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Computes the similarity between two histograms

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

comparison_ids | system/array_1d_uint64/1 | Input |

probe | system/array_1d_floats/1 | Input |

probe_id | system/uint64/1 | Input |

probe_client_id | system/uint64/1 | Input |

scores | tutorial/probe_scores/1 | Output |

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

model_id | system/uint64/1 | Input |

model | system/array_1d_floats/1 | Input |

model_client_id | system/uint64/1 | Input |

The code for this algorithm in Python

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This algorithm computes the similarity between two histograms using bob.math.chi_square.
For two given histograms *H* and *M*, the χ^{2} distance is defined as:

χ^{2}(*H*, *M*) = ^{ }⎲⎳_{i}((*H*_{i} − *M*_{i})^{2})/((*H*_{i} + *M*_{i}))

Since χ^{2} is a distance measure, but scores should be similarity values (with higher values being better), the negative χ^{2} distance is actually returned.

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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