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in linq i'm tring to create gridview with dynamic sort columns
can any one help me what is worng on below code and why it does't work
// i created this function to get column value which i need to sorty by
private static string GetReflectedPropertyValue( object subject, string field)
{
object reflectedValue = subject.GetType().GetProperty(field).GetValue(subject, null);
return reflectedValue != null ? reflectedValue.ToString() : "";
}
// this is my grid query
List<ticketSearchRes> tickets = new List<ticketSearchRes>();
// here i deteermined sort direction ascending or desc
bool asc = (gridViewInputsVM.SortDirection == "asc") ? true : false;
bool desc = (gridViewInputsVM.SortDirection != "asc") ? true : false;
IQueryable<ticketSearchRes> source = (from ticket in _db.TblTicket
where (searchRes.assignTic == 1) ? ticket.AssignedTo == CurrentuserId : true
where (searchRes.myTicket == 1 && searchRes.forOthers != 1) ? ticket.CreatedFor == CurrentuserId : true
orderby
asc ? GetReflectedPropertyValue(ticket, "TicketTitle") : "",
// here i need to get dynamic column which i need to sort by
desc ? GetReflectedPropertyValue(ticket, "TicketTitle") : "" descending // doesn't work
select new ticketSearchRes
{
title = (ticket.TicketTitle != null) ? ticket.TicketTitle.ToString() : "",
ticId = ticket.TicketId.ToString()
}).AsQueryable();
How I would solve this is;
The partial class TicketSearchResList is part that fills in the partial method CustomSort. CustomSort accepts a property name and a sort direction and uses Reflection to sort on the named property. So far it should be easy to follow.
public partial class TicketSearchResList : List<TicketSearchRes>
{
partial void CustomSort(string propertyName, string direction);
public void Dump()
{
CustomSort("TicketTitle", "desc");
foreach(var ticket in this)
Console.WriteLine(ticket.ToString()); // For demo purposes
}
}
public partial class TicketSearchResList {
private string propertyName;
private string direction;
partial void CustomSort(string propertyName, string direction)
{
this.propertyName = propertyName;
this.direction = direction;
Sort(Comparer);
}
private int Comparer(TicketSearchRes x, TicketSearchRes y)
{
int directionChanger = direction == "asc" ? 1 : -1;
try
{
PropertyInfo lhs = x.GetType().GetProperty(propertyName);
PropertyInfo rhs = y.GetType().GetProperty(propertyName);
object o1 = lhs.GetValue(x, null);
object o2 = rhs.GetValue(y, null);
if(o1 is IComparable && o2 is IComparable)
{
return ((IComparable)o1).CompareTo(o2) * directionChanger;
}
// No sort
return 0;
}
catch(Exception ex)
{
Debug.WriteLine(ex.Message); // Should log something
return 0;
}
}
The comparison is done using Reflection in the Comparer method. The direction is
used to determine whether to multiply the result by 1 or -1. CompareTo returns
an integer where -1 means less than, 0 means equal to, and 1 means greater than. Thus, if
you multiply the result by -1, you change the direction of the sort.
Finally, the TicketSearchResList class inherits from List<TicketResearchRes>. As you can see, the Dump method calls CustomSort, which, if implemented, yields ordered output.
Also, have a look at the Sort Method documented here by Microsoft
Initial data:
public class Stats {
int passesNumber;
int tacklesNumber;
public Stats(int passesNumber, int tacklesNumber) {
this.passesNumber = passesNumber;
this.tacklesNumber = tacklesNumber;
}
public int getPassesNumber() {
return passesNumber;
}
public void setPassesNumber(int passesNumber) {
this.passesNumber = passesNumber;
}
public int getTacklesNumber() {
return tacklesNumber;
}
public void setTacklesNumber(int tacklesNumber) {
this.tacklesNumber = tacklesNumber;
}
}
Map<String, List<Stats>> statsByPosition = new HashMap<>();
statsByPosition.put("Defender", Arrays.asList(new Stats(10, 50), new Stats(15, 60), new Stats(12, 100)));
statsByPosition.put("Attacker", Arrays.asList(new Stats(80, 5), new Stats(90, 10)));
I need to calculate an average of Stats by position. So result should be a map with the same keys, however values should be aggregated to single Stats object (List should be reduced to single Stats object)
{
"Defender" => Stats((10 + 15 + 12) / 3, (50 + 60 + 100) / 3),
"Attacker" => Stats((80 + 90) / 2, (5 + 10) / 2)
}
I don't think there's anything new in Java8 that could really help in solving this problem, at least not efficiently.
If you look carefully at all new APIs, then you will see that majority of them are aimed at providing more powerful primitives for working on single values and their sequences - that is, on sequences of double, int, ? extends Object, etc.
For example, to compute an average on sequence on double, JDK introduces a new class - DoubleSummaryStatistics which does an obvious thing - collects a summary over arbitrary sequence of double values.
I would actually suggest that you yourself go for similar approach: make your own StatsSummary class that would look along the lines of this:
// assuming this is what your Stats class look like:
class Stats {
public final double a ,b; //the two stats
public Stats(double a, double b) {
this.a = a; this.b = b;
}
}
// summary will go along the lines of:
class StatsSummary implements Consumer<Stats> {
DoubleSummaryStatistics a, b; // summary of stats collected so far
StatsSummary() {
a = new DoubleSummaryStatistics();
b = new DoubleSummaryStatistics();
}
// this is how we collect it:
#Override public void accept(Stats stat) {
a.accept(stat.a); b.accept(stat.b);
}
public void combine(StatsSummary other) {
a.combine(other.a); b.combine(other.b);
}
// now for actual methods that return stuff. I will implement only average and min
// but rest of them are not hard
public Stats average() {
return new Stats(a.getAverage(), b.getAverage());
}
public Stats min() {
return new Stats(a.getMin(), b.getMin());
}
}
Now, above implementation will actually allow you to express your proper intents when using Streams and such: by building a rigid API and using classes available in JDK as building blocks, you get less errors overall.
However, if you only want to compute average once somewhere and don't need anything else, coding this class is a little overkill, and here's a quick-and-dirty solution:
Map<String, Stats> computeAverage(Map<String, List<Stats>> statsByPosition) {
Map<String, Stats> averaged = new HashMap<>();
statsByPosition.forEach((position, statsList) -> {
averaged.put(position, averageStats(statsList));
});
return averaged;
}
Stats averageStats(Collection<Stats> stats) {
double a, b;
int len = stats.size();
for(Stats stat : stats) {
a += stat.a;
b += stat.b;
}
return len == 0d? new Stats(0,0) : new Stats(a/len, b/len);
}
There is probably a cleaner solution with Java 8, but this works well and isn't too complex:
Map<String, Stats> newMap = new HashMap<>();
statsByPosition.forEach((key, statsList) -> {
newMap.put(key, new Stats(
(int) statsList.stream().mapToInt(Stats::getPassesNumber).average().orElse(0),
(int) statsList.stream().mapToInt(Stats::getTacklesNumber).average().orElse(0))
);
});
The functional forEach method lets you iterate over every key value pair of your given map.
You just put a new entry in your map for the averaged values. There you take the key you have already in your given map. The new value is a new Stats, where the arguments for the constructor are calculated directly.
Just take the value of your old map, which is the statsList in the forEach function, map the values from the given stats to Integer value with mapToInt and use the average function.
This function returns an OptionalDouble which is nearly the same as Optional<Double>. Preventing that anything didn't work, you use its orElse() method and pass a default value (like 0). Since the average values are double you have to cast the value to int.
As mentioned, there doubld probably be a even shorter version, using reduce.
You might as well use custom collector. Let's add the following methods to Stats class:
public Stats() {
}
public void accumulate(Stats stats) {
passesNumber += stats.passesNumber;
tacklesNumber += stats.tacklesNumber;
}
public Stats combine(Stats acc) {
passesNumber += acc.passesNumber;
tacklesNumber += acc.tacklesNumber;
return this;
}
#Override
public String toString() {
return "Stats{" +
"passesNumber=" + passesNumber +
", tacklesNumber=" + tacklesNumber +
'}';
}
Now we can use Stats in collect method:
System.out.println(statsByPosition.entrySet().stream().collect(
Collectors.toMap(
entity -> entity.getKey(),
entity -> {
Stats entryStats = entity.getValue().stream().collect(
Collector.of(Stats::new, Stats::accumulate, Stats::combine)
); // get stats for each map key.
// get average
entryStats.setPassesNumber(entryStats.getPassesNumber() / entity.getValue().size());
// get average
entryStats.setTacklesNumber(entryStats.getTacklesNumber() / entity.getValue().size());
return entryStats;
}
))); // {Attacker=Stats{passesNumber=85, tacklesNumber=7}, Defender=Stats{passesNumber=12, tacklesNumber=70}}
If java-9 is available and StreamEx, you could do :
public static Map<String, Stats> third(Map<String, List<Stats>> statsByPosition) {
return statsByPosition.entrySet().stream()
.collect(Collectors.groupingBy(e -> e.getKey(),
Collectors.flatMapping(e -> e.getValue().stream(),
MoreCollectors.pairing(
Collectors.averagingDouble(Stats::getPassesNumber),
Collectors.averagingDouble(Stats::getTacklesNumber),
(a, b) -> new Stats(a, b)))));
}
I have a datatable of 200,000 rows and want to validate each row with that of list and return that string codesList..
It is taking very long time..I want to improve the performance.
for (int i = 0; i < dataTable.Rows.Count; i++)
{
bool isCodeValid = CheckIfValidCode(codevar, codesList,out CodesCount);
}
private bool CheckIfValidCode(string codevar, List<Codes> codesList, out int count)
{
List<Codes> tempcodes= codesList.Where(code => code.StdCode.Equals(codevar)).ToList();
if (tempcodes.Count == 0)
{
RetVal = false;
for (int i = 0; i < dataTable.Rows.Count; i++)
{
bool isCodeValid = CheckIfValidCode(codevar, codesList,out CodesCount);
}
}
}
private bool CheckIfValidCode(string codevar, List<Codes> codesList, out int count)
{
List<Codes> tempcodes= codesList.Where(code => code.StdCode.Equals(codevar)).ToList();
if (tempcodes.Count == 0)
{
RetVal = false;
}
else
{
RetVal=true;
}
return bRetVal;
}
codelist is a list which also contains 200000 records. Please suggest. I used findAll which takes same time and also used LINQ query which also takes same time.
A few optimizations come to mind:
You could start by removing the Tolist() altogether
replace the Count() with .Any(), which returns true if there are items in the result
It's probably also a lot faster when you replace the List with a HashSet<Codes> (this requires your Codes class to implement HashCode and Equals properly. Alternatively you could populate a HashSet<string> with the contents of Codes.StdCode
It looks like you're not using the out count at all. Removing it would make this method a lot faster. Computing a count requires you to check all codes.
You could also split the List into a Dictionary> which you populate with by taking the first character of the code. That would reduce the number of codes to check drastically, since you can exclude 95% of the codes by their first character.
Tell string.Equals to use a StringComparison of type Ordinal or OrdinalIgnoreCase to speed up the comparison.
It looks like you can stop processing a lot earlier as well, the use of .Any takes care of that in the second method. A similar construct can be used in the first, instead of using for and looping through each row, you could short-circuit after the first failure is found (unless this code is incomplete and you mark each row as invalid individually).
Something like:
private bool CheckIfValidCode(string codevar, List<Codes> codesList)
{
Hashset<string> codes = new Hashset(codesList.Select(c ==> code.StdCode));
return codes.Contains(codevar);
// or: return codes.Any(c => string.Equals(codevar, c, StringComparison.Ordinal);
}
If you're adamant about the count:
private bool CheckIfValidCode(string codevar, List<Codes> codesList, out int count)
{
Hashset<string> codes = new Hashset(codesList.Select(c ==> code.StdCode));
count = codes.Count(codevar);
// or: count = codes.Count(c => string.Equals(codevar, c, StringComparison.Ordinal);
return count > 0;
}
You can optimize further by creating the HashSet outside of the call and re-use the instance:
InCallingCode
{
...
Hashset<string> codes = new Hashset(codesList.Select(c ==> code.StdCode));
for (/*loop*/) {
bool isValid = CheckIfValidCode(codevar, codes, out int count)
}
....
}
private bool CheckIfValidCode(string codevar, List<Codes> codesList, out int count)
{
count = codes.Count(codevar);
// or: count = codes.Count(c => string.Equals(codevar, c, StringComparison.Ordinal);
return count > 0;
}
What is the best way to solve this:
I have a group of arrays with 3-4 characters inside each like so:
{p, {a, {t, {m,
q, b, u, n,
r, c v o
s } } }
}
I also have an array of dictionary words.
What is the best/fastest way to find if the array of characters can combine to form one of the dictionary words? For example, the above arrays could make the words:
"pat","rat","at","to","bum"(lol)but not "nub" or "mat"Should i loop through the dictionary to see if words can be made or get all the combinations from the letters then compare those to the dictionary
I had some Scrabble code laying around, so I was able to throw this together. The dictionary I used is sowpods (267751 words). The code below reads the dictionary as a text file with one uppercase word on each line.
The code is C#:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.IO;
using System.Diagnostics;
namespace SO_6022848
{
public struct Letter
{
public const string Chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
public static implicit operator Letter(char c)
{
return new Letter() { Index = Chars.IndexOf(c) };
}
public int Index;
public char ToChar()
{
return Chars[Index];
}
public override string ToString()
{
return Chars[Index].ToString();
}
}
public class Trie
{
public class Node
{
public string Word;
public bool IsTerminal { get { return Word != null; } }
public Dictionary<Letter, Node> Edges = new Dictionary<Letter, Node>();
}
public Node Root = new Node();
public Trie(string[] words)
{
for (int w = 0; w < words.Length; w++)
{
var word = words[w];
var node = Root;
for (int len = 1; len <= word.Length; len++)
{
var letter = word[len - 1];
Node next;
if (!node.Edges.TryGetValue(letter, out next))
{
next = new Node();
if (len == word.Length)
{
next.Word = word;
}
node.Edges.Add(letter, next);
}
node = next;
}
}
}
}
class Program
{
static void GenWords(Trie.Node n, HashSet<Letter>[] sets, int currentArrayIndex, List<string> wordsFound)
{
if (currentArrayIndex < sets.Length)
{
foreach (var edge in n.Edges)
{
if (sets[currentArrayIndex].Contains(edge.Key))
{
if (edge.Value.IsTerminal)
{
wordsFound.Add(edge.Value.Word);
}
GenWords(edge.Value, sets, currentArrayIndex + 1, wordsFound);
}
}
}
}
static void Main(string[] args)
{
const int minArraySize = 3;
const int maxArraySize = 4;
const int setCount = 10;
const bool generateRandomInput = true;
var trie = new Trie(File.ReadAllLines("sowpods.txt"));
var watch = new Stopwatch();
var trials = 10000;
var wordCountSum = 0;
var rand = new Random(37);
for (int t = 0; t < trials; t++)
{
HashSet<Letter>[] sets;
if (generateRandomInput)
{
sets = new HashSet<Letter>[setCount];
for (int i = 0; i < setCount; i++)
{
sets[i] = new HashSet<Letter>();
var size = minArraySize + rand.Next(maxArraySize - minArraySize + 1);
while (sets[i].Count < size)
{
sets[i].Add(Letter.Chars[rand.Next(Letter.Chars.Length)]);
}
}
}
else
{
sets = new HashSet<Letter>[] {
new HashSet<Letter>(new Letter[] { 'P', 'Q', 'R', 'S' }),
new HashSet<Letter>(new Letter[] { 'A', 'B', 'C' }),
new HashSet<Letter>(new Letter[] { 'T', 'U', 'V' }),
new HashSet<Letter>(new Letter[] { 'M', 'N', 'O' }) };
}
watch.Start();
var wordsFound = new List<string>();
for (int i = 0; i < sets.Length - 1; i++)
{
GenWords(trie.Root, sets, i, wordsFound);
}
watch.Stop();
wordCountSum += wordsFound.Count;
if (!generateRandomInput && t == 0)
{
foreach (var word in wordsFound)
{
Console.WriteLine(word);
}
}
}
Console.WriteLine("Elapsed per trial = {0}", new TimeSpan(watch.Elapsed.Ticks / trials));
Console.WriteLine("Average word count per trial = {0:0.0}", (float)wordCountSum / trials);
}
}
}
Here is the output when using your test data:
PA
PAT
PAV
QAT
RAT
RATO
RAUN
SAT
SAU
SAV
SCUM
AT
AVO
BUM
BUN
CUM
TO
UM
UN
Elapsed per trial = 00:00:00.0000725
Average word count per trial = 19.0
And the output when using random data (does not print each word):
Elapsed per trial = 00:00:00.0002910
Average word count per trial = 62.2
EDIT: I made it much faster with two changes: Storing the word at each terminal node of the trie, so that it doesn't have to be rebuilt. And storing the input letters as an array of hash sets instead of an array of arrays, so that the Contains() call is fast.
There are probably many way of solving this.
What you are interested in is the number of each character you have available to form a word, and how many of each character is required for each dictionary word. The trick is how to efficiently look up this information in the dictionary.
Perhaps you can use a prefix tree (a trie), some kind of smart hash table, or similar.
Anyway, you will probably have to try out all your possibilities and check them against the dictionary. I.e., if you have three arrays of three values each, there will be 3^3+3^2+3^1=39 combinations to check out. If this process is too slow, then perhaps you could stick a Bloom filter in front of the dictionary, to quickly check if a word is definitely not in the dictionary.
EDIT: Anyway, isn't this essentially the same as Scrabble? Perhaps try Googling for "scrabble algorithm" will give you some good clues.
The reformulated question can be answered just by generating and testing. Since you have 4 letters and 10 arrays, you've only got about 1 million possible combinations (10 million if you allow a blank character). You'll need an efficient way to look them up, use a BDB or some sort of disk based hash.
The trie solution previously posted should work as well, you are just restricted more by what characters you can choose at each step of the search. It should be faster as well.
I just made a very large nested for loop like this:
for(NSString*s1 in [letterList objectAtIndex:0]{
for(NSString*s2 in [letterList objectAtIndex:1]{
8 more times...
}
}
Then I do a binary search on the combination to see if it is in the dictionary and add it to an array if it is
I am looking for a pattern, algorithm, or library that will take a set of dates and return a description of the recurrence if one exits, i.e. the set [11-01-2010, 11-08-2010, 11-15-2010, 11-22-2010, 11-29-2010] would yield something like "Every Monday in November".
Has anyone seen anything like this before or have any suggestions on the best way to implement it?
Grammatical Evolution (GE) is suitable for this kind of problem, because you are searching for an answer that adheres to a certain language. Grammatical Evolution is also used for program generation, composing music, designing, etcetera.
I'd approach the task like this:
Structure the problem space with a grammar.
Construct a Context-free Grammar that can represent all desired recurrence patterns. Consider production rules like these:
datepattern -> datepattern 'and' datepattern
datepattern -> frequency bounds
frequency -> 'every' ordinal weekday 'of the month'
frequency -> 'every' weekday
ordinal -> ordinal 'and' ordinal
ordinal -> 'first' | 'second' | 'third'
bounds -> 'in the year' year
An example of a pattern generated by these rules is: 'every second and third wednesday of the month in the year 2010 and every tuesday in the year 2011'
One way to implement such a grammar would be through a class hierarchy that you will later operate on through reflection, as I've done in the example below.
Map this language to a set of dates
You should create a function that takes a clause from your language and recursively returns the set of all dates covered by it. This allows you to compare your answers to the input.
Guided by the grammar, search for potential solutions
You could use a Genetic algorithm or Simulated Annealing to match the dates to the grammar, try your luck with Dynamic Programming or start simple with a brute force enumeration of all possible clauses.
Should you go with a Genetic Algorithm, your mutation concept should consist of substituting an expression for another one based on the application of one of your production rules.
Have a look at the following GE-related sites for code and information:
http://www.bangor.ac.uk/~eep201/jge/
http://nohejl.name/age/
http://www.geneticprogramming.us/Home_Page.html
Evaluate each solution
The fitness function could take into account the textual length of the solution, the number of dates generated more than once, the number of dates missed, as well as the number of wrong dates generated.
Example code
By request, and because it's such an interesting challenge, I've written a rudimentary implementation of the algorithm to get you started. Although it works it is by no means finished, the design should definitively get some more thought, and once you have gleaned the fundamental take-aways from this example I recommend you consider using one the libraries I've mentioned above.
/// <summary>
/// This is a very basic example implementation of a grammatical evolution algorithm for formulating a recurrence pattern in a set of dates.
/// It needs significant extensions and optimizations to be useful in a production setting.
/// </summary>
static class Program
{
#region "Class hierarchy that codifies the grammar"
class DatePattern
{
public Frequency frequency;
public Bounds bounds;
public override string ToString() { return "" + frequency + " " + bounds; }
public IEnumerable<DateTime> Dates()
{
return frequency == null ? new DateTime[] { } : frequency.FilterDates(bounds.GetDates());
}
}
abstract class Bounds
{
public abstract IEnumerable<DateTime> GetDates();
}
class YearBounds : Bounds
{
/* in the year .. */
public int year;
public override string ToString() { return "in the year " + year; }
public override IEnumerable<DateTime> GetDates()
{
var firstDayOfYear = new DateTime(year, 1, 1);
return Enumerable.Range(0, new DateTime(year, 12, 31).DayOfYear)
.Select(dayOfYear => firstDayOfYear.AddDays(dayOfYear));
}
}
abstract class Frequency
{
public abstract IEnumerable<DateTime> FilterDates(IEnumerable<DateTime> Dates);
}
class WeeklyFrequency : Frequency
{
/* every .. */
public DayOfWeek dayOfWeek;
public override string ToString() { return "every " + dayOfWeek; }
public override IEnumerable<DateTime> FilterDates(IEnumerable<DateTime> Dates)
{
return Dates.Where(date => (date.DayOfWeek == dayOfWeek));
}
}
class MonthlyFrequency : Frequency
{
/* every .. */
public Ordinal ordinal;
public DayOfWeek dayOfWeek;
/* .. of the month */
public override string ToString() { return "every " + ordinal + " " + dayOfWeek + " of the month"; }
public override IEnumerable<DateTime> FilterDates(IEnumerable<DateTime> Dates)
{
return Dates.Where(date => (date.DayOfWeek == dayOfWeek) && (int)ordinal == (date.Day - 1) / 7);
}
}
enum Ordinal { First, Second, Third, Fourth, Fifth }
#endregion
static Random random = new Random();
const double MUTATION_RATE = 0.3;
static Dictionary<Type, Type[]> subtypes = new Dictionary<Type, Type[]>();
static void Main()
{
// The input signifies the recurrence 'every first thursday of the month in 2010':
var input = new DateTime[] {new DateTime(2010,12,2), new DateTime(2010,11,4),new DateTime(2010,10,7),new DateTime(2010,9,2),
new DateTime(2010,8,5),new DateTime(2010,7,1),new DateTime(2010,6,3),new DateTime(2010,5,6),
new DateTime(2010,4,1),new DateTime(2010,3,4),new DateTime(2010,2,4),new DateTime(2010,1,7) };
for (int cTests = 0; cTests < 20; cTests++)
{
// Initialize with a random population
int treesize = 0;
var population = new DatePattern[] { (DatePattern)Generate(typeof(DatePattern), ref treesize), (DatePattern)Generate(typeof(DatePattern), ref treesize), (DatePattern)Generate(typeof(DatePattern), ref treesize) };
Run(input, new List<DatePattern>(population));
}
}
private static void Run(DateTime[] input, List<DatePattern> population)
{
var strongest = population[0];
int strongestFitness = int.MinValue;
int bestTry = int.MaxValue;
for (int cGenerations = 0; cGenerations < 300 && strongestFitness < -100; cGenerations++)
{
// Select the best individuals to survive:
var survivers = population
.Select(individual => new { Fitness = Fitness(input, individual), individual })
.OrderByDescending(pair => pair.Fitness)
.Take(5)
.Select(pair => pair.individual)
.ToArray();
population.Clear();
// The survivers are the foundation for the next generation:
foreach (var parent in survivers)
{
for (int cChildren = 0; cChildren < 3; cChildren++)
{
int treeSize = 1;
DatePattern child = (DatePattern)Mutate(parent, ref treeSize); // NB: procreation may also be done through crossover.
population.Add((DatePattern)child);
var childFitness = Fitness(input, child);
if (childFitness > strongestFitness)
{
bestTry = cGenerations;
strongestFitness = childFitness;
strongest = child;
}
}
}
}
Trace.WriteLine("Found best match with fitness " + Fitness(input, strongest) + " after " + bestTry + " generations: " + strongest);
}
private static object Mutate(object original, ref int treeSize)
{
treeSize = 0;
object replacement = Construct(original.GetType());
foreach (var field in original.GetType().GetFields())
{
object newFieldValue = field.GetValue(original);
int subtreeSize;
if (field.FieldType.IsEnum)
{
subtreeSize = 1;
if (random.NextDouble() <= MUTATION_RATE)
newFieldValue = ConstructRandomEnumValue(field.FieldType);
}
else if (field.FieldType == typeof(int))
{
subtreeSize = 1;
if (random.NextDouble() <= MUTATION_RATE)
newFieldValue = (random.Next(2) == 0
? Math.Min(int.MaxValue - 1, (int)newFieldValue) + 1
: Math.Max(int.MinValue + 1, (int)newFieldValue) - 1);
}
else
{
subtreeSize = 0;
newFieldValue = Mutate(field.GetValue(original), ref subtreeSize); // mutate pre-maturely to find out subtreeSize
if (random.NextDouble() <= MUTATION_RATE / subtreeSize) // makes high-level nodes mutate less.
{
subtreeSize = 0; // init so we can track the size of the subtree soon to be made.
newFieldValue = Generate(field.FieldType, ref subtreeSize);
}
}
field.SetValue(replacement, newFieldValue);
treeSize += subtreeSize;
}
return replacement;
}
private static object ConstructRandomEnumValue(Type type)
{
var vals = type.GetEnumValues();
return vals.GetValue(random.Next(vals.Length));
}
private static object Construct(Type type)
{
return type.GetConstructor(new Type[] { }).Invoke(new object[] { });
}
private static object Generate(Type type, ref int treesize)
{
if (type.IsEnum)
{
return ConstructRandomEnumValue(type);
}
else if (typeof(int) == type)
{
return random.Next(10) + 2005;
}
else
{
if (type.IsAbstract)
{
// pick one of the concrete subtypes:
var subtypes = GetConcreteSubtypes(type);
type = subtypes[random.Next(subtypes.Length)];
}
object newobj = Construct(type);
foreach (var field in type.GetFields())
{
treesize++;
field.SetValue(newobj, Generate(field.FieldType, ref treesize));
}
return newobj;
}
}
private static int Fitness(DateTime[] input, DatePattern individual)
{
var output = individual.Dates().ToArray();
var avgDateDiff = Math.Abs((output.Average(d => d.Ticks / (24.0 * 60 * 60 * 10000000)) - input.Average(d => d.Ticks / (24.0 * 60 * 60 * 10000000))));
return
-individual.ToString().Length // succinct patterns are preferred.
- input.Except(output).Count() * 300 // Forgetting some of the dates is bad.
- output.Except(input).Count() * 3000 // Spurious dates cause even more confusion to the user.
- (int)(avgDateDiff) * 30000; // The difference in average date is the most important guide.
}
private static Type[] GetConcreteSubtypes(Type supertype)
{
if (subtypes.ContainsKey(supertype))
{
return subtypes[supertype];
}
else
{
var types = AppDomain.CurrentDomain.GetAssemblies().ToList()
.SelectMany(s => s.GetTypes())
.Where(p => supertype.IsAssignableFrom(p) && !p.IsAbstract).ToArray();
subtypes.Add(supertype, types);
return types;
}
}
}
Hope this gets you on track. Be sure to share your actual solution somewhere; I think it will be quite useful in lots of scenarios.
If your purpose is to generate human-readable descriptions of the pattern, as in your "Every Monday in November", then you probably want to start by enumerating the possible descriptions. Descriptions can be broken down into frequency and bounds, for example,
Frequency:
Every day ...
Every other/third/fourth day ...
Weekdays/weekends ...
Every Monday ...
Alternate Mondays ...
The first/second/last Monday ...
...
Bounds:
... in January
... between 25 March and 25 October
...
There won't be all that many of each, and you can check for them one by one.
What I would do:
Create samples of the data
Use a clustering algorithm
Generate samples using the algorithm
Creating a fitness function to measure how well it correlates to the full data set. The clustering algorithm will come up with either 0 or 1 suggestions and you can meassure it against how well it fits in with the full set.
Elementate/merge the occurrence with the already found sets and rerun this algorithm.
Looking at that you may want to use either Simulated Annealing, or an Genetic Algorithm. Also, if you have the descriptions, you may want to compare the descriptions to generate a sample.
You could access the system date or system dateandtime and construct crude calendar points in memory based on the date and the day of the week as returned by the call or function result. Then use the number of days in relevant months to sum them and add on the number of days of the day variable in the input and/or access the calendar point for the relevant week starting sunday or monday and calculate or increment index forward to the correct day. Construct text string using fixed characters and insert the relevant variable such as the full name of the day of the week as required. There may be multiple traversals needed to obtain all the events of which the occurrences are to be displayed or counted.
First, find a sequence, if it exists:
step = {day,month,year}
period=0
for d = 1 to dates.count-1
interval(d,step)=datedifference(s,date(d),date(d+1))
next
' Find frequency with largest interval
for s = year downto day
found=true
for d = 1 to dates.count-2
if interval(d,s)=interval(d+1,s) then
found=false
exit for
end if
next
if found then
period=s
frequency=interval(1,s)
exit for
end if
next
if period>0
Select case period
case day
if frequency mod 7 = 0 then
say "every" dayname(date(1))
else
say "every" frequency "days"
end if
case month
say "every" frequency "months on day" daynumber(date(1))
case years
say "every" frequency "years on" daynumber(date(1)) monthname(date(1))
end select
end if
Finally, deal with "in November", "from 2007 to 2010" etc., should be obvious.
HTH
I like #arjen answer but I don't think there is any need for complex algorithm. This is so so simple. If there is a pattern, there is a pattern... therefore a simple algorithm would work. First we need to think of the types of patterns we are looking for: daily, weekly, monthly and yearly.
How to recognize?
Daily: there is a record every day
Weekly: there is a record every week
Monthly: there is a record every month
Yearly: there is a record every year
Difficult? No. Just count how many repetitions you have and then classify.
Here is my implementation
RecurrencePatternAnalyser.java
public class RecurrencePatternAnalyser {
// Local copy of calendars by add() method
private ArrayList<Calendar> mCalendars = new ArrayList<Calendar>();
// Used to count the uniqueness of each year/month/day
private HashMap<Integer, Integer> year_count = new HashMap<Integer,Integer>();
private HashMap<Integer, Integer> month_count = new HashMap<Integer,Integer>();
private HashMap<Integer, Integer> day_count = new HashMap<Integer,Integer>();
private HashMap<Integer, Integer> busday_count = new HashMap<Integer,Integer>();
// Used for counting payments before due date on weekends
private int day_goodpayer_ocurrences = 0;
private int day_goodPayer = 0;
// Add a new calendar to the analysis
public void add(Calendar date)
{
mCalendars.add(date);
addYear( date.get(Calendar.YEAR) );
addMonth( date.get(Calendar.MONTH) );
addDay( date.get(Calendar.DAY_OF_MONTH) );
addWeekendDays( date );
}
public void printCounts()
{
System.out.println("Year: " + getYearCount() +
" month: " + getMonthCount() + " day: " + getDayCount());
}
public RecurrencePattern getPattern()
{
int records = mCalendars.size();
if (records==1)
return null;
RecurrencePattern rp = null;
if (getYearCount()==records)
{
rp = new RecurrencePatternYearly();
if (records>=3)
rp.setConfidence(1);
else if (records==2)
rp.setConfidence(0.9f);
}
else if (getMonthCount()==records)
{
rp = new RecurrencePatternMonthly();
if (records>=12)
rp.setConfidence(1);
else
rp.setConfidence(1-(-0.0168f * records + 0.2f));
}
else
{
calcDaysRepetitionWithWeekends();
if (day_goodpayer_ocurrences==records)
{
rp = new RecurrencePatternMonthly();
rp.setPattern(RecurrencePattern.PatternType.MONTHLY_GOOD_PAYER);
if (records>=12)
rp.setConfidence(0.95f);
else
rp.setConfidence(1-(-0.0168f * records + 0.25f));
}
}
return rp;
}
// Increment one more year/month/day on each count variable
private void addYear(int key_year) { incrementHash(year_count, key_year); }
private void addMonth(int key_month) { incrementHash(month_count, key_month); }
private void addDay(int key_day) { incrementHash(day_count, key_day); }
// Retrieve number of unique entries for the records
private int getYearCount() { return year_count.size(); }
private int getMonthCount() { return month_count.size(); }
private int getDayCount() { return day_count.size(); }
// Generic function to increment the hash by 1
private void incrementHash(HashMap<Integer, Integer> var, Integer key)
{
Integer oldCount = var.get(key);
Integer newCount = 0;
if ( oldCount != null ) {
newCount = oldCount;
}
newCount++;
var.put(key, newCount);
}
// As Bank are closed during weekends, some dates might be anticipated
// to Fridays. These will be false positives for the recurrence pattern.
// This function adds Saturdays and Sundays to the count when a date is
// Friday.
private void addWeekendDays(Calendar c)
{
int key_day = c.get(Calendar.DAY_OF_MONTH);
incrementHash(busday_count, key_day);
if (c.get(Calendar.DAY_OF_WEEK) == Calendar.FRIDAY)
{
// Adds Saturday
c.add(Calendar.DATE, 1);
key_day = c.get(Calendar.DAY_OF_MONTH);
incrementHash(busday_count, key_day);
// Adds Sunday
c.add(Calendar.DATE, 1);
key_day = c.get(Calendar.DAY_OF_MONTH);
incrementHash(busday_count, key_day);
}
}
private void calcDaysRepetitionWithWeekends()
{
Iterator<Entry<Integer, Integer>> it =
busday_count.entrySet().iterator();
while (it.hasNext()) {
#SuppressWarnings("rawtypes")
Map.Entry pair = (Map.Entry)it.next();
if ((int)pair.getValue() > day_goodpayer_ocurrences)
{
day_goodpayer_ocurrences = (int) pair.getValue();
day_goodPayer = (int) pair.getKey();
}
//it.remove(); // avoids a ConcurrentModificationException
}
}
}
RecurrencePattern.java
public abstract class RecurrencePattern {
public enum PatternType {
YEARLY, MONTHLY, WEEKLY, DAILY, MONTHLY_GOOD_PAYER
}
public enum OrdinalType {
FIRST, SECOND, THIRD, FOURTH, FIFTH
}
protected PatternType pattern;
private float confidence;
private int frequency;
public PatternType getPattern() {
return pattern;
}
public void setPattern(PatternType pattern) {
this.pattern = pattern;
}
public float getConfidence() {
return confidence;
}
public void setConfidence(float confidence) {
this.confidence = confidence;
}
public int getFrequency() {
return frequency;
}
public void setFrequency(int frequency) {
this.frequency = frequency;
}
}
RecurrencePatternMonthly.java
public class RecurrencePatternMonthly extends RecurrencePattern {
private boolean isDayFixed;
private boolean isDayOrdinal;
private OrdinalType ordinaltype;
public RecurrencePatternMonthly()
{
this.pattern = PatternType.MONTHLY;
}
}
RecurrencePatternYearly.java
public class RecurrencePatternYearly extends RecurrencePattern {
private boolean isDayFixed;
private boolean isMonthFixed;
private boolean isDayOrdinal;
private OrdinalType ordinaltype;
public RecurrencePatternYearly()
{
this.pattern = PatternType.YEARLY;
}
}
Main.java
public class Algofin {
static Connection c = null;
public static void main(String[] args) {
//openConnection();
//readSqlFile();
RecurrencePatternAnalyser r = new RecurrencePatternAnalyser();
//System.out.println(new GregorianCalendar(2015,1,30).get(Calendar.MONTH));
r.add(new GregorianCalendar(2015,0,1));
r.add(new GregorianCalendar(2015,0,30));
r.add(new GregorianCalendar(2015,1,27));
r.add(new GregorianCalendar(2015,3,1));
r.add(new GregorianCalendar(2015,4,1));
r.printCounts();
RecurrencePattern rp;
rp=r.getPattern();
System.out.println("Pattern: " + rp.getPattern() + " confidence: " + rp.getConfidence());
}
}
I think you'll have to build it, and I think it will be a devil in the details kind of project. Start by getting much more thorough requirements. Which date patterns do you want to recognize? Come up with a list of examples that you want your algorithm to successfully identify. Write your algorithm to meet your examples. Put your examples in a test suite so when you get different requirements later you can make sure you didn't break the old ones.
I predict you will write 200 if-then-else statements.
OK, I do have one idea. Get familiar with the concepts of sets, unions, coverage, intersection and so on. Have a list of short patterns that you search for, say, "Every day in October", "Every day in November", and "Every day in December." If these short patterns are contained within the set of dates, then define a union function that can combine shorter patterns in intelligent ways. For example, let's say you matched the three patterns I mention above. If you Union them together you get, "Every day in October through December." You could aim to return the most succinct set of unions that cover your set of dates or something like that.
Have a look at your favourite calendar program. See what patterns of event recurrence it can generate. Reverse engineer them.