NearestNeighbours:机器学习算法

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  • 2022-06-09 08:46
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最近的邻居 机器学习算法 输入包含两个文件。 第一个文件包含交叉验证信息,第二个文件包含数据。 第一个档案 每行中的数字用单个空格分隔。 第一个数字是k折交叉验证方案中使用的k折的k。 第二个数字是m,即示例数。 第三个数字是t,即随机排列的数量。 第二档 每行中的数字和字符用单个空格分隔。 第一行有两个数字:cols行。 然后是cols大小的行的网格。 网格中的每个条目均位于{+;-;.}的eof上,其中+表示正例,-表示负例,而。 表示该位置不是示例。 输出 输出文件包含k = 1的k个最近邻居的评估; 2; 3; 4; 5.在每种情况下都会产生以下内容: 误差的估计值e。 误差标准偏差的估计sigma。 根据k最近邻对整个网格进行标记。
NearestNeighbours-master.zip
内容介绍
# NearestNeighbours Machine Learning Algorithm The input consists of two files. The first file contains cross-validation information, and the second file contains the data. The first file The numbers in each row are separated by a single space. The first number is the k of k-fold, to be used in the k-fold cross validation scheme. The second number is m, the number of examples. The third number is t, the number of random permutations. The second file The numbers and characters in each row are separated by a single space. The first line has two numbers: rows cols. This is followed by a grid of size rows by cols. Each entry in the grid is on eof {+;-;.}, where + indicates a positive example, - indicates a negative example, and . indicates that the location is not an example. Output The output file contains evaluation of k-nearest neighbors for k = 1; 2; 3; 4; 5. In each case the following is produced: 1. The estimate e of for the error. 2. The estimate sigma for the error standard deviation. 3. The labeling of the entire grid according to k-nearest neighbors.
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