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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.

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 |

The 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.

- Inputs:
- featureSet: a 2d float array of size Nxd, where N is the number of patterns and d is the length of each pattern. class: a text label. The label can take one of two values: 'real' or 'attack'

[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.

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