public void GaussianDynamicalTimeWarpingConstructorTest3()
{
// 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:
//
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
Assert.AreEqual(0, error);
Assert.AreEqual(-1, resultA);
Assert.AreEqual(+1, resultB);
}
