zhao.algorithmMagic.algorithm.classificationAlgorithm.NoSampleClassification Maven / Gradle / Ivy
package zhao.algorithmMagic.algorithm.classificationAlgorithm;
import zhao.algorithmMagic.operands.matrix.ColumnDoubleMatrix;
import zhao.algorithmMagic.operands.matrix.ColumnIntegerMatrix;
import zhao.algorithmMagic.operands.matrix.DoubleMatrix;
import zhao.algorithmMagic.operands.matrix.IntegerMatrix;
import zhao.algorithmMagic.operands.vector.ColumnDoubleVector;
import zhao.algorithmMagic.operands.vector.ColumnIntegerVector;
import zhao.algorithmMagic.operands.vector.DoubleVector;
import zhao.algorithmMagic.operands.vector.IntegerVector;
import java.util.ArrayList;
import java.util.HashMap;
/**
* 无样本数据分类计算组件的抽象接口，其中包含了无样本的数据分类计算函数。
*
* The abstract interface of the non-sample data classification calculation component, which contains the non-sample data classification calculation function.
*/
public interface NoSampleClassification {
/**
* 无样本的距离计算，您在此进行分类，不需要传递很多的数据样本，只需要由实现类按照自己的算法进行类别推断即可。
*
* For distance calculation without samples, you can classify here. You don't need to pass a lot of data samples. You only need to infer the category by the implementation class according to its own algorithm.
*
* @param keys 指定的一些数据类别，按照索引与 ints 参数一一对应，其中如果为 ? 代表是未知类别
*
* Some specified data categories correspond to the ints parameter one by one according to the index, in which, if it is? Represents an unknown category
* @param ints 指定的类别索引对应的数据特征本身，是需要分类的关键对象。
*
* The data feature corresponding to the specified category index is the key object to be classified.
* @return 分类结果。
*
* Classification results.
*/
HashMap> classification(String[] keys, int[]... ints);
/**
* 无样本的距离计算，您在此进行分类，不需要传递很多的数据样本，只需要由实现类按照自己的算法进行类别推断即可。
*
* For distance calculation without samples, you can classify here. You don't need to pass a lot of data samples. You only need to infer the category by the implementation class according to its own algorithm.
*
* @param keys 指定的一些数据类别，按照索引与 ints 参数一一对应，其中如果为 ? 代表是未知类别
*
* Some specified data categories correspond to the ints parameter one by one according to the index, in which, if it is? Represents an unknown category
* @param doubles 指定的类别索引对应的数据特征本身，是需要分类的关键对象。
*
* The data feature corresponding to the specified category index is the key object to be classified.
* @return 分类结果。
*
* Classification results.
*/
HashMap> classification(String[] keys, double[]... doubles);
/**
* 无样本的距离计算，您在此进行分类，不需要传递很多的数据样本，只需要由实现类按照自己的算法进行类别推断即可。
*
* For distance calculation without samples, you can classify here. You don't need to pass a lot of data samples. You only need to infer the category by the implementation class according to its own algorithm.
*
* @param keys 指定的一些数据类别，按照索引与 ints 参数一一对应，其中如果为 ? 代表是未知类别
*
* Some specified data categories correspond to the ints parameter one by one according to the index, in which, if it is? Represents an unknown category
* @param ints 指定的类别索引对应的数据特征本身，是需要分类的关键对象。
*
* The data feature corresponding to the specified category index is the key object to be classified.
* @return 分类结果。
*
* Classification results.
*/
HashMap> classification(String[] keys, IntegerMatrix ints);
/**
* 无样本的距离计算，您在此进行分类，不需要传递很多的数据样本，只需要由实现类按照自己的算法进行类别推断即可。
*
* For distance calculation without samples, you can classify here. You don't need to pass a lot of data samples. You only need to infer the category by the implementation class according to its own algorithm.
*
* @param keys 指定的一些数据类别，按照索引与 ints 参数一一对应，其中如果为 ? 代表是未知类别
*
* Some specified data categories correspond to the ints parameter one by one according to the index, in which, if it is? Represents an unknown category
* @param doubles 指定的类别索引对应的数据特征本身，是需要分类的关键对象。
*
* The data feature corresponding to the specified category index is the key object to be classified.
* @return 分类结果。
*
* Classification results.
*/
HashMap> classification(String[] keys, DoubleMatrix doubles);
/**
* 无样本的距离计算，您在此进行分类，不需要传递很多的数据样本，只需要由实现类按照自己的算法进行类别推断即可。
*
* For distance calculation without samples, you can classify here. You don't need to pass a lot of data samples. You only need to infer the category by the implementation class according to its own algorithm.
*
* @param keys 指定的一些数据类别，按照索引与 columnIntegerMatrix 参数一一对应，其中如果为 ? 代表是未知类别
*
* Some specified data categories correspond to the columnIntegerMatrix parameter one by one according to the index, in which, if it is? Represents an unknown category
* @param columnIntegerMatrix 指定的类别索引对应的数据特征本身，是需要分类的关键对象。
*
* The data feature corresponding to the specified category index is the key object to be classified.
* @return 分类结果。
*
* Classification results.
*/
HashMap> classification(String[] keys, ColumnIntegerMatrix columnIntegerMatrix);
/**
* 无样本的距离计算，您在此进行分类，不需要传递很多的数据样本，只需要由实现类按照自己的算法进行类别推断即可。
*
* For distance calculation without samples, you can classify here. You don't need to pass a lot of data samples. You only need to infer the category by the implementation class according to its own algorithm.
*
* @param keys 指定的一些数据类别，按照索引与 columnDoubleMatrix 参数一一对应，其中如果为 ? 代表是未知类别
*
* Some specified data categories correspond to the columnDoubleMatrix parameter one by one according to the index, in which, if it is? Represents an unknown category
* @param columnDoubleMatrix 指定的类别索引对应的数据特征本身，是需要分类的关键对象。
*
* The data feature corresponding to the specified category index is the key object to be classified.
* @return 分类结果。
*
* Classification results.
*/
HashMap> classification(String[] keys, ColumnDoubleMatrix columnDoubleMatrix);
}