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formatting work

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This commit is contained in:
eugenp
2017-01-29 16:03:33 +02:00
parent 44bf48068f
commit 034cde6e20
42 changed files with 455 additions and 700 deletions
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39
core-java/src/main/java/com/baeldung/algorithms/RunAlgorithm.java
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@@ -7,25 +7,24 @@ import com.baeldung.algorithms.slope_one.SlopeOne;
public class RunAlgorithm {
public static void main(String[] args) {
Scanner in = new Scanner(System.in);
System.out.println("Run algorithm:");
System.out.println("1 - Simulated Annealing");
System.out.println("2 - Slope One");
int decision = in.nextInt();
switch (decision) {
case 1:
System.out.println(
"Optimized distance for travel: " + SimulatedAnnealing.simulateAnnealing(10, 10000, 0.9995));
break;
case 2:
SlopeOne.slopeOne(3);
break;
default:
System.out.println("Unknown option");
break;
}
in.close();
}
public static void main(String[] args) {
Scanner in = new Scanner(System.in);
System.out.println("Run algorithm:");
System.out.println("1 - Simulated Annealing");
System.out.println("2 - Slope One");
int decision = in.nextInt();
switch (decision) {
case 1:
System.out.println("Optimized distance for travel: " + SimulatedAnnealing.simulateAnnealing(10, 10000, 0.9995));
break;
case 2:
SlopeOne.slopeOne(3);
break;
default:
System.out.println("Unknown option");
break;
}
in.close();
}
}

22
core-java/src/main/java/com/baeldung/algorithms/annealing/City.java
View File

@@ -5,18 +5,18 @@ import lombok.Data;
@Data
public class City {
private int x;
private int y;
private int x;
private int y;
public City() {
this.x = (int) (Math.random() * 500);
this.y = (int) (Math.random() * 500);
}
public City() {
this.x = (int) (Math.random() * 500);
this.y = (int) (Math.random() * 500);
}
public double distanceToCity(City city) {
int x = Math.abs(getX() - city.getX());
int y = Math.abs(getY() - city.getY());
return Math.sqrt(Math.pow(x, 2) + Math.pow(y, 2));
}
public double distanceToCity(City city) {
int x = Math.abs(getX() - city.getX());
int y = Math.abs(getY() - city.getY());
return Math.sqrt(Math.pow(x, 2) + Math.pow(y, 2));
}
}

2
core-java/src/main/java/com/baeldung/algorithms/annealing/SimulatedAnnealing.java
View File

@@ -24,7 +24,7 @@ public class SimulatedAnnealing {
}
t *= coolingRate;
} else {
continue;
continue;
}
if (i % 100 == 0) {
System.out.println("Iteration #" + i);

39
core-java/src/main/java/com/baeldung/algorithms/slope_one/InputData.java
View File

@@ -11,26 +11,25 @@ import lombok.Data;
@Data
public class InputData {
protected static List<Item> items = Arrays.asList(new Item("Candy"), new Item("Drink"), new Item("Soda"), new Item("Popcorn"),
new Item("Snacks"));
public static Map<User, HashMap<Item, Double>> initializeData(int numberOfUsers) {
Map<User, HashMap<Item, Double>> data = new HashMap<>();
HashMap<Item, Double> newUser;
Set<Item> newRecommendationSet;
for (int i = 0; i < numberOfUsers; i++) {
newUser = new HashMap<Item, Double>();
newRecommendationSet = new HashSet<>();
for (int j = 0; j < 3; j++) {
newRecommendationSet.add(items.get((int) (Math.random() * 5)));
}
for (Item item : newRecommendationSet) {
newUser.put(item, Math.random());
}
data.put(new User("User " + i), newUser);
}
return data;
}
protected static List<Item> items = Arrays.asList(new Item("Candy"), new Item("Drink"), new Item("Soda"), new Item("Popcorn"), new Item("Snacks"));
public static Map<User, HashMap<Item, Double>> initializeData(int numberOfUsers) {
Map<User, HashMap<Item, Double>> data = new HashMap<>();
HashMap<Item, Double> newUser;
Set<Item> newRecommendationSet;
for (int i = 0; i < numberOfUsers; i++) {
newUser = new HashMap<Item, Double>();
newRecommendationSet = new HashSet<>();
for (int j = 0; j < 3; j++) {
newRecommendationSet.add(items.get((int) (Math.random() * 5)));
}
for (Item item : newRecommendationSet) {
newUser.put(item, Math.random());
}
data.put(new User("User " + i), newUser);
}
return data;
}
}

2
core-java/src/main/java/com/baeldung/algorithms/slope_one/Item.java
View File

@@ -9,5 +9,5 @@ import lombok.NoArgsConstructor;
@AllArgsConstructor
public class Item {
private String itemName;
private String itemName;
}

208
core-java/src/main/java/com/baeldung/algorithms/slope_one/SlopeOne.java
View File

@@ -11,114 +11,114 @@ import java.util.Map.Entry;
*/
public class SlopeOne {
private static Map<Item, Map<Item, Double>> diff = new HashMap<>();
private static Map<Item, Map<Item, Integer>> freq = new HashMap<>();
private static Map<User, HashMap<Item, Double>> inputData;
private static Map<User, HashMap<Item, Double>> outputData = new HashMap<>();
private static Map<Item, Map<Item, Double>> diff = new HashMap<>();
private static Map<Item, Map<Item, Integer>> freq = new HashMap<>();
private static Map<User, HashMap<Item, Double>> inputData;
private static Map<User, HashMap<Item, Double>> outputData = new HashMap<>();
public static void slopeOne(int numberOfUsers) {
inputData = InputData.initializeData(numberOfUsers);
System.out.println("Slope One - Before the Prediction\n");
buildDifferencesMatrix(inputData);
System.out.println("\nSlope One - With Predictions\n");
predict(inputData);
}
public static void slopeOne(int numberOfUsers) {
inputData = InputData.initializeData(numberOfUsers);
System.out.println("Slope One - Before the Prediction\n");
buildDifferencesMatrix(inputData);
System.out.println("\nSlope One - With Predictions\n");
predict(inputData);
}
/**
* Based on the available data, calculate the relationships between the
* items and number of occurences
*
* @param data
* existing user data and their items' ratings
*/
private static void buildDifferencesMatrix(Map<User, HashMap<Item, Double>> data) {
for (HashMap<Item, Double> user : data.values()) {
for (Entry<Item, Double> e : user.entrySet()) {
if (!diff.containsKey(e.getKey())) {
diff.put(e.getKey(), new HashMap<Item, Double>());
freq.put(e.getKey(), new HashMap<Item, Integer>());
}
for (Entry<Item, Double> e2 : user.entrySet()) {
int oldCount = 0;
if (freq.get(e.getKey()).containsKey(e2.getKey())) {
oldCount = freq.get(e.getKey()).get(e2.getKey()).intValue();
}
double oldDiff = 0.0;
if (diff.get(e.getKey()).containsKey(e2.getKey())) {
oldDiff = diff.get(e.getKey()).get(e2.getKey()).doubleValue();
}
double observedDiff = e.getValue() - e2.getValue();
freq.get(e.getKey()).put(e2.getKey(), oldCount + 1);
diff.get(e.getKey()).put(e2.getKey(), oldDiff + observedDiff);
}
}
}
for (Item j : diff.keySet()) {
for (Item i : diff.get(j).keySet()) {
double oldValue = diff.get(j).get(i).doubleValue();
int count = freq.get(j).get(i).intValue();
diff.get(j).put(i, oldValue / count);
}
}
printData(data);
}
/**
* Based on the available data, calculate the relationships between the
* items and number of occurences
*
* @param data
* existing user data and their items' ratings
*/
private static void buildDifferencesMatrix(Map<User, HashMap<Item, Double>> data) {
for (HashMap<Item, Double> user : data.values()) {
for (Entry<Item, Double> e : user.entrySet()) {
if (!diff.containsKey(e.getKey())) {
diff.put(e.getKey(), new HashMap<Item, Double>());
freq.put(e.getKey(), new HashMap<Item, Integer>());
}
for (Entry<Item, Double> e2 : user.entrySet()) {
int oldCount = 0;
if (freq.get(e.getKey()).containsKey(e2.getKey())) {
oldCount = freq.get(e.getKey()).get(e2.getKey()).intValue();
}
double oldDiff = 0.0;
if (diff.get(e.getKey()).containsKey(e2.getKey())) {
oldDiff = diff.get(e.getKey()).get(e2.getKey()).doubleValue();
}
double observedDiff = e.getValue() - e2.getValue();
freq.get(e.getKey()).put(e2.getKey(), oldCount + 1);
diff.get(e.getKey()).put(e2.getKey(), oldDiff + observedDiff);
}
}
}
for (Item j : diff.keySet()) {
for (Item i : diff.get(j).keySet()) {
double oldValue = diff.get(j).get(i).doubleValue();
int count = freq.get(j).get(i).intValue();
diff.get(j).put(i, oldValue / count);
}
}
printData(data);
}
/**
* Based on existing data predict all missing ratings. If prediction is not
* possible, the value will be equal to -1
*
* @param data
* existing user data and their items' ratings
*/
private static void predict(Map<User, HashMap<Item, Double>> data) {
HashMap<Item, Double> uPred = new HashMap<Item, Double>();
HashMap<Item, Integer> uFreq = new HashMap<Item, Integer>();
for (Item j : diff.keySet()) {
uFreq.put(j, 0);
uPred.put(j, 0.0);
}
for (Entry<User, HashMap<Item, Double>> e : data.entrySet()) {
for (Item j : e.getValue().keySet()) {
for (Item k : diff.keySet()) {
try {
double predictedValue = diff.get(k).get(j).doubleValue() + e.getValue().get(j).doubleValue();
double finalValue = predictedValue * freq.get(k).get(j).intValue();
uPred.put(k, uPred.get(k) + finalValue);
uFreq.put(k, uFreq.get(k) + freq.get(k).get(j).intValue());
} catch (NullPointerException e1) {
}
}
}
HashMap<Item, Double> clean = new HashMap<Item, Double>();
for (Item j : uPred.keySet()) {
if (uFreq.get(j) > 0) {
clean.put(j, uPred.get(j).doubleValue() / uFreq.get(j).intValue());
}
}
for (Item j : InputData.items) {
if (e.getValue().containsKey(j)) {
clean.put(j, e.getValue().get(j));
} else {
clean.put(j, -1.0);
}
}
outputData.put(e.getKey(), clean);
}
printData(outputData);
}
/**
* Based on existing data predict all missing ratings. If prediction is not
* possible, the value will be equal to -1
*
* @param data
* existing user data and their items' ratings
*/
private static void predict(Map<User, HashMap<Item, Double>> data) {
HashMap<Item, Double> uPred = new HashMap<Item, Double>();
HashMap<Item, Integer> uFreq = new HashMap<Item, Integer>();
for (Item j : diff.keySet()) {
uFreq.put(j, 0);
uPred.put(j, 0.0);
}
for (Entry<User, HashMap<Item, Double>> e : data.entrySet()) {
for (Item j : e.getValue().keySet()) {
for (Item k : diff.keySet()) {
try {
double predictedValue = diff.get(k).get(j).doubleValue() + e.getValue().get(j).doubleValue();
double finalValue = predictedValue * freq.get(k).get(j).intValue();
uPred.put(k, uPred.get(k) + finalValue);
uFreq.put(k, uFreq.get(k) + freq.get(k).get(j).intValue());
} catch (NullPointerException e1) {
}
}
}
HashMap<Item, Double> clean = new HashMap<Item, Double>();
for (Item j : uPred.keySet()) {
if (uFreq.get(j) > 0) {
clean.put(j, uPred.get(j).doubleValue() / uFreq.get(j).intValue());
}
}
for (Item j : InputData.items) {
if (e.getValue().containsKey(j)) {
clean.put(j, e.getValue().get(j));
} else {
clean.put(j, -1.0);
}
}
outputData.put(e.getKey(), clean);
}
printData(outputData);
}
private static void printData(Map<User, HashMap<Item, Double>> data) {
for (User user : data.keySet()) {
System.out.println(user.getUsername() + ":");
print(data.get(user));
}
}
private static void printData(Map<User, HashMap<Item, Double>> data) {
for (User user : data.keySet()) {
System.out.println(user.getUsername() + ":");
print(data.get(user));
}
}
private static void print(HashMap<Item, Double> hashMap) {
NumberFormat formatter = new DecimalFormat("#0.000");
for (Item j : hashMap.keySet()) {
System.out.println(" " + j.getItemName() + " --> " + formatter.format(hashMap.get(j).doubleValue()));
}
}
private static void print(HashMap<Item, Double> hashMap) {
NumberFormat formatter = new DecimalFormat("#0.000");
for (Item j : hashMap.keySet()) {
System.out.println(" " + j.getItemName() + " --> " + formatter.format(hashMap.get(j).doubleValue()));
}
}
}

4
core-java/src/main/java/com/baeldung/algorithms/slope_one/User.java
View File

@@ -8,7 +8,7 @@ import lombok.NoArgsConstructor;
@NoArgsConstructor
@AllArgsConstructor
public class User {
private String username;
private String username;
}

9
core-java/src/main/java/com/baeldung/chainedexception/LogWithChain.java
View File

@@ -23,8 +23,7 @@ public class LogWithChain {
try {
howIsManager();
} catch (ManagerUpsetException e) {
throw new TeamLeadUpsetException(
"Team lead is not in good mood", e);
throw new TeamLeadUpsetException("Team lead is not in good mood", e);
}
}
@@ -36,9 +35,7 @@ public class LogWithChain {
}
}
private static void howIsGirlFriendOfManager()
throws GirlFriendOfManagerUpsetException {
throw new GirlFriendOfManagerUpsetException(
"Girl friend of manager is in bad mood");
private static void howIsGirlFriendOfManager() throws GirlFriendOfManagerUpsetException {
throw new GirlFriendOfManagerUpsetException("Girl friend of manager is in bad mood");
}
}

9
core-java/src/main/java/com/baeldung/chainedexception/LogWithoutChain.java
View File

@@ -25,8 +25,7 @@ public class LogWithoutChain {
howIsManager();
} catch (ManagerUpsetException e) {
e.printStackTrace();
throw new TeamLeadUpsetException(
"Team lead is not in good mood");
throw new TeamLeadUpsetException("Team lead is not in good mood");
}
}
@@ -39,9 +38,7 @@ public class LogWithoutChain {
}
}
private static void howIsGirlFriendOfManager()
throws GirlFriendOfManagerUpsetException {
throw new GirlFriendOfManagerUpsetException(
"Girl friend of manager is in bad mood");
private static void howIsGirlFriendOfManager() throws GirlFriendOfManagerUpsetException {
throw new GirlFriendOfManagerUpsetException("Girl friend of manager is in bad mood");
}
}

5
core-java/src/main/java/com/baeldung/concurrent/countdownlatch/WaitingWorker.java
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@@ -10,10 +10,7 @@ public class WaitingWorker implements Runnable {
private final CountDownLatch callingThreadBlocker;
private final CountDownLatch completedThreadCounter;
public WaitingWorker(final List<String> outputScraper,
final CountDownLatch readyThreadCounter,
final CountDownLatch callingThreadBlocker,
CountDownLatch completedThreadCounter) {
public WaitingWorker(final List<String> outputScraper, final CountDownLatch readyThreadCounter, final CountDownLatch callingThreadBlocker, CountDownLatch completedThreadCounter) {
this.outputScraper = outputScraper;
this.readyThreadCounter = readyThreadCounter;

8
core-java/src/main/java/com/baeldung/concurrent/future/SquareCalculator.java
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@@ -5,14 +5,14 @@ import java.util.concurrent.ExecutorService;
import java.util.concurrent.Future;
public class SquareCalculator {
private final ExecutorService executor;
public SquareCalculator(ExecutorService executor) {
this.executor = executor;
}
public Future<Integer> calculate(Integer input) {
public Future<Integer> calculate(Integer input) {
return executor.submit(new Callable<Integer>() {
@Override
public Integer call() throws Exception {

2
core-java/src/main/java/com/baeldung/java/map/MyLinkedHashMap.java
View File

@@ -10,13 +10,11 @@ public class MyLinkedHashMap<K, V> extends LinkedHashMap<K, V> {
*/
private static final long serialVersionUID = 1L;
private static final int MAX_ENTRIES = 5;
public MyLinkedHashMap(int initialCapacity, float loadFactor, boolean accessOrder) {
super(initialCapacity, loadFactor, accessOrder);
}
@Override
protected boolean removeEldestEntry(Map.Entry eldest) {
return size() > MAX_ENTRIES;