Finding iterators to any of two elemens in a container - search

I want to find iterators to any of two elements in a container. For instance I have a vector of {1, 2, 3, 4} and I want to search for elements 2 or 5 in it (each one of them found first is OK, because my container only contains one of them or none of them). I am using two std::find to look for each of two elements (2 and 5), but is there a more efficient method?
Thank you,
Shahab

Don't use std::find to search for the two elements twice, it can be done by searching only once. The problem is that std::find returns as soon as a single value is found, so it can't be used on multiple values.
{
while (first!=last) {
if (*first==val) return first;
++first;
}
return last;
}
the solution to this would be creating your own loop to traverse your list and checking if the list contains (a || b)

Related

Difference between tuples and lists [duplicate]

What's the difference between tuples/lists and what are their advantages/disadvantages?
Apart from tuples being immutable there is also a semantic distinction that should guide their usage. Tuples are heterogeneous data structures (i.e., their entries have different meanings), while lists are homogeneous sequences. Tuples have structure, lists have order.
Using this distinction makes code more explicit and understandable.
One example would be pairs of page and line number to reference locations in a book, e.g.:
my_location = (42, 11) # page number, line number
You can then use this as a key in a dictionary to store notes on locations. A list on the other hand could be used to store multiple locations. Naturally one might want to add or remove locations from the list, so it makes sense that lists are mutable. On the other hand it doesn't make sense to add or remove items from an existing location - hence tuples are immutable.
There might be situations where you want to change items within an existing location tuple, for example when iterating through the lines of a page. But tuple immutability forces you to create a new location tuple for each new value. This seems inconvenient on the face of it, but using immutable data like this is a cornerstone of value types and functional programming techniques, which can have substantial advantages.
There are some interesting articles on this issue, e.g. "Python Tuples are Not Just Constant Lists" or "Understanding tuples vs. lists in Python". The official Python documentation also mentions this
"Tuples are immutable, and usually contain an heterogeneous sequence ...".
In a statically typed language like Haskell the values in a tuple generally have different types and the length of the tuple must be fixed. In a list the values all have the same type and the length is not fixed. So the difference is very obvious.
Finally there is the namedtuple in Python, which makes sense because a tuple is already supposed to have structure. This underlines the idea that tuples are a light-weight alternative to classes and instances.
Difference between list and tuple
Literal
someTuple = (1,2)
someList = [1,2]
Size
a = tuple(range(1000))
b = list(range(1000))
a.__sizeof__() # 8024
b.__sizeof__() # 9088
Due to the smaller size of a tuple operation, it becomes a bit faster, but not that much to mention about until you have a huge number of elements.
Permitted operations
b = [1,2]
b[0] = 3 # [3, 2]
a = (1,2)
a[0] = 3 # Error
That also means that you can't delete an element or sort a tuple.
However, you could add a new element to both list and tuple with the only difference that since the tuple is immutable, you are not really adding an element but you are creating a new tuple, so the id of will change
a = (1,2)
b = [1,2]
id(a) # 140230916716520
id(b) # 748527696
a += (3,) # (1, 2, 3)
b += [3] # [1, 2, 3]
id(a) # 140230916878160
id(b) # 748527696
Usage
As a list is mutable, it can't be used as a key in a dictionary, whereas a tuple can be used.
a = (1,2)
b = [1,2]
c = {a: 1} # OK
c = {b: 1} # Error
If you went for a walk, you could note your coordinates at any instant in an (x,y) tuple.
If you wanted to record your journey, you could append your location every few seconds to a list.
But you couldn't do it the other way around.
The key difference is that tuples are immutable. This means that you cannot change the values in a tuple once you have created it.
So if you're going to need to change the values use a List.
Benefits to tuples:
Slight performance improvement.
As a tuple is immutable it can be used as a key in a dictionary.
If you can't change it neither can anyone else, which is to say you don't need to worry about any API functions etc. changing your tuple without being asked.
Lists are mutable; tuples are not.
From docs.python.org/2/tutorial/datastructures.html
Tuples are immutable, and usually contain an heterogeneous sequence of
elements that are accessed via unpacking (see later in this section)
or indexing (or even by attribute in the case of namedtuples). Lists
are mutable, and their elements are usually homogeneous and are
accessed by iterating over the list.
This is an example of Python lists:
my_list = [0,1,2,3,4]
top_rock_list = ["Bohemian Rhapsody","Kashmir","Sweet Emotion", "Fortunate Son"]
This is an example of Python tuple:
my_tuple = (a,b,c,d,e)
celebrity_tuple = ("John", "Wayne", 90210, "Actor", "Male", "Dead")
Python lists and tuples are similar in that they both are ordered collections of values. Besides the shallow difference that lists are created using brackets "[ ... , ... ]" and tuples using parentheses "( ... , ... )", the core technical "hard coded in Python syntax" difference between them is that the elements of a particular tuple are immutable whereas lists are mutable (...so only tuples are hashable and can be used as dictionary/hash keys!). This gives rise to differences in how they can or can't be used (enforced a priori by syntax) and differences in how people choose to use them (encouraged as 'best practices,' a posteriori, this is what smart programers do). The main difference a posteriori in differentiating when tuples are used versus when lists are used lies in what meaning people give to the order of elements.
For tuples, 'order' signifies nothing more than just a specific 'structure' for holding information. What values are found in the first field can easily be switched into the second field as each provides values across two different dimensions or scales. They provide answers to different types of questions and are typically of the form: for a given object/subject, what are its attributes? The object/subject stays constant, the attributes differ.
For lists, 'order' signifies a sequence or a directionality. The second element MUST come after the first element because it's positioned in the 2nd place based on a particular and common scale or dimension. The elements are taken as a whole and mostly provide answers to a single question typically of the form, for a given attribute, how do these objects/subjects compare? The attribute stays constant, the object/subject differs.
There are countless examples of people in popular culture and programmers who don't conform to these differences and there are countless people who might use a salad fork for their main course. At the end of the day, it's fine and both can usually get the job done.
To summarize some of the finer details
Similarities:
Duplicates - Both tuples and lists allow for duplicates
Indexing, Selecting, & Slicing - Both tuples and lists index using integer values found within brackets. So, if you want the first 3 values of a given list or tuple, the syntax would be the same:
>>> my_list[0:3]
[0,1,2]
>>> my_tuple[0:3]
[a,b,c]
Comparing & Sorting - Two tuples or two lists are both compared by their first element, and if there is a tie, then by the second element, and so on. No further attention is paid to subsequent elements after earlier elements show a difference.
>>> [0,2,0,0,0,0]>[0,0,0,0,0,500]
True
>>> (0,2,0,0,0,0)>(0,0,0,0,0,500)
True
Differences: - A priori, by definition
Syntax - Lists use [], tuples use ()
Mutability - Elements in a given list are mutable, elements in a given tuple are NOT mutable.
# Lists are mutable:
>>> top_rock_list
['Bohemian Rhapsody', 'Kashmir', 'Sweet Emotion', 'Fortunate Son']
>>> top_rock_list[1]
'Kashmir'
>>> top_rock_list[1] = "Stairway to Heaven"
>>> top_rock_list
['Bohemian Rhapsody', 'Stairway to Heaven', 'Sweet Emotion', 'Fortunate Son']
# Tuples are NOT mutable:
>>> celebrity_tuple
('John', 'Wayne', 90210, 'Actor', 'Male', 'Dead')
>>> celebrity_tuple[5]
'Dead'
>>> celebrity_tuple[5]="Alive"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
Hashtables (Dictionaries) - As hashtables (dictionaries) require that its keys are hashable and therefore immutable, only tuples can act as dictionary keys, not lists.
#Lists CAN'T act as keys for hashtables(dictionaries)
>>> my_dict = {[a,b,c]:"some value"}
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'list'
#Tuples CAN act as keys for hashtables(dictionaries)
>>> my_dict = {("John","Wayne"): 90210}
>>> my_dict
{('John', 'Wayne'): 90210}
Differences - A posteriori, in usage
Homo vs. Heterogeneity of Elements - Generally list objects are homogenous and tuple objects are heterogeneous. That is, lists are used for objects/subjects of the same type (like all presidential candidates, or all songs, or all runners) whereas although it's not forced by), whereas tuples are more for heterogenous objects.
Looping vs. Structures - Although both allow for looping (for x in my_list...), it only really makes sense to do it for a list. Tuples are more appropriate for structuring and presenting information (%s %s residing in %s is an %s and presently %s % ("John","Wayne",90210, "Actor","Dead"))
It's been mentioned that the difference is largely semantic: people expect a tuple and list to represent different information. But this goes further than a guideline; some libraries actually behave differently based on what they are passed. Take NumPy for example (copied from another post where I ask for more examples):
>>> import numpy as np
>>> a = np.arange(9).reshape(3,3)
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> idx = (1,1)
>>> a[idx]
4
>>> idx = [1,1]
>>> a[idx]
array([[3, 4, 5],
[3, 4, 5]])
The point is, while NumPy may not be part of the standard library, it's a major Python library, and within NumPy lists and tuples are completely different things.
Lists are for looping, tuples are for structures i.e. "%s %s" %tuple.
Lists are usually homogeneous, tuples are usually heterogeneous.
Lists are for variable length, tuples are for fixed length.
The values of list can be changed any time but the values of tuples can't be change.
The advantages and disadvantages depends upon the use. If you have such a data which you never want to change then you should have to use tuple, otherwise list is the best option.
Difference between list and tuple
Tuples and lists are both seemingly similar sequence types in Python.
Literal syntax
We use parenthesis () to construct tuples and square brackets [ ] to get a new list. Also, we can use call of the appropriate type to get required structure — tuple or list.
someTuple = (4,6)
someList = [2,6]
Mutability
Tuples are immutable, while lists are mutable. This point is the base the for the following ones.
Memory usage
Due to mutability, you need more memory for lists and less memory for tuples.
Extending
You can add a new element to both tuples and lists with the only difference that the id of the tuple will be changed (i.e., we’ll have a new object).
Hashing
Tuples are hashable and lists are not. It means that you can use a tuple as a key in a dictionary. The list can't be used as a key in a dictionary, whereas a tuple can be used
tup = (1,2)
list_ = [1,2]
c = {tup : 1} # ok
c = {list_ : 1} # error
Semantics
This point is more about best practice. You should use tuples as heterogeneous data structures, while lists are homogenous sequences.
Lists are intended to be homogeneous sequences, while tuples are heterogeneous data structures.
As people have already answered here that tuples are immutable while lists are mutable, but there is one important aspect of using tuples which we must remember
If the tuple contains a list or a dictionary inside it, those can be changed even if the tuple itself is immutable.
For example, let's assume we have a tuple which contains a list and a dictionary as
my_tuple = (10,20,30,[40,50],{ 'a' : 10})
we can change the contents of the list as
my_tuple[3][0] = 400
my_tuple[3][1] = 500
which makes new tuple looks like
(10, 20, 30, [400, 500], {'a': 10})
we can also change the dictionary inside tuple as
my_tuple[4]['a'] = 500
which will make the overall tuple looks like
(10, 20, 30, [400, 500], {'a': 500})
This happens because list and dictionary are the objects and these objects are not changing, but the contents its pointing to.
So the tuple remains immutable without any exception
The PEP 484 -- Type Hints says that the types of elements of a tuple can be individually typed; so that you can say Tuple[str, int, float]; but a list, with List typing class can take only one type parameter: List[str], which hints that the difference of the 2 really is that the former is heterogeneous, whereas the latter intrinsically homogeneous.
Also, the standard library mostly uses the tuple as a return value from such standard functions where the C would return a struct.
As people have already mentioned the differences I will write about why tuples.
Why tuples are preferred?
Allocation optimization for small tuples
To reduce memory fragmentation and speed up allocations, Python reuses old tuples. If a
tuple no longer needed and has less than 20 items instead of deleting
it permanently Python moves it to a free list.
A free list is divided into 20 groups, where each group represents a
list of tuples of length n between 0 and 20. Each group can store up
to 2 000 tuples. The first (zero) group contains only 1 element and
represents an empty tuple.
>>> a = (1,2,3)
>>> id(a)
4427578104
>>> del a
>>> b = (1,2,4)
>>> id(b)
4427578104
In the example above we can see that a and b have the same id. That is
because we immediately occupied a destroyed tuple which was on the
free list.
Allocation optimization for lists
Since lists can be modified, Python does not use the same optimization as in tuples. However,
Python lists also have a free list, but it is used only for empty
objects. If an empty list is deleted or collected by GC, it can be
reused later.
>>> a = []
>>> id(a)
4465566792
>>> del a
>>> b = []
>>> id(b)
4465566792
Source: https://rushter.com/blog/python-lists-and-tuples/
Why tuples are efficient than lists? -> https://stackoverflow.com/a/22140115
The most important difference is time ! When you do not want to change the data inside the list better to use tuple ! Here is the example why use tuple !
import timeit
print(timeit.timeit(stmt='[1,2,3,4,5,6,7,8,9,10]', number=1000000)) #created list
print(timeit.timeit(stmt='(1,2,3,4,5,6,7,8,9,10)', number=1000000)) # created tuple
In this example we executed both statements 1 million times
Output :
0.136621
0.013722200000000018
Any one can clearly notice the time difference.
A direction quotation from the documentation on 5.3. Tuples and Sequences:
Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable, and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples). Lists are mutable, and their elements are usually homogeneous and are accessed by iterating over the list.
In other words, TUPLES are used to store group of elements where the contents/members of the group would not change while LISTS are used to store group of elements where the members of the group can change.
For instance, if i want to store IP of my network in a variable, it's best i used a tuple since the the IP is fixed. Like this my_ip = ('192.168.0.15', 33, 60). However, if I want to store group of IPs of places I would visit in the next 6 month, then I should use a LIST, since I will keep updating and adding new IP to the group. Like this
places_to_visit = [
('192.168.0.15', 33, 60),
('192.168.0.22', 34, 60),
('192.168.0.1', 34, 60),
('192.168.0.2', 34, 60),
('192.168.0.8', 34, 60),
('192.168.0.11', 34, 60)
]
First of all, they both are the non-scalar objects (also known as a compound objects) in Python.
Tuples, ordered sequence of elements (which can contain any object with no aliasing issue)
Immutable (tuple, int, float, str)
Concatenation using + (brand new tuple will be created of course)
Indexing
Slicing
Singleton (3,) # -> (3) instead of (3) # -> 3
List (Array in other languages), ordered sequence of values
Mutable
Singleton [3]
Cloning new_array = origin_array[:]
List comprehension [x**2 for x in range(1,7)] gives you
[1,4,9,16,25,36] (Not readable)
Using list may also cause an aliasing bug (two distinct paths
pointing to the same object).
Just a quick extension to list vs tuple responses:
Due to dynamic nature, list allocates more bit buckets than the actual memory required. This is done to prevent costly reallocation operation in case extra items are appended in the future.
On the other hand, being static, lightweight tuple object does not reserve extra memory required to store them.
Lists are mutable and tuples are immutable.
Just consider this example.
a = ["1", "2", "ra", "sa"] #list
b = ("1", "2", "ra", "sa") #tuple
Now change index values of list and tuple.
a[2] = 1000
print a #output : ['1', '2', 1000, 'sa']
b[2] = 1000
print b #output : TypeError: 'tuple' object does not support item assignment.
Hence proved the following code is invalid with tuple, because we attempted to update a tuple, which is not allowed.
Lists are mutable. whereas tuples are immutable. Accessing an offset element with index makes more sense in tuples than lists, Because the elements and their index cannot be changed.
List is mutable and tuples is immutable. The main difference between mutable and immutable is memory usage when you are trying to append an item.
When you create a variable, some fixed memory is assigned to the variable. If it is a list, more memory is assigned than actually used. E.g. if current memory assignment is 100 bytes, when you want to append the 101th byte, maybe another 100 bytes will be assigned (in total 200 bytes in this case).
However, if you know that you are not frequently add new elements, then you should use tuples. Tuples assigns exactly size of the memory needed, and hence saves memory, especially when you use large blocks of memory.

Fast way to return the other element in a list containing two elements in Python

In Python 3, assume a list l contains two object A and B, what is the most elegant way to return B if given A, or A given B?
I could think of
[i for i in l if i != A][0]
or the other round.
Since this process will be repeated for a large number of times, I want the most efficient way.
you can use a ternary operator
a=l[0] if l[0]!=A else l[1]

Simple adding two arrays using numpy in python?

This might be a simple question. However, I wanted to get some clarifications of how the following code works.
a = np.arange(8)
a
array([1,2,3,4,5,6,7])
Example Function = a[0:-1]+a[1:]/2.0
In the Example Function, I want to draw your attention to the plus sign between the array a[0:-1]+a[1:]. How does that work? What does that look like?
For instance, is the plus sign (addition) adding the first index of each array? (e.g 1+2) or add everything together? (e.g 1+2+2+3+3+4+4+5+5+6+6+7)
Then, I assume /2.0 is just dividing it by 2...
A numpy array uses vector algebra in that you can only add two arrays if they have the same dimensions as you are adding element by element
a = [1,2,3,4,5]
b = [1,1,1]
a+b # will throw an error
whilst
a = [1,2,3,4,5]
b = [1,1,1,1,1]
a+b # is ok
The division is also element by element.
Now to your question about the indexing
a = [1,2,3,4,5]
a[0:-1]= [1,2,3,4]
a[1:] = [2,3,4,5]
or more generally a[index_start: index_end] is inclusive at the start_index but exclusive at the end_index - unless you are given a a[start_index:]where it includes everything up to and including the last element.
My final tip is just to try and play around with the structures - there is no harm in trying different things, the computer will not explode with a wrong value here or there. Unless you trying to do so of course.
If arrays have identical shapes, they can be added:
new_array = first_array.__add__(second_array)
This simple operation adds each value from first_array to each value in second_array and puts result into new_array.

How Does the any Method Work in Groovy?

I came across this bit of code:
n = args[0] as Long
[*n..1, n].any{ println ' '*it + '*'*(n - ~n - it*2) }
It's used for printing a tree form of structure. Like this:
*
***
*****
*******
*
(for n=4)
How does the code [*n..1,n] produce [4, 3, 2, 1, 4]?
How does any method works here? The Doc doesn't help me much. What is a predictive that can be passed to any(as mentioned in Doc's)?
Whats the use of any and how its handled in this case?
Q1a: * "unpacks" an array. .. creates a range. [] creates a collection.
Q1b: *n..1 unpacks [4,3,2,1] into its individual parts.
Q1c: [4,3,2,1,n] == [4,3,2,1,4]
Q2: I don't know why any was used here; each works just as well, and makes more sense in context. any does loop over the connection, so the println side-effect functions as intended.
Normally any would be used to determine if any collection elements met a criteria, for example:
[*n..1,n].any { it > 10 } // Returns false, no elements are > 10
[*n..1,n].any { it == 3 } // Returns true, because at least one element is 3
The last statement of the closure is used to determine if each item meets the criteria. println returns null, so any will return false. The value is unused and discarded.
The only reason I can think of that someone might have used any is to avoid seeing the return value of each in the console. each returns the original collection.
1) n..1 is called a range literal, it creates a groovy.lang.Range object that decrements by 1 from n to 1. This is then merged into the surrounding list context using the "Spread operator (*)"
2) the any method is defined in DefaultGroovyMethods and it is a predicate function that returns true if an element in a collection satisfies the supplied predicate closure. In this example, the code doesn't check the return value, so original other could have produced the same output using an each call instead.

Methods for nearby numbers in Groovy

In groovy are there any methods that can find the near by numbers? For example :
def list = [22,33,37,56]
def number = 25
//any method to find $number is near to 22 rather than 33.
Is there any method for the above mentioned purpose, or i have to construct my own method or closure for this purpose.
Thanks in advance.
The following combination of Groovy's collection methods will give you the closest number in the list:
list.groupBy { (it - number).abs() }.min { it.key }.value.first()
The list.groupBy { (it - number).abs() } will transform the list into a map, where each map entry consists of the distance to the number as key and the original list entry as the value:
[3:[22], 8:[33], 12:[37], 31:[56]]
The values are now each a list on their own, as theoretically the original list could contain two entries with equal distance. On the map you then select the entry with the smallest key, take its value and return the first entry of the value's list.
Edit:
Here's a simpler version that sorts the original list based on the distance and return the first value of the sorted list:
list.sort { (it - number).abs() }.first()
If it's a sorted List, Collections.binarySearch() does nearly the same job. So does Arrays.binarySearch().

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