public static int[][] dataSetToArray(Dataset dataset) {
int[][] result = new int[dataset.size()][];
for (int i = 0; i < dataset.size(); i++) {
Instance instance = dataset.get(i);
result[i] = instanceToArray(instance);
}
return result;
}
/**
* converts two dimensional array of vectors to the dataset
*
* @param points
* @return
*/
public static Dataset arrayToDataSet(int[][] points) {
Dataset dataset = new DefaultDataset();
for (int i = 0; i < points[0].length; i++) {
double[] values = new double[points.length];
for (int j = 0; j < points.length; j++) {
values[j] = points[j][i];
}
dataset.add(new DenseInstance(values));
}
return dataset;
}
/**
* Tests a classifier on a data set
*
* @param cls the classifier to test
* @param data the data set to test on
* @return the performance for each class
*/
public static Map<Object, PerformanceMeasure> testDataset(Classifier cls, Dataset data) {
Map<Object, PerformanceMeasure> out = new HashMap<Object, PerformanceMeasure>();
for (Object o : data.classes()) {
out.put(o, new PerformanceMeasure());
}
for (Instance instance : data) {
Object prediction = cls.classify(instance);
if (instance.classValue().equals(prediction)) { // prediction
// ==class
for (Object o : out.keySet()) {
if (o.equals(instance.classValue())) {
out.get(o).tp++;
} else {
out.get(o).tn++;
}
}
} else { // prediction != class
for (Object o : out.keySet()) {
/* prediction is positive class */
if (prediction.equals(o)) {
out.get(o).fp++;
}
/* instance is positive class */
else if (o.equals(instance.classValue())) {
out.get(o).fn++;
}
/* none is positive class */
else {
out.get(o).tn++;
}
}
}
}
return out;
}
public setsMeanStdDev produceDataSets(Vector<String> all, int tooHigh, int tooLow) {
double mean = 0;
double standardDev = 0;
double standardDevTimesN = 0;
double sum = 0;
double curCount = 0;
DexComReading garbage = new DexComReading(0, "empty", 0);
Vector<DexComReading> dexReadings = new Vector<DexComReading>();
for (String curString : all) {
try {
DexComReading newRead = produceReading(curString);
dexReadings.add(newRead);
} catch (NumberFormatException e) {
Log.d("KNNMethods", "Bad Date Format");
}
}
Vector<Boolean> dangerListHigh = new Vector<Boolean>();
Vector<Boolean> dangerListLow = new Vector<Boolean>();
for (int i = 0; i < 12; ++i) {
dangerListHigh.add(false);
dangerListLow.add(false);
}
for (int i = 12; i < dexReadings.size() - 6; ++i) {
sum = sum + dexReadings.get(i + 6).getSgv();
++curCount;
}
mean = sum / curCount;
curCount = 0;
for (int i = 12; i < dexReadings.size() - 6; ++i) {
standardDevTimesN = standardDevTimesN + Math.pow((dexReadings.get(i).getSgv() - mean), 2);
++curCount;
if (dexReadings.get(i + 6).getSgv() > tooHigh) {
dangerListHigh.add(true);
dangerListLow.add(false);
} else if (dexReadings.get(i + 6).getSgv() > tooLow) {
dangerListHigh.add(false);
dangerListLow.add(false);
} else if (dexReadings.get(i + 6).getSgv() < tooLow) {
dangerListLow.add(true);
dangerListHigh.add(false);
}
}
standardDev = standardDevTimesN / curCount;
double[][] sets13 = new double[dangerListHigh.size()][13];
for (int i = 12; i < dexReadings.size() - 6; ++i) {
double[] set13 = new double[13];
for (int j = 0; j < 12; ++j) {
set13[11 - j] = (dexReadings.get(i - j).getDoubleSgv() - mean) / standardDev;
}
set13[12] = dexReadings.get(i).getTime();
sets13[i - 12] = set13;
}
Dataset dataHigh = new DefaultDataset();
Dataset dataLow = new DefaultDataset();
Log.d(TAG, "Value=" + tooHigh);
for (int i = 0; i < sets13.length; ++i) {
Instance instanceWClassValueHigh = new DenseInstance(sets13[i], dangerListHigh.get(i));
Instance instanceWClassValueLow = new DenseInstance(sets13[i], dangerListLow.get(i));
dataHigh.add(instanceWClassValueHigh);
dataLow.add(instanceWClassValueLow);
}
Vector<Dataset> dataSets = new Vector<Dataset>();
dataSets.add(dataHigh);
dataSets.add(dataLow);
setsMeanStdDev toReturn = new setsMeanStdDev(dataSets, mean, standardDev);
return toReturn;
}
