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 |
|---|---|---|
| featureSet | system/array_2d_floats/1 | Input |
| class | system/text/1 | Input |
| subspace | tutorial/linear_machine/1 | Output |
xxxxxxxxxximport numpyimport bobimport bob.learn.linear#same functionality as version 2, but implemented using bob-v2.class Algorithm: def __init__(self): self.data = [[], []] def process(self, inputs, outputs): # accumulates the input data in different # containers for hit or miss flat_image = inputs["featureSet"].data.value if inputs["class"].data.text == 'real': self.data[1].append(flat_image) #positive/real class else: self.data[0].append(flat_image) #negative/attack class if not(inputs.hasMoreData()): # trains the LDA projection self.data[0] = numpy.vstack(self.data[0]) self.data[1] = numpy.vstack(self.data[1]) #first, normalize the input features trSet = numpy.vstack((self.data[1], self.data[0])) trMean = trSet.mean(axis=0) trStd = trSet.std(axis=0) posTS = self.data[1] - trMean posTS /= trStd negTS = self.data[0] - trMean negTS /=trStd del trSet trainer = bob.learn.linear.FisherLDATrainer(use_pinv=True) machine, eigen_values = trainer.train([negTS, posTS]) machine.resize(machine.shape[0], 1) # save the normalization parameters machine.input_subtract = trMean machine.input_divide = trStd #using training-data scores to 'set the sign' of the classifier flipSign = 1 scores_train_pos = machine(posTS)[:,0] scores_train_neg = machine(negTS)[:,0] medPosScore = numpy.median(scores_train_pos) medNegScore = numpy.median(scores_train_neg) if medPosScore < medNegScore : flipSign = -1 machine.weights = flipSign*machine.weights # outputs data outputs["subspace"].write({ 'input_subtract': machine.input_subtract, 'input_divide': machine.input_divide, 'weights': machine.weights, 'biases': machine.biases, }) return TrueThe code for this algorithm in Python
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This algorithm will run Linear Discriminant Analysis [LDA] for a binary classification problem using images as inputs.
| [LDA] | http://en.wikipedia.org/wiki/Linear_Discriminant_analysis |
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| sbhatta/sbhatta/iqm-face-antispoofing-test/2/replay2-antispoofing-iqm-lda | replay/2@grandtest | sbhatta/iqm_spoof_eer_analyzer/9 |
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.