The problem might have been that the teaching algorithm expects the training sequences to be in the form `double[12][32][18]`

, rather than `double[12][32,18]`

. The training data should be a collection of sequences of multivariate points. It should also be necessary to note that, if you have 11 possible classes of gestures, the integer labels given in the `int[12]`

array should be comprised of values between 0 and 10 only.

Thus if you have 12 gesture samples, each containing 32 frames, and each frame is a vector of 18 points, you should be feeding the teacher with a `double[12][32][18]`

array containing the observations and a `int[12]`

array containing the expected class labels.

The example below, extracted from the HiddenMarkovClassifierLearning documentation page should help to give an idea how the vectors should be organized!

```
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 2, 3 },
new double[] { 3, 4 },
new double[] { 4, 5 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4, 3 },
new double[] { 3, 2 },
new double[] { 2, 1 },
new double[] { 1, 0 },
new double[] { 0, -1 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
```

In the above code, we have set the problem for 2 sequences of observations, where each sequence containing 5 observations, and in which each observations is comprised of 2 values. As you can see, this is a double[2][5][2] array. The array of class labels is given by a int[2], containing only values ranging from 0 to 1.

Now, to make the example more complete, we can continue creating and training the model using the following code:

```
var initialDensity = new MultivariateNormalDistribution(2);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier
```