java实现线性回归预测示例
时间: 2023-10-08 07:08:06 浏览: 45
好的,我了解了。以下是一个使用Java实现线性回归预测的示例代码:
```java
import java.util.ArrayList;
import java.util.List;
public class LinearRegressionExample {
public static void main(String[] args) {
// 预测的数据
double[] input = new double[]{1.0, 2.0, 3.0, 4.0, 5.0};
// 输出的数据
double[] output = new double[]{2.0, 4.0, 6.0, 8.0, 10.0};
// 构建输入矩阵
List<List<Double>> X = new ArrayList<>();
for (double d : input) {
List<Double> row = new ArrayList<>();
row.add(1.0);
row.add(d);
X.add(row);
}
// 构建输出矩阵
List<List<Double>> Y = new ArrayList<>();
for (double d : output) {
List<Double> row = new ArrayList<>();
row.add(d);
Y.add(row);
}
// 构建模型
LinearRegressionModel model = new LinearRegressionModel();
// 训练模型
model.fit(X, Y);
// 预测
double[] pred_input = new double[]{6.0, 7.0};
List<List<Double>> pred_X = new ArrayList<>();
for (double d : pred_input) {
List<Double> row = new ArrayList<>();
row.add(1.0);
row.add(d);
pred_X.add(row);
}
List<List<Double>> pred_Y = model.predict(pred_X);
System.out.println("预测输出:" + pred_Y);
}
}
class LinearRegressionModel {
private List<List<Double>> theta;
LinearRegressionModel() {
}
public void fit(List<List<Double>> X, List<List<Double>> Y) {
Matrix X_mtx = new Matrix(X);
Matrix Y_mtx = new Matrix(Y);
// 求解 theta
Matrix theta_mtx = X_mtx.transpose().dot(X_mtx).inverse().dot(X_mtx.transpose()).dot(Y_mtx);
theta = theta_mtx.getMatrix();
}
public List<List<Double>> predict(List<List<Double>> X) {
Matrix X_mtx = new Matrix(X);
Matrix Y_mtx = X_mtx.dot(new Matrix(theta));
return Y_mtx.getMatrix();
}
}
class Matrix {
private final double[][] data;
private final int row;
private final int column;
Matrix(List<List<Double>> data) {
this.row = data.size();
this.column = data.get(0).size();
this.data = new double[row][column];
for (int i = 0; i < row; i++) {
for (int j = 0; j < column; j++) {
this.data[i][j] = data.get(i).get(j);
}
}
}
Matrix(int row, int column) {
this.row = row;
this.column = column;
this.data = new double[row][column];
}
Matrix(double[][] data) {
this.row = data.length;
this.column = data[0].length;
this.data = data;
}
public Matrix dot(Matrix other) {
if (this.column != other.row) {
throw new IllegalArgumentException("行列数不符合");
}
Matrix result = new Matrix(this.row, other.column);
for (int i = 0; i < this.row; i++) {
for (int j = 0; j < other.column; j++) {
double sum = 0.0;
for (int k = 0; k < this.column; k++) {
sum += this.data[i][k] * other.data[k][j];
}
result.data[i][j] = sum;
}
}
return result;
}
public Matrix transpose() {
Matrix result = new Matrix(this.column, this.row);
for (int i = 0; i < this.row; i++) {
for (int j = 0; j < this.column; j++) {
result.data[j][i] = this.data[i][j];
}
}
return result;
}
public Matrix inverse() {
if (this.row != this.column) {
throw new IllegalArgumentException("矩阵不是方阵");
}
int n = row;
Matrix A = new Matrix(this.data);
Matrix[] LUP = LUP(A);
Matrix L = LUP[0];
Matrix U = LUP[1];
Matrix P = LUP[2];
Matrix B = new Matrix(n, n);
for (int j = 0; j < n; j++) {
Matrix X = new Matrix(n, 1);
for (int i = 0; i < n; i++) {
if (i == j) {
X.data[i][0] = 1.0;
} else {
X.data[i][0] = 0.0;
}
}
for (int k = 0; k < n; k++) {
if (k < j) {
double sum = 0.0;
for (int i = k; i < j; i++) {
sum += U.data[i][k] * X.data[i][0];
}
X.data[j][0] = X.data[j][0] - sum;
} else {
if (k > j) {
double sum = 0.0;
for (int i = j; i < k; i++) {
sum += L.data[k][i] * X.data[i][0];
}
X.data[j][0] = (X.data[j][0] - sum) / L.data[k][k];
}
}
}
for (int i = n - 1; i >= 0; i--) {
if (i == j) {
B.data[i][j] = X.data[i][0];
} else {
double sum = 0.0;
for (int k = i + 1; k <= j; k++) {
sum += U.data[i][k] * B.data[k][j];
}
B.data[i][j] = X.data[i][0] - sum;
}
}
}
return P.dot(B);
}
public Matrix[] LUP(Matrix A) {
int n = A.row;
Matrix L = new Matrix(n, n);
Matrix U = new Matrix(A.data);
Matrix P = new Matrix(n, n);
for (int i = 0; i < n; i++) {
P.data[i][i] = 1.0;
}
for (int j = 0; j < n; j++) {
int p = j;
for (int i = j + 1; i < n; i++) {
if (Math.abs(U.data[i][j]) > Math.abs(U.data[p][j])) {
p = i;
}
}
if (p != j) {
double[] temp = U.data[p];
U.data[p] = U.data[j];
U.data[j] = temp;
temp = P.data[p];
P.data[p] = P.data[j];
P.data[j] = temp;
for (int i = 0; i < j; i++) {
double t = L.data[p][i];
L.data[p][i] = L.data[j][i];
L.data[j][i] = t;
}
}
for (int i = j + 1; i < n; i++) {
L.data[i][j] = U.data[i][j] / U.data[j][j];
for (int k = j; k < n; k++) {
U.data[i][k] -= L.data[i][j] * U.data[j][k];
}
}
}
return new Matrix[]{L, U, P};
}
public List<List<Double>> getMatrix() {
List<List<Double>> result = new ArrayList<>();
for (int i = 0; i < row; i++) {
List<Double> row = new ArrayList<>();
for (int j = 0; j < column; j++) {
row.add(data[i][j]);
}
result.add(row);
}
return result;
}
}
```
希望能够帮助你!
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