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How to have parametrizable "methods" in Elm data-structures

By : viral
Date : October 01 2020, 02:00 AM
I wish this help you You can use a custom type to represent the language then pattern match on the language to perform your index.
code :
type Language
    = English
    | French

type alias Book =
    { title : String
    , text : String
    , language : Language

wordsInBook : Book -> Set String
wordsInBook { language, text } =
    case language of
        English ->
            doSomethingWithEnglish text

        French ->
            doSomethingWithFrench text

type Book
    = Book Language Data

type Language
    = English
    | French

type alias Data =
    { title : String
    , text : String

wordsInBook : Book -> Set String
wordsInBook (Book language data) =
    case language of
        English ->
            doSomethingWithEnglish data

        French ->
            doSomethingWithFrench data

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Overlap in C++ and Java data structures: stack "top" vs "peek"

Date : March 29 2020, 07:55 AM
seems to work fine Writing a custom interface just to make method names the same would be a colossal waste of time. What exactly would be the point? You wouldn't be able to easily copy-and-paste most code between the languages you've mentioned even with such a feature.
Personally, I don't like the name of the STL vector method push_back(). I would prefer if it were just called add(), for one thing it'd be less typing. It never occurred to me that I might change it, however. Doing so would just make my code less portable and less readable for others. Now, I suppose this could be done fairly easily with a pre-processor macro, but even that would be a waste of time in my mind.

SCALA: Which data structures are optimal in which situations when using ".contains()" or ".exists()"

By : Wowdrali
Date : March 29 2020, 07:55 AM
wish help you to fix your issue Set and Map (with a default hash table implementation) are by far the fastest at contains since they compute the hash value and jump to the right location immediately. For example, if you want to find an arbitrary string out of a list of a thousand, contains on a set is about 100x faster than contains on List or Vector or Array.
With exists, you really just care about how fast the collection is to traverse--you have to traverse everything anyway. There, List is usually the champ (unless you want to traverse an array by hand), but only Set and so on are usually particularly bad (e.g. exists on List is ~8x faster than on a Set when each have 1000 elements). The others are within about 2.5x of List (usually 1.5x, but Vector has an underlying tree structure which is not all that fast to traverse).

Sonar alert : "private" methods that don't access instance data should be "static"

By : Yadiee Adolfo Arseni
Date : March 29 2020, 07:55 AM
help you fix your problem The problem was solved with the upgrade of Sonar to the version 5.1.2

How to store "complex" data structure as a Persistent Data Structures using ethereum & serpent

By : Clark Hwang
Date : March 29 2020, 07:55 AM
I hope this helps . IMPORTANT : Please note that according to this Vitalik Tweet, Serpent is now an "outdated tech".
The Serpent README has been updated to :
code :
#!/usr/bin/env python
# -*- coding: UTF-8 -*-

import serpent
import json,math
from ethereum import tester, utils, abi

serpent_code = '''
data mystorage[]

def test_data_storage(key,value):
    if not self.mystorage[key]:

def get_value_mystorage(key):
    if not self.mystorage[key]:

def test_self_storage(key,value):
    if not self.storage[key]:

def rebuild_complex_data_storage(key):
    if not self.storage[key]:

def get_value_self_storage(key):
    if not self.storage[key]:

def put_complex_data_storage(key,value=""):

def get_complex_data_storage(key):


s = tester.state()
c = s.abi_contract(serpent_code)

#example with self storage
print c.get_value_self_storage("keyA") #store and access data works in self.storage!

#example with mystorage
print c.get_value_mystorage("keyB") #store and access data works in data as persistant data storage!


def block_chain_encode(c,key,my_complex_data):


    global start_block

    print "\n","*","ENCODE","*"

    #fail example with complex data

    for each in my_complex_data_json_format:
        int_str=int_str+ str(ord(each)).zfill(4)
    print int_str
    #toHex = lambda x:"".join([hex(ord(c))[2:].zfill(2) for c in x])
    print "need to declare 32 bit words", math.ceil(len(int_str)/32.),"times"
    #max 32 char for each key

    for i in range(0,max_size):
        print "block",i," content",block_content
        if i==0:

        res=c.test_data_storage(key,int(block_content)) #store block part in block chain
        print " - substorage",key,"",

    #print "data storage part is done !"
    return 1

def block_chain_decode(c,key):


    global start_block
    print "\n","*","DECODE","*"

    lenght= c.get_value_mystorage("keyComplex_len") #get lenght to know how many block we need to get for all data from block chain
    for i in range(0,lenght):
        print key,c.get_value_mystorage(key)

    sp_int_str= int_str.split(start_block)
    if len(sp_int_str)==3:
        if sp_int_str[-1]==sp_int_str[0] and sp_int_str[0]=="":
            print "ok"

    if content_!="":
        for i in range(0,int(len(content_)/4.)):
            js_str+=chr(int(content_[i*4:(i+1)*4])) #recover char from asci code

    return my_complex_data

#define complex data
my_complex_data={"keyA":1,"keyB":2, "keyC":[1,2,3], "keyD":{"a":1,"b":2}}
print "Initial complex data",my_complex_data

#encode and push to block chain

#get complex variable from block chain
my_complex_data_back = block_chain_decode(c,"keyComplex")
#acces data
print my_complex_data_back["keyD"]["a"]+my_complex_data_back["keyD"]["b"]
print "Extracted complex data from blockchain",my_complex_data
print "Integrity test pass:",my_complex_data_back==my_complex_data
Initial complex data {'keyC': [1, 2, 3], 'keyB': 2, 'keyA': 1, 'keyD': {'a': 1, 'b': 2}}

need to declare 32 bit words 9.0 times
block 0  content 99699012300340107010101210067003
 - substorage keyComplex_000  block 1  content 40058003200910049004400320050004
 - substorage keyComplex_001  block 2  content 40032005100930044003200340107010
 - substorage keyComplex_002  block 3  content 10121006600340058003200500044003
 - substorage keyComplex_003  block 4  content 20034010701010121006500340058003
 - substorage keyComplex_004  block 5  content 20049004400320034010701010121006
 - substorage keyComplex_005  block 6  content 80034005800320123003400970034005
 - substorage keyComplex_006  block 7  content 80032004900440032003400980034005
 - substorage keyComplex_007  block 8  content 8003200500125012599699
 - substorage keyComplex_008  
keyComplex_000 99699012300340107010101210067003
keyComplex_001 40058003200910049004400320050004
keyComplex_002 40032005100930044003200340107010
keyComplex_003 10121006600340058003200500044003
keyComplex_004 20034010701010121006500340058003
keyComplex_005 20049004400320034010701010121006
keyComplex_006 80034005800320123003400970034005
keyComplex_007 80032004900440032003400980034005
keyComplex_008 8003200500125012599699
Extracted complex data from blockchain {'keyC': [1, 2, 3], 'keyB': 2, 'keyA': 1, 'keyD': {'a': 1, 'b': 2}}
Integrity test pass: True

Why are "Algorithms" and "Data Structures" treated as separate disciplines?

By : Mulitcore
Date : March 29 2020, 07:55 AM
wish help you to fix your issue They are different. Consider graphs, or trees to be more specific. Now, a tree appears to only be a tree. But you can browse it in preorder, inorder or postorder (3 algorithms for one structure).
You can have multiple or only 2 children for one node. The tree can be balanced (like AVL) or contain additional information (like B-tree indexes in data bases). That's different structures. But still you traverse them with the same algorithm.
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