具有动态时间扭曲内核的SVM返回错误率大于0
我在研究中使用Accord.net。 我有一个可变大小的矢量序列作为输入，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]} ?inputs[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]: -2.215158998843094E-31 [66]: 1.041379591973569E-31 [67]: -4.3897715186113276E-31 [68]: 4.248432864156974E-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 ?outputs Count = 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
使用该代码，错误返回为0.5。
问题：
-
这是否意味着我的训练数据存在问题？
-
有没有其他内核可以用于我的可变大小序列？
谢谢。
我将举例说明如何使用DynamicTimeWarping内核结合高斯内核进行序列分类，这有望提供更好的结果。
序列分类问题的第一个任务是正确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（这是使用序列机器时最可能的错误来源，所以请花一两个时间来理解以下代码在做什么）。
// Suppose you have sequences of multivariate observations, and that // those sequences could be of arbitrary length. On the other hand, // each observation have a fixed, delimited number of dimensions. // In this example, we have sequences of 3-dimensional observations. // Each sequence can have an arbitrary length, but each observation // will always have length 3: double[][][] sequences = { new double[][] // first sequence { new double[] { 1, 1, 1 }, // first observation of the first sequence new double[] { 1, 2, 1 }, // second observation of the first sequence new double[] { 1, 4, 2 }, // third observation of the first sequence new double[] { 2, 2, 2 }, // fourth observation of the first sequence }, new double[][] // second sequence (note that this sequence has a different length) { new double[] { 1, 1, 1 }, // first observation of the second sequence new double[] { 1, 5, 6 }, // second observation of the second sequence new double[] { 2, 7, 1 }, // third observation of the second sequence }, new double[][] // third sequence { new double[] { 8, 2, 1 }, // first observation of the third sequence }, new double[][] // fourth sequence { new double[] { 8, 2, 5 }, // first observation of the fourth sequence new double[] { 1, 5, 4 }, // second observation of the fourth sequence } };
这些是我们的输入序列。 现在，由于我们正在尝试执行分类问题，因此我们必须具有与每个序列相关联的输出类标签。 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}) };
现在已经定义了问题，它必须进行一些转换，以便DTW-SVM可以处理它们：
// 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(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
在此之后，应该已经创建了机器。 参数C应该由机器自动猜测，但您可以稍后对其进行微调以尝试提高机器的泛化性能。 在任何情况下，
// 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
我没有你的原始数据来测试它，但有两件事我想起你可能会尝试它们。 首先，您的输入没有恒定的长度。 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。 其次，您将第一个参数设置为MulticlassSupportVectorMachine 0.这是输入的长度，您应该正确地确定它。 另外，我强烈建议您在培训阶段之前扩展数据。