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src/b/i/biopython-1.63/Bio/HMM/MarkovModel.py   biopython(Download)
        # probable state preceding the i-th state.
        state = last_state
        traceback_seq.append(state)
        for i in loop_seq:
            state = pred_state_seq[(i - 1, state)]
            traceback_seq.append(state)

src/b/i/biopython-HEAD/Bio/HMM/MarkovModel.py   biopython(Download)
        # probable state preceding the i-th state.
        state = last_state
        traceback_seq.append(state)
        for i in loop_seq:
            state = pred_state_seq[(i - 1, state)]
            traceback_seq.append(state)

src/b/i/biopython-1.63/Bio/GA/Organism.py   biopython(Download)
        for gene_num in range(genome_size):
            new_gene = letter_rand.choice(genome_alphabet.letters)
            new_genome.append(new_gene)
 
        # add the new organism with this genome

src/b/i/biopython-HEAD/Bio/GA/Organism.py   biopython(Download)
        for gene_num in range(genome_size):
            new_gene = letter_rand.choice(genome_alphabet.letters)
            new_genome.append(new_gene)
 
        # add the new organism with this genome

src/b/i/biopython-1.63/Tests/test_HMMCasino.py   biopython(Download)
    # generate the sequence
    for roll in range(num_rolls):
        state_seq.append(cur_state)
        # generate a random number
        chance_num = random.random()
 
        # add on a new roll to the sequence
        new_roll = _loaded_dice_roll(chance_num, cur_state)
        roll_seq.append(new_roll)

src/b/i/biopython-HEAD/Tests/test_HMMCasino.py   biopython(Download)
    # generate the sequence
    for roll in range(num_rolls):
        state_seq.append(cur_state)
        # generate a random number
        chance_num = random.random()
 
        # add on a new roll to the sequence
        new_roll = _loaded_dice_roll(chance_num, cur_state)
        roll_seq.append(new_roll)