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

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: main

Endpoint Name Data Format Nature
image system/array_2d_uint8/1 Input
client_id system/uint64/1 Input
subspace_lda tutorial/linear_machine/1 Output
subspace_pca tutorial/linear_machine/1 Output

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
number-of-pca-components uint32 5
number-of-lda-components uint32 2

The code for this algorithm in Python
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This algorithm performs principal component analysis (PCA) [PCA] on a given dataset using the singular value decomposition (SVD) [SVD], followed by linear discriminant analysis (LDA) [LDA].

This implementation relies on the Bob library.

The inputs are:

  • image: A training set of floating point vectors as a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of training samples, and the number of columns to the dimensionality of the training samples.
  • client_id: Client (class/subject) identifier as an unsigned 64 bits integer.

The outputs are subspace_pca and subspace_lda for the PCA and LDA transformation, respectively.

[SVD]http://en.wikipedia.org/wiki/Singular_value_decomposition
[PCA]http://en.wikipedia.org/wiki/Principal_component_analysis
[LDA]http://en.wikipedia.org/wiki/Linear_discriminant_analysis
No experiments are using this algorithm.

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.

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