This version of the UBM training, normalize the data before the kmeans training.
Forked from tpereira/ubm_training/2
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| features | system/array_2d_floats/1 | Input |
| ubm | tutorial/gmm/1 | Output |
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
| Name | Description | Type | Default | Range/Choices |
|---|---|---|---|---|
| number-of-gaussians | uint32 | 100 | ||
| maximum-number-of-iterations | uint32 | 10 |
xxxxxxxxxximport bobimport numpyfrom bob.machine import GMMMachineclass Algorithm: def __init__(self): self.number_of_gaussians = 100 self.max_iterations = 10 self.data = [] def setup(self, parameters): self.number_of_gaussians = parameters.get('number-of-gaussians', self.number_of_gaussians) self.max_iterations = parameters.get('maximum-number-of-iterations', self.max_iterations) return True def __normalize_std_array__(self, array): """Applies a unit variance normalization to an array""" # Initializes variables n_samples = array.shape[0] length = array.shape[1] mean = numpy.ndarray((length,), 'float64') std = numpy.ndarray((length,), 'float64') mean.fill(0) std.fill(0) # Computes mean and variance for k in range(n_samples): x = array[k,:].astype('float64') mean += x std += (x ** 2) mean /= n_samples std /= n_samples std -= (mean ** 2) std = std ** 0.5 # sqrt(std) ar_std_list = [] for k in range(n_samples): ar_std_list.append(array[k,:].astype('float64') / std) ar_std = numpy.vstack(ar_std_list) return (ar_std,std) def __multiply_vectors_by_factors__(self, matrix, vector): """Used to unnormalize some data""" for i in range(0, matrix.shape[0]): for j in range(0, matrix.shape[1]): matrix[i, j] *= vector[j] def process(self, inputs, outputs): self.data.append(inputs["features"].data.value) if not(inputs.hasMoreData()): # create array set used for training training_set = numpy.vstack(self.data) input_size = training_set.shape[1] # create the KMeans and UBM machine kmeans = bob.machine.KMeansMachine(int(self.number_of_gaussians), input_size) ubm = bob.machine.GMMMachine(int(self.number_of_gaussians), input_size) # create the KMeansTrainer kmeans_trainer = bob.trainer.KMeansTrainer() kmeans_trainer.initialization_method = bob.trainer.KMeansTrainer.RANDOM_NO_DUPLICATE kmeans_trainer.max_iterations = int(self.max_iterations) # train using the KMeansTrainer normalized_data, std_array = self.__normalize_std_array__(training_set) #normlize before training kmeans_trainer.train(kmeans, normalized_data) (variances, weights) = kmeans.get_variances_and_weights_for_each_cluster(normalized_data) means = kmeans.means #undoing the normalization self.__multiply_vectors_by_factors__(means, std_array) self.__multiply_vectors_by_factors__(variances, std_array ** 2) # initialize the GMM ubm.means = means ubm.variances = variances ubm.weights = weights # train the GMM trainer = bob.trainer.ML_GMMTrainer() trainer.max_iterations = int(self.max_iterations) trainer.train(ubm, training_set) # outputs data outputs["ubm"].write({ 'weights': ubm.weights, 'means': ubm.means, 'variances': ubm.variances, 'variance_thresholds': ubm.variance_thresholds, }) return TrueThe code for this algorithm in Python
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For a Gaussian Mixture Models (GMM), this algorithm implements the Universal Background Model (UBM) training described in [Reynolds2000].
First, this algorithm estimates the means, diagonal covariance matrix and the weights of each gaussian component using the KMeans clustering. After, only the means are re-estimated using the Maximum Likelihood (ML) estimator.
This version of the UBM training normalizes the input data before the kmeans training.
This algorithm relies on the Bob library.
The input, features, is a training set of floating point vectors as a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of training samples, and the number of columns to the dimensionality of the training samples. The output, ubm, is the GMM trained using the ML estimator.
| [Reynolds2000] |
|
This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.