BEAT - anthony_larcher/DER

This algorithm is a sequential one. The platform will call its process() method once per data incoming on its inputs.

This algorithm is an analyzer. It can only be used on analysis blocks.

Endpoint Groups 1

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.

Group: group0

Endpoint Name	Data Format	Nature
DER	system/float/1	Input
file_info	anthony_larcher/file_info_sd/1	Input
speech_duration	system/float/1	Input

Results 1

Analyzers may produce any number of results. Once experiments using this analyzer are done, you may display the results or filter experiments using criteria based on them.

Name	Type
DER	float32

# You may import any python packages that will be available in the environment you will run this algorithm in
# Environments can change based on the experiment's settings

import numpy as np

class Algorithm:
    # initialise fields to store cross-input data (e.g. machines, aggregations, etc.)
    def __init__(self):
        self.total_der = 0.0
        self.total_time = 0.0

# this will be called each time the sync'd input has more data available to be processed
    def process(self, inputs, dataloader, output):
        # Groups available:
        # Group 0:
        #   Input "DER" with type  "system/float/1"
        #   Input "speech_duration" with type  "system/float/1"
        #   Input "file_info" with type  "allies/file_info/1"

# Results available:
        #   Result "DER" with type "float32"

# to check if there is more data waiting in the inputs
        # (if it is False, you have processed all the inputs and this "process" function won't be called again):
        #       if inputs.hasMoreData():

# to check if a specific input is done:
        #       if inputs["input1"].isDataUnitDone():

# to manually fetch the next input of a specific input
        # (e.g. the block is not sync'd to the input but you want the input immediately)
        #       inputs['input1'].next()
        # you can then access that input value as normal:
        #       self.val1 = inputs['input1'].data

# to get the data for an input (note that the value will be of the type specified in the metadata!):
        #       data_value = inputs['input1'].data

# to write to an output:
        #       outputs['output1'].write({
        #           'output_field_1': 1,
        #           'output_field_2': 'output'
        #       })

# always return True, it signals BEAT to continue processing
        fi =  inputs["file_info"].data.file_id
        sup = inputs["file_info"].data.supervision
        print("file {}: DER = {}, supervision: {}".format(fi, inputs["DER"].data.value, sup))
        tm = inputs["speech_duration"].data.value
        self.total_time += tm
        self.total_der += tm * inputs["DER"].data.value

if not inputs.hasMoreData():
            output.write({"DER": np.cast["float32"](self.total_der/self.total_time)})

return True

The code for this algorithm in Python
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Could not find any documentation for this object.

No experiments are using this algorithm.

Allies (0.0.1) 1

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.

algorithms anthony_larcher DER_collate 1

Endpoint Groups 1

Group: group0

Results 1

algorithms

anthony_larcher

DER_collate

1