Answer:
Hi there Foodalexandre! The question is good to revise knowledge on the concepts of classes and inheritance. Please find the answer with explanation below.
Explanation:
We can use a number of different object-oriented programming languages to implement this solution in, such as Java, C++, Ruby, etc. I have chosen to use Python as the language to implement because of the ease with which it can be used. First, I have defined the Vehicle class based on the description from the question, where the constructor (the __init__ method) initializes the door count and the engine sound, and the original Move() method belonging to the Vehicle class is defined. Then I define the Car class which inherits from the Vehicle class making it inherit the Vehicle properties, and initialize the Car class to have door count of 4 and engine sound as 'rrrrrr'. Defining the Move() method again in the Car class overrides the one in the Vehicle class, and the RoadTrip() method is added to return the string as requested in the question.
class Vehicle(object):
def __init__(self, door_count, engine_sound):
door_count: door_count
engine_sound: engine_sound
def Move()
:
return ‘rrrrrr’
class Car(Vehicle):
def __init__(self, door_count, engine_sound):
super().__init__(4, ‘rrrrrr’)
def Move():
return ‘vrumm’
def RoadTrip()
:
return “Not a care in the world”
Answer:
Digital Restrictions
Explanation:
This is the process that arises as a result of not meeting specific ethical standards and procedures for operating an online business on the form of electronic commerce. Such situation could be as a result of important factors like:
1. Unduly registered or incorporated businesses
2. Lack of patent or trademark to shown authenticity of product or services
3. Inability to provide certified prove of ownership to avoid fraudulent acts and others.
Answer:
Implementing on Python for the question, the following is the code.
Explanation:
def intialMatch(l):
word_dict={}
for word in l.split():
if word_dict.get(word)==None:
word_dict[word]=[]
for key in word_dict.keys():
if key[0]==word[0] and (word is not key) :
values = word_dict.get(key)
if word not in values:
values.append(word)
for key,values in word_dict.items():
for value in values:
if value==key:values.remove(value)
return word_dict
t='do what you can with what you have'
print(intialMatch(t))
B. A generated report will include all records that a query fetches.
Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
