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 |
| projections | chichan/a-collection-of-data/2 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| subspace | chichan/a-collection-of-linear_machines/1 | Input |
xxxxxxxxxximport bobimport numpydef linear_machines_from_data(data): """Unmangles a bob.machine.LinearMachine from a BEAT Data object""" machines={} data_array=data.machine_array num_of_machine= len(data_array) if num_of_machine>0 : for i in xrange(num_of_machine) : machine= None machine = bob.machine.LinearMachine(data_array[i].weights) machine.biases = data_array[i].biases machine.input_subtract = data_array[i].input_subtract machine.input_divide = data_array[i].input_divide machines[i]=machine print machines[i].biases print machines[num_of_machine-1].biases return machinesdef data_projections(self, data): features=[] for i in xrange(data.shape[0]): features.append({'value': self.machines[i].forward(data[i])}) return featuresclass Algorithm: def __init__(self): self.machines = None self.projections = None self.current_projection = 0 def process(self, inputs, outputs): # retrieve the linear machine once if self.machines is None: inputs['subspace'].next() self.machines = linear_machines_from_data(inputs['subspace'].data) # collect all the image projections for the current template features = inputs['features'].data.value projections = data_projections(self, features) outputs["projections"].write({ "data_array" : projections }) return TrueThe code for this algorithm in Python
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This algorithm linearizes and accumulates images into a buffer, before applying a linear transformation (e.g. using a projection matrix computed by principal component analysis). The linear transformation relies on the Bob library.
The inputs are:
The output projections is a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of accumulated images (with the same identifier), and the number of columns to the output dimensionality after applying the linear transformation.
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.