<jupyter_start><jupyter_text>梯度下降法需要对数据进行标准化  而在正规方程方法中，不需要对数据进行标准化处理而是直接运用方程，进行计算<jupyter_code># 样本数
m=1000
# 特征数
n=5000

big_x=np.random.random(size=(m,n))
true_theta=np.random.uniform(0.0, 100.0, size=n+1)

big_y = big_x.dot(true_theta[1:]) +true_theta[0] + np.random.normal(0.0, 10.0, size=m)
lin_reg2=LinearRegression()

%time
lin_reg2.fit(big_x ,big_y)
lin_reg2.coef_
lin_reg.intercept_<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15639810426540285, 'fraction_numerical': 0.05924170616113744, 'mean_word_length': 8.837209302325581, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '663522', 'license': 'Apache-2.0', 'n_tokens_mistral': 253, 'n_tokens_neox': 246, 'n_words': 22, 'path': 'imooc/GradientDescent/data-standard.ipynb', 'repo_name': 'imgoodman/play-with-machine-learning', 'size': '422'}