ダイナミックタイムワーピングカーネルを使ってマルチクラスSVMを作成したい

Question

私はaccord.netを使用しています。次の例は正常に動作していますが、私はMultiクラスの分類子を作りたいと思います。 MulticlassSupportVectorMachine（）関数を使用しようとしましたが、特定の入力に対して正しい出力を出さなかった動的タイムワーピングクラスカーネルに対して、0.6エラーでデータを訓練しました。

// 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 
} 
}; 
// 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}) 
}; 

// 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: 
// 
DynamicTimeWarping kernel = 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) 
{ 
Complexity = 1.5 
}; 

// 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 

私は以上の二つのタイプの入力のための機械を訓練し、入力の種類ごとに出力ラベルを持ってMulticlassSupportVectorMachine（）を使用して、マルチクラスSVM分類器を実装するために助けが必要。 P.S：MulticlassSupportVectorMachine（）関数が動的タイムワーピングカーネルをサポートしていない場合。上記のDynamic Timeワーピングカーネルで1つのMulticlass svmテクニックを使う方法と、1つのテクニックを使って複数のクラシファイアを作る方法を教えてください。 あなたのご協力は非常に高く評価されます。 ありがとうございました。

Answer 1

このコードは私

var smo = new MulticlassSupportVectorLearning<DynamicTimeWarping, double[][]>() 
      { 
       // Set the parameters of the kernel 
       Kernel = new DynamicTimeWarping(alpha: 1, degree: 1) 
      }; 
      // And use it to learn a machine! 
      var svm = smo.Learn(words, labels); 
      // Create the multi-class learning algorithm for the machine 
      var calibration = new MulticlassSupportVectorLearning<DynamicTimeWarping, double[][]>() 
      { 
       Model = svm, // We will start with an existing machine 

       // Configure the learning algorithm to use SMO to train the 
       // underlying SVMs in each of the binary class subproblems. 
       Learner = (param) => new ProbabilisticOutputCalibration<DynamicTimeWarping, double[][]>() 
       { 
        Model = param.Model // Start with an existing machine 
       } 
      }; 
      // Configure parallel execution options 
      calibration.ParallelOptions.MaxDegreeOfParallelism = 1; 
      // Learn a machine 
      calibration.Learn(words, labels); 
      // Obtain class predictions for each sample 
      int[] predicted = svm.Decide(words); 
      int[] expected = new int[words.Length]; 


      double correct = 0; 
      for (int i = 0; i < words.Length; i++) 
      { 
       expected[i] = labels[i]; 
       predicted[i] = svm.Decide(words[i]); 
       if (svm.Decide(words[i]) == labels[i]) 
       { 
        correct++; 
       } 

      } 
      string Accurecy = "SMO Accurecy = " + (correct/predicted.Length).ToString() + Environment.NewLine; // ori