对原始数据集进行预处理，窗口划分，特征提取，特征融合，分类识别.zip

package edu.xaut.activityrecognition; /** * LDA（线性判别分析）算法实现 * 需要JAMAjar包的支持 * http://www.psychometrica.de/lda.html * */ import java.util.ArrayList; import Jama.Matrix; public class LDA { private double[][] groupMean; private double[][] pooledInverseCovariance; private double[] probability; private ArrayList<Integer> groupList = new ArrayList<Integer>(); /** * LDA算法主要实现代码： * @param d data---存储数据信息，其数据个数必须与group分组个数一致(7)，即一一对应关系---x(i) * @param g group---分组数据信息---y(i) * @param p Set to true, if the probability estimation should be based on * the real group sizes (true), or if the share of each group * should be equal */ @SuppressWarnings("unchecked") public LDA(double[][] d, int[] g, boolean p) { // 检查数据和分组信息是否一致，即一一对应关系 if (d.length != g.length) return; // 存储data数据信息{ { 2.95, 6.63 }, { 2.53, 7.79 }, { 3.57, 5.65 }, // { 3.16, 5.47 }, { 2.58, 4.46 }, { 2.16, 6.22 }, { 3.27, 3.52 } } double[][] data = new double[d.length][d[0].length]; for (int i = 0; i < d.length; i++) { for (int j = 0; j < d[i].length; j++) { data[i][j] = d[i][j]; } } // 存储group数据信息{1,1,1,1,2,2,2} int[] group = new int[g.length]; for (int j = 0; j < g.length; j++) { group[j] = g[j]; } // 原始数据（不区分类别）均值 double[] globalMean; // 协方差 double[][][] covariance; // 通过groupList存储标签信息1,2 for (int i = 0; i < group.length; i++) { if (!groupList.contains(group[i])) { groupList.add(group[i]); } } // 根据标签信息将数据划分为一个个子集，同一子集内的数据信息所对应的标签（类别）相同 ArrayList<double[]>[] subset = new ArrayList[groupList.size()]; for (int i = 0; i < subset.length; i++) { subset[i] = new ArrayList<double[]>(); for (int j = 0; j < data.length; j++) { if (group[j] == groupList.get(i)) { subset[i].add(data[j]); } } } // 计算每一个子集（标签1,2）内的中心点，即均值mean groupMean = new double[subset.length][data[0].length]; for (int i = 0; i < groupMean.length; i++) { for (int j = 0; j < groupMean[i].length; j++) { groupMean[i][j] = getGroupMean(j, subset[i]); } } // 计算原始数据（不区分类别）的均值 globalMean = new double[data[0].length]; for (int i = 0; i < data[0].length; i++) { globalMean[i] = getGlobalMean(i, data); } // 将每一个子集内的数据-globalMean，存储在原来的位置,为下一步计算协方差准备好数据 for (int i = 0; i < subset.length; i++) { for (int j = 0; j < subset[i].size(); j++) { double[] v = subset[i].get(j); for (int k = 0; k < v.length; k++) v[k] = v[k] - globalMean[k]; subset[i].set(j, v); } } // 计算协方差 covariance = new double[subset.length][globalMean.length][globalMean.length]; for (int i = 0; i < covariance.length; i++) { for (int j = 0; j < covariance[i].length; j++) { for (int k = 0; k < covariance[i][j].length; k++) { // 为每一个子集计算协方差，因为subset中存储的数据已经减去了globalMean，所以只需要相乘求和取其均值即可 for (int l = 0; l < subset[i].size(); l++) covariance[i][j][k] += (subset[i].get(l)[j] * subset[i] .get(l)[k]); covariance[i][j][k] = covariance[i][j][k] / subset[i].size(); } } } // 逆协方差池 pooledInverseCovariance = new double[globalMean.length][globalMean.length]; for (int j = 0; j < pooledInverseCovariance.length; j++) { for (int k = 0; k < pooledInverseCovariance[j].length; k++) { for (int l = 0; l < subset.length; l++) { pooledInverseCovariance[j][k] += ((double) subset[l].size() / (double) data.length) * covariance[l][j][k]; } } } pooledInverseCovariance = new Matrix(pooledInverseCovariance).inverse() .getArray(); // 计算测试数据属于不同分组的概率 this.probability = new double[subset.length]; // 如果p为false的话，则每种类别的概率相同1/n，n为类别数 if (!p) { double prob = 1.0d / groupList.size(); for (int i = 0; i < groupList.size(); i++) { this.probability[i] = prob; } } else { // 如果p为true的话，则每种类别的概率等于属于该分组的条目数除以总数 for (int i = 0; i < subset.length; i++) { this.probability[i] = (double) subset[i].size() / (double) data.length; } } } private double getGroupMean(int column, ArrayList<double[]> data) { double[] d = new double[data.size()]; for (int i = 0; i < data.size(); i++) { d[i] = data.get(i)[column]; } return getMean(d); } private double getGlobalMean(int column, double data[][]) { double[] d = new double[data.length]; for (int i = 0; i < data.length; i++) { d[i] = data[i][column]; } return getMean(d); } /** * 计算不同分组的判别函数值 * * @param values * @return */ public double[] getDiscriminantFunctionValues(double[] values) { double[] function = new double[groupList.size()]; for (int i = 0; i < groupList.size(); i++) { // tmp就是w的转置 double[] tmp = matrixMultiplication(groupMean[i], pooledInverseCovariance); function[i] = (matrixMultiplication(tmp, values)) - (.5d * matrixMultiplication(tmp, groupMean[i])) + Math.log(probability[i]); } return function; } /** * 根据马氏距离计算算不同分组的判别函数值 * * @param values * @return */ public double[] getMahalanobisDistance(double[] values) { double[] function = new double[groupList.size()]; for (int i = 0; i < groupList.size(); i++) { double[] dist = new double[groupMean[i].length]; for (int j = 0; j < dist.length; j++) dist[j] = values[j] - groupMean[i][j]; function[i] = matrixMultiplication(matrixMultiplication(dist, this.pooledInverseCovariance), dist); } return function; } /** * 通过玛氏距离预测测试数据属于哪个类别 * * @param values * @return the group */ public int predictM(double[] values) { int group = -1; double max = Double.NEGATIVE_INFINITY; double[] discr = this.getMahalanobisDistance(values); for (int i = 0; i < discr.length; i++) { if (discr[i] > max) { max = discr[i]; group = groupList.get(i); } } return group; } /** * 计算测试数据属于不同分组的概率 * * @param values * @return the probabilities */ public double[] getProbabilityEstimates(double[] values) { // TODO return new double[] {}; } /** * 计算权值 * * @return the weights */ public double[] getFisherWeights() { double[] tmp = null; for (int i = 0; i < groupList.size(); i++) { // tmp就是w的转置 tmp = matrixMultiplication(groupMean[i], pooledInverseCovariance); } return tmp; } /** * 预测一个测试数据属于哪个类别 * * @param values * @return the group */ public int predict(double[] values) { int group = -1; double max = Double.NEGATIVE_INFINITY; double[] discr = this.getDiscriminantFunctionValues(values); for (int i = 0; i < discr.length; i++) { if (discr[i] > max) { max = discr[i]; group = groupList.get(i); } } return group; } // /** // * 两个矩阵相乘并将计算结果以 double[][]-array的形式返回. // * // * @param a // * the first matrix // * @param b // * the second matrix // * @return the resulting matrix // */ // @SuppressWarnings("unused") // private double[][] matrixMultiplication(final double[][] matrixA, // final double[][] matrixB) { // int rowA = matrixA.length; // int colA = matrixA[0].length; // int colB = matrixB[0].length; // // double c[][] = new double[rowA][colB]; // for (i