text = """
To be or not to be, that is the question;
Whether 'tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take arms against a sea of troubles
And by opposing end them. To die—to sleep,
No more; and by a sleep to say we end
The heart-ache and the thousand natural shocks
That flesh is heir to, 'tis a consummation
Devoutly to be wish'd. To die, to sleep;
To sleep, perchance to dream—ay, there's the rub,
For in that sleep of death what dreams may come,
When we have shuffled off this mortal coil,
Must give us pause—there's the respect
That makes calamity of so long life;
The oppressor's wrong, the proud man's contumely,
The pangs of despis'd love, the law's delay,
The insolence of office, and the spurns
That patient merit of the unworthy takes,
When he himself might his quietus make
With a bare bodkin? Who would these fardels bear,
To grunt and sweat under a weary life,
But that the dread of something after death—
The undiscover'd country, from whose bourn
No traveller returns—puzzles the will,
And makes us rather bear those ills we have
Than fly to others that we know not of?
Thus conscience does make cowards of us all;
And thus the native hue of resolution
Is sicklied o'er with the pale cast of thought,
And enterprises of great pith and moment
With this regard their currents turn awry,
And lose the name of action.
"""

# 去掉換行符，并將文本轉(zhuǎn)換為小寫
text = text.replace('\n', ' ').lower()

def create_ngram_model(text, n):
    words = text.split()  # 將文本分割成單詞
    ngrams = []  # 用于存儲n-grams的列表

    for i in range(len(words) - n + 1):
        ngram = ' '.join(words[i:i + n])  # 創(chuàng)建一個n-gram
        ngrams.append(ngram)

    return ngrams

n = 2  # 選擇2-gram模型
ngram_model = create_ngram_model(text, n)

# 打印前10個2-grams
print(ngram_model[:10])

import random

def generate_text(ngram_model, n, length=50):
    generated_text = random.choice(ngram_model)  # 隨機(jī)選擇一個n-gram作為起始點(diǎn)
    words = generated_text.split()

    while len(words) < length:
        possible_next_ngrams = [ngram for ngram in ngram_model if ' '.join(words[-n + 1:]) in ngram]
        if not possible_next_ngrams:
            break
        next_ngram = random.choice(possible_next_ngrams)
        words.extend(next_ngram.split())

    generated_text = ' '.join(words)
    return generated_text

generated_text = generate_text(ngram_model, n, length=100)

print(generated_text)

# 增加n-gram的大小為3
n = 3
ngram_model = create_ngram_model(text, n)

def generate_text_with_temperature(ngram_model, n, length=50, temperature=1.0):
    generated_text = random.choice(ngram_model)
    words = generated_text.split()

    while len(words) < length:
        possible_next_ngrams = [ngram for ngram in ngram_model if ' '.join(words[-n + 1:]) in ngram]
        if not possible_next_ngrams:
            break
        # 根據(jù)溫度參數(shù)調(diào)整選擇下一個n-gram的隨機(jī)性
        next_ngram = random.choices(possible_next_ngrams, weights=[1.0 / temperature] * len(possible_next_ngrams))[0]
        words.extend(next_ngram.split())

    generated_text = ' '.join(words)
    return generated_text

# 使用溫度參數(shù)為0.5生成文本
generated_text = generate_text_with_temperature(ngram_model, n, length=100, temperature=0.5)