I'm learning about delegates and think I may have found a use for one. Basically what I have is a series of string properties that take in a minimum value and a maximum value, like so:
string weightInvalid(min as int32, max as int32)
There are several messages like this, all with unique messages but all sharing the same signature of minimum and maximum. I think that a delegate could be used here, but how would I go about doing so? It would really help me to see some code so I could get a grasp on this delegate stuff.
Below is a simple Console application example that may help...
public delegate string foo(int min, int max);
class Program
{
static void Main(string[] args)
{
CallFoo(foo1);
CallFoo(foo2);
CallFoo(foo3);
Console.WriteLine("Press ENTER to exit...");
Console.ReadLine();
}
private static void CallFoo(foo foo)
{
Console.WriteLine(foo(1, 2));
}
private static string foo1(int min, int max)
{
return "foo1";
}
private static string foo2(int min, int max)
{
return "foo2";
}
private static string foo3(int min, int max)
{
return "foo3";
}
on c#:
delegate string weightInvalid(int min, int max);
string MyWeightInvalid(int min, int max)
{
return "";
}
string SomeMethod()
{
weightInvalid myFunc = new weightInvalid(MyWeightInvalid);
return myFunc(0, 1);
}
Related
I am trying to solve the problem using extra space. In the pop() function, when I compare the top of both the stacks inside the if condition, the following test case is failing:
["MinStack","push","push","push","push","pop","getMin","pop","getMin","pop","getMin"]\ [[],[512],[-1024],[-1024],[512],[],[],[],[],[],[]]
When I store the top of the first stack and then compare it with the top of the second stack, all the test cases pass.
Can someone please help me understand what is causing this?
The below code caused the test case to fail.
class MinStack {
Stack<Integer> s;
Stack<Integer> auxStack;
public MinStack() {
s = new Stack<Integer>();
auxStack = new Stack<Integer>();
}
public void push(int val) {
this.s.push(val);
if (this.auxStack.empty() || val <= this.auxStack.peek()) {
this.auxStack.push(val);
}
}
public void pop() {
if (this.s.peek() == this.auxStack.peek()) {
this.auxStack.pop();
}
this.s.pop();
}
public int top() {
return this.s.peek();
}
public int getMin() {
return this.auxStack.peek();
}
}
The below code worked for all the test cases.
class MinStack {
Stack<Integer> s;
Stack<Integer> auxStack;
public MinStack() {
s = new Stack<Integer>();
auxStack = new Stack<Integer>();
}
public void push(int val) {
this.s.push(val);
if (this.auxStack.empty() || val <= this.auxStack.peek()) {
this.auxStack.push(val);
}
}
public void pop() {
int ans = this.s.pop();
if (ans == this.auxStack.peek()) {
this.auxStack.pop();
}
}
public int top() {
return this.s.peek();
}
public int getMin() {
return this.auxStack.peek();
}
}
The problem is that you are comparing Integer objects, not int values. The data type stored on the stack is Integer. So the peek() method returns an Integer, not int, which means that the following comparison is always false:
this.s.peek() == this.auxStack.peek()
Fix this by explicitly converting at least one of those two Integer objects to an int:
this.s.peek().intValue() == this.auxStack.peek()
Or use the equals method:
this.s.peek().equals(this.auxStack.peek())
I have seen other questions on SO asking why the last iteration in counting sort, where we place elements on the sorted array cannot start from the start. The reason is that that way the sort algorithm loses its stability.
But what if we reversed the count also? Instead of counting the no of elements present before a specific element, what if we count the no of elements present after that specific element? I have implemented it like the following.
public class TestCountSort {
public static void main(String[] args) {
Element[] arr=new Element[]{new Element("One",1),new Element("Three",2),new Element("Two",1)};
System.out.println("Array before - "+Arrays.toString(arr));
countSort(arr);
System.out.println("Array after - "+Arrays.toString(arr));
}
public static void countSort(Element[] arr){
int n=arr.length;
int max=arr[0].key;
int min=arr[0].key;
for(Element i:arr){
if(i.key>max){
max=i.key;
}
if(i.key<min){
min=i.key;
}
}
int range=max-min+1;
int[] count=new int[range];
Element[] sortedArray=new Element[n];
for(Element i:arr){
count[i.key-min]++;
}
for(int i=range-2;i>=0;i--){
count[i]=count[i]+count[i+1];
}
for(int i=0;i<n;i++){
sortedArray[n-count[arr[i].key-min]]=arr[i];
count[arr[i].key-min]--;
}
for(int i=0;i<n;i++){
arr[i]=sortedArray[i];
}
}
}
class Element{
private String name;
public int key;
public Element(String name, int key){
this.name=name;
this.key=key;
}
public String toString(){
return "{"+name+":"+key+"}";
}
}
Will this preserve the stability and provide sorting? Or is there something I am missing?
I have a number from 1 to 10,000 stored in an array of long. When adding them sequentially it will give a result of 50,005,000.
I have writing an Spliterator where if a size of array is longer than 1000, it will be splitted to another array.
Here is my code. But when I run it, the result from addition is far greater than 50,005,000. Can someone tell me what is wrong with my code?
Thank you so much.
import java.util.Arrays;
import java.util.Optional;
import java.util.Spliterator;
import java.util.function.Consumer;
import java.util.stream.LongStream;
import java.util.stream.Stream;
import java.util.stream.StreamSupport;
public class SumSpliterator implements Spliterator<Long> {
private final long[] numbers;
private int currentPosition = 0;
public SumSpliterator(long[] numbers) {
super();
this.numbers = numbers;
}
#Override
public boolean tryAdvance(Consumer<? super Long> action) {
action.accept(numbers[currentPosition++]);
return currentPosition < numbers.length;
}
#Override
public long estimateSize() {
return numbers.length - currentPosition;
}
#Override
public int characteristics() {
return SUBSIZED;
}
#Override
public Spliterator<Long> trySplit() {
int currentSize = numbers.length - currentPosition;
if( currentSize <= 1_000){
return null;
}else{
currentPosition = currentPosition + 1_000;
return new SumSpliterator(Arrays.copyOfRange(numbers, 1_000, numbers.length));
}
}
public static void main(String[] args) {
long[] twoThousandNumbers = LongStream.rangeClosed(1, 10_000).toArray();
Spliterator<Long> spliterator = new SumSpliterator(twoThousandNumbers);
Stream<Long> stream = StreamSupport.stream(spliterator, false);
System.out.println( sumValues(stream) );
}
private static long sumValues(Stream<Long> stream){
Optional<Long> optional = stream.reduce( ( t, u) -> t + u );
return optional.get() != null ? optional.get() : Long.valueOf(0);
}
}
I have the strong feeling that you didn’t get the purpose of splitting right. It’s not meant to copy the underlying data but just provide access to a range of it. Keep in mind that spliterators provide read-only access. So you should pass the original array to the new spliterator and configure it with an appropriate position and length instead of copying the array.
But besides the inefficiency of copying, the logic is obviously wrong: You pass Arrays.copyOfRange(numbers, 1_000, numbers.length) to the new spliterator, so the new spliterator contains the elements from position 1000 to the end of the array and you advance the current spliterator’s position by 1000, so the old spliterator covers the elements from currentPosition + 1_000 to the end of the array. So both spliterators will cover elements at the end of the array while at the same time, depending on the previous value of currentPosition, elements at the beginning might not be covered at all. So when you want to advance the currentPosition by 1_000 the skipped range is expressed by Arrays.copyOfRange(numbers, currentPosition, 1_000) instead, referring to the currentPosition before advancing.
It’s should also be noted, that a spliterator should attempt to split balanced, that is, in the middle if the size is known. So splitting off thousand elements is not the right strategy for an array.
Further, your tryAdvance method is wrong. It should not test after calling the consumer but before, returning false if there are no more elements, which also implies that the consumer has not been called.
Putting it all together, the implementation may look like
public class MyArraySpliterator implements Spliterator<Long> {
private final long[] numbers;
private int currentPosition, endPosition;
public MyArraySpliterator(long[] numbers) {
this(numbers, 0, numbers.length);
}
public MyArraySpliterator(long[] numbers, int start, int end) {
this.numbers = numbers;
currentPosition=start;
endPosition=end;
}
#Override
public boolean tryAdvance(Consumer<? super Long> action) {
if(currentPosition < endPosition) {
action.accept(numbers[currentPosition++]);
return true;
}
return false;
}
#Override
public long estimateSize() {
return endPosition - currentPosition;
}
#Override
public int characteristics() {
return ORDERED|NONNULL|SIZED|SUBSIZED;
}
#Override
public Spliterator<Long> trySplit() {
if(estimateSize()<=1000) return null;
int middle = (endPosition + currentPosition)>>>1;
MyArraySpliterator prefix
= new MyArraySpliterator(numbers, currentPosition, middle);
currentPosition=middle;
return prefix;
}
}
But of course, it’s recommended to provide a specialized forEachRemaining implementation, where possible:
#Override
public void forEachRemaining(Consumer<? super Long> action) {
int pos=currentPosition, end=endPosition;
currentPosition=end;
for(;pos<end; pos++) action.accept(numbers[pos]);
}
As a final note, for the task of summing longs from an array, a Spliterator.OfLong and a LongStream is preferred and that work has already been done, see Arrays.spliterator() and LongStream.sum(), making the whole task as simple as Arrays.stream(numbers).sum().
Consider this class: (From Hadoop: The definitive guide 3rd edition):
import java.io.*;
import org.apache.hadoop.io.*;
public class TextPair implements WritableComparable<TextPair> {
private Text first;
private Text second;
public TextPair() {
set(new Text(), new Text());
}
public TextPair(String first, String second) {
set(new Text(first), new Text(second));
}
public TextPair(Text first, Text second) {
set(first, second);
}
public void set(Text first, Text second) {
this.first = first;
this.second = second;
}
public Text getFirst() {
return first;
}
public Text getSecond() {
return second;
}
#Override
public void write(DataOutput out) throws IOException {
first.write(out);
second.write(out);
}
#Override
public void readFields(DataInput in) throws IOException {
first.readFields(in);
second.readFields(in);
}
#Override
public int hashCode() {
return first.hashCode() * 163 + second.hashCode();
}
#Override
public boolean equals(Object o) {
if (o instanceof TextPair) {
TextPair tp = (TextPair) o;
return first.equals(tp.first) && second.equals(tp.second);
}
return false;
}
#Override
public String toString() {
return first + "\t" + second;
}
#Override
public int compareTo(TextPair tp) {
int cmp = first.compareTo(tp.first);
if (cmp != 0) {
return cmp;
}
return second.compareTo(tp.second);
}
// ^^ TextPair
// vv TextPairComparator
public static class Comparator extends WritableComparator {
private static final Text.Comparator TEXT_COMPARATOR = new Text.Comparator();
public Comparator() {
super(TextPair.class);
}
#Override
public int compare(byte[] b1, int s1, int l1,
byte[] b2, int s2, int l2) {
try {
int firstL1 = WritableUtils.decodeVIntSize(b1[s1]) + readVInt(b1, s1);
int firstL2 = WritableUtils.decodeVIntSize(b2[s2]) + readVInt(b2, s2);
int cmp = TEXT_COMPARATOR.compare(b1, s1, firstL1, b2, s2, firstL2);
if (cmp != 0) {
return cmp;
}
return TEXT_COMPARATOR.compare(b1, s1 + firstL1, l1 - firstL1,
b2, s2 + firstL2, l2 - firstL2);
} catch (IOException e) {
throw new IllegalArgumentException(e);
}
}
}
static {
WritableComparator.define(TextPair.class, new Comparator());
}
// ^^ TextPairComparator
// vv TextPairFirstComparator
public static class FirstComparator extends WritableComparator {
private static final Text.Comparator TEXT_COMPARATOR = new Text.Comparator();
public FirstComparator() {
super(TextPair.class);
}
#Override
public int compare(byte[] b1, int s1, int l1,
byte[] b2, int s2, int l2) {
try {
int firstL1 = WritableUtils.decodeVIntSize(b1[s1]) + readVInt(b1, s1);
int firstL2 = WritableUtils.decodeVIntSize(b2[s2]) + readVInt(b2, s2);
return TEXT_COMPARATOR.compare(b1, s1, firstL1, b2, s2, firstL2);
} catch (IOException e) {
throw new IllegalArgumentException(e);
}
}
#Override
public int compare(WritableComparable a, WritableComparable b) {
if (a instanceof TextPair && b instanceof TextPair) {
return ((TextPair) a).first.compareTo(((TextPair) b).first);
}
return super.compare(a, b);
}
}
// ^^ TextPairFirstComparator
// vv TextPair
}
// ^^ TextPair
There are two kinds of comparators defined:
one is sorting by first followed by second which is the default comparator.
The other is sorting by first ONLY, which is the firstComparator.
If I have to use use firstComparator for sorting my keys, how do I achieve that?
That is, how do I override my default comparator with the first comparator, I defined above.
Secondly, how would I unitTest this since the output of map job is not sorted. ?
If I have to use use firstComparator for sorting my keys, how do I achieve that? That is, how do I override my default comparator with the first comparator, I defined above.
I assume you expect a method something like setComparator(firstComparator). As far as I know there is no such method. The keys are sorted (on the mapper side) using the compareTo() of the Writeable type representing the keys. In your case, the compareTo() method checks the first value and then the second one. In other words, the keys will be sorted by the first value and, then, the keys in the same group (i.e. having the same first value) will be sorted by their second value.
All in all, this means that your keys will always be sorted by the first value (+ by the second value if the first one isn't able to take the decision). Which in turn means that there is no need to have a different comparator (firstComparator) which looks only at the first value because that is already achieved with the compareTo() method of your TextPair class.
On the other hand, if the firstComparator sorts the keys completely differently, the only solution is to move the logic in firstComparator to the compareTo() method of the Writable class representing your key. I don't see any reason why you wouldn't do that. If you already have the firstComparator and want to reuse it, you can instantiate it and invoke it in the compareTo() method of the TexPair Writable.
You might also want to take a look at the GroupingComparator which is used to decide which keys are used together in the same call of the reduce() method. Since you didn't describe exactly what you want to achieve, I can't say for sure if this will be helpful or not.
Secondly, how would I unitTest this since the output of map job is not sorted. ?
Unit testing, as the name says, implies testing a single unit of code (most of the time a method/function/procedure). If you want to unit-test your reduce method you have to provide the interesting input cases and to check that the method under test outputs the expected result. More concretely, you have to create/mock a sorted Iterable over your keys and invoke your reduce function with it. Unit testing a reduce method shouldn't rely on the execution of the corresponding map method.
I am trying to implement the following in a Raw Comparator but not sure how to write this?
the tumestamp field here is of tyoe LongWritable.
if (this.getNaturalKey().compareTo(o.getNaturalKey()) != 0) {
return this.getNaturalKey().compareTo(o.getNaturalKey());
} else if (this.timeStamp != o.timeStamp) {
return timeStamp.compareTo(o.timeStamp);
} else {
return 0;
}
I found a hint here, but not sure how do I implement this dealing with a LongWritabel type?
http://my.safaribooksonline.com/book/databases/hadoop/9780596521974/serialization/id3548156
Thanks for your help
Let say i have a CompositeKey that represents a pair of (String stockSymbol, long timestamp).
We can do a primary grouping pass on the stockSymbol field to get all of the data of one type together, and then our "secondary sort" during the shuffle phase uses the timestamp long member to sort the timeseries points so that they arrive at the reducer partitioned and in sorted order.
public class CompositeKey implements WritableComparable<CompositeKey> {
// natural key is (stockSymbol)
// composite key is a pair (stockSymbol, timestamp)
private String stockSymbol;
private long timestamp;
......//Getter setter omiited for clarity here
#Override
public void readFields(DataInput in) throws IOException {
this.stockSymbol = in.readUTF();
this.timestamp = in.readLong();
}
#Override
public void write(DataOutput out) throws IOException {
out.writeUTF(this.stockSymbol);
out.writeLong(this.timestamp);
}
#Override
public int compareTo(CompositeKey other) {
if (this.stockSymbol.compareTo(other.stockSymbol) != 0) {
return this.stockSymbol.compareTo(other.stockSymbol);
}
else if (this.timestamp != other.timestamp) {
return timestamp < other.timestamp ? -1 : 1;
}
else {
return 0;
}
}
Now the CompositeKey comparator would be:
public class CompositeKeyComparator extends WritableComparator {
protected CompositeKeyComparator() {
super(CompositeKey.class, true);
}
#Override
public int compare(WritableComparable wc1, WritableComparable wc2) {
CompositeKey ck1 = (CompositeKey) wc1;
CompositeKey ck2 = (CompositeKey) wc2;
int comparison = ck1.getStockSymbol().compareTo(ck2.getStockSymbol());
if (comparison == 0) {
// stock symbols are equal here
if (ck1.getTimestamp() == ck2.getTimestamp()) {
return 0;
}
else if (ck1.getTimestamp() < ck2.getTimestamp()) {
return -1;
}
else {
return 1;
}
}
else {
return comparison;
}
}
}
Are you asking about way to compare LongWritable type provided by hadoop ?
If yes, then the answer is to use compare() method. For more details, scroll down here.
The best way to correctly implement RawComparator is to extend WritableComparator and override compare() method. The WritableComparator is very good written, so you can easily understand it.
It is already implemented from what I see in the LongWritable class:
/** A Comparator optimized for LongWritable. */
public static class Comparator extends WritableComparator {
public Comparator() {
super(LongWritable.class);
}
public int compare(byte[] b1, int s1, int l1,
byte[] b2, int s2, int l2) {
long thisValue = readLong(b1, s1);
long thatValue = readLong(b2, s2);
return (thisValue<thatValue ? -1 : (thisValue==thatValue ? 0 : 1));
}
}
That byte comparision is the override of the RawComparator.