def viterbi_segment(text, P):
    """Find the best segmentation of the string of characters, given the
    UnigramTextModel P."""
    # best[i] = best probability for text[0:i]
    # words[i] = best word ending at position i
    n = len(text)
    words = [''] + list(text)
    best = [1.0] + [0.0] * n
    ## Fill in the vectors best, words via dynamic programming
    for i in range(n+1):
        for j in range(0, i):
            w = text[j:i]
            if P[w] * best[i - len(w)] >= best[i]:
                best[i] = P[w] * best[i - len(w)]
                words[i] = w
    ## Now recover the sequence of best words
    sequence = []; i = len(words)-1
    while i > 0:
        sequence[0:0] = [words[i]]
        i = i - len(words[i])
    ## Return sequence of best words and overall probability
    return sequence, best[-1]
def viterbi_segment(text, P):
    """Find the best segmentation of the string of characters, given the
    UnigramTextModel P."""
    # best[i] = best probability for text[0:i]
    # words[i] = best word ending at position i
    n = len(text)
    words = [''] + list(text)
    best = [1.0] + [0.0] * n
    ## Fill in the vectors best, words via dynamic programming
    for i in range(n+1):
        for j in range(0, i):
            w = text[j:i]
            if P[w] * best[i - len(w)] >= best[i]:
                best[i] = P[w] * best[i - len(w)]
                words[i] = w
    ## Now recover the sequence of best words
    sequence = []; i = len(words)-1
    while i > 0:
        sequence[0:0] = [words[i]]
        i = i - len(words[i])
    ## Return sequence of best words and overall probability
    return sequence, best[-1]
def viterbi_segment ( text , P ) : # best[i] = best probability for text[0:i] # words[i] = best word ending at position i n = len ( text ) words = [ '' ] + list ( text ) best = [ 1.0 ] + [ 0.0 ] * n ## Fill in the vectors best, words via dynamic programming for i in range ( n + 1 ) : for j in range ( 0 , i ) : w = text [ j : i ] if P [ w ] * best [ i - len ( w ) ] >= best [ i ] : best [ i ] = P [ w ] * best [ i - len ( w ) ] words [ i ] = w ## Now recover the sequence of best words sequence = [ ] i = len ( words ) - 1 while i > 0 : sequence [ 0 : 0 ] = [ words [ i ] ] i = i - len ( words [ i ] ) ## Return sequence of best words and overall probability return sequence , best [ - 1 ]
Find the best segmentation of the string of characters, given the
    UnigramTextModel P.
Find the best segmentation of the string of characters given the UnigramTextModel P .