assignLabel(unknownLabel, l, currentSettings.getPredictSimilaritySettings(), l.getOneLabel());
}
}
The
“inSphere” method checks if new unknown value is in the class representing
sphere. It iterates through each furthest vector, then finds closest opposite
class vector. Using this vector it finds closest opposite class vector again,
but this time it is its own closest vector. These are used to find midpoint and
then radius which represents class points inside the radius. The next iteration
checks each closest vector and their own circle radius in case they are not in range
of furthest vector radius.
public boolean
inSphere(VectorBox unknownLabel,
List<VectorBox> closestOnes, List<VectorBox> furthestOnes, List<VectorBox> closestOthers, SimilaritySettingsBox similaritySettings) {
for (int i = 0; i < furthestOnes.size(); i++) {
//
furthestOne--->closestOne---><---closestOther<---furthestOther
VectorBox furthestOne = furthestOnes.get(i);
//
furthestOne-----------------><---closestOtherToFurthestOne------------
VectorBox closestOtherToFurthest = findClosest(furthestOne, closestOthers, similaritySettings);
//
--------->closestOneToClosestOther---><---closestOtherToFurthestOne---
VectorBox closestOneToClosestOther = findClosest(closestOtherToFurthest, closestOnes, similaritySettings);
VectorBox midPoint = extraMath.middle(closestOtherToFurthest, closestOneToClosestOther);
double midDist = similarity(midPoint, closestOtherToFurthest, similaritySettings);
double safeDist = similarity(furthestOne, closestOtherToFurthest, similaritySettings) - midDist;
safeDist *= currentSettings.getPredictionSearchSensitivity();
double distance = similarity(unknownLabel, furthestOne, similaritySettings);
if (distance <= safeDist) {
return true;
}
}
for (int j = 0; j < closestOnes.size(); j++) {
VectorBox closestOne = closestOnes.get(j);
VectorBox closestOther = findClosest(closestOne, closestOthers, similaritySettings);
double dist1 = similarity(unknownLabel, closestOne, similaritySettings);
VectorBox midP = extraMath.middle(closestOne, closestOther);
double midDist = similarity(midP, closestOne, similaritySettings);
if (dist1 <= midDist) {
return true;
}
}
return false;
}
Inseparable
problem solution by using slack variable can be seen inside the above code
snippet as “safeDist*=currentSettings.getPredictionSearchSensitivity()”. It
modifies the sphere radius by multiplying it with given value.
Slow Machine
To
improve learning the option to search for best learning subset was also
implemented. It randomly creates subsets of learn data, learns it, predicts,
reset original data to unknowns, then compares the prediction precision and
tracks which was the best subset. Due to difficulties and complexity for
calculating precision when using subsets, these were not used for results even
though it had better results. One of those difficulties arise when subset becomes
vectors of one class label, which results to 100% precision because there is no
other class.
public
List<VectorBox> tryFindBestTrainSet(List<VectorBox> learn, List<VectorBox> predict, SettingsBox s, int times, int subsetSize) {
List<List<VectorBox>>
randomSubsets = getRandomSubsets(learn, times, subsetSize);
double[] results = new double[times];
double high = 0;
for (int i = 0; i < times; i++) {
List<VectorBox> randomSubset = randomSubsets.get(i);
FastMachine lss = new FastMachine(s);
lss.learn(randomSubset);
resetDataLabels(predict);
List<VectorBox> predicted = lss.predict(predict);
results[i] =
predictonPrecision(predicted, learn);
if (results[i] > high) {
high = results[i];
addDisplayInfo("Trying to find best learning subset: " + "Try: " + (i + 1) + " Prediction: " + high + "%");
}
}
double highest = results[0];
List<VectorBox>
bestTrainingSubset = randomSubsets.get(0);
for (int i = 0; i < results.length; i++) {
if (highest < results[i]) {
highest = results[i];
bestTrainingSubset = randomSubsets.get(i);
}
}
return bestTrainingSubset;
}
Find
best settings and find best closest similarity settings use randomly generated
settings and “FastMachine” multiple times by comparing the prediction
precision.
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