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 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 kmeans_trainer.train(kmeans, training_set) (variances, weights) = kmeans.get_variances_and_weights_for_each_cluster(training_set) means = kmeans.means # 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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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.