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

This algorithm is **splittable**

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 |
---|---|---|

image | system/array_2d_uint8/1 | Input |

id | system/uint64/1 | Input |

projections | system/array_2d_floats/1 | Output |

Endpoint Name | Data Format | Nature |
---|---|---|

subspace_lda | tutorial/linear_machine/1 | Input |

subspace_pca | tutorial/linear_machine/1 | Input |

The code for this algorithm in Python

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This algorithm linearizes and accumulates images into a buffer, before applying two consecutive linear transformations (using projection matrices computed by principal component analysis (PCA) and by linear discriminant analysis (LDA)). The linear transformations rely on the Bob library.

The inputs are:

- image: an image as a two-dimensional arrays of floats (64 bits)
- id: an identifier which is used as follows: all images with the
- same identifier are accumulated into the same buffer

- subspace_pca: a PCA-learnt linear transformation as a collection of
- weights, biases, input subtraction and input division factors.

- subspace_lda: a LDA-learnt linear transformation as a collection of
- weights, biases, input subtraction and input division factors.

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 transformations.

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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