C# 动态时间规整核返回错误率大于0的支持向量机

我正在使用Accord.net进行研究。 我有一个可变大小的向量序列作为输入，所以我使用DynamicTimeWarping作为MulticlassSupportVectorMachine的内核

IKernel kernel = new DynamicTimeWarping(dimension);
        var machine = new MulticlassSupportVectorMachine(0, kernel, 2);
        // Create the Multi-class learning algorithm for the machine
        var teacher = new MulticlassSupportVectorLearning(machine, inputs.ToArray(), outputs.ToArray());
        // Configure the learning algorithm to use SMO to train the
        //  underlying SVMs in each of the binary class subproblems.
        teacher.Algorithm = (svm, classInputs, classOutputs, i, j) =>
            new SequentialMinimalOptimization(svm, classInputs, classOutputs)
            {
                Complexity = 1.5
            };
        // Run the learning algorithm
        double error = teacher.Run();

输入和输出如下所示：

？inputs.ToArray（）
{double[22][]}
[0]：{double[10656]}
[1] ：{double[9360]}
[2] ：{double[9216]}
[3] ：{double[9864]}
[4] ：{double[10296]}
[5] ：{double[10152]}
[6] ：{double[9936]}
[7] ：{double[9216]}
[8] ：{double[10944]}
[9] ：{double[9504]}
[10] ：{double[11880]}
[11] ：{double[22752]}
[12] ：{double[23688]}
[13] ：{double[29880]}
[14] ：{double[32328]}
[15] ：{double[37224]}
[16] ：{double[30024]}
[17] ：{double[27288]}
[18] ：{double[26064]}
[19] ：{double[22032]}
[20] ：{double[21672]}
[21]：{double[22680]}
？输入[0]
{double[10656]}
[0]: 7.6413027545068823
[1]: -61.607203372756942
[2]: 7.7375128997886513
[3]: -25.704529598536471
[4]: -0.4124927191531238
[5]: 9.6820255661415011
[6]: 3.0674374003781861
[7]: 4.6364653722537668
[8]: 3.3559314278499177
[9]: 0.93969394152714925
[10]: -6.3800159552064146
[11]: 1.4239779356781062
[12]: -2.25349154655782
[13]: -1.5457194406236221
[14]: -0.7612541874802764
[15]: -3.3364791133985348
[16]: 0.67816801816804861
[17]: -3.4117217877592343
[18]: 1.5785492543017225
[19]: 0.31091690789261689
[20]: -2.5526646739208712
[21]: -1.0550268575680164
[22]: -0.9598271201088191
[23]: -1.1797916101998056
[24]: 0.56157735657438412
[25]: -0.16309890421998655
[26]: 0.29765136770064271
[27]: -0.35684735108472643
[28]: -0.52382117896006564
[29]: -0.052087258844925849
[30]: -0.45363669419489172
[31]: -0.16216259086709361
[32]: -0.25958480481802632
[33]: 0.081248839173330589
[34]: -0.019783293216956807
[35]: 0.14139773316964666
[36]: 0.088466551256948273
[37]: -0.019528343614348152
[38]: 0.087073343332064762
[39]: 0.048432068369313144
[40]: -0.0069171431858626713
[41]: -0.0095272766950126042
[42]: 0.016639887499893875
[43]: -0.009108847017642599
[44]: 0.0017424263597747487
[45]: 0.0042160613810267641
[46]: -0.002793626734247919
[47]: 0.00092130299196750763
[48]: 0.0024488939699103319
[49]: 0.0021684669072286468
[50]: 0.000000000000010673294119695543
[51]: -0.000000000000014072530108313123
[52]: 0.000000000000000069063495074940116
[53]：8.73342598612937E-17
[54]: 0.000000000000000030048643853749834
[55]：-6.95380121971215E-17
[56]: 0.00000000000000010093927767292201
[57]: 0.000000000000000046158366228268829
[58]: 0.000000000000000039070100378142324
[59]: 0.00000000000000010492059540665321
[60]: -0.000000000000000014254591247067773
[61]: -0.0000000000000000015902697756329909
[62]: 0.0000000000000000017024249964704589
[63]: 0.0000000000000000010277956708903136
[64]：3.5875442986020568E-28
[65]：-2.21515899884843094E-31
[66]：1.041379591973569E-31
[67]：-4.3897715186113276E-31
[68]：4.24843286416974E-34
[69]：4.3718530099471368E-47
[70]：1.4551856970655856E-50
[71]: 0.0
[72]: 11.031182384920639
[73]: -63.434486026723626
[74]: 1.7731679007864651
[75]: -23.968196466652273
[76]: 2.2753564408666507
[77]: 9.5492641110324534
[78]: 3.4465209481281054
[79]: 4.7979691924966161
[80]: 2.0147801482840508
[81]: 1.1858337013571998
[82]: -4.607944757859336
[83]: 0.75637871318664485
[84]: -3.8397810581420115
[85]: -2.1276086210477514
[86]: -0.4060620782117581
[87]: -2.9313848427777227
[88]: 0.052605148372525556
[89]: -1.5948208186863277
[90]: 0.36061926783486992
[91]: -0.12623742266247567
[92]: -1.1889713301479885
[93]: -0.33299631607409635
[94]: -0.00912650336180437
[95]: -0.52707950657313729
[96]: 0.52115933681848092
[97]: 0.46870463636533816
[98]: -0.18482093982467213
[99]: -0.49350561475314514
<更多。。。（显示了10656项中的前100项。）>
？输出
计数=22
[0]: 0
[1]: 0
[2]: 0
[3]: 0
[4]: 0
[5]: 0
[6]: 0
[7]: 0
[8]: 0
[9]: 0
[10]: 0
[11]: 1
[12]: 1
[13]: 1
[14]: 1
[15]: 1
[16]: 1
[17]: 1
[18]: 1
[19]: 1
[20]: 1
[21]：1

我没有你的原始数据来测试它，但我想到两件事，也许你应该试试。 首先，输入的长度不是恒定的。例如，第一个具有10656长度，第二个具有9360长度的特征向量。就我而言，所有特征向量的长度必须相同。 第二个参数是MulticlassSupportVectorMachine 0。这是输入的长度，您应该正确确定它。

---

另外，我强烈建议您在培训阶段之前缩放数据。

我将给出一个示例，说明如何使用DynamicTimeWarping内核与高斯内核相结合来执行序列分类，这有望获得更好的结果

序列分类问题的第一个任务是正确地组织序列，以提供学习算法。每个序列都可以由多变量向量组成，因此，必须相应地组织输入数据（这是使用序列机时最可能的错误源，因此请花一两分钟来了解以下代码的作用）

这些是我们的输入序列。现在，由于我们正试图执行一个分类问题，我们必须有一个输出类标签与这些序列中的每一个相关联。如果我们有4个序列，那么我们需要4个类标签：

// Now, we will also have different class labels associated which each 
// sequence. We will assign -1 to sequences whose observations start 
// with { 1, 1, 1 } and +1 to those that do not:

int[] outputs =
{
    -1,-1,  // First two sequences are of class -1 (those start with {1,1,1})
        1, 1,  // Last two sequences are of class +1  (don't start with {1,1,1})
};

既然问题已经确定，就必须对其进行一些转换，以便DTW支持向量机能够处理这些问题：

// At this point, we will have to "flat" out the input sequences from double[][][]
// to a double[][] so they can be properly understood by the SVMs. The problem is 
// that, normally, SVMs usually expect the data to be comprised of fixed-length 
// input vectors and associated class labels. But in this case, we will be feeding
// them arbitrary-length sequences of input vectors and class labels associated with
// each sequence, instead of each vector.

double[][] inputs = new double[sequences.Length][];
for (int i = 0; i < sequences.Length; i++)
    inputs[i] = Matrix.Concatenate(sequences[i]);


// Now we have to setup the Dynamic Time Warping kernel. We will have to
// inform the length of the fixed-length observations contained in each
// arbitrary-length sequence:
// 
var kernel = new Gaussian<DynamicTimeWarping>(new DynamicTimeWarping(length: 3));

// Now we can create the machine. When using variable-length
// kernels, we will need to pass zero as the input length:
var svm = new KernelSupportVectorMachine(kernel, inputs: 0);

// Create the Sequential Minimal Optimization learning algorithm
var smo = new SequentialMinimalOptimization(svm, inputs, outputs);

// And start learning it!
double error = smo.Run(); // error will be 0.0

---

感谢您尝试回答！易居

// At this point, we will have to "flat" out the input sequences from double[][][]
// to a double[][] so they can be properly understood by the SVMs. The problem is 
// that, normally, SVMs usually expect the data to be comprised of fixed-length 
// input vectors and associated class labels. But in this case, we will be feeding
// them arbitrary-length sequences of input vectors and class labels associated with
// each sequence, instead of each vector.

double[][] inputs = new double[sequences.Length][];
for (int i = 0; i < sequences.Length; i++)
    inputs[i] = Matrix.Concatenate(sequences[i]);


// Now we have to setup the Dynamic Time Warping kernel. We will have to
// inform the length of the fixed-length observations contained in each
// arbitrary-length sequence:
// 
var kernel = new Gaussian<DynamicTimeWarping>(new DynamicTimeWarping(length: 3));

// Now we can create the machine. When using variable-length
// kernels, we will need to pass zero as the input length:
var svm = new KernelSupportVectorMachine(kernel, inputs: 0);

// Create the Sequential Minimal Optimization learning algorithm
var smo = new SequentialMinimalOptimization(svm, inputs, outputs);

// And start learning it!
double error = smo.Run(); // error will be 0.0

// At this point, we should have obtained an useful machine. Let's
// see if it can understand a few examples it hasn't seem before:

double[][] a = 
{ 
    new double[] { 1, 1, 1 },
    new double[] { 7, 2, 5 },
    new double[] { 2, 5, 1 },
};

double[][] b =
{
    new double[] { 7, 5, 2 },
    new double[] { 4, 2, 5 },
    new double[] { 1, 1, 1 },
};

// Following the aforementioned logic, sequence (a) should be
// classified as -1, and sequence (b) should be classified as +1.

int resultA = System.Math.Sign(svm.Compute(Matrix.Concatenate(a))); // -1
int resultB = System.Math.Sign(svm.Compute(Matrix.Concatenate(b))); // +1