This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Endpoint Groups 3

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: probes

Endpoint Name	Data Format	Nature
comparison_ids	system/array_1d_text/1	Input
probe_statistics	tutorial/gmm_statistics/1	Input
probe_id	system/text/1	Input
probe_client_id	system/text/1	Input
scores	elie_khoury/string_probe_scores/1	Output

Group: templates

Endpoint Name	Data Format	Nature
template_client_id	system/text/1	Input
template_id	system/text/1	Input
template_model	tutorial/gmm/1	Input

Unnamed group

Endpoint Name	Data Format	Nature
ubm	tutorial/gmm/1	Input

import bob
import numpy
from bob.machine import GMMMachine
from bob.machine import GMMStats

def gmm_from_data(data):
    """Unmangles a bob.machine.GMMMachine from a BEAT Data object"""

dim_c, dim_d = data.means.shape
    gmm = GMMMachine(dim_c, dim_d)
    gmm.weights = data.weights
    gmm.means = data.means
    gmm.variances = data.variances
    gmm.variance_thresholds = data.variance_thresholds
    return gmm

def stats_from_data(data):
    """Unmangles a bob.machine.GMMStats from a BEAT Data object"""

dim_c, dim_d = data.sum_px.shape
    stat = GMMStats(dim_c, dim_d)
    stat.t = long(data.t)
    stat.n = data.n
    stat.sum_px = data.sum_px
    stat.sum_pxx = data.sum_pxx
    return stat

class Algorithm:

def __init__(self):
        self.ubm       = None
        self.templates = None

def process(self, inputs, outputs):

# retrieve the UBM once
        if self.ubm is None:
            inputs['ubm'].next()
            self.ubm = gmm_from_data(inputs['ubm'].data)

# retrieve all the templates once
        if self.templates is None:
            self.templates = {}
            group = inputs.groupOf('template_model')

while group.hasMoreData():
                group.next()

template_id = group['template_id'].data.text

self.templates[template_id] = dict(
                    client_id = group['template_client_id'].data.text,
                    model = gmm_from_data(group['template_model'].data),
                )

# process the probe
        comparison_ids = inputs['comparison_ids'].data.text
        statistics = stats_from_data(inputs['probe_statistics'].data)

scores = []
        for comparison_id in comparison_ids:
            template_client_identity = self.templates[comparison_id]['client_id']

score = bob.machine.linear_scoring([self.templates[comparison_id]['model']],
                                               self.ubm, [statistics])[0,0]

scores.append({
                'template_identity': template_client_identity,
                'score': score,
            })

outputs['scores'].write({
                'client_identity': inputs['probe_client_id'].data.text,
                'scores': scores
            },
            end_data_index=inputs['probe_id'].data_index_end
        )

return True

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

For a given set of feature vectors, a Gaussian Mixture Model (GMM) of the target client and an UBM-GMM, this algorithm computes the scoring using the linear scoring implemented on the `Bob https://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/machine/generated/bob.machine.linear_scoring.html?highlight=linear%20scoring#bob.machine.linear_scoring`_

This algorithm relies on the Bob library.

Docutils System Messages

System Message: ERROR/3 (<string>, line 1); backlink

Unknown target name: "bob https://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/machine/generated/bob.machine.linear_scoring.html?highlight=linear%20scoring#bob.machine.linear_scoring".

No experiments are using this algorithm.

This algorithm was never executed.

algorithms elie_khoury gmm_scoring 2

Endpoint Groups 3

Group: probes

Group: templates

Unnamed group

Docutils System Messages

algorithms

elie_khoury

gmm_scoring

2