Implement the naive gaussian Bayes estimator. The training must be done from scikit-learn.
The parameters can be easily generated from the scikit-learn object. Some examples are given in DSP/Testing/PatternGeneration/Bayes.py
- Parameters
-
[in] | *S | points to a naive bayes instance structure |
[in] | *in | points to the elements of the input vector. |
[out] | *pOutputProbabilities | points to a buffer of length numberOfClasses containing estimated probabilities |
[out] | *pBufferB | points to a temporary buffer of length numberOfClasses |
- Returns
- The predicted class
- Parameters
-
[in] | *S | points to a naive bayes instance structure |
[in] | *in | points to the elements of the input vector. |
[out] | *pOutputProbabilities | points to a buffer of length numberOfClasses containing estimated probabilities |
[out] | *pBufferB | points to a temporary buffer of length numberOfClasses |
- Returns
- The predicted class