I am writing a code which makes a lot of combinations (Combinations might not be the right word here, sequences of string in the order they are actually present in the string) that already exist in a string. The loop starts adding combinations to a List<string> but unfortunately, my loop takes a lot of time when dealing with any file over 200 bytes. I want to be able to work with hundreds of MBs here.
Let me explain what I actually want in the simplest of ways.
Lets say I have a string that is "Afnan is awesome" (-> main string), what I would want is a list of string which encompasses different substring sequences of the main string. For example-> A,f,n,a,n, ,i,s, ,a,w,e,s,o,m,e. Now this is just the first iteration of the loop. With each iteration, my substring length increases, yielding these results for the second iteration -> Af,fn,na,n , i,is,s , a,aw,we,es,so,om,me. The third iteration would look like this: Afn,fna,nan,an ,n i, is,is ,s a, aw, awe, wes, eso, som, ome. This will keep going on until my substring length reaches half the length of my main string.
My code is as follows:
string data = File.ReadAllText("MyFilePath");
//Creating my dictionary
List<string> dictionary = new List<string>();
int stringLengthIncrementer = 1;
for (int v = 0; v < (data.Length / 2); v++)
{
for (int x = 0; x < data.Length; x++)
{
if ((x + stringLengthIncrementer) > data.Length) break; //So index does not go out of bounds
if (dictionary.Contains(data.Substring(x, stringLengthIncrementer)) == false) //So no repetition takes place
{
dictionary.Add(data.Substring(x, stringLengthIncrementer)); //To add the substring to my List<string> -> dictionary
}
}
stringLengthIncrementer++; //To increase substring length with each iteration
}
I use data.Length / 2 because I only need combinations at most half the length of the entire string. Note that I search the entire string for combinations, not half of it.
To further simplify what I am trying to do -> Suppose I have an input string =
"abcd"
the output would be =
a, b, c, d, ab, bc, cd, This rest will be cut out as it is longer than half the length of my primary string -> //abc, bcd, abcd
I was hoping if some regex method may help me achieve this. Anything that doesn't consist of loops. Anything that is exponentially faster than this? Some simple code with less complexity which is more efficient?
Update
When I used Hashset instead of List<string> for my dictionary, I did not experience any change of performance and also got an OutOfMemoryException:
You can use linq to simplify the code and very easily parallelize it, but it's not going to be orders of magnitude faster, as you would need to run it on files of 100s of MBs (that's very likely impossible).
var data = File.ReadAllText("MyFilePath");
var result = Enumerable.Range(1, data.Length / 2)
.AsParallel()
.Select(len => new HashSet<string>(
Enumerable.Range(0, data.Length - len + 1) //Adding the +1 here made it work perfectly
.Select(x => data.Substring(x, len))))
.SelectMany(t=>t)
.ToList();
General improvements, that you can do in your code to improve the performance (I don't consider if there're other more optimal solutions).
calculate data.Substring(x, stringLengthIncrementer) only once
as you do search, use SortedList, it will be faster.
initialize the List (or SortedList, or whatever) with calculated number of items. Like new List(CalucatedCapacity).
or you can try to write an algorithm that produces combinations without checking for duplicates.
You may be able to use HashSet combined with MoreLINQ's Batch feature (available on NuGet) to simplify the code a little.
public static void Main()
{
string data = File.ReadAllText("MyFilePath");
//string data = "Afnan is awesome";
var dictionary = new HashSet<string>();
for (var stringLengthIncrementer = 1; stringLengthIncrementer <= (data.Length / 2); stringLengthIncrementer++)
{
foreach (var skipper in Enumerable.Range(0, stringLengthIncrementer))
{
var batched = data.Skip(skipper).Batch(stringLengthIncrementer);
foreach (var batch in batched)
{
dictionary.Add(new string(batch.ToArray()));
}
}
}
Console.WriteLine(dictionary);
dictionary.ForEach(z => Console.WriteLine(z));
Console.ReadLine();
}
For this input:
"Afnan is awesome askdjkhaksjhd askjdhaksjsdhkajd asjsdhkajshdkjahsd asksdhkajshdkjashd aksjdhkajsshd98987ad asdhkajsshd98xcx98asdjaksjsd askjdakjshcc98z98asdsad"
performance is roughly 10x faster than your current code.
Related
I found something interesting while doing a HW question.
The howework question asks to code the Median Maintenance algorithm.
The formal statement is as follows:
The goal of this problem is to implement the "Median Maintenance" algorithm (covered in the Week 5 lecture on heap applications). The text file contains a list of the integers from 1 to 10000 in unsorted order; you should treat this as a stream of numbers, arriving one by one. Letting xi denote the ith number of the file, the kth median mk is defined as the median of the numbers x1,…,xk. (So, if k is odd, then mk is ((k+1)/2)th smallest number among x1,…,xk; if k is even, then m1 is the (k/2)th smallest number among x1,…,xk.)
In order to get O(n) running time, this should be implemented using heaps obviously. Anyways, I coded this using Brute Force (deadline was too soon and needed an answer right away) (O(n2)) with the following steps:
Read data in
Sort array
Find Median
Add it to running time
I ran the algorithm through several test cases (with a known answer) and got the correct results, however when I was running the same algorithm on a larger data set I was getting the wrong answer. I was doing all the operations using Int64 ro represent the data.
Then I tried switching to Int32 and magically I got the correct answer which makes no sense to me.
The code is below, and it is also found here (the data is in the repo). The algorithm starts to give erroneous results after the 3810 index:
private static void Main(string[] args)
{
MedianMaintenance("Question2.txt");
}
private static void MedianMaintenance(string filename)
{
var txtData = File.ReadLines(filename).ToArray();
var inputData32 = new List<Int32>();
var medians32 = new List<Int32>();
var sums32 = new List<Int32>();
var inputData64 = new List<Int64>();
var medians64 = new List<Int64>();
var sums64 = new List<Int64>();
var sum = 0;
var sum64 = 0f;
var i = 0;
foreach (var s in txtData)
{
//Add to sorted list
var intToAdd = Convert.ToInt32(s);
inputData32.Add(intToAdd);
inputData64.Add(Convert.ToInt64(s));
//Compute sum
var count = inputData32.Count;
inputData32.Sort();
inputData64.Sort();
var index = 0;
if (count%2 == 0)
{
//Even number of elements
index = count/2 - 1;
}
else
{
//Number is odd
index = ((count + 1)/2) - 1;
}
var val32 = Convert.ToInt32(inputData32[index]);
var val64 = Convert.ToInt64(inputData64[index]);
if (i > 3810)
{
var t = sum;
var t1 = sum + val32;
}
medians32.Add(val32);
medians64.Add(val64);
//Debug.WriteLine("Median is {0}", val);
sum += val32;
sums32.Add(Convert.ToInt32(sum));
sum64 += val64;
sums64.Add(Convert.ToInt64(sum64));
i++;
}
Console.WriteLine("Median Maintenance result is {0}", (sum).ToString("N"));
Console.WriteLine("Median Maintenance result is {0}", (medians32.Sum()).ToString("N"));
Console.WriteLine("Median Maintenance result is {0} - Int64", (sum64).ToString("N"));
Console.WriteLine("Median Maintenance result is {0} - Int64", (medians64.Sum()).ToString("N"));
}
What's more interesting is that the running sum (in the sum64 variable) yields a different result than summing all items in the list with LINQ's Sum() function.
The results (the thirs one is the one that's wrong):
These are the computer details:
I'll appreciate if someone can give me some insights on why is this happening.
Thanks,
0f is initializing a 32 bit float variable, you meant 0d or 0.0 to receive a 64 bit floating point.
As for linq, you'll probably get better results if you use strongly typed lists.
new List<int>()
new List<long>()
The first thing I notice is what the commenter did: var sum64 = 0f initializes sum64 as a float. As the median value of a collection of Int64s will itself be an Int64 (the specified rules don't use the mean between two midpoint values in a collection of even cardinality), you should instead declare this variable explicitly as a long. In fact, I would go ahead and replace all usages of var in this code example; the convenience of var is being lost here in causing type-related bugs.
I'm implementing the K-nearest neighbours classification algorithm in C# for a training and testing set of about 20,000 samples each, and 25 dimensions.
There are only two classes, represented by '0' and '1' in my implementation. For now, I have the following simple implementation :
// testSamples and trainSamples consists of about 20k vectors each with 25 dimensions
// trainClasses contains 0 or 1 signifying the corresponding class for each sample in trainSamples
static int[] TestKnnCase(IList<double[]> trainSamples, IList<double[]> testSamples, IList<int[]> trainClasses, int K)
{
Console.WriteLine("Performing KNN with K = "+K);
var testResults = new int[testSamples.Count()];
var testNumber = testSamples.Count();
var trainNumber = trainSamples.Count();
// Declaring these here so that I don't have to 'new' them over and over again in the main loop,
// just to save some overhead
var distances = new double[trainNumber][];
for (var i = 0; i < trainNumber; i++)
{
distances[i] = new double[2]; // Will store both distance and index in here
}
// Performing KNN ...
for (var tst = 0; tst < testNumber; tst++)
{
// For every test sample, calculate distance from every training sample
Parallel.For(0, trainNumber, trn =>
{
var dist = GetDistance(testSamples[tst], trainSamples[trn]);
// Storing distance as well as index
distances[trn][0] = dist;
distances[trn][1] = trn;
});
// Sort distances and take top K (?What happens in case of multiple points at the same distance?)
var votingDistances = distances.AsParallel().OrderBy(t => t[0]).Take(K);
// Do a 'majority vote' to classify test sample
var yea = 0.0;
var nay = 0.0;
foreach (var voter in votingDistances)
{
if (trainClasses[(int)voter[1]] == 1)
yea++;
else
nay++;
}
if (yea > nay)
testResults[tst] = 1;
else
testResults[tst] = 0;
}
return testResults;
}
// Calculates and returns square of Euclidean distance between two vectors
static double GetDistance(IList<double> sample1, IList<double> sample2)
{
var distance = 0.0;
// assume sample1 and sample2 are valid i.e. same length
for (var i = 0; i < sample1.Count; i++)
{
var temp = sample1[i] - sample2[i];
distance += temp * temp;
}
return distance;
}
This takes quite a bit of time to execute. On my system it takes about 80 seconds to complete. How can I optimize this, while ensuring that it would also scale to larger number of data samples? As you can see, I've tried using PLINQ and parallel for loops, which did help (without these, it was taking about 120 seconds). What else can I do?
I've read about KD-trees being efficient for KNN in general, but every source I read stated that they're not efficient for higher dimensions.
I also found this stackoverflow discussion about this, but it seems like this is 3 years old, and I was hoping that someone would know about better solutions to this problem by now.
I've looked at machine learning libraries in C#, but for various reasons I don't want to call R or C code from my C# program, and some other libraries I saw were no more efficient than the code I've written. Now I'm just trying to figure out how I could write the most optimized code for this myself.
Edited to add - I cannot reduce the number of dimensions using PCA or something. For this particular model, 25 dimensions are required.
Whenever you are attempting to improve the performance of code, the first step is to analyze the current performance to see exactly where it is spending its time. A good profiler is crucial for this. In my previous job I was able to use the dotTrace profiler to good effect; Visual Studio also has a built-in profiler. A good profiler will tell you exactly where you code is spending time method-by-method or even line-by-line.
That being said, a few things come to mind in reading your implementation:
You are parallelizing some inner loops. Could you parallelize the outer loop instead? There is a small but nonzero cost associated to a delegate call (see here or here) which may be hitting you in the "Parallel.For" callback.
Similarly there is a small performance penalty for indexing through an array using its IList interface. You might consider declaring the array arguments to "GetDistance()" explicitly.
How large is K as compared to the size of the training array? You are completely sorting the "distances" array and taking the top K, but if K is much smaller than the array size it might make sense to use a partial sort / selection algorithm, for instance by using a SortedSet and replacing the smallest element when the set size exceeds K.
I am looking for a concise way to generate an array of integers 1 to 100 in c# , ie
int[] values = {1,2,3 ..., 100};
so that I can use the array in a foreach loop:
foreach (var i in values)
{
// do whatever
}
Any ideas?
Using Enumerable.Range:
Enumerable.Range(1, 100).ToArray();
There's probably not much point in me putting this - Oded's 18 votes (including my own +1) pretty much say it all, but just to point out that if you're then going to use the integers in the array, to produce something - let's say an object - then you can roll the whole thing up.
So, let's say you want some strings:
var strings = Enumerable.Range(1,100)
.Select(i => i.ToString()).ToArray();
Gives you an array of strings.
Or perhaps a MyWidget that's produced from a method call, which uses the index for something:
var myWidgets = Enumerable.Range(1,100)
.Select(i => ProduceMyWidget(i)).ToArray();
If the original foreach code block was more than one line of code - then you just use a {} block
var myWidgets = Enumerable.Range(1,100)
.Select(i => {
if(i == 57)
return ProduceSpecial57Widget(i);
else
ProduceByWidget(i);
}).ToArray();
Obviously this last example is a bit silly - but it illustrates often how a traditional foreach can be replaced with a Select plus ToArray() call.
The following class parses through a very large string (an entire novel of text) and breaks it into consecutive 4-character strings that are stored as a Tuple. Then each tuple can be assigned a probability based on a calculation. I am using this as part of a monte carlo/ genetic algorithm to train the program to recognize a language based on syntax only (just the character transitions).
I am wondering if there is a faster way of doing this. It takes about 400ms to look up the probability of any given 4-character tuple. The relevant method _Probablity() is at the end of the class.
This is a computationally intensive problem related to another post of mine: Algorithm for computing the plausibility of a function / Monte Carlo Method
Ultimately I'd like to store these values in a 4d-matrix. But given that there are 26 letters in the alphabet that would be a HUGE task. (26x26x26x26). If I take only the first 15000 characters of the novel then performance improves a ton, but my data isn't as useful.
Here is the method that parses the text 'source':
private List<Tuple<char, char, char, char>> _Parse(string src)
{
var _map = new List<Tuple<char, char, char, char>>();
for (int i = 0; i < src.Length - 3; i++)
{
int j = i + 1;
int k = i + 2;
int l = i + 3;
_map.Add
(new Tuple<char, char, char, char>(src[i], src[j], src[k], src[l]));
}
return _map;
}
And here is the _Probability method:
private double _Probability(char x0, char x1, char x2, char x3)
{
var subset_x0 = map.Where(x => x.Item1 == x0);
var subset_x0_x1_following = subset_x0.Where(x => x.Item2 == x1);
var subset_x0_x2_following = subset_x0_x1_following.Where(x => x.Item3 == x2);
var subset_x0_x3_following = subset_x0_x2_following.Where(x => x.Item4 == x3);
int count_of_x0 = subset_x0.Count();
int count_of_x1_following = subset_x0_x1_following.Count();
int count_of_x2_following = subset_x0_x2_following.Count();
int count_of_x3_following = subset_x0_x3_following.Count();
decimal p1;
decimal p2;
decimal p3;
if (count_of_x0 <= 0 || count_of_x1_following <= 0 || count_of_x2_following <= 0 || count_of_x3_following <= 0)
{
p1 = e;
p2 = e;
p3 = e;
}
else
{
p1 = (decimal)count_of_x1_following / (decimal)count_of_x0;
p2 = (decimal)count_of_x2_following / (decimal)count_of_x1_following;
p3 = (decimal)count_of_x3_following / (decimal)count_of_x2_following;
p1 = (p1 * 100) + e;
p2 = (p2 * 100) + e;
p3 = (p3 * 100) + e;
}
//more calculations omitted
return _final;
}
}
EDIT - I'm providing more details to clear things up,
1) Strictly speaking I've only worked with English so far, but its true that different alphabets will have to be considered. Currently I only want the program to recognize English, similar to whats described in this paper: http://www-stat.stanford.edu/~cgates/PERSI/papers/MCMCRev.pdf
2) I am calculating the probabilities of n-tuples of characters where n <= 4. For instance if I am calculating the total probability of the string "that", I would break it down into these independent tuples and calculate the probability of each individually first:
[t][h]
[t][h][a]
[t][h][a][t]
[t][h] is given the most weight, then [t][h][a], then [t][h][a][t]. Since I am not just looking at the 4-character tuple as a single unit, I wouldn't be able to just divide the instances of [t][h][a][t] in the text by the total no. of 4-tuples in the next.
The value assigned to each 4-tuple can't overfit to the text, because by chance many real English words may never appear in the text and they shouldn't get disproportionally low scores. Emphasing first-order character transitions (2-tuples) ameliorates this issue. Moving to the 3-tuple then the 4-tuple just refines the calculation.
I came up with a Dictionary that simply tallies the count of how often the tuple occurs in the text (similar to what Vilx suggested), rather than repeating identical tuples which is a waste of memory. That got me from about ~400ms per lookup to about ~40ms per, which is a pretty great improvement. I still have to look into some of the other suggestions, however.
In yoiu probability method you are iterating the map 8 times. Each of your wheres iterates the entire list and so does the count. Adding a .ToList() ad the end would (potentially) speed things. That said I think your main problem is that the structure you've chossen to store the data in is not suited for the purpose of the probability method. You could create a one pass version where the structure you store you're data in calculates the tentative distribution on insert. That way when you're done with the insert (which shouldn't be slowed down too much) you're done or you could do as the code below have a cheap calculation of the probability when you need it.
As an aside you might want to take puntuation and whitespace into account. The first letter/word of a sentence and the first letter of a word gives clear indication on what language a given text is written in by taking punctuaion charaters and whitespace as part of you distribution you include those characteristics of the sample data. We did that some years back. Doing that we shown that using just three characters was almost as exact (we had no failures with three on our test data and almost as exact is an assumption given that there most be some weird text where the lack of information would yield an incorrect result). as using more (we test up till 7) but the speed of three letters made that the best case.
EDIT
Here's an example of how I think I would do it in C#
class TextParser{
private Node Parse(string src){
var top = new Node(null);
for (int i = 0; i < src.Length - 3; i++){
var first = src[i];
var second = src[i+1];
var third = src[i+2];
var fourth = src[i+3];
var firstLevelNode = top.AddChild(first);
var secondLevelNode = firstLevelNode.AddChild(second);
var thirdLevelNode = secondLevelNode.AddChild(third);
thirdLevelNode.AddChild(fourth);
}
return top;
}
}
public class Node{
private readonly Node _parent;
private readonly Dictionary<char,Node> _children
= new Dictionary<char, Node>();
private int _count;
public Node(Node parent){
_parent = parent;
}
public Node AddChild(char value){
if (!_children.ContainsKey(value))
{
_children.Add(value, new Node(this));
}
var levelNode = _children[value];
levelNode._count++;
return levelNode;
}
public decimal Probability(string substring){
var node = this;
foreach (var c in substring){
if(!node.Contains(c))
return 0m;
node = node[c];
}
return ((decimal) node._count)/node._parent._children.Count;
}
public Node this[char value]{
get { return _children[value]; }
}
private bool Contains(char c){
return _children.ContainsKey(c);
}
}
the usage would then be:
var top = Parse(src);
top.Probability("test");
I would suggest changing the data structure to make that faster...
I think a Dictionary<char,Dictionary<char,Dictionary<char,Dictionary<char,double>>>> would be much more efficient since you would be accessing each "level" (Item1...Item4) when calculating... and you would cache the result in the innermost Dictionary so next time you don't have to calculate at all..
Ok, I don't have time to work out details, but this really calls for
neural classifier nets (Just take any off the shelf, even the Controllable Regex Mutilator would do the job with way more scalability) -- heuristics over brute force
you could use tries (Patricia Tries a.k.a. Radix Trees to make a space optimized version of your datastructure that can be sparse (the Dictionary of Dictionaries of Dictionaries of Dictionaries... looks like an approximation of this to me)
There's not much you can do with the parse function as it stands. However, the tuples appear to be four consecutive characters from a large body of text. Why not just replace the tuple with an int and then use the int to index the large body of text when you need the character values. Your tuple based method is effectively consuming four times the memory the original text would use, and since memory is usually the bottleneck to performance, it's best to use as little as possible.
You then try to find the number of matches in the body of text against a set of characters. I wonder how a straightforward linear search over the original body of text would compare with the linq statements you're using? The .Where will be doing memory allocation (which is a slow operation) and the linq statement will have parsing overhead (but the compiler might do something clever here). Having a good understanding of the search space will make it easier to find an optimal algorithm.
But then, as has been mentioned in the comments, using a 264 matrix would be the most efficent. Parse the input text once and create the matrix as you parse. You'd probably want a set of dictionaries:
SortedDictionary <int,int> count_of_single_letters; // key = single character
SortedDictionary <int,int> count_of_double_letters; // key = char1 + char2 * 32
SortedDictionary <int,int> count_of_triple_letters; // key = char1 + char2 * 32 + char3 * 32 * 32
SortedDictionary <int,int> count_of_quad_letters; // key = char1 + char2 * 32 + char3 * 32 * 32 + char4 * 32 * 32 * 32
Finally, a note on data types. You're using the decimal type. This is not an efficient type as there is no direct mapping to CPU native type and there is overhead in processing the data. Use a double instead, I think the precision will be sufficient. The most precise way will be to store the probability as two integers, the numerator and denominator and then do the division as late as possible.
The best approach here is to using sparse storage and pruning after each each 10000 character for example. Best storage strucutre in this case is prefix tree, it will allow fast calculation of probability, updating and sparse storage. You can find out more theory in this javadoc http://alias-i.com/lingpipe/docs/api/com/aliasi/lm/NGramProcessLM.html
Let's assume we have a large list of points List<Point> pointList (already stored in memory) where each Point contains X, Y, and Z coordinate.
Now, I would like to select for example N% of points with biggest Z-values of all points stored in pointList. Right now I'm doing it like that:
N = 0.05; // selecting only 5% of points
double cutoffValue = pointList
.OrderBy(p=> p.Z) // First bottleneck - creates sorted copy of all data
.ElementAt((int) pointList.Count * (1 - N)).Z;
List<Point> selectedPoints = pointList.Where(p => p.Z >= cutoffValue).ToList();
But I have here two memory usage bottlenecks: first during OrderBy (more important) and second during selecting the points (this is less important, because we usually want to select only small amount of points).
Is there any way of replacing OrderBy (or maybe other way of finding this cutoff point) with something that uses less memory?
The problem is quite important, because LINQ copies the whole dataset and for big files I'm processing it sometimes hits few hundreds of MBs.
Write a method that iterates through the list once and maintains a set of the M largest elements. Each step will only require O(log M) work to maintain the set, and you can have O(M) memory and O(N log M) running time.
public static IEnumerable<TSource> TakeLargest<TSource, TKey>
(this IEnumerable<TSource> items, Func<TSource, TKey> selector, int count)
{
var set = new SortedDictionary<TKey, List<TSource>>();
var resultCount = 0;
var first = default(KeyValuePair<TKey, List<TSource>>);
foreach (var item in items)
{
// If the key is already smaller than the smallest
// item in the set, we can ignore this item
var key = selector(item);
if (first.Value == null ||
resultCount < count ||
Comparer<TKey>.Default.Compare(key, first.Key) >= 0)
{
// Add next item to set
if (!set.ContainsKey(key))
{
set[key] = new List<TSource>();
}
set[key].Add(item);
if (first.Value == null)
{
first = set.First();
}
// Remove smallest item from set
resultCount++;
if (resultCount - first.Value.Count >= count)
{
set.Remove(first.Key);
resultCount -= first.Value.Count;
first = set.First();
}
}
}
return set.Values.SelectMany(values => values);
}
That will include more than count elements if there are ties, as your implementation does now.
You could sort the list in place, using List<T>.Sort, which uses the Quicksort algorithm. But of course, your original list would be sorted, which is perhaps not what you want...
pointList.Sort((a, b) => b.Z.CompareTo(a.Z));
var selectedPoints = pointList.Take((int)(pointList.Count * N)).ToList();
If you don't mind the original list being sorted, this is probably the best balance between memory usage and speed
You can use Indexed LINQ to put index on the data which you are processing. This can result in noticeable improvements in some cases.
If you combine the two there is a chance a little less work will be done:
List<Point> selectedPoints = pointList
.OrderByDescending(p=> p.Z) // First bottleneck - creates sorted copy of all data
.Take((int) pointList.Count * N);
But basically this kind of ranking requires sorting, your biggest cost.
A few more ideas:
if you use a class Point (instead of a struct Point) there will be much less copying.
you could write a custom sort that only bothers to move the top 5% up. Something like (don't laugh) BubbleSort.
If your list is in memory already, I would sort it in place instead of making a copy - unless you need it un-sorted again, that is, in which case you'll have to weigh having two copies in memory vs loading it again from storage):
pointList.Sort((x,y) => y.Z.CompareTo(x.Z)); //this should sort it in desc. order
Also, not sure how much it will help, but it looks like you're going through your list twice - once to find the cutoff value, and once again to select them. I assume you're doing that because you want to let all ties through, even if it means selecting more than 5% of the points. However, since they're already sorted, you can use that to your advantage and stop when you're finished.
double cutoffValue = pointlist[(int) pointList.Length * (1 - N)].Z;
List<point> selectedPoints = pointlist.TakeWhile(p => p.Z >= cutoffValue)
.ToList();
Unless your list is extremely large, it's much more likely to me that cpu time is your performance bottleneck. Yes, your OrderBy() might use a lot of memory, but it's generally memory that for the most part is otherwise sitting idle. The cpu time really is the bigger concern.
To improve cpu time, the most obvious thing here is to not use a list. Use an IEnumerable instead. You do this by simply not calling .ToList() at the end of your where query. This will allow the framework to combine everything into one iteration of the list that runs only as needed. It will also improve your memory use because it avoids loading the entire query into memory at once, and instead defers it to only load one item at a time as needed. Also, use .Take() rather than .ElementAt(). It's a lot more efficient.
double N = 0.05; // selecting only 5% of points
int count = (1-N) * pointList.Count;
var selectedPoints = pointList.OrderBy(p=>p.Z).Take(count);
That out of the way, there are three cases where memory use might actually be a problem:
Your collection really is so large as to fill up memory. For a simple Point structure on a modern system we're talking millions of items. This is really unlikely. On the off chance you have a system this large, your solution is to use a relational database, which can keep this items on disk relatively efficiently.
You have a moderate size collection, but there are external performance constraints, such as needing to share system resources with many other processes as you might find in an asp.net web site. In this case, the answer is either to 1) again put the points in a relational database or 2) offload the work to the client machines.
Your collection is just large enough to end up on the Large Object Heap, and the HashSet used in the OrderBy() call is also placed on the LOH. Now what happens is that the garbage collector will not properly compact memory after your OrderBy() call, and over time you get a lot of memory that is not used but still reserved by your program. In this case, the solution is, unfortunately, to break your collection up into multiple groups that are each individually small enough not to trigger use of the LOH.
Update:
Reading through your question again, I see you're reading very large files. In that case, the best performance can be obtained by writing your own code to parse the files. If the count of items is stored near the top of the file you can do much better, or even if you can estimate the number of records based on the size of the file (guess a little high to be sure, and then truncate any extras after finishing), you can then build your final collection as your read. This will greatly improve cpu performance and memory use.
I'd do it by implementing "half" a quicksort.
Consider your original set of points, P, where you are looking for the "top" N items by Z coordinate.
Choose a pivot x in P.
Partition P into L = {y in P | y < x} and U = {y in P | x <= y}.
If N = |U| then you're done.
If N < |U| then recurse with P := U.
Otherwise you need to add some items to U: recurse with N := N - |U|, P := L to add the remaining items.
If you choose your pivot wisely (e.g., median of, say, five random samples) then this will run in O(n log n) time.
Hmmmm, thinking some more, you may be able to avoid creating new sets altogether, since essentially you're just looking for an O(n log n) way of finding the Nth greatest item from the original set. Yes, I think this would work, so here's suggestion number 2:
Make a traversal of P, finding the least and greatest items, A and Z, respectively.
Let M be the mean of A and Z (remember, we're only considering Z coordinates here).
Count how many items there are in the range [M, Z], call this Q.
If Q < N then the Nth greatest item in P is somewhere in [A, M). Try M := (A + M)/2.
If N < Q then the Nth greatest item in P is somewhere in [M, Z]. Try M := (M + Z)/2.
Repeat until we find an M such that Q = N.
Now traverse P, removing all items greater than or equal to M.
That's definitely O(n log n) and creates no extra data structures (except for the result).
Howzat?
You might use something like this:
pointList.Sort(); // Use you own compare here if needed
// Skip OrderBy because the list is sorted (and not copied)
double cutoffValue = pointList.ElementAt((int) pointList.Length * (1 - N)).Z;
// Skip ToList to avoid another copy of the list
IEnumerable<Point> selectedPoints = pointList.Where(p => p.Z >= cutoffValue);
If you want a small percentage of points ordered by some criterion, you'll be better served using a Priority queue data structure; create a size-limited queue(with the size set to however many elements you want), and then just scan through the list inserting every element. After the scan, you can pull out your results in sorted order.
This has the benefit of being O(n log p) instead of O(n log n) where p is the number of points you want, and the extra storage cost is also dependent on your output size instead of the whole list.
int resultSize = pointList.Count * (1-N);
FixedSizedPriorityQueue<Point> q =
new FixedSizedPriorityQueue<Point>(resultSize, p => p.Z);
q.AddEach(pointList);
List<Point> selectedPoints = q.ToList();
Now all you have to do is implement a FixedSizedPriorityQueue that adds elements one at a time and discards the largest element when it is full.
You wrote, you are working with a DataSet. If so, you can use DataView to sort your data once and use them for all future accessing the rows.
Just tried with 50,000 rows and 100 times accessing 30% of them. My performance results are:
Sort With Linq: 5.3 seconds
Use DataViews: 0.01 seconds
Give it a try.
[TestClass]
public class UnitTest1 {
class MyTable : TypedTableBase<MyRow> {
public MyTable() {
Columns.Add("Col1", typeof(int));
Columns.Add("Col2", typeof(int));
}
protected override DataRow NewRowFromBuilder(DataRowBuilder builder) {
return new MyRow(builder);
}
}
class MyRow : DataRow {
public MyRow(DataRowBuilder builder) : base(builder) {
}
public int Col1 { get { return (int)this["Col1"]; } }
public int Col2 { get { return (int)this["Col2"]; } }
}
DataView _viewCol1Asc;
DataView _viewCol2Desc;
MyTable _table;
int _countToTake;
[TestMethod]
public void MyTestMethod() {
_table = new MyTable();
int count = 50000;
for (int i = 0; i < count; i++) {
_table.Rows.Add(i, i);
}
_countToTake = _table.Rows.Count / 30;
Console.WriteLine("SortWithLinq");
RunTest(SortWithLinq);
Console.WriteLine("Use DataViews");
RunTest(UseSoredDataViews);
}
private void RunTest(Action method) {
int iterations = 100;
Stopwatch watch = Stopwatch.StartNew();
for (int i = 0; i < iterations; i++) {
method();
}
watch.Stop();
Console.WriteLine(" {0}", watch.Elapsed);
}
private void UseSoredDataViews() {
if (_viewCol1Asc == null) {
_viewCol1Asc = new DataView(_table, null, "Col1 ASC", DataViewRowState.Unchanged);
_viewCol2Desc = new DataView(_table, null, "Col2 DESC", DataViewRowState.Unchanged);
}
var rows = _viewCol1Asc.Cast<DataRowView>().Take(_countToTake).Select(vr => (MyRow)vr.Row);
IterateRows(rows);
rows = _viewCol2Desc.Cast<DataRowView>().Take(_countToTake).Select(vr => (MyRow)vr.Row);
IterateRows(rows);
}
private void SortWithLinq() {
var rows = _table.OrderBy(row => row.Col1).Take(_countToTake);
IterateRows(rows);
rows = _table.OrderByDescending(row => row.Col2).Take(_countToTake);
IterateRows(rows);
}
private void IterateRows(IEnumerable<MyRow> rows) {
foreach (var row in rows)
if (row == null)
throw new Exception("????");
}
}