I have a stream of vectors ... then i have N number of vectors to best represent this stream. (smallest dist )
To do that I can use algorithm similar to Self Organizing Maps.. i.e. pick the closest vector and move it closer to the data vector. rinse and repeat.
Now my question is:
I want the number of vectors N to be variable and dynamic. For this I will probably need to maximize/minimize some external function ?
What will that function be ?
I will also probably need rules for when to : Merging, Creating or Deleting vectors
Related
As part of a larger algorithm, I need to produce the residuals of an array relative to a specified limit. In other words, I need to produce an array which, given someArray, comprises elements which encode the amount by which the corresponding element of someArray exceeds a limit value. My initial inclination was to use a distributed comparison to determine when a value has exceeded the threshold. As follows:
# Generate some test data.
residualLimit = 1
someArray = 2.1.*(rand(10,10,3).-0.5)
# Determine the residuals.
someArrayResiduals = (residualLimit-someArray)[(residualLimit-someArray.<0)]
The problem is that the someArrayResiduals is a one-dimensional vector containing the residual values, rather than a mask of (residualLimit-someArray). If you check [(residualLimit-someArray.<0)] you'll find that it is behaving as expected; it's producing a BitArray. The question is, why doesn't Julia allow to use this BitArray to mask someArray?
Casting the Bools in the BitArray to Ints using int() and distributing using .*produces the desired result, but is a bit inelegant... See the following:
# Generate some test data.
residualLimit = 1
someArray = 2.1.*(rand(10,10,3).-0.5)
# Determine the residuals.
someArrayResiduals = (residualLimit-someArray).*int(residualLimit-someArray.<0)
# This array should be (and is) limited at residualLimit. This is correct...
someArrayLimited = someArray + someArrayResiduals
Anyone know why a BitArray can't be used to mask an array? Or, any way that this entire process can be simplified?
Thanks, all!
Indexing with a logical array simply selects the elements at indices where the logical array is true. You can think of it as transforming the logical index array with find before doing the indexing expression. Note that this can be used in both array indexing and indexed assignment. These logical arrays are often themselves called masks, but indexing is more like a "selection" operation than a clamping operation.
The suggestions in the comments are good, but you can also solve your problem using logical indexing with indexed assignment:
overLimitMask = someArray .> residualLimit
someArray[overLimitMask] = residualLimit
In this case, though, I think the most readable way to solve this problem is with min or clamp: min(someArray, residualLimit) or clamp(someArray, -residualLimit, residualLimit)
I know Vector in C++ and Java, it's like dynamic Array, but I can't find any general definition of Vector data structure. So what is Vector? Is Vector a general data structure(like arrray, stack, queue, tree,...) or it just a data type depending on language?
The word "vector" as applied to computer science/programming is borrowed from math, which can make the use confusing (even your question could be on multiple subjects).
The simplest example of vectors in math is the number line, used to teach elementary math (especially to help visualize negative numbers, subtraction of negative numbers, addition of negative numbers, etc).
The vector is a distance and direction from a point. This is why it can confuse the discussion, because a vector data structure COULD be three points, X,Y,Z, in a structure used in 3D graphics engines, or a 2D point (just X,Y). In that context, the subtraction of two such points results in a vector - the vector describes how far and in what direction to travel from one of the source operands to the other.
This applies to storage, like stl vectors or Java vectors, in that storage is represented as a distance from an address (where a memory address is similar to a point in space, or on a number line).
The concept is related to arrays, because arrays could be the storage allocated for a vector, but I submit that the vector is a larger concept than the array. A vector must include the concept of distance from a starting point, and if you think of the beginning of an array as the starting point, the distance to the end of the array is it's size.
So, the data structure representing a vector must include the size, whereas an array doesn't have storage to include the size, it's assumed by the way it's allocated. That is to say, if you dynamically allocate an array, there is no data structure storing the size of that array, the programmer must assume to know that size, or store it in a some integer or long.
The vector data structure (say, the design of a vector class) DOES need to store the size, so at a minimum, there would be a starting point (the base of an array, or some address in memory) and a distance from that point indicating size.
That's really "RAM" oriented, though, in description, because there's one more point not yet described which must be part of the data describing the vector - the notion of element size. If a vector represents bytes, and memory storage is typically measured in bytes, an address and a distance (or size) would represent a vector of bytes, but nothing else - and that's a very machine level thinking. A higher thought, that of some structure, has it's own size - say, the size of a float or double, or of a structure or class in C++. Whatever the element size is, the memory required to store N of them requires that the vector data structure have some knowledge of WHAT it's storing, and how large that thing is. This is why you'd think in terms of "a vector of strings" or "a vector of points". A vector must also store an element size.
So, a basic vector data structure must have:
An address (the starting point)
An element size (each thing it stores is X bytes long)
A number of elements stored (how many elements times element size is 'minimum' storage size).
One important "assumption" made in this simple 3 item list of entries in the vector data structure is that the address is allocated memory, which must be freed at some point, and is to be guarded against access beyond the end of the vector.
That means there's something missing. In order to make a vector class work, there is a recognizable difference between the number of ITEMS stored in the vector, and the amount of memory ALLOCATED for that storage. Typically, as you might realize from the use of vector from the STL, it may "know" it has room to store 10 items, but currently only has 2 of them.
So, a working vector class would ALSO have to store the amount of memory allocation. This would be how it could dynamically extend itself - it would now have sufficient information to expand storage automatically.
Thinking through just how you would make a vector class operate gives you the structure of data required to operate a vector class.
It's an array with dynamically allocated space, everytime you exceed this space new place in memory is allocated and old array is copied to the new one. Old one is freed then.
Moreover, vector usually allocates more memory, than it needs to, so it does not have to copy all the data, when new element is added.
It may seem, that lists then are much much better, but it's not necessarily so. If you do not change your vector often (in terms of size), then computer's cache memory functions much better with vectors, than lists, because they are continuus in memory space. Disadvantage is when you have large vector, that you need to expand. Then you have to agree to copy large amount of data to another space in memory.
What's more. You can add new data to the end and to the front of the vector. Because Vector's are array-like, then every time you want to add element to the beginning of the vector all the array has to be copied. Adding elements to the end of vector is far more efficient. There's no such an issue with linked lists.
Vector gives random access to it's internal kept data, while lists,queues,stacks do not.
Vectors are the same as dynamic arrays with the ability to resize
itself automatically when an element is inserted or deleted.
Vector elements are placed in contiguous storage so that they can be
accessed and traversed using iterators.
In vectors, data is inserted at the end.
Working through the first edition of "Introduction to Functional Programming", by Bird & Wadler, which uses a theoretical lazy language with Haskell-ish syntax.
Exercise 3.2.3 asks:
Using a list comprehension, define a function for counting the number
of negative numbers in a list
Now, at this point we're still scratching the surface of lists. I would assume the intention is that only concepts that have been introduced at that point should be used, and the following have not been introduced yet:
A function for computing list length
List indexing
Pattern matching i.e. f (x:xs) = ...
Infinite lists
All the functions and operators that act on lists - with one exception - e.g. ++, head, tail, map, filter, zip, foldr, etc
What tools are available?
A maximum function that returns the maximal element of a numeric list
List comprehensions, with possibly multiple generator expressions and predicates
The notion that the output of the comprehension need not depend on the generator expression, implying the generator expression can be used for controlling the size of the generated list
Finite arithmetic sequence lists i.e. [a..b] or [a, a + step..b]
I'll admit, I'm stumped. Obviously one can extract the negative numbers from the original list fairly easily with a comprehension, but how does one then count them, with no notion of length or indexing?
The availability of the maximum function would suggest the end game is to construct a list whose maximal element is the number of negative numbers, with the final result of the function being the application of maximum to said list.
I'm either missing something blindingly obvious, or a smart trick, with a horrible feeling it may be the former. Tell me SO, how do you solve this?
My old -- and very yellowed copy of the first edition has a note attached to Exercise 3.2.3: "This question needs # (length), which appears only later". The moral of the story is to be more careful when setting exercises. I am currently finishing a third edition, which contains answers to every question.
By the way, did you answer Exercise 1.2.1 which asks for you to write down all the ways that
square (square (3 + 7)) can be reduced to normal form. It turns out that there are 547 ways!
I think you may be assuming too many restrictions - taking the length of the filtered list seems like the blindingly obvious solution to me.
An couple of alternatives but both involve using some other function that you say wasn't introduced:
sum [1 | x <- xs, x < 0]
maximum (0:[index | (index, ()) <- zip [1..] [() | x <- xs, x < 0]])
I have a std::vector of double values. Now I need to know if two succeeding elements are within a certain distance in order to process them. I have sorted the vector previously with std::sort.
I have thought about a solution to determine these elements with std::find_if and a lambda expression (c++11), like this:
std::vector<std::vector<double>::iterator> foundElements;
std::vector<double>::iterator foundElement;
while (foundElement != gradients.end())
{
foundElement = std::find_if(gradients.begin(), gradients.end(),
[] (double grad)->bool{
return ...
});
foundElements.push_back(foundElement);
}
But what should the predicate actually return? The plan is that I use the vector of iterators to later modify the vector.
Am I on the right track with this approach or is it too complicated/impossible? What are other, propably more practical solutions?
EDIT: I think I will go after the std::adjacent_find function, as proposed by one answerer.
Read about std::adjacent_find.
Can you enhance the grammar of your question?
"I have a std::vector with different double values. Now I need to know if two succeeding ones (I have sorted the vector previously with std::sort) are within a certain distance to process them)."
Do you imply that each element of a vector of type double is a unique value? If that's the case, can it be reasonably inferred that your goal to find the distance between each of these elements?
I'm new to OCaml, and I'd like to implement Gaussian Elimination as an exercise. I can easily do it with a stateful algorithm, meaning keep a matrix in memory and recursively operating on it by passing around a reference to it.
This statefulness, however, smacks of imperative programming. I know there are capabilities in OCaml to do this, but I'd like to ask if there is some clever functional way I haven't thought of first.
OCaml arrays are mutable, and it's hard to avoid treating them just like arrays in an imperative language.
Haskell has immutable arrays, but from my (limited) experience with Haskell, you end up switching to monadic, mutable arrays in most cases. Immutable arrays are probably amazing for certain specific purposes. I've always imagined you could write a beautiful implementation of dynamic programming in Haskell, where the dependencies among array entries are defined entirely by the expressions in them. The key is that you really only need to specify the contents of each array entry one time. I don't think Gaussian elimination follows this pattern, and so it seems it might not be a good fit for immutable arrays. It would be interesting to see how it works out, however.
You can use a Map to emulate a matrix. The key would be a pair of integers referencing the row and column. You'll want to use your own get x y function to ensure x < n and y < n though, instead of accessing the Map directly. (edit) You can use the compare function in Pervasives directly.
module OrderedPairs = struct
type t = int * int
let compare = Pervasives.compare
end
module Pairs = Map.Make (OrderedPairs)
let get_ n set x y =
assert( x < n && y < n );
Pairs.find (x,y) set
let set_ n set x y v =
assert( x < n && y < n );
Pairs.add (x,y) set v
Actually, having a general set of functions (get x y and set x y at a minimum), without specifying the implementation, would be an even better option. The functions then can be passed to the function, or be implemented in a module through a functor (a better solution, but having a set of functions just doing what you need would be a first step since you're new to OCaml). In this way you can use a Map, Array, Hashtbl, or a set of functions to access a file on the hard-drive to implement the matrix if you wanted. This is the really important aspect of functional programming; that you trust the interface over exploiting the side-effects, and not worry about the underlying implementation --since it's presumed to be pure.
The answers so far are using/emulating mutable data-types, but what does a functional approach look like?
To see, let's decompose the problem into some functional components:
Gaussian elimination involves a sequence of row operations, so it is useful first to define a function taking 2 rows and scaling factors, and returning the resultant row operation result.
The row operations we want should eliminate a variable (column) from a particular row, so lets define a function which takes a pair of rows and a column index and uses the previously defined row operation to return the modified row with that column entry zero.
Then we define two functions, one to convert a matrix into triangular form, and another to back-substitute a triangular matrix to the diagonal form (using the previously defined functions) by eliminating each column in turn. We could iterate or recurse over the columns, and the matrix could be defined as a list, vector or array of lists, vectors or arrays. The input is not changed, but a modified matrix is returned, so we can finally do:
let out_matrix = to_diagonal (to_triangular in_matrix);
What makes it functional is not whether the data-types (array or list) are mutable, but how they they are used. This approach may not be particularly 'clever' or be the most efficient way to do Gaussian eliminations in OCaml, but using pure functions lets you express the algorithm cleanly.