/// <summary>2个数据集的相关度计算类</summary>
public static class Correlation { /// <summary>计算皮尔逊积差相关系数</summary> /// <param name="dataA">数据样本A.</param> /// <param name="dataB">数据样本B.</param> /// <returns>返回皮尔逊积差相关系数.</returns> public static double Pearson(IEnumerable<double> dataA, IEnumerable<double> dataB) { int n = 0; double r = 0.0; double meanA = 0; double meanB = 0; double varA = 0; double varB = 0; using (IEnumerator<double> ieA = dataA.GetEnumerator()) using (IEnumerator<double> ieB = dataB.GetEnumerator()) { while (ieA.MoveNext()) { if (!ieB.MoveNext()) { throw new ArgumentOutOfRangeException("dataB", Resources.ArgumentArraysSameLength); } double currentA = ieA.Current; double currentB = ieB.Current; double deltaA = currentA - meanA; double scaleDeltaA = deltaA/++n; double deltaB = currentB - meanB; double scaleDeltaB = deltaB/n; meanA += scaleDeltaA; meanB += scaleDeltaB; varA += scaleDeltaA*deltaA*(n - 1); varB += scaleDeltaB*deltaB*(n - 1); r += (deltaA*deltaB*(n - 1))/n; } if (ieB.MoveNext()) { throw new ArgumentOutOfRangeException("dataA", Resources.ArgumentArraysSameLength); } } return r/Math.Sqrt(varA*varB); } /// <summary>计算加权皮尔逊积差相关系数.</summary> /// <param name="dataA">数据样本A.</param> /// <param name="dataB">数据样本B.</param> /// <param name="weights">数据权重.</param> /// <returns>加权皮尔逊积差相关系数.</returns> public static double WeightedPearson(IEnumerable<double> dataA, IEnumerable<double> dataB, IEnumerable<double> weights) { int n = 0; double meanA = 0; double meanB = 0; double varA = 0; double varB = 0; double sumWeight = 0; double covariance = 0; using (IEnumerator<double> ieA = dataA.GetEnumerator()) using (IEnumerator<double> ieB = dataB.GetEnumerator()) using (IEnumerator<double> ieW = weights.GetEnumerator()) { while (ieA.MoveNext()) { if (!ieB.MoveNext()) { throw new ArgumentOutOfRangeException("dataB", Resources.ArgumentArraysSameLength); } if (!ieW.MoveNext()) { throw new ArgumentOutOfRangeException("weights", Resources.ArgumentArraysSameLength); } ++n; double xi = ieA.Current; double yi = ieB.Current; double wi = ieW.Current; double temp = sumWeight + wi; double deltaX = xi - meanA; double rX = deltaX*wi/temp; meanA += rX; varA += sumWeight*deltaX*rX; double deltaY = yi - meanB; double rY = deltaY*wi/temp; meanB += rY; varB += sumWeight*deltaY*rY; sumWeight = temp; covariance += deltaX*deltaY*(n - 1)*wi/n; } if (ieB.MoveNext()) { throw new ArgumentOutOfRangeException("dataB", Resources.ArgumentArraysSameLength); } if (ieW.MoveNext()) { throw new ArgumentOutOfRangeException("weights", Resources.ArgumentArraysSameLength); } } return covariance/Math.Sqrt(varA*varB); } /// <summary>计算皮尔逊积差相关矩阵</summary> /// <param name="vectors">数据矩阵</param> /// <returns>皮尔逊积差相关矩阵.</returns> public static Matrix<double> PearsonMatrix(params double[][] vectors) { var m = Matrix<double>.Build.DenseIdentity(vectors.Length); for (int i = 0; i < vectors.Length; i++) { for (int j = i + 1; j < vectors.Length; j++) { var c = Pearson(vectors[i], vectors[j]); m.At(i, j, c); m.At(j, i, c); } } return m; } /// <summary> 计算皮尔逊积差相关矩阵</summary>