OptimizationMethod defines a type enum which (using constants declared below) lets a user pass in a optimization method to use when creating a new mod

* TFIDF is a Term Frequency- Inverse Document Frequency model that is created from a trained NaiveBayes model (they are very similar so you can just t

Ascendable is an interface that can be used with batch gradient descent where the parameter vector theta is in one dimension only (so softmax regressi

Datapoint is used in some models where it is cleaner to pass data as a struct rather than just as 1D and 2D arrays like Generalized Linear Models are

DistanceMeasure is any function that maps two vectors of float64s to a float64. Used for vector distance calculations

Frequencies is an array of word frequencies (stored as separate type to be able to sort)

Frequency holds word frequency information so you don't have to hold a map[string]float64 and can, then, sort

* KMeans implements the k-means unsupervised clustering algorithm. The batch version of the model used k=means++ as the instantiation of the model. Th

* KNN implements the KNN algorithm for classification, where an input is classified by finding the K nearest (by some distance metric) data points, an

KernelPerceptron represents the perceptron online learning model, where you input features and the model's state reacts to the input and changes weigh

LeastSquares implements a standard linear regression model with a Least Squares cost function. https://en.wikipedia.org/wiki/Least_squares The model

LocalLinear implements a locally weighted linear least squares regression. https://en.wikipedia.org/wiki/Least_squares http://cs229.stanford.edu/note

Logistic represents the logistic classification model with a sigmoidal hypothesis https://en.wikipedia.org/wiki/Logistic_regression The model is cur

Model is an interface that can Train based on a 2D array of data (called x) and an array (y) of solution data. Model trains in a supervised manor. Pre

* NaiveBayes is a general classification model that calculates the probability that a datapoint is part of a class by using Bayes Rule: P(y|x) = P(x|

OnlineModel differs from Model because the learning can take place in a goroutine because the data is passed through a channel, ending when the channe

OnlineParams is used to pass optional parameters in to creating a new K-Means model if you want to learn using the online version of the model

OnlineTextModel holds the interface for text classifiers. They have the refular learn & predict functions, but don't include an updating callback func

Perceptron represents the perceptron online learning model, where you input features and the model's state reacts to the input and changes weights (pa

SimpleTokenizer splits sentences into tokens delimited by its SplitOn string – space, for example

Softmax represents a softmax classification model in 'k' demensions. It is generally thought of as a generalization of the Logistic Regression model.

StochasticAscendable is an interface that can be used with stochastic gradient descent where the parameter vector theta is in one dimension only (so s

TextDatapoint is the data structure expected for text classification models. The passed types, therefore, are inherently different from the other stru

Tokenizer accepts a sentence as input and breaks it down into a slice of tokens

* TriangleKMeans implements the k-means unsupervised clustering algorithm sped up to use the Triangle Inequality to reduce the number of reduntant dis

Word holds the structural information needed to calculate the probability of

concurrentMap allows concurrency-friendly map access via its exported Get and Set methods

nn represents an encapsulation of the Nearest Neighbor data for each datapoint to facilitate easy sorting

pointInfo stores information needed to use the Triangle Inequality to reduce the number of distance calculations.