How to make an n-dimensional datastructure within lab.open-roberta.org using calliope as system? It seems there is no way to declare a variable as a list within a list. As a workaround one could think of making a list of String and then read the characters the string consists of - but it seems there is no way to do that either.
The Image is a fine matrix but it's fixed to 5x5 elements.
Has anybody an idea how to make a more complex datastructure (I know an n-dimensional list can be stored in a one-dimesional list but it's so hard to read that as a human).
Related
I am writing a Huffman Coding/Decoding algorithm and I am running into the problem that the storing the Huffman tree is taking up way to much room. Currently, I am converting the tree into a hashMap as such -> hashMap<Character(s),Huffman Code> and then storing that hash map. The issue is that, while the string is compressed great, adding the Huffman Tree data stored in the hash map is adding so much overhead that it's actually ending up bigger than the original. Currently I am just naively writing [data, value] pairs to the file, but I imagine there must be some sort of trickier way to do that. Any ideas?
You do not need the tree in order to encode. All you need is the bit lengths for each symbol and a way to order the symbols. See Canonical Huffman Code.
In fact, all you need is the symbols that are coded ordered by bit length, and within bit length sorted by symbol, and then the number of codes of each length. With just those two things you can encode.
I have a function that predicts a words being typed and returns the possibilities in an array. Unfortunately those aren’t sorted by frequency used. So I have a list of 10K ordered words listed by most frequent to less frequent. What would be an efficient way to compare the words in the array and the ordered list to return the most frequent one? (i.e the one it encounters first?)
I was tipped off by a friend to use a binary search tree but I really don't see how that helps me. From what I understood from the following website, only numerical values can be used.. Am I wrong in thinking so? Is there a better way of doing the aforementioned task?
Thanks in advance
You could create a dictionary with words as keys and frequencies as values. Then iterate over your result array, use the dictionary to obtain the frequency value for each item, and predict the item with the highest frequency.
I wouldn't use a vanilla binary search tree here. It would be possible - as Taylor Kirkpatrick says, you could just create a tree with words as keys and frequencies and use that to find the frequency for each result word, in much the same way as the dictionary solution.
The problem is that you cannot guarantee that a simple binary tree will be balanced. From the sound of it your data would probably be OK, since your words are in frequency order. The worst case would be if the words were in alphabetic order - then your binary tree would end up being identical to a linked list - it would never branch, since every node would attach to the right of the previous one. So the computational complexity of a search would be the same as iterating over the array of words - O(n) instead of O(log2N) (which is the best case for binary trees).
Of course, you could guard against this by randomising the list of words before doing the insert. But to my mind it's just easier to use a dictionary. I don't know what the actual implementation of Swift dictionaries is (and we won't until they open source it in a couple of months), but you can take it as read that it will out perform a vanilla BT for value retrieval.
I don't know what the background to this problem is - if you are learning CS it might be worth implementing the BST just for intellectual growth - in this case, with only 10,000 items you might find the performance differences are ultimately quite small. But if you are a working programmer trying to solve a problem, go with the dictionary approach.
You put all your words into a dictionary or a set. That's it. Dictionary if you have data associated with the words, set if you have no data and just want to know if the word is in the list or not.
You might want to use a Trie.
Put your word list into it. For every character entered, you traverse the Trie as deeply as you can and then show all paths to leaf nodes as possible completions.
Since the world like you have is likely static, you can precompute the Trie and load from disk/network/whatever at startup if performance is a concern.
You can use a binary search tree with anything as the actual value. To actually make use of the tree, use the frequency of the words as the numerical value. This is actually a pretty good solution to your problem. Each node of the tree will contain this word and a numeric value that represents the frequency of the word.
Here are a few links to help you out with making it.
Hope that helps.
I'm trying to learn how to think in a functional programming way, for this, I'm trying to learn Erlang and solving easy problems from codingbat. I came with the common problem of comparing elements inside a list. For example, compare a value of the i-th position element with the value of the i+1-th position of the list. So, I have been thinking and searching how to do this in a functional way in Erlang (or any functional language).
Please, be gentle with me, I'm very newb in this functional world, but I want to learn
Thanks in advance
Define a list:
L = [1,2,3,4,4,5,6]
Define a function f, which takes a list
If it matches a list of one element or an empty list, return the empty list
If it matches the first element and the second element then take the first element and construct a new list by calling the rest of the list recursivly
Otherwise skip the first element of the list.
In Erlang code
f ([]) -> [];
f ([_]) -> [];
f ([X, X|Rest]) -> [X | f(Rest)];
f ([_|Rest]) -> f(Rest).
Apply function
f(L)
This should work... haven't compiled and run it but it should get you started. Also in case you need to do modifications to it to behave differently.
Welcome to Erlang ;)
I try to be gentle ;-) So main thing in functional approach is thinking in terms: What is input? What should be output? There is nothing like comparing the i-th element with the i+1-th element alone. There have to be always purpose of it which will lead to data transformation. Even Mazen Harake's example doing it. In this example there is function which returns only elements which are followed by same value i.e. filters given list. Typically there are very different ways how do similar thing which depends of purpose of it. List is basic functional structure and you can do amazing things with it as Lisp shows us but you have to remember it is not array.
Each time you need access i-th element repeatable it indicates you are using wrong data structure. You can build up different data structures form lists and tuples in Erlang which can serve your purposes better. So when you face problem to compare i-th with i+1-th element you should stop and think. What is purpose of it? Do you need perform some stream data transformation as Mazen Harake does or You need random access? If second you should use different data structure (array for example). Even then you should think about your task characteristics. If you will be mostly read and almost never write then you can use list_to_tuple(L) and then read using element/2. When you need write occasionally you will start thinking about partition it to several tuples and as your write ratio will grow you will end up with array implementation.
So you can use lists:nth/2 if you will do it only once or several times but on short list and you are not performance freak as I'm. You can improve it using [X1,X2|_] = lists:nthtail(I-1, L) (L = lists:nthtail(0,L) works as expected). If you are facing bigger lists and you want call it many times you have to rethink your approach.
P.S.: There are many other fascinating data structures except lists and trees. Zippers for example.
If I want to make a time series type in F# to hold stock prices, which basic type should I use? We need
Select a subset based on time index,
Calculate basic statistics for a subset like mean, STD or for several subsets like correlations,
Append item for new data and fast update statistics or technical indicators,
Do linear regression between time series, etc
I have read that array has a better performance, seq has a smaller memory footnote, list is better for adding items and F# vector is easier for certain math calculation. To balance all the trade offs, how would you model a stock price time series in f#? Thanks.
As a concrete representation you can choose either array or list or some other .NET colllection type. A sequence seq<'T> is an abstract type and both array and list are automatically also sequences - this means that when you write some code that works with sequences, it will work with any concrete data type (array, list or any other .NET collection).
So, when writing data processing, you can use Seq by default (as it gives you great flexibility - it doesn't matter what concrete representation you use) and then optimize some operations to use the concrete representation (whatever that will be) if you need something to run faster.
Regarding the concrete representation - I think the crucial question is whether you want to add elements without changing original data structure (immutable list or array used in an immutable way) or whether you want to mutate the data structure (e.g. use some mutable .NET collection).
If you need to add new items freuqently then you can either use immutable list (which supports appending elements to front) or a mutable collection (array won't do as it cannot be resized).
If you're working on a more sophisticated system, I would recommend taking a look at ObservableCollection<T> (see MSDN). This is a collection that automatically notifies you when it is changed. In response to the notification, you could update your statistics (it also tells you which elements were added, so you don't need to recalculate everything). However, F# doesn't have any libraries for working with this type, so you'll need to write a lot of things yourself.
If you're adding data only rarely or adding them in larger groups, you could use array (and allocate new array each time you add items). If you have only relatively small number of items in the collection, you could use lists (where adding item is easy).
For numerical calculations, the F# PowerPack (and types like vector) offer only quite limitied set of features, so you may need to look at some thrid party libraries. Extreme optimizations is a commercial library with some F# examples and Math.NET is an open source alternative.
Otherwise, it is difficult to give any concrete advice - can you add some more details about your system? (e.g. how large the data set is, how many items need to be added how often etc...)
I have an application for storing many strings in a TStringList. The strings will be largely similar to one another and it occurs to me that one could compress them on the fly - i.e. store a given string in terms of a mixture of unique text fragments plus references to previously stored fragments. StringLists such as lists of fully-qualified path and filenames should be able to be compressed greatly.
Does anyone know of a TStringlist descendant that implement this - i.e. provides read and write access to the uncompressed strings but stores them internally compressed, so that a TStringList.SaveToFile produces a compressed file?
While you could implement this by uncompressing the entire stringlist before each access and re-compressing it afterwards, it would be unnecessarily slow. I'm after something that is efficient for incremental operations and random "seeks" and reads.
TIA
Ross
I don't think there's any freely available implementation around for this (not that I know of anyway, although I've written at least 3 similar constructs in commercial code), so you'd have to roll your own.
The remark Marcelo made about adding items in order is very relevant, as I suppose you'll probably want to compress the data at addition time - having quick access to entries already similar to the one being added, gives a much better performance than having to look up a 'best fit entry' (needed for similarity-compression) over the entire set.
Another thing you might want to read up about, are 'ropes' - a conceptually different type than strings, which I already suggested to Marco Cantu a while back. At the cost of a next-pointer per 'twine' (for lack of a better word) you can concatenate parts of a string without keeping any duplicate data around. The main problem is how to retrieve the parts that can be combined into a new 'rope', representing your original string. Once that problem is solved, you can reconstruct the data as a string at any time, while still having compact storage.
If you don't want to go the 'rope' route, you could also try something called 'prefix reduction', which is a simple form of compression - just start out each string with an index of a previous string and the number of characters that should be treated as a prefix for the new string. Be aware that you should not recurse this too far back, or access-speed will suffer greatly. In one simple implementation, I did a mod 16 on the index, to establish the entry at which prefix-reduction started, which gave me on average about 40% memory savings (this number is completely data-dependant of course).
You could try to wrap a Delphi or COM API around Judy arrays. The JudySL type would do the trick, and has a fairly simple interface.
EDIT: I assume you are storing unique strings and want to (or are happy to) store them in lexicographical order. If these constraints aren't acceptable, then Judy arrays are not for you. Mind you, any compression system will suffer if you don't sort your strings.
I suppose you expect general flexibility from the list (including delete operation), in this case I don't know about any out of the box solution, but I'd suggest one of the two approaches:
You split your string into words and
keep separated growning dictionary
to reference the words and save list of indexes internally
You implement something related to
zlib stream available in Delphi, but operating by the block that
for example can contains 10-100
strings. In this case you still have
to recompress/compress the complete
block, but the "price" you pay is lower.
I dont think you really want to compress TStrings items in memory, because it terribly ineffecient. I suggest you to look at TStream implementation in Zlib unit. Just wrap regular stream into TDecompressionStream on load and TCompressionStream on save (you can even emit gzip header there).
Hint: you will want to override LoadFromStream/SaveToStream instead of LoadFromFile/SaveToFile