vectorized

简明释义

英[ˈvɛktəraɪzd]美[ˈvɛk.təˌraɪzd]

向量化

英英释义

单词用法

同义词

反义词

例句

1.The curve fairing after raster curve has been vectorized is researched in this paper.

研究了在光栅矢量化后的光顺问题。

2.So even in this vectorized implementation, there is a lot of room for improvement!

因此即使在向量化实现中，也有很多改进空间！

3.This paper concentrates on how to track and extract the isogram cluster from the map image so that they can be vectorized and inputted into the spatial database rapidly and automatically.

研究地图中等值线群的快速输入问题，给出了自动跟踪提取等值线群的有效算法。

4.Correcting manually for vectorized errors is needed and it is a time-consuming process.

校正错误的时间往往远大于矢量化的时间。

5.EPS is a vectorized format used by graphic designers for professional purposes.

EPS是图表设计者用于专业目的的一种矢量格式。

6.The view spot triggering data can be vectorized which is better than the mode of triggering the zone in an graphics mode.

在景点触发数据都可以矢量化，大大优于以图形方式设置触发区域的做 法；

7.Vectorized Requests: "almost everywhere we accept an id we'll accept up to 100 of them"?

矢量化请求：“无论何时，如果我们能接受一个id，那么我们就可以接受100个”？

8.The library provides vectorized 向量化的 functions that make it easier to work with large datasets.

该库提供了vectorized 向量化的 函数，使得处理大数据集变得更加简单。

9.By converting the data to a vectorized 向量化的 format, we improved performance significantly.

通过将数据转换为vectorized 向量化的 格式，我们显著提高了性能。

10.Using vectorized 向量化的 operations can greatly reduce the execution time of your code.

使用vectorized 向量化的 操作可以大大减少代码的执行时间。

11.In machine learning, vectorized 向量化的 computations allow for faster training of models.

在机器学习中，vectorized 向量化的 计算可以加快模型的训练速度。

12.The algorithm was optimized by using vectorized 向量化的 operations to speed up the calculations.

通过使用vectorized 向量化的 操作来加速计算，算法得到了优化。

作文

In the world of programming and data science, the term vectorized refers to a method of performing operations on entire arrays or collections of data without the need for explicit loops. This concept is particularly important in languages such as Python, where libraries like NumPy allow for vectorized operations that can significantly enhance performance and efficiency. By leveraging vectorized functions, developers can write cleaner and more concise code while also taking advantage of optimized underlying implementations that are often written in lower-level languages.For example, consider a scenario where we want to add two lists of numbers together. In a traditional, loop-based approach, we would iterate through each element of both lists, summing them one by one. However, with vectorized operations, we can simply use a single line of code to achieve the same result. This not only reduces the amount of code we need to write but also speeds up execution time, especially when dealing with large datasets.The benefits of vectorized operations extend beyond mere speed. They also promote better readability and maintainability of code. When operations are expressed in a vectorized manner, it becomes easier for other programmers (or even the original author) to understand what the code is doing at a glance. This is crucial in collaborative environments where multiple developers work on the same codebase.Moreover, vectorized operations can lead to fewer bugs. Since these operations are typically implemented in highly optimized libraries, they have been tested extensively and are less prone to errors compared to custom loop implementations. This reliability is vital in data-intensive applications where accuracy is paramount.Another critical aspect of vectorized computations is their ability to utilize modern hardware effectively. Many processors today come equipped with features that allow them to perform multiple operations simultaneously, known as SIMD (Single Instruction, Multiple Data). Vectorized operations are designed to take advantage of these capabilities, leading to even greater performance improvements over traditional methods.In conclusion, the concept of vectorized operations is an essential part of modern programming and data analysis. By allowing developers to perform computations on entire datasets at once, vectorized methods not only improve performance but also enhance code clarity and reliability. As the field of data science continues to evolve, mastering vectorized programming techniques will be crucial for anyone looking to work efficiently with large amounts of data.

在编程和数据科学的世界中，术语vectorized指的是一种对整个数组或数据集合执行操作的方法，而无需显式的循环。这一概念在Python等语言中尤为重要，因为像NumPy这样的库允许进行vectorized操作，这可以显著提高性能和效率。通过利用vectorized函数，开发者可以编写更简洁、更清晰的代码，同时也能利用通常用低级语言编写的优化底层实现。例如，考虑一个场景，我们想将两个数字列表相加。在传统的基于循环的方法中，我们需要逐个迭代两个列表的每个元素，将它们相加。然而，通过vectorized操作，我们只需一行代码即可实现相同的结果。这不仅减少了我们需要编写的代码量，还加快了执行时间，特别是在处理大型数据集时。Vectorized操作的好处不仅限于速度。它们还促进了代码的可读性和可维护性。当操作以vectorized方式表达时，其他程序员（甚至是原作者）可以一目了然地理解代码的功能。这在多个开发人员共同工作于同一代码库的协作环境中至关重要。此外，vectorized操作可以导致更少的错误。由于这些操作通常在高度优化的库中实现，因此经过了广泛测试，相较于自定义的循环实现，它们不太容易出错。这种可靠性在数据密集型应用中至关重要，因为准确性是重中之重。Vectorized计算的另一个关键方面是它们能够有效利用现代硬件。如今，许多处理器都配备了允许它们同时执行多个操作的功能，称为SIMD（单指令多数据）。Vectorized操作旨在利用这些能力，从而比传统方法带来更大的性能提升。总之，vectorized操作的概念是现代编程和数据分析的重要组成部分。通过允许开发者一次性对整个数据集进行计算，vectorized方法不仅提高了性能，还增强了代码的清晰度和可靠性。随着数据科学领域的不断发展，掌握vectorized编程技术对于任何希望高效处理大量数据的人来说都是至关重要的。