So for a given prime number 31, how can I write a hash function for a string parameter?
Here is my attempt.
private int hash(String key){
int c = 31;
int hash = 0;
for (int i = 0; i < key.length(); i++ ) {
int ascii = key.charAt(i);
hash += c * hash + ascii;
}
return (hash % sizetable);} // sizetable is an integer which is declared outside. You can see it as a table.length().
So, since I can not run any other function in my work and I need to be sure about the process here, I need your answers and help! Thank you so much.
Your implementation looks quite similar to what is documented as standard String.hashCode() implementation, this even uses also 31 as prime factor, so it should be good enough.
I just would not assign 31 to a variable, but declare a private static final field or use it directly as magic number - not OK in general, but might be OK in this case.
Additionally you should add some tests - if you already know about the concept of unit tests - to prove that your method gives different hashes for different strings. And pick the samples clever, so they are different (for the case of the homework ;)
I have been thinking of it but have ran out of idea's. I have 10 arrays each of length 18 and having 18 double values in them. These 18 values are features of an image. Now I have to apply k-means clustering on them.
For implementing k-means clustering I need a unique computational value for each array. Are there any mathematical or statistical or any logic that would help me to create a computational value for each array, which is unique to it based upon values inside it. Thanks in advance.
Here is my array example. Have 10 more
[0.07518284315321135
0.002987851573676068
0.002963866526639678
0.002526139418225552
0.07444872939213325
0.0037219653347541617
0.0036979802877177715
0.0017920256571474585
0.07499695903867931
0.003477831820276616
0.003477831820276616
0.002036159171625004
0.07383539747505984
0.004311312204791184
0.0043352972518275745
0.0011786937400740452
0.07353130134299131
0.004339580295941216]
Did you checked the Arrays.hashcode in Java 7 ?
/**
* Returns a hash code based on the contents of the specified array.
* For any two <tt>double</tt> arrays <tt>a</tt> and <tt>b</tt>
* such that <tt>Arrays.equals(a, b)</tt>, it is also the case that
* <tt>Arrays.hashCode(a) == Arrays.hashCode(b)</tt>.
*
* <p>The value returned by this method is the same value that would be
* obtained by invoking the {#link List#hashCode() <tt>hashCode</tt>}
* method on a {#link List} containing a sequence of {#link Double}
* instances representing the elements of <tt>a</tt> in the same order.
* If <tt>a</tt> is <tt>null</tt>, this method returns 0.
*
* #param a the array whose hash value to compute
* #return a content-based hash code for <tt>a</tt>
* #since 1.5
*/
public static int hashCode(double a[]) {
if (a == null)
return 0;
int result = 1;
for (double element : a) {
long bits = Double.doubleToLongBits(element);
result = 31 * result + (int)(bits ^ (bits >>> 32));
}
return result;
}
I dont understand why #Marco13 mentioned " this is not returning unquie for arrays".
UPDATE
See #Macro13 comment for the reason why it cannot be unquie..
UPDATE
If we draw a graph using your input points, ( 18 elements) has one spike and 3 low values and the pattern goes..
if that is true.. you can find the mean of your Peak ( 1, 4, 8,12,16 ) and find the low Mean from remaining values.
So that you will be having Peak mean and Low mean . and you find the unquie number to represent these two also preserve the values using bijective algorithm described in here
This Alogirthm also provides formulas to reverse i.e take the Peak and Low mean from the unquie value.
To find unique pair < x; y >= x + (y + ( (( x +1 ) /2) * (( x +1 ) /2) ) )
Also refer Exercise 1 in pdf page 2 to reverse x and y.
For finding Mean and find paring value.
public static double mean(double[] array){
double peakMean = 0;
double lowMean = 0;
for (int i = 0; i < array.length; i++) {
if ( (i+1) % 4 == 0 || i == 0){
peakMean = peakMean + array[i];
}else{
lowMean = lowMean + array[i];
}
}
peakMean = peakMean / 5;
lowMean = lowMean / 13;
return bijective(lowMean, peakMean);
}
public static double bijective(double x,double y){
double tmp = ( y + ((x+1)/2));
return x + ( tmp * tmp);
}
for test
public static void main(String[] args) {
double[] arrays = {0.07518284315321135,0.002963866526639678,0.002526139418225552,0.07444872939213325,0.0037219653347541617,0.0036979802877177715,0.0017920256571474585,0.07499695903867931,0.003477831820276616,0.003477831820276616,0.002036159171625004,0.07383539747505984,0.004311312204791184,0.0043352972518275745,0.0011786937400740452,0.07353130134299131,0.004339580295941216};
System.out.println(mean(arrays));
}
You can use this the peak and low values to find the similar images.
You can simply sum the values, using double precision, the result value will unique most of the times. On the other hand, if the value position is relevant, then you can apply a sum using the index as multiplier.
The code could be as simple as:
public static double sum(double[] values) {
double val = 0.0;
for (double d : values) {
val += d;
}
return val;
}
public static double hash_w_order(double[] values) {
double val = 0.0;
for (int i = 0; i < values.length; i++) {
val += values[i] * (i + 1);
}
return val;
}
public static void main(String[] args) {
double[] myvals =
{ 0.07518284315321135, 0.002987851573676068, 0.002963866526639678, 0.002526139418225552, 0.07444872939213325, 0.0037219653347541617, 0.0036979802877177715, 0.0017920256571474585, 0.07499695903867931, 0.003477831820276616,
0.003477831820276616, 0.002036159171625004, 0.07383539747505984, 0.004311312204791184, 0.0043352972518275745, 0.0011786937400740452, 0.07353130134299131, 0.004339580295941216 };
System.out.println("Computed value based on sum: " + sum(myvals));
System.out.println("Computed value based on values and its position: " + hash_w_order(myvals));
}
The output for that code, using your list of values is:
Computed value based on sum: 0.41284176550504803
Computed value based on values and its position: 3.7396448842464496
Well, here's a method that works for any number of doubles.
public BigInteger uniqueID(double[] array) {
final BigInteger twoToTheSixtyFour =
BigInteger.valueOf(Long.MAX_VALUE).add(BigInteger.ONE);
BigInteger count = BigInteger.ZERO;
for (double d : array) {
long bitRepresentation = Double.doubleToRawLongBits(d);
count = count.multiply(twoToTheSixtyFour);
count = count.add(BigInteger.valueOf(bitRepresentation));
}
return count;
}
Explanation
Each double is a 64-bit value, which means there are 2^64 different possible double values. Since a long is easier to work with for this sort of thing, and it's the same number of bits, we can get a 1-to-1 mapping from doubles to longs using Double.doubleToRawLongBits(double).
This is awesome, because now we can treat this like a simple combinations problem. You know how you know that 1234 is a unique number? There's no other number with the same value. This is because we can break it up by its digits like so:
1234 = 1 * 10^3 + 2 * 10^2 + 3 * 10^1 + 4 * 10^0
The powers of 10 would be "basis" elements of the base-10 numbering system, if you know linear algebra. In this way, base-10 numbers are like arrays consisting of only values from 0 to 9 inclusively.
If we want something similar for double arrays, we can discuss the base-(2^64) numbering system. Each double value would be a digit in a base-(2^64) representation of a value. If there are 18 digits, there are (2^64)^18 unique values for a double[] of length 18.
That number is gigantic, so we're going to need to represent it with a BigInteger data-structure instead of a primitive number. How big is that number?
(2^64)^18 = 61172327492847069472032393719205726809135813743440799050195397570919697796091958321786863938157971792315844506873509046544459008355036150650333616890210625686064472971480622053109783197015954399612052812141827922088117778074833698589048132156300022844899841969874763871624802603515651998113045708569927237462546233168834543264678118409417047146496
There are that many unique configurations of 18-length double arrays and this code lets you uniquely describe them.
I'm going to suggest three methods, with different pros and cons which I will outline.
Hash Code
This is the obvious "solution", though it has been correctly pointed out that it will not be unique. However, it will be very unlikely that any two arrays will have the same value.
Weighted Sum
Your elements appear to be bounded; perhaps they range from a minimum of 0 to a maximum of 1. If this is the case, you can multiply the first number by N^0, the second by N^1, the third by N^2 and so on, where N is some large number (ideally the inverse of your precision). This is easily implemented, particularly if you use a matrix package, and very fast. We can make this unique if we choose.
Euclidean Distance from Mean
Subtract the mean of your arrays from each array, square the results, sum the squares. If you have an expected mean, you can use that. Again, not unique, there will be collisions, but you (almost) can't avoid that.
The difficulty of uniqueness
It has already been explained that hashing will not give you a unique solution. A unique number is possible in theory, using the Weighted Sum, but we have to use numbers of a very large size. Let's say your numbers are 64 bits in memory. That means that there are 2^64 possible numbers they can represent (slightly less using floating point). Eighteen such numbers in an array could represent 2^(64*18) different numbers. That's huge. If you use anything less, you will not be able to guarantee uniqueness due to the pigeonhole principle.
Let's look at a trivial example. If you have four letters, a, b, c and d, and you have to number them each uniquely using the numbers 1 to 3, you can't. That's the pigeonhole principle. You have 2^(18*64) possible numbers. You can't number them uniquely with less than 2^(18*64) numbers, and hashing doesn't give you that.
If you use BigDecimal, you can represent (almost) arbitrarily large numbers. If the largest element you can get is 1 and the smallest 0, then you can set N = 1/(precision) and apply the Weighted Sum mentioned above. This will guarantee uniqueness. The precision for doubles in Java is Double.MIN_VALUE. Note that the array of weights needs to be stored in _Big Decimal_s!
That satisfies this part of your question:
create a computational value for each array, which is unique to it
based upon values inside it
However, there is a problem:
1 and 2 suck for K Means
I am assuming from your discussion with Marco 13 that you are performing the clustering on the single values, not the length 18 arrays. As Marco has already mentioned, Hashing sucks for K means. The whole idea is that the smallest change in the data will result in a large change in Hash Values. That means that two images which are similar, produce two very similar arrays, produce two very different "unique" numbers. Similarity is not preserved. The result will be pseudo random!!!
Weighted Sums are better, but still bad. It will basically ignore all the elements except for the last one, unless the last element is the same. Only then will it look at the next to last, and so on. Similarity is not really preserved.
Euclidean distance from the mean (or at least some point) will at least group things together in a sort of sensible way. Direction will be ignored, but at least things that are far from the mean won't be grouped with things that are close. Similarity of one feature is preserved, the other features are lost.
In summary
1 is very easy, but is not unique and doesn't preserve similarity.
2 is easy, can be unique and doesn't preserve similarity.
3 is easy, but is not unique and preserves some similarity.
Implementatio of Weighted Sum. Not really tested.
public class Array2UniqueID {
private final double min;
private final double max;
private final double prec;
private final int length;
/**
* Used to provide a {#code BigInteger} that is unique to the given array.
* <p>
* This uses weighted sum to guarantee that two IDs match if and only if
* every element of the array also matches. Similarity is not preserved.
*
* #param min smallest value an array element can possibly take
* #param max largest value an array element can possibly take
* #param prec smallest difference possible between two array elements
* #param length length of each array
*/
public Array2UniqueID(double min, double max, double prec, int length) {
this.min = min;
this.max = max;
this.prec = prec;
this.length = length;
}
/**
* A convenience constructor which assumes the array consists of doubles of
* full range.
* <p>
* This will result in very large IDs being returned.
*
* #see Array2UniqueID#Array2UniqueID(double, double, double, int)
* #param length
*/
public Array2UniqueID(int length) {
this(-Double.MAX_VALUE, Double.MAX_VALUE, Double.MIN_VALUE, length);
}
public BigDecimal createUniqueID(double[] array) {
// Validate the data
if (array.length != length) {
throw new IllegalArgumentException("Array length must be "
+ length + " but was " + array.length);
}
for (double d : array) {
if (d < min || d > max) {
throw new IllegalArgumentException("Each element of the array"
+ " must be in the range [" + min + ", " + max + "]");
}
}
double range = max - min;
/* maxNums is the maximum number of numbers that could possibly exist
* between max and min.
* The ID will be in the range 0 to maxNums^length.
* maxNums = range / prec + 1
* Stored as a BigDecimal for convenience, but is an integer
*/
BigDecimal maxNums = BigDecimal.valueOf(range)
.divide(BigDecimal.valueOf(prec))
.add(BigDecimal.ONE);
// For convenience
BigDecimal id = BigDecimal.valueOf(0);
// 2^[ (el-1)*length + i ]
for (int i = 0; i < array.length; i++) {
BigDecimal num = BigDecimal.valueOf(array[i])
.divide(BigDecimal.valueOf(prec))
.multiply(maxNums).pow(i);
id = id.add(num);
}
return id;
}
As I understand, you are going to make k-clustering, based on the double values.
Why not just wrap double value in an object, with array and position identifier, so you would know in which cluster it ended up?
Something like:
public class Element {
final public double value;
final public int array;
final public int position;
public Element(double value, int array, int position) {
this.value = value;
this.array = array;
this.position = position;
}
}
If you need to cluster array as a whole,
You can transform original arrays of length 18 to array of length 19 with last or first element being unique id, that you will ignore during clustering, but, to which you could refer after clustering finished. That way this have a small memory footprint - of 8 additional bytes for an array, and easy association with the original value.
If space is absolutely a problem, and you have all values of an array lesser than 1, you can add unique id, greater or equal to 1 to each array, and cluster, based on reminder of division to 1, 0.07518284315321135 stays 0.07518284315321135 for the 1st, and 0.07518284315321135 becomes 1.07518284315321135 for the 2nd, although this increases complexity of computation during clustering.
First of all, let's try to understand what you need mathematically:
Uniquely mapping an array of m real numbers to a single number is in fact a bijection between R^m and R, or at least N.
Since floating points are in fact rational numbers, your problem is to find a bijection between Q^m and N, which can be transformed to N^n to N, because you know your values will always be greater than 0 (just multiply your values by the precision).
Thus you need to map N^m to N. Take a look at the Cantor Pairing Function for some ideas
A guaranteed way to generate a unique result based on the array is to convert it to one big string, and use that for your computational value.
It may be slow, but it will be unique based on the array's values.
Implementation examples:
Best way to convert an ArrayList to a string
I need a hashCode implementation in Java which ignores the order of the fields in my class Edge. It should be possible that Node first could be Node second, and second could be Node first.
Here is my method is depend on the order:
public class Edge {
private Node first, second;
#Override
public int hashCode() {
int hash = 17;
int hashMultiplikator = 79;
hash = hashMultiplikator * hash
+ first.hashCode();
hash = hashMultiplikator * hash
+ second.hashCode();
return hash;
}
}
Is there a way to compute a hash which is for the following Edges the same but unique?
Node n1 = new Node("a");
Node n2 = new Node("b");
Edge ab = new Edge(n1,n2);
Edge ba = new Edge(n2,n1);
ab.hashCode() == ba.hashCode() should be true.
You can use some sort of commutative operation instead of what you have now, like addition:
#Override
public int hashCode() {
int hash = 17;
int hashMultiplikator = 79;
int hashSum = first.hashCode() + second.hashCode();
hash = hashMultiplikator * hash * hashSum;
return hash;
}
I'd recommend that you still use the multiplier since it provides some entropy to your hash code. See my answer here, which says:
Some good rules to follow for hashing are:
Mix up your operators. By mixing your operators, you can cause the results to vary more. Using simply x * y in this test, I had a very
large number of collisions.
Use prime numbers for multiplication. Prime numbers have interesting binary properties that cause multiplication to be more volatile.
Avoid using shift operators (unless you really know what you're doing). They insert lots of zeroes or ones into the binary of the
number, decreasing volatility of other operations and potentially even
shrinking your possible number of outputs.
To solve you problem you have to combine both hashCodes of the components.
An example could be:
#Override
public int hashCode() {
int prime = 17;
return prime * (first.hashCode() + second.hashCode());
}
Please check if this matches your requirements. Also a multiplikation or an XOR insted of an addition could be possible.
How do you in a general (and performant) way implement hashcode while minimizing collisions for objects with 2 or more integers?
update: as many stated, you cant ofcource eliminate colisions entierly (honestly didnt think about it). So my question should be how do you minimize collisions in a proper way, edited to reflect that.
Using NetBeans' autogeneration fails; for example:
public class HashCodeTest {
#Test
public void testHashCode() {
int loopCount = 0;
HashSet<Integer> hashSet = new HashSet<Integer>();
for (int outer = 0; outer < 18; outer++) {
for (int inner = 0; inner < 2; inner++) {
loopCount++;
hashSet.add(new SimpleClass(inner, outer).hashCode());
}
}
org.junit.Assert.assertEquals(loopCount, hashSet.size());
}
private class SimpleClass {
int int1;
int int2;
public SimpleClass(int int1, int int2) {
this.int1 = int1;
this.int2 = int2;
}
#Override
public int hashCode() {
int hash = 5;
hash = 17 * hash + this.int1;
hash = 17 * hash + this.int2;
return hash;
}
}
}
Can you in a general (and performant) way implement hashcode without
colisions for objects with 2 or more integers.
It is technically impossible to have zero collision when hashing to 32 bits (one integer) something made of more than 32 bits (like 2 or more integers).
This is what eclipse auto-generates:
#Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + getOuterType().hashCode();
result = prime * result + int1;
result = prime * result + int2;
return result;
}
And with this code your testcase passes...
PS: And don't forget to implement equals()!
There is no way to eliminate hash collisions entirely. Your approach is basically the preferred one to minimize collisions.
Creating a hash method with zero collisions is impossible. The idea of a hash method is you're taking a large set of objects and mapping it to a smaller set of integers. The best you can do is minimize the number of collisions you get within a subset of your objects.
As others have said, it's more important to minimize collisions that to eliminate them -- especially since you didn't say how many buckets you're aiming for. It's going to be much easier to have zero collisions with 5 items in 1000 buckets than if you have 5 items in 2 buckets! And even if there are plenty of buckets, your collisions could look very different with 1000 buckets vs 1001.
Another thing to note is that there's a good chance that the hash you provide won't even be the one the HashMap eventually uses. If you take a look at the OpenJDK HashMap code, for instance, you'll see that your keys' hashCodes are put through a private hash method (line 264 in that link) which re-hashes them. So, if you're going through the trouble of creating a carefully constructed custom hash function to reduce collisions (rather than just a simple, auto-generated one), make sure you also understand who's going to use it, and how.
I've recently encountered an odd situation when computing the hash Code of tuples of doubles in java. Suppose that you have the two tuples (1.0,1.0) and (Double.POSITIVE_INFINITY,Double.POSITIVE_INFINITY). Using the idiom stated in Joshua Bloch's Effective Java(Item 7), these two tuples would not be considered equal (Imagine that these tuples are objects). However, using the formula stated in Item 8 to compute hashCode() of each tuple evaluates to the same value.
So my question is: is there something strange about this formula that I missed out on when I was writing my formulas, or is it just an odd case of hash-code collisions?
Here is my short, comparative method to illustrate the situation (I wrote it as a JUnit4 test, but it should be pretty easily converted to a main method).
#Test
public void testDoubleHashCodeAndInfinity(){
double a = 1.0;
double b = 1.0;
double c = Double.POSITIVE_INFINITY;
double d = Double.POSITIVE_INFINITY;
int prime = 31;
int result1 = 17;
int result2 = 17;
long temp1 = Double.doubleToLongBits(a);
long temp2 = Double.doubleToLongBits(c);
//this assertion passes successfully
assertTrue("Double.doubleToLongBits(Double.POSITIVE_INFINITY" +
"==Double.doubleToLongBits(1.0)",temp1!=temp2);
result1 = prime*result1 + (int)(temp1^(temp1>>>32));
result2 = prime*result2 + (int)(temp2^(temp2>>>32));
//this assertion passes successfully
assertTrue("Double.POSITIVE_INFINITY.hashCode()" +
"==(1.0).hashCode()",result1!=result2);
temp1 = Double.doubleToLongBits(b);
temp2 = Double.doubleToLongBits(d);
//this assertion should pass successfully
assertTrue("Double.doubleToLongBits(Double.POSITIVE_INFINITY" +
"==Double.doubleToLongBits(1.0)",temp1!=temp2);
result1 = prime*result1+(int)(temp1^(temp1>>>32));
result2 = prime*result2+(int)(temp2^(temp2>>>32));
//this assertion fails!
assertTrue("(1.0,1.0).hashCode()==" +
"(Double.POSITIVE_INFINITY,Double.POSITIVE_INFINITY).hashCode()",
result1!=result2);
}
It's just a coincidence. However, it's an interesting one. Try this:
Double d1 = 1.0;
Double d2 = Double.POSITIVE_INFINITY;
int hash1 = d1.hashCode();
int hash2 = d2.hashCode();
// These both print -1092616192
// This was me using the wrong hash combinator *and*
// the wrong tuples... but it's interesting
System.out.println(hash1 * 17 + hash2);
System.out.println(hash2 * 17 + hash1);
// These both print -33554432
System.out.println(hash1 * 31 + hash1);
System.out.println(hash2 * 31 + hash2);
Basically the bit patterns of the hash determine this. hash1 (1.0's hash code) is 0x3ff00000 and hash2 (infinity's hash code) is 0x7ff00000. That sort of hash and those sort of multipliers produces that sort of effect...
Executive summary: it's a coincidence, but don't worry about it :)
It may be a coincidence, but that sure does not help when you are trying to use the hashCode in a Map to cache objects that have doubles in tuples. I ran into this when creating a map of Thermostat temp settings classes. Then other tests were failing because I was getting the wrong object out of the Map when using the hashCode as the key.
The solution I found to fix this was to create an appended String of the 2 double parameters and called hashCode() on the String. To avoid the String overhead I cached the hashcode.
private volatile hashCode;
#Override public int hashCode()
{
int result = hashCode;
if (result == 0) {
String value = new StringBuilder().append(d1).append(d2).toString();
result = value.hashCode();
hashCode = result;
}
return result;
}