大样品随机双盲测试
This post aims to explore a step-by-step approach to create a K-Nearest Neighbors Algorithm without the help of any third-party library. In practice, this Algorithm should be useful enough for us to classify our data whenever we have already made classifications (in this case, color), which will serve as a starting point to find neighbors.
这篇文章旨在探索逐步方法，以在无需任何第三方库的帮助下创建K最近邻居算法 。 在实践中，只要我们已经进行了分类(在这种情况下为颜色)，该算法就足以对我们进行数据分类，这将成为寻找邻居的起点。
For this post, we will use a specific dataset which can be downloaded here. It contains 539 two dimensional data points, each with a specific color classification. Our goal will be to separate them into two groups (train and test) and try to guess our test sample colors based on our algorithm recommendation.
对于这篇文章，我们将使用一个特定的数据集，可以在此处下载 。 它包含539个二维数据点，每个数据点都有特定的颜色分类。 我们的目标是将它们分为两组(训练和测试)，并根据算法建议尝试猜测测试样本的颜色。
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