1.1 重构的重要性 重构,就如同对一幅日渐模糊的油画进行细致入微的修复和重新布局,它不是改变画的主题,而是使之更加鲜明生动。在编程领域,重构是指在不改变代码外部行为的前提下,对其内部结构进行改进的过程,旨在提升代码的可读性、可维护性和可扩展性。 1.1.1 何为重构 想象一下,你接手了一段复杂的Python代码,...
lambda: It is the keyword that defines the function. arguments: They are the inputs to the function, that can be zero or more. expression: It is a single-line expression that is evaluated and returned. Example: Python 1 2 3 4 # creating lambda function cube = lambda x: x * x *...
.index(category) for audio in os.listdir(path): audio_path = os.path.join(path, audio) audio_array, sr_array = librosa.load(audio_path, duration=30) time_features = extract_time_domain_features(audio_array) freq_features = extract_frequency_domain_features(audio_array) features = time_...
def selected_sort(alist): n = len(alist) # 需要进行n-1次选择操作 for i in range(n-1): # 记录最小位置 min_index = i # 从i+1位置到末尾,选择出最小的元素 for j in range(i+1, n): if alist[j] < alist[min_index]: min_index = j # 如果选择出的元素不在正确位置,进行交换...
If you don’t like calling the complex() factory function, you can create a type alias with a better-suited name or use the literal form of a complex number to save a few keystrokes: Python CityCoordinates = complex miami_fl = CityCoordinates(-80.191788, 25.761681) miami_fl = -80.191788...
You use functions in programming to bundle a set of instructions that you want to use repeatedly or that, because of their complexity, are better self-contained in a sub-program and called when needed. That means that a function is a piece of code written to carry out a specified task. ...
empty((n_samples, n_output_features)) for i, index_combs in enumerate(combinations): X_new[:, i] = np.prod(X[:, index_combs], axis=1) return X_new 这个是计算多项式特征函数。什么是多项式特征呢? 以sklearn中的为例:使用sklearn.preprocessing.PolynomialFeatures来进行特征的构造。它是使用...
这是一位大佬翻译的GooglePython代码风格指南,很全面。可以作为公司的code review 标准,也可以作为自己编写代码的风格指南。希望对你有帮助。 Translator: shendeguize@github Link: https://github.com/shendeguize/GooglePythonStyleGuideCN 本翻译囿于水平,可能有不准确的地方,欢迎指出,谢谢大家 ...
Our review of the four data structures confirms that thesearch4vowelsfunction returns a set. But, other than calling the function and checking the return type, how can users of our function know this ahead of time? How do they know what to expect?
We can then serialize this model using Pickle’s dump() function: with open("linear_regression.pkl", "wb") as f: pickle.dump(linear_model, f) Powered By The model is now saved as a Pickle file. Let’s deserialize it using the load() function: with open("linear_regression.pkl", ...