HWcpp

See the lecture

In both tasks, you should implement all functionalities in two versions:

• Pure Python
• Python interface with C++ implementation

Then, you should compare the running speed of both implementations and report it in your report. You should submit a report and also your implementation files.

Task A

You are given a list of documents. Each document consists of multiple words separated by space. You should design and implement an indexing datastructure that allows the following two operations:

• Add a document to the index (your index should assign it a unique ID)
• Retrieve a document with the given ID
• Find all documents that contain the given word

You should ideally implement this as an inverted index, i.e. for each word keep a list of documents containing it. In /tasks/cpp/documents.txt is an example file with documents (one document per each line). In /tasks/cpp/queries.txt is an example file with queries (one query per line).

This is the recommended interface for your datastructure with its example usage:

from typing import List

class DocumentIndex:
  def __init__(self):
    """Initialize what you need here"""
  
  def add_document(self, document:str) -> int:
    """Adds document to index, generates unique id for it"""

  def get_document(self, document_id:int) -> str:
    """Returns document for given ID"""
   
  def search(self, word:str) -> List[int]:
    """Returns list of document IDs containing the given word"""

"""Example usage"""
index = DocumentIndex()

index.add_document("this is document one about frog") # Returns 47
index.add_document("this is document two about dog") # Returns 42
index.get_document(42) # Returns "this is document two about dog"
index.search("dog") # Returns [42]
index.search("cat") # Returns []
index.search("this") # Returns [47, 42] or [42, 47] order of documents is not relevant

Task B

Extend you index so that it support following query:

• Given list of words (W1, W2, ...) find all documents, which contain all of the words (so each document has to contain all of the word from the query).

You should ideally build on top of functionality from previous task. For each word find documents containing it and then find overlap in the results.

This is the recommended interface for your datastructure with its example usage:

from typing import List

class DocumentIndex:
  def __init__(self):
    """Initialize what you need here"""
  
  def add_document(self, document:str) -> int:
    """Adds document to index, generates unique id for it"""

  def get_document(self, document_id:int) -> str:
    """Returns document for given ID"""
   
  def search(self, word:str) -> List[int]:
    """Returns list of document IDs containing the given word"""

  def multi_search(self, words: List[str]) -> List[int]:
    """Returns list of document IDs that contains all of the words"""

"""Example usage"""
index = DocumentIndex()

index.add_document("this is document one about frog") # Returns 47
index.add_document("this is document two about dog") # Returns 42
index.get_document(42) # Returns "this is document two about dog"
index.search("dog") # Returns [42]
index.search("cat") # Returns []
index.search("this") # Returns [47, 42] or [42, 47] order of documents is not relevant
index.multi_search(["two", "frog"]) # Returns []
index.multi_search(["two", "dog"]) # Returns [42]