Я пробую априорный алгоритм и этот код, который я получил от Google, который загружался 4 года go. Затем, когда я попытаюсь запустить, я просто буду работать в версии Android Nougat. И ошибка в другой версии android. Что я должен делать? любой может мне помочь. Я здесь новенький. спасибо
@RequiresApi(api = Build.VERSION_CODES.N)
private void apriori() {
aprioriReference.removeValue();
AprioriFrequentItemsetGenerator<String> generator =
new AprioriFrequentItemsetGenerator<>();
List<Set<String>> itemsetList = new ArrayList<>();
databaseReference.addValueEventListener(new ValueEventListener() {
@Override
public void onDataChange(@NonNull DataSnapshot dataSnapshot) {
itemsetList.clear();
for (DataSnapshot dataSnapshot1:dataSnapshot.getChildren()){
Set<String> set=new HashSet<>();
for(DataSnapshot dataSnapshot11:dataSnapshot1.child("trProduct").getChildren()){
set.add(dataSnapshot11.child("productId").getValue().toString());
}
itemsetList.add(set);
}
FrequentItemsetData<String> data = generator.generate(itemsetList, 0.2);
int i = 0;
for (Set<String> itemset : data.getFrequentItemsetList()) {
logs.append((i++)+":\t"+itemset+" with support "+data.getSupport(itemset)+"\n");
aprioriReference.child(String.valueOf(i)).child("itemSet").setValue(itemset.toString());
aprioriReference.child(String.valueOf(i)).child("support").setValue(data.getSupport(itemset));
}
}
@Override
public void onCancelled(@NonNull DatabaseError databaseError) {
}
});
FrequentItemsetData. java
package site.team2dev.adminag;
import java.util.List;
import java.util.Map;
import java.util.Set;
public class FrequentItemsetData<I> {
private final List<Set<I>> frequentItemsetList;
private final Map<Set<I>, Integer> supportCountMap;
private final double minimumSupport;
private final int numberOfTransactions;
FrequentItemsetData(List<Set<I>> frequentItemsetList,
Map<Set<I>, Integer> supportCountMap,
double minimumSupport,
int transactionNumber) {
this.frequentItemsetList = frequentItemsetList;
this.supportCountMap = supportCountMap;
this.minimumSupport = minimumSupport;
this.numberOfTransactions = transactionNumber;
}
public List<Set<I>> getFrequentItemsetList() {
return frequentItemsetList;
}
public Map<Set<I>, Integer> getSupportCountMap() {
return supportCountMap;
}
public double getMinimumSupport() {
return minimumSupport;
}
public int getTransactionNumber() {
return numberOfTransactions;
}
public double getSupport(Set<I> itemset) {
return 1.0 * supportCountMap.get(itemset) / numberOfTransactions;
}
}
AprioriFrequentItemsetGenerator. java
package site.team2dev.adminag;
import android.os.Build;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Set;
import androidx.annotation.RequiresApi;
public class AprioriFrequentItemsetGenerator<I> {
/**
* Generates the frequent itemset data.
*
* @param transactionList the list of transactions to mine.
* @param minimumSupport the minimum support.
* @return the object describing the result of this task.
*/
@RequiresApi(api = Build.VERSION_CODES.N)
public FrequentItemsetData<I> generate(List<Set<I>> transactionList,
double minimumSupport) {
Objects.requireNonNull(transactionList, "The itemset list is empty.");
checkSupport(minimumSupport);
if (transactionList.isEmpty()) {
return null;
}
// Maps each itemset to its support count. Support count is simply the
// number of times an itemset appeares in the transaction list.
Map<Set<I>, Integer> supportCountMap = new HashMap<>();
// Get the list of 1-itemsets that are frequent.
List<Set<I>> frequentItemList = findFrequentItems(transactionList,
supportCountMap,
minimumSupport);
// Maps each 'k' to the list of frequent k-itemsets.
Map<Integer, List<Set<I>>> map = new HashMap<>();
map.put(1, frequentItemList);
// 'k' denotes the cardinality of itemsets processed at each iteration
// of the following loop.
int k = 1;
do {
++k;
// First generate the candidates.
List<Set<I>> candidateList =
generateCandidates(map.get(k - 1));
for (Set<I> transaction : transactionList) {
List<Set<I>> candidateList2 = subset(candidateList,
transaction);
for (Set<I> itemset : candidateList2) {
supportCountMap.put(itemset,
supportCountMap.getOrDefault(itemset,
0) + 1);
}
}
map.put(k, getNextItemsets(candidateList,
supportCountMap,
minimumSupport,
transactionList.size()));
} while (!map.get(k).isEmpty());
return new FrequentItemsetData<>(extractFrequentItemsets(map),
supportCountMap,
minimumSupport,
transactionList.size());
}
/**
* This method simply concatenates all the lists of frequent itemsets into
* one list.
*
* @param map the map mapping an itemset size to the list of frequent
* itemsets of that size.
* @return the list of all frequent itemsets.
*/
private List<Set<I>>
extractFrequentItemsets(Map<Integer, List<Set<I>>> map) {
List<Set<I>> ret = new ArrayList<>();
for (List<Set<I>> itemsetList : map.values()) {
ret.addAll(itemsetList);
}
return ret;
}
/**
* This method gathers all the frequent candidate itemsets into a single
* list.
*
* @param candidateList the list of candidate itemsets.
* @param supportCountMap the map mapping each itemset to its support count.
* @param minimumSupport the minimum support.
* @param transactions the total number of transactions.
* @return a list of frequent itemset candidates.
*/
private List<Set<I>> getNextItemsets(List<Set<I>> candidateList,
Map<Set<I>, Integer> supportCountMap,
double minimumSupport,
int transactions) {
List<Set<I>> ret = new ArrayList<>(candidateList.size());
for (Set<I> itemset : candidateList) {
if (supportCountMap.containsKey(itemset)) {
int supportCount = supportCountMap.get(itemset);
double support = 1.0 * supportCount / transactions;
if (support >= minimumSupport) {
ret.add(itemset);
}
}
}
return ret;
}
/**
* Computes the list of itemsets that are all subsets of
* {@code transaction}.
*
* @param candidateList the list of candidate itemsets.
* @param transaction the transaction to test against.
* @return the list of itemsets that are subsets of {@code transaction}
* itemset.
*/
private List<Set<I>> subset(List<Set<I>> candidateList,
Set<I> transaction) {
List<Set<I>> ret = new ArrayList<>(candidateList.size());
for (Set<I> candidate : candidateList) {
if (transaction.containsAll(candidate)) {
ret.add(candidate);
}
}
return ret;
}
/**
* Generates the next candidates. This is so called F_(k - 1) x F_(k - 1)
* method.
*
* @param itemsetList the list of source itemsets, each of size <b>k</b>.
* @return the list of candidates each of size <b>k + 1</b>.
*/
private List<Set<I>> generateCandidates(List<Set<I>> itemsetList) {
List<List<I>> list = new ArrayList<>(itemsetList.size());
for (Set<I> itemset : itemsetList) {
List<I> l = new ArrayList<>(itemset);
Collections.<I>sort(l, ITEM_COMPARATOR);
list.add(l);
}
int listSize = list.size();
List<Set<I>> ret = new ArrayList<>(listSize);
for (int i = 0; i < listSize; ++i) {
for (int j = i + 1; j < listSize; ++j) {
Set<I> candidate = tryMergeItemsets(list.get(i), list.get(j));
if (candidate != null) {
ret.add(candidate);
}
}
}
return ret;
}
/**
* Attempts the actual construction of the next itemset candidate.
* @param itemset1 the list of elements in the first itemset.
* @param itemset2 the list of elements in the second itemset.
*
* @return a merged itemset candidate or {@code null} if one cannot be
* constructed from the input itemsets.
*/
private Set<I> tryMergeItemsets(List<I> itemset1, List<I> itemset2) {
int length = itemset1.size();
for (int i = 0; i < length - 1; ++i) {
if (!itemset1.get(i).equals(itemset2.get(i))) {
return null;
}
}
if (itemset1.get(length - 1).equals(itemset2.get(length - 1))) {
return null;
}
Set<I> ret = new HashSet<>(length + 1);
for (int i = 0; i < length - 1; ++i) {
ret.add(itemset1.get(i));
}
ret.add(itemset1.get(length - 1));
ret.add(itemset2.get(length - 1));
return ret;
}
private static final Comparator ITEM_COMPARATOR = new Comparator() {
@Override
public int compare(Object o1, Object o2) {
return ((Comparable) o1).compareTo(o2);
}
};
/**
* Computes the frequent itemsets of size 1.
*
* @param itemsetList the entire database of transactions.
* @param supportCountMap the support count map to which to write the
* support counts of each item.
* @param minimumSupport the minimum support.
* @return the list of frequent one-itemsets.
*/
private List<Set<I>> findFrequentItems(List<Set<I>> itemsetList,
Map<Set<I>, Integer> supportCountMap,
double minimumSupport) {
Map<I, Integer> map = new HashMap<>();
// Count the support counts of each item.
for (Set<I> itemset : itemsetList) {
for (I item : itemset) {
Set<I> tmp = new HashSet<>(1);
tmp.add(item);
if (supportCountMap.containsKey(tmp)) {
supportCountMap.put(tmp, supportCountMap.get(tmp) + 1);
} else {
supportCountMap.put(tmp, 1);
}
map.put(item, map.getOrDefault(item, 0) + 1);
}
}
List<Set<I>> frequentItemsetList = new ArrayList<>();
for (Map.Entry<I, Integer> entry : map.entrySet()) {
if (1.0 * entry.getValue() / map.size() >= minimumSupport) {
Set<I> itemset = new HashSet<>(1);
itemset.add(entry.getKey());
frequentItemsetList.add(itemset);
}
}
return frequentItemsetList;
}
private void checkSupport(double support) {
if (Double.isNaN(support)) {
throw new IllegalArgumentException("The input support is NaN.");
}
if (support > 1.0) {
throw new IllegalArgumentException(
"The input support is too large: " + support + ", " +
"should be at most 1.0");
}
if (support < 0.0) {
throw new IllegalArgumentException(
"The input support is too small: " + support + ", " +
"should be at least 0.0");
}
}
}