week2/pagerank/pagerank.py
def sample_pagerank(corpus, damping_factor, n):
    """
    Return PageRank values for each page by sampling `n` pages
    according to transition model, starting with a page at random.

    Return a dictionary where keys are page names, and values are
    their estimated PageRank value (a value between 0 and 1). All
    PageRank values should sum to 1.
    """
    distribution = {}
    for page in corpus:
        distribution[page] = 0
    
    page = random.choice(list(corpus.keys()))

    for i in range(1, n):
        current_distribution = transition_model(corpus, page, damping_factor)
        for page in distribution:
            distribution[page] = ((i-1) * distribution[page] + current_distribution[page]) / i
        
        page = random.choices(list(distribution.keys()), list(distribution.values()), k=1)[0]

    return distribution

    Return PageRank values for each page by sampling `n` pages
    according to transition model, starting with a page at random.

    Return a dictionary where keys are page names, and values are
    their estimated PageRank value (a value between 0 and 1). All
    PageRank values should sum to 1.
    
Return PageRank values for each page by sampling `n` pages
according to transition model, starting with a page at random.

Return a dictionary where keys are page names, and values are
their estimated PageRank value (a value between 0 and 1). All
PageRank values should sum to 1.
Return PageRank values for each page by sampling ` n ` pages according to transition model starting with a page at random . Return a dictionary where keys are page names and values are their estimated PageRank value ( a value between 0 and 1 ) . All PageRank values should sum to 1 .
def sample_pagerank(corpus, damping_factor, n):
    distribution = {}
    for page in corpus:
        distribution[page] = 0
    page = random.choice(list(corpus.keys()))
    for i in range(1, n):
        current_distribution = transition_model(corpus, page, damping_factor)
        for page in distribution:
            distribution[page] = ((i-1) * distribution[page] + current_distribution[page]) / i
        page = random.choices(list(distribution.keys()), list(distribution.values()), k=1)[0]
    return distribution
def sample_pagerank ( corpus , damping_factor , n ) : distribution = { } for page in corpus : distribution [ page ] = 0 page = random . choice ( list ( corpus . keys ( ) ) ) for i in range ( 1 , n ) : current_distribution = transition_model ( corpus , page , damping_factor ) for page in distribution : distribution [ page ] = ( ( i - 1 ) * distribution [ page ] + current_distribution [ page ] ) / i page = random . choices ( list ( distribution . keys ( ) ) , list ( distribution . values ( ) ) , k = 1 ) [ 0 ] return distribution
Return PageRank values for each page by sampling `n` pages
according to transition model, starting with a page at random.
Return PageRank values for each page by sampling ` n ` pages according to transition model starting with a page at random .
"""
    Return PageRank values for each page by sampling `n` pages
    according to transition model, starting with a page at random.

    Return a dictionary where keys are page names, and values are
    their estimated PageRank value (a value between 0 and 1). All
    PageRank values should sum to 1.
    """
corpus
damping_factor
n