/**
* Returns the impurity of a node.
*
* @param count the sample count in each class.
* @param n the number of samples in the node.
* @return the impurity of a node
*/
private double impurity(int[] count, int n) {
double impurity = 0.0;
switch (rule) {
case GINI:
impurity = 1.0;
for (int i = 0; i < count.length; i++) {
if (count[i] > 0) {
double p = (double) count[i] / n;
impurity -= p * p;
}
}
break;
case ENTROPY:
for (int i = 0; i < count.length; i++) {
if (count[i] > 0) {
double p = (double) count[i] / n;
impurity -= p * Math.log2(p);
}
}
break;
case CLASSIFICATION_ERROR:
impurity = 0;
for (int i = 0; i < count.length; i++) {
if (count[i] > 0) {
impurity = Math.max(impurity, count[i] / (double) n);
}
}
impurity = Math.abs(1 - impurity);
break;
}
return impurity;
}
/**
* Constructor. Learns a gradient tree boosting for classification.
*
* @param attributes the attribute properties.
* @param x the training instances.
* @param y the class labels.
* @param T the number of iterations (trees).
* @param J the number of leaves in each tree.
* @param shrinkage the shrinkage parameter in (0, 1] controls the learning rate of procedure.
* @param f the sampling fraction for stochastic tree boosting.
*/
public GradientTreeBoost(
Attribute[] attributes, double[][] x, int[] y, int T, int J, double shrinkage, double f) {
if (x.length != y.length) {
throw new IllegalArgumentException(
String.format("The sizes of X and Y don't match: %d != %d", x.length, y.length));
}
if (shrinkage <= 0 || shrinkage > 1) {
throw new IllegalArgumentException("Invalid shrinkage: " + shrinkage);
}
if (f <= 0 || f > 1) {
throw new IllegalArgumentException("Invalid sampling fraction: " + f);
}
if (attributes == null) {
int p = x[0].length;
attributes = new Attribute[p];
for (int i = 0; i < p; i++) {
attributes[i] = new NumericAttribute("V" + (i + 1));
}
}
this.T = T;
this.J = J;
this.shrinkage = shrinkage;
this.f = f;
this.k = Math.max(y) + 1;
if (k < 2) {
throw new IllegalArgumentException("Only one class or negative class labels.");
}
importance = new double[attributes.length];
if (k == 2) {
train2(attributes, x, y);
for (RegressionTree tree : trees) {
double[] imp = tree.importance();
for (int i = 0; i < imp.length; i++) {
importance[i] += imp[i];
}
}
} else {
traink(attributes, x, y);
for (RegressionTree[] grove : forest) {
for (RegressionTree tree : grove) {
double[] imp = tree.importance();
for (int i = 0; i < imp.length; i++) {
importance[i] += imp[i];
}
}
}
}
}
/**
* Constructor.
*
* @param keys the keys of data objects.
* @param data the data objects.
* @param w the width of random projections. It should be sufficiently away from 0. But we should
* not choose an w value that is too large, which will increase the query time.
* @param H the size of universal hash tables.
*/
public LSH(double[][] keys, E[] data, double w, int H) {
this(
keys[0].length,
Math.max(50, (int) Math.pow(keys.length, 0.25)),
Math.max(3, (int) Math.log10(keys.length)),
w,
H);
if (keys.length != data.length) {
throw new IllegalArgumentException("The array size of keys and data are different.");
}
if (H < keys.length) {
throw new IllegalArgumentException("Hash table size is too small: " + H);
}
int n = keys.length;
for (int i = 0; i < n; i++) {
put(keys[i], data[i]);
}
}
/** Test of learn method, of class FPGrowth. */
@Test
public void testKosarak() {
System.out.println("kosarak");
List<int[]> dataList = new ArrayList<int[]>(1000);
try {
InputStream stream = getClass().getResourceAsStream("/smile/data/transaction/kosarak.dat");
BufferedReader input = new BufferedReader(new InputStreamReader(stream));
String line;
for (int nrow = 0; (line = input.readLine()) != null; nrow++) {
if (line.trim().isEmpty()) {
continue;
}
String[] s = line.split(" ");
Set<Integer> items = new HashSet<Integer>();
for (int i = 0; i < s.length; i++) {
items.add(Integer.parseInt(s[i]));
}
int j = 0;
int[] point = new int[items.size()];
for (int i : items) {
point[j++] = i;
}
dataList.add(point);
}
} catch (IOException ex) {
System.err.println(ex);
}
int[][] data = dataList.toArray(new int[dataList.size()][]);
int n = Math.max(data);
System.out.format("%d transactions, %d items\n", data.length, n);
long time = System.currentTimeMillis();
FPGrowth fpgrowth = new FPGrowth(data, 1500);
System.out.format(
"Done building FP-tree: %.2f secs.\n", (System.currentTimeMillis() - time) / 1000.0);
time = System.currentTimeMillis();
List<ItemSet> results = fpgrowth.learn();
System.out.format(
"%d frequent item sets discovered: %.2f secs.\n",
results.size(), (System.currentTimeMillis() - time) / 1000.0);
assertEquals(219725, results.size());
}
/** Test of learn method, of class FPGrowth. */
@Test
public void testPima() {
System.out.println("pima");
List<int[]> dataList = new ArrayList<int[]>(1000);
try {
InputStream stream =
getClass().getResourceAsStream("/smile/data/transaction/pima.D38.N768.C2");
BufferedReader input = new BufferedReader(new InputStreamReader(stream));
String line;
for (int nrow = 0; (line = input.readLine()) != null; nrow++) {
if (line.trim().isEmpty()) {
continue;
}
String[] s = line.split(" ");
int[] point = new int[s.length];
for (int i = 0; i < s.length; i++) {
point[i] = Integer.parseInt(s[i]);
}
dataList.add(point);
}
} catch (IOException ex) {
System.err.println(ex);
}
int[][] data = dataList.toArray(new int[dataList.size()][]);
int n = Math.max(data);
System.out.format("%d transactions, %d items\n", data.length, n);
long time = System.currentTimeMillis();
FPGrowth fpgrowth = new FPGrowth(data, 20);
System.out.format(
"Done building FP-tree: %.2f secs.\n", (System.currentTimeMillis() - time) / 1000.0);
time = System.currentTimeMillis();
long numItemsets = fpgrowth.learn(System.out);
System.out.format(
"%d frequent item sets discovered: %.2f secs.\n",
numItemsets, (System.currentTimeMillis() - time) / 1000.0);
assertEquals(1803, numItemsets);
assertEquals(1803, fpgrowth.learn().size());
}
/** Test of learn method, of class ARM. */
@Test
public void testLearnKosarak() {
System.out.println("kosarak");
List<int[]> dataList = new ArrayList<int[]>(1000);
try {
InputStream stream = getClass().getResourceAsStream("/smile/data/transaction/kosarak.dat");
BufferedReader input = new BufferedReader(new InputStreamReader(stream));
String line;
for (int nrow = 0; (line = input.readLine()) != null; nrow++) {
if (line.trim().isEmpty()) {
continue;
}
String[] s = line.split(" ");
Set<Integer> items = new HashSet<Integer>();
for (int i = 0; i < s.length; i++) {
items.add(Integer.parseInt(s[i]));
}
int j = 0;
int[] point = new int[items.size()];
for (int i : items) {
point[j++] = i;
}
dataList.add(point);
}
} catch (IOException ex) {
System.err.println(ex);
}
int[][] data = dataList.toArray(new int[dataList.size()][]);
int n = Math.max(data);
System.out.format("%d transactions, %d items\n", data.length, n);
ARM instance = new ARM(data, 0.003);
long numRules = instance.learn(0.5, System.out);
System.out.format("%d association rules discovered\n", numRules);
assertEquals(17932, numRules);
}
/** Test of learn method, of class ARM. */
@Test
public void testLearnPima() {
System.out.println("pima");
List<int[]> dataList = new ArrayList<int[]>(1000);
try {
InputStream stream =
getClass().getResourceAsStream("/smile/data/transaction/pima.D38.N768.C2");
BufferedReader input = new BufferedReader(new InputStreamReader(stream));
String line;
for (int nrow = 0; (line = input.readLine()) != null; nrow++) {
if (line.trim().isEmpty()) {
continue;
}
String[] s = line.split(" ");
int[] point = new int[s.length];
for (int i = 0; i < s.length; i++) {
point[i] = Integer.parseInt(s[i]);
}
dataList.add(point);
}
} catch (IOException ex) {
System.err.println(ex);
}
int[][] data = dataList.toArray(new int[dataList.size()][]);
int n = Math.max(data);
System.out.format("%d transactions, %d items\n", data.length, n);
ARM instance = new ARM(data, 20);
long numRules = instance.learn(0.9, System.out);
System.out.format("%d association rules discovered\n", numRules);
assertEquals(6803, numRules);
assertEquals(6803, instance.learn(0.9).size());
}
/**
* Constructor. Clustering data into k clusters.
*
* @param data the dataset for clustering.
* @param distance the distance/dissimilarity measure.
* @param k the number of clusters.
* @param maxNeighbor the maximum number of neighbors examined during a random search of local
* minima.
*/
public CLARANS(T[] data, Distance<T> distance, int k, int maxNeighbor) {
this(data, distance, k, maxNeighbor, Math.max(2, MulticoreExecutor.getThreadPoolSize()));
}
