zhao.algorithmMagic.algorithm.modelAlgorithm.LinearRegression Maven / Gradle / Ivy
package zhao.algorithmMagic.algorithm.modelAlgorithm;
import zhao.algorithmMagic.algorithm.OperationAlgorithm;
import zhao.algorithmMagic.algorithm.OperationAlgorithmManager;
import zhao.algorithmMagic.exception.TargetNotRealizedException;
import zhao.algorithmMagic.operands.matrix.DoubleMatrix;
import zhao.algorithmMagic.operands.matrix.IntegerMatrix;
import zhao.algorithmMagic.utils.ASClass;
import zhao.algorithmMagic.utils.ASMath;
/**
* 线性回归计算组件，其能够接收一个初始线性模型，并将初始线性模型中的回归系数不断完善，然后返回最为准确的回归系数。
*
* The linear regression calculation component can receive an initial linear model, continuously improve the regression coefficient in the initial linear model, and then return the most accurate regression coefficient.
*
* @author zhao
*/
public final class LinearRegression extends ModelAlgorithm {
private int featureIndex = 0;
private int targetIndex = 0;
private LinearRegression(String AlgorithmName) {
super(AlgorithmName);
}
/**
* 获取到该算法的类对象，
*
* @param Name 该算法的名称
* @return 算法类对象
* @throws TargetNotRealizedException 当您传入的算法名称对应的组件不能被成功提取的时候会抛出异常
*/
public static LinearRegression getInstance(String Name) {
if (OperationAlgorithmManager.containsAlgorithmName(Name)) {
OperationAlgorithm operationAlgorithm = OperationAlgorithmManager.getInstance().get(Name);
if (operationAlgorithm instanceof LinearRegression) {
return ASClass.transform(operationAlgorithm);
} else {
throw new TargetNotRealizedException("您提取的[" + Name + "]算法被找到了，但是它不属于LinearRegression类型，请您为这个算法重新定义一个名称。\n" +
"The [" + Name + "] algorithm you extracted has been found, but it does not belong to the LinearRegression type. Please redefine a name for this algorithm.");
}
} else {
LinearRegression LinearRegression = new LinearRegression(Name);
OperationAlgorithmManager.getInstance().register(LinearRegression);
return LinearRegression;
}
}
/**
* 设置自变量的列编号
*
* @param featureIndex 自变量的列编号
*/
public void setFeatureIndex(int featureIndex) {
this.featureIndex = featureIndex;
}
/**
* 设置因变量的列编号
*
* @param targetIndex 因变量的列编号
*/
public void setTargetIndex(int targetIndex) {
this.targetIndex = targetIndex;
}
/**
* 通过给定的一个模型，不断修正模型中的参数或其它方式，最终返回在最接近样本本身时所有参数组成的数组
*
* Through a given model, continuously modify the parameters in the model or other ways, and finally return the array of all parameters when they are closest to the sample itself.
*
* @param integerMatrix 需要被计算的所有数据行组成的数组，其中每一个数组代表一行数据。
*
* An array of all data rows to be calculated, where each array represents one row of data.
* @return 每一个计算时的待推断参数的推断结果组成的一个数组。
*
* An array composed of the inference results of the parameters to be inferred during each calculation.
*/
public double[] modelInference(IntegerMatrix integerMatrix) {
return modelInference(targetIndex, integerMatrix);
}
/**
* 通过给定的一个模型，不断修正模型中的参数或其它方式，最终返回在最接近样本本身时所有参数组成的数组
*
* Through a given model, continuously modify the parameters in the model or other ways, and finally return the array of all parameters when they are closest to the sample itself.
*
* @param doubleMatrix 需要被计算的所有数据行组成的数组，其中每一个数组代表一行数据。
*
* An array of all data rows to be calculated, where each array represents one row of data.
* @return 每一个计算时的待推断参数的推断结果组成的一个数组。
*
* An array composed of the inference results of the parameters to be inferred during each calculation.
*/
public double[] modelInference(DoubleMatrix doubleMatrix) {
return modelInference(targetIndex, doubleMatrix);
}
/**
* 通过给定的一个模型，不断修正模型中的参数或其它方式，最终返回在最接近样本本身时所有参数组成的数组
*
* Through a given model, continuously modify the parameters in the model or other ways, and finally return the array of all parameters when they are closest to the sample itself.
*
* @param targetIndex 目标值索引，该索引所标记的列将会作为本次计算的目标值！
*
* Target value index, the column marked by this index will be the target value of this calculation!
* @param integerMatrix 需要被计算的所有数据行组成的数组，其中每一个数组代表一行数据。
*
* An array of all data rows to be calculated, where each array represents one row of data.
* @return 每一个计算时的待推断参数的推断结果组成的一个数组。
*
* An array composed of the inference results of the parameters to be inferred during each calculation.
*/
@Override
public double[] modelInference(int targetIndex, IntegerMatrix integerMatrix) {
return ASMath.linearRegression(integerMatrix.getArrayByColIndex(featureIndex), integerMatrix.getArrayByColIndex(targetIndex));
}
/**
* 通过给定的一个模型，不断修正模型中的参数或其它方式，最终返回在最接近样本本身时所有参数组成的数组
*
* Through a given model, continuously modify the parameters in the model or other ways, and finally return the array of all parameters when they are closest to the sample itself.
*
* @param targetIndex 目标值索引，该索引所标记的列将会作为本次计算的目标值！
*
* Target value index, the column marked by this index will be the target value of this calculation!
* @param doubleMatrix 需要被计算的所有数据行组成的数组，其中每一个数组代表一行数据。
*
* An array of all data rows to be calculated, where each array represents one row of data.
* @return 每一个计算时的待推断参数的推断结果组成的一个数组。
*
* An array composed of the inference results of the parameters to be inferred during each calculation.
*/
@Override
public double[] modelInference(int targetIndex, DoubleMatrix doubleMatrix) {
return ASMath.linearRegression(doubleMatrix.getArrayByColIndex(featureIndex), doubleMatrix.getArrayByColIndex(targetIndex));
}
}