same as above but with StochasticGA

Theta returns the parameter vector θ for use in persisting the model, and optimizing the model through gradient descent ( or other methods like Newton

Theta returns the parameter vector θ for use in persisting the model, and optimizing the model through gradient descent ( or other methods like Newton

Theta returns the parameter vector θ for use in persisting the model, and optimizing the model through gradient descent ( or other methods like Newton

Theta returns a pointer to the parameter vector theta, which is 1D vector of floats

UpdateLearningRate set's the learning rate of the model to the given float64.

UpdateLearningRate set's the learning rate of the model to the given float64.

UpdateLearningRate set's the learning rate of the model to the given float64.

UpdateLearningRate set's the learning rate of the model to the given float64.

UpdateLearningRate set's the learning rate of the model to the given float64.

UpdateLearningRate set's the learning rate of the model to the given float64.

UpdateSanitize updates the NaiveBayes model's text sanitization transformation function

UpdateStream updates the datastream channel used in learning for the algorithm

UpdateStream updates the datastream channel used in learning for the algorithm

UpdateStream updates the NaiveBayes model's text datastream

UpdateTokenizer updates NaiveBayes model's tokenizer function. The default implementation will convert the input to lower case and split on the space

UpdateTrainingSet takes in a new training set (variable x) as well as a new result set (y). This could be useful if you want to retrain a model starti

UpdateTrainingSet takes in a new training set (variable x) as well as a new result set (y). This could be useful if you want to retrain a model starti

UpdateTrainingSet takes in a new training set (variable x) as well as a new result set (y). This could be useful if you want to retrain a model starti

UpdateTrainingSet takes in a new training set (variable x) as well as a new result set (y). This could be useful if you want to retrain a model starti

UpdateTrainingSet takes in a new training set (variable x.) Will reset the hidden 'guesses' param of the KMeans model.

UpdateTrainingSet takes in a new training set (variable x.)

UpdateTrainingSet takes in a new training set (variable x.) Will reset the hidden 'guesses' param of the KMeans model.

tests basically make a bunch of planes where when the input is above the plane the resultant output is 1.0, else 0.0

tests basically make a bunch of Softmaxs where when the input is above the Softmax the resultant output is 1.0, else 0.0