// 获取降维后新样本
public static ArrayList GetMDSampleList()
{
int sourceFeatureDimension = Constant.dimension; //获取原始特征向量的维数
sourceSampleList = FeatureHelper.GetFeaturesList();;
int n_samepleNum = sourceSampleList.Count;
MatrixHelper sourceSamples = new MatrixHelper(sourceFeatureDimension, n_samepleNum); //计算原始样本特征矩阵
for (int i = 0; i < n_samepleNum; i++)
{
Features feature = (Features)sourceSampleList[i];
for (int k = 0; k < sourceFeatureDimension; k++)
{
sourceSamples[k, i] = feature.feature_vector[k];
}
}
MatrixHelper W_T = GetMatrix().Transpose();
MatrixHelper targetSamples = W_T * sourceSamples; // S' = W * S
mdSampleList = new ArrayList();
for (int i = 0; i < n_samepleNum; i++)
{
Features feature = (Features)sourceSampleList[i];
Features newSample = new Features();
newSample.classID = feature.classID;
newSample.feature_vector = new double[W_T.Rows];
for (int k = 0; k < W_T.Rows; k++)
{
newSample.feature_vector[k] = targetSamples[k, i];
}
mdSampleList.Add(newSample);
}
return(mdSampleList);
}
private static ArrayList mdSampleList; //降维后的训练样本
#endregion
#region 静态方法
// 获取降维后的新矩阵
public static MatrixHelper GetMatrix()
{
if (matrix == null)
{
#region 数据
int sourceFeatureDimension = MVHelper.FeatureDimension; //从样本类工厂获取原始特征维数
ArrayList sampleClassList = MVHelper.GetClassFeatureList(FeatureHelper.GetFeaturesList()); //从样本类工厂获取所有样本类
int num = 0; // 记录所有样本个数
MatrixHelper S_W = new MatrixHelper(sourceFeatureDimension, sourceFeatureDimension); //类内散布矩阵
MatrixHelper S_B = new MatrixHelper(sourceFeatureDimension, sourceFeatureDimension); //类间散布矩阵
MatrixHelper M_V = new MatrixHelper(sourceFeatureDimension, 1); //总体均值向量
#endregion
#region 计算 总类内散布矩阵 和 总均值向量
for (int i = 0; i < sampleClassList.Count; i++)
{
Sample sample = (Sample)sampleClassList[i];
int n_SampleNum = sample.ClassSampleList.Count; // 各类样本数
S_W += sample.ConMatrix.Multiply(n_SampleNum); //类内散布矩阵 = 类协方差矩阵 * 类样本数;
M_V += sample.MeanVector.Multiply(n_SampleNum); //类向量和 = 类均值向量 * 类样本数
num += n_SampleNum;
}
M_V = M_V.Multiply(1.00 / num); //总体均值向量 = 所有向量和 / 向量个数
#endregion
#region 计算 类间散布矩阵
for (int i = 0; i < sampleClassList.Count; i++)
{
Sample sample = (Sample)sampleClassList[i];
int n_SampleNum = sample.ClassSampleList.Count;
MatrixHelper tempVector = sample.MeanVector - M_V; // Mi - M
MatrixHelper tempMatrix = tempVector * tempVector.Transpose(); // ( Mi - M) * ( Mi - M)t
S_B += tempMatrix.Multiply(n_SampleNum); //类内散布矩阵 = 类协方差矩阵 * 类样本数
}
#endregion
#region 计算最佳投影 W
S_W.InvertGaussJordan(); // S_W 求逆
MatrixHelper featureMatrix = S_W * S_B;
double[] feaValue = new double[sourceFeatureDimension];
MatrixHelper feaMatrix = new MatrixHelper(sourceFeatureDimension, sourceFeatureDimension);
bool tag = featureMatrix.ComputeEvJacobi(feaValue, feaMatrix, 0.00000001); //计算 特征值 和 特征向量
#endregion
#region 对特征值排序
ArrayList feaValueList = new ArrayList();
for (int i = 0; i < feaValue.Length; i++)
{
feaValueList.Add(feaValue[i]);
}
ArrayList feaValueSortList = new ArrayList(feaValueList);
feaValueSortList.Sort(); //特征值排序
#endregion
#region 找出较大的k个特征值对应的特征向量组成 W
int targetDimension = (int)Math.Ceiling(1.0 * num / sampleClassList.Count / 5 + 1); // 自动计算降维后的维数
matrix = new MatrixHelper(sourceFeatureDimension, targetDimension);
for (int i = sourceFeatureDimension - 1; i >= (sourceFeatureDimension - targetDimension); i--)
{
double[] colVector = new double[sourceFeatureDimension];
feaMatrix.GetColVector(feaValueList.IndexOf(feaValueSortList[i]), colVector);
int j = sourceFeatureDimension - 1 - i;
for (int k = 0; k < sourceFeatureDimension; k++)
{
matrix[k, j] = colVector[k];
}
}
#endregion
}
return(matrix);
}
