@Override
public double[][] infer(double[][] features, KObject origin, KInternalDataManager manager) {
int maxOutput = ((MetaEnum) origin.metaClass().outputs()[0].type()).literals().length;
KMemoryChunk ks =
manager.closestChunk(
origin.universe(),
origin.now(),
origin.uuid(),
origin.metaClass(),
((AbstractKObject) origin).previousResolved());
int dependenciesIndex = origin.metaClass().dependencies().index();
// check if chunk is empty
int size = (maxOutput + 1) * (origin.metaClass().inputs().length * NUMOFFIELDS + 1);
if (ks.getDoubleArraySize(dependenciesIndex, origin.metaClass()) == 0) {
return null;
}
Array1D state =
new Array1D(size, 0, origin.metaClass().dependencies().index(), ks, origin.metaClass());
double[][] result = new double[features.length][1];
for (int j = 0; j < features.length; j++) {
result[j] = new double[1];
double maxprob = 0;
double prob = 0;
for (int output = 0; output < maxOutput; output++) {
prob = getProba(features[j], output, state, origin.metaClass().dependencies());
if (prob > maxprob) {
maxprob = prob;
result[j][0] = output;
}
}
}
return result;
}
@Override
public void train(
double[][] trainingSet,
double[][] expectedResultSet,
KObject origin,
KInternalDataManager manager) {
int maxOutput = ((MetaEnum) origin.metaClass().outputs()[0].type()).literals().length;
KMemoryChunk ks =
manager.preciseChunk(
origin.universe(),
origin.now(),
origin.uuid(),
origin.metaClass(),
((AbstractKObject) origin).previousResolved());
int dependenciesIndex = origin.metaClass().dependencies().index();
// Create initial chunk if empty
int size = (maxOutput + 1) * (origin.metaClass().inputs().length * NUMOFFIELDS + 1);
if (ks.getDoubleArraySize(dependenciesIndex, origin.metaClass()) == 0) {
ks.extendDoubleArray(origin.metaClass().dependencies().index(), size, origin.metaClass());
for (int i = 0; i < size; i++) {
ks.setDoubleArrayElem(dependenciesIndex, i, 0, origin.metaClass());
}
}
Array1D state =
new Array1D(size, 0, origin.metaClass().dependencies().index(), ks, origin.metaClass());
// update the state
for (int i = 0; i < trainingSet.length; i++) {
int output = (int) expectedResultSet[i][0];
for (int j = 0; j < origin.metaClass().inputs().length; j++) {
// If this is the first datapoint
if (state.get(getCounter(output, origin.metaClass().dependencies())) == 0) {
state.set(getIndex(j, output, MIN, origin.metaClass().dependencies()), trainingSet[i][j]);
state.set(getIndex(j, output, MAX, origin.metaClass().dependencies()), trainingSet[i][j]);
state.set(getIndex(j, output, SUM, origin.metaClass().dependencies()), trainingSet[i][j]);
state.set(
getIndex(j, output, SUMSQUARE, origin.metaClass().dependencies()),
trainingSet[i][j] * trainingSet[i][j]);
} else {
if (trainingSet[i][j]
< state.get(getIndex(j, output, MIN, origin.metaClass().dependencies()))) {
state.set(
getIndex(j, output, MIN, origin.metaClass().dependencies()), trainingSet[i][j]);
}
if (trainingSet[i][j]
> state.get(getIndex(j, output, MAX, origin.metaClass().dependencies()))) {
state.set(
getIndex(j, output, MAX, origin.metaClass().dependencies()), trainingSet[i][j]);
}
state.add(getIndex(j, output, SUM, origin.metaClass().dependencies()), trainingSet[i][j]);
state.add(
getIndex(j, output, SUMSQUARE, origin.metaClass().dependencies()),
trainingSet[i][j] * trainingSet[i][j]);
}
// update global stat
if (state.get(getCounter(maxOutput, origin.metaClass().dependencies())) == 0) {
state.set(
getIndex(j, maxOutput, MIN, origin.metaClass().dependencies()), trainingSet[i][j]);
state.set(
getIndex(j, maxOutput, MAX, origin.metaClass().dependencies()), trainingSet[i][j]);
state.set(
getIndex(j, maxOutput, SUM, origin.metaClass().dependencies()), trainingSet[i][j]);
state.set(
getIndex(j, maxOutput, SUMSQUARE, origin.metaClass().dependencies()),
trainingSet[i][j] * trainingSet[i][j]);
} else {
if (trainingSet[i][j]
< state.get(getIndex(j, maxOutput, MIN, origin.metaClass().dependencies()))) {
state.set(
getIndex(j, maxOutput, MIN, origin.metaClass().dependencies()), trainingSet[i][j]);
}
if (trainingSet[i][j]
> state.get(getIndex(j, maxOutput, MAX, origin.metaClass().dependencies()))) {
state.set(
getIndex(j, maxOutput, MAX, origin.metaClass().dependencies()), trainingSet[i][j]);
}
state.add(
getIndex(j, maxOutput, SUM, origin.metaClass().dependencies()), trainingSet[i][j]);
state.add(
getIndex(j, maxOutput, SUMSQUARE, origin.metaClass().dependencies()),
trainingSet[i][j] * trainingSet[i][j]);
}
}
// Update Global counters
state.add(getCounter(output, origin.metaClass().dependencies()), 1);
state.add(getCounter(maxOutput, origin.metaClass().dependencies()), 1);
}
}
