private void performDrag(MotionEvent event) {
mLastGesture = ChartGesture.DRAG;
mMatrix.set(mSavedMatrix);
OnChartGestureListener l = mChart.getOnChartGestureListener();
float dX, dY;
// check if axis is inverted
if (mChart.isAnyAxisInverted()
&& mClosestDataSetToTouch != null
&& mChart.getAxis(mClosestDataSetToTouch.getAxisDependency()).isInverted()) {
// if there is an inverted horizontalbarchart
if (mChart instanceof HorizontalBarChart) {
dX = -(event.getX() - mTouchStartPoint.x);
dY = event.getY() - mTouchStartPoint.y;
} else {
dX = event.getX() - mTouchStartPoint.x;
dY = -(event.getY() - mTouchStartPoint.y);
}
} else {
dX = event.getX() - mTouchStartPoint.x;
dY = event.getY() - mTouchStartPoint.y;
}
mMatrix.postTranslate(dX, dY);
if (l != null) l.onChartTranslate(event, dX, dY);
}
public List<SelectionDetail> getSelectionDetailsAtIndex(int xIndex) {
List<SelectionDetail> vals = new ArrayList<>();
for (int i = 0; i < mData.getDataSetCount(); i++) {
DataSet<?> dataSet = mData.getDataSetByIndex(i);
// extract all y-values from all DataSets at the given x-index
final float yVal = dataSet.getYValForXIndex(xIndex);
if (yVal == Float.NaN) continue;
vals.add(new SelectionDetail(yVal, i, dataSet));
}
return vals;
}
private Label makeClassLabel(int classId) {
float[] labels = tempLabels[classId];
for (int i = 0; i < labels.length; i++) {
if (trainData.getLabel(i) == classId) labels[i] = 1.0F;
labels[i] -= 1 / classCount;
}
return new Label(labels, null);
}
public PointF getTrans(float x, float y) {
ViewPortHandler vph = mChart.getViewPortHandler();
float xTrans = x - vph.offsetLeft();
float yTrans = 0f;
// check if axis is inverted
if (mChart.isAnyAxisInverted()
&& mClosestDataSetToTouch != null
&& mChart.isInverted(mClosestDataSetToTouch.getAxisDependency())) {
yTrans = -(y - vph.offsetTop());
} else {
yTrans = -(mChart.getMeasuredHeight() - y - vph.offsetBottom());
}
return new PointF(xTrans, yTrans);
}
@Override
public void initialize(DataSet d) {
trainData = d;
indices = new TIntArrayList(RandomUtils.getIndexes(d.getInstanceLength()));
classCount = trainData.getLabels().getClassIndexMap().size();
scoreCache = new float[trainData.getInstanceLength()][classCount];
roundData = new DataSet[classCount];
roundIndices = new int[(int) (trainData.getInstanceLength() * SAMPLE_RATE)];
tempLabels = new float[classCount][trainData.getInstanceLength()];
indices.shuffle(new Random());
for (int j = 0; j < roundIndices.length; j++) roundIndices[j] = indices.get(j);
for (int j = 0; j < classCount; j++) {
roundData[j] = new DataSet(trainData.getFeatureMatrix(), makeClassLabel(j));
}
log.info(
"initialize finished, tempLabels + ScoreCache MEM use ~ {} GB",
2.0 * scoreCache.length * scoreCache[0].length * 4 / 1024 / 1024 / 1024);
}
@Override
public void train() {
for (int i = 0; i < boosters.length; i++) {
long t1 = System.currentTimeMillis();
final int ROUND = i;
service = Executors.newFixedThreadPool(MAX_THREADS);
countDownLatch = new CountDownLatch(classCount);
IntStream.range(0, classCount)
.forEach(
j ->
service.submit(
() -> {
long tic = System.currentTimeMillis();
try {
boosters[ROUND][j].boostInitialize(roundData[j], roundIndices);
boosters[ROUND][j].boost();
} catch (Throwable t) {
log.error(t.getMessage(), t);
}
long toc = System.currentTimeMillis();
log.debug(
"round {}, task: {}/{} finished, elapsed {} ms",
ROUND,
j,
classCount,
toc - tic);
countDownLatch.countDown();
}));
try {
TimeUnit.SECONDS.sleep(10);
countDownLatch.await();
} catch (Throwable t) {
log.error(t.getMessage(), t);
}
service.shutdown();
roundIndicator[ROUND] = true;
long t2 = System.currentTimeMillis();
float[] probs = new float[classCount];
for (int j = 0; j < trainData.getInstanceLength(); j++) {
double[] x = trainData.getInstance(j);
double y = trainData.getLabel(j);
float[] ys = new float[classCount];
ys[(int) y] = 1;
for (int k = 0; k < classCount; k++) {
scoreCache[j][k] += LEARNING_RATE * boosters[ROUND][k].boostPredict(x);
probs[k] = (float) Math.exp(scoreCache[j][k]);
}
ArraySumUtil.normalize(probs);
roundKL[ROUND] += (float) ArrayUtil.KLDivergence(ys, probs);
for (int k = 0; k < classCount; k++) tempLabels[k][j] = ys[k] - probs[k];
}
roundKL[ROUND] /= trainData.getInstanceLength();
for (int j = 0; j < classCount; j++)
roundData[j] = new DataSet(trainData.getFeatureMatrix(), new Label(tempLabels[j], null));
indices.shuffle(new Random());
for (int j = 0; j < roundIndices.length; j++) roundIndices[j] = indices.get(j);
long t3 = System.currentTimeMillis();
if (NEED_REPORT) {
statisticReport(ROUND);
}
long t4 = System.currentTimeMillis();
log.info("round {}, KL {}", ROUND, roundKL[ROUND]);
log.info(
"boost {} |update gradient {} |report {}| total {}", t2 - t1, t3 - t2, t4 - t3, t4 - t1);
}
log.info("GradientBoostClassification training finished ...");
if (NEED_REPORT) {
printRoundReport();
}
}
