* implement sort.Interface for Frequency list *// Len gives the length of a frequency array

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ if normalize

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ if normalize

NormalizePoint is the same as Normalize, but it only operates on one singular datapoint, normalizing it's value to unit length.

NewLogistic takes in a learning rate alpha, a regularization parameter value (0 means no regularization, higher value means higher bias on the model,)

NewLeastSquares returns a pointer to the linear model initialized with the learning rate alpha, the training set trainingSet, and the expected results

Learn takes the struct's dataset and expected results and runs batch gradient descent on them, optimizing theta so you can predict based on those resu

NewSoftmax takes in a learning rate alpha, a regularization parameter value (0 means no regularization, higher value means higher bias on the model,)

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ if normalize

Learn takes the struct's dataset and expected results and runs gradient descent on them, optimizing theta so you can predict accurately based on those

diff returns the square magnitude of the vector subtraction between u and v. This is also known as the Squared Euclidean Distance: diff(u, v) == |u -

Learn takes the struct's dataset and expected results and runs batch gradient descent on them, optimizing theta so you can predict based on those resu

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ if normalize

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ

maxI returns the index of the maximum value of a slice of float64's

SaveDataToCSV takes in a absolute filepath, as well as a 2D array of 'X' values and a 1D array of 'Y', or expected values, concatenates the format to

NewKernelPerceptron takes in a learning rate alpha, the number of features (not including the constant term) being evaluated by the model, the update

OnlineLearn runs off of the datastream within the Perceptron structure. Whenever the model makes a wrong prediction the parameter vector theta is upda

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ

String implements the fmt interface for clean printing. Here we're using it to print the model as the equation h(θ)=... where h is the perceptron hypo

Predict takes in a document, predicts the class of the document based on the training data passed so far, and returns the class estimated for the docu

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ if normalize

NewNaiveBayes returns a NaiveBayes model the given number of classes instantiated, ready to learn off the given data stream. The sanitization function

LinearKernel is the base kernel function. It will return a valid kernel for use within models that can use the Kernel Trick. The resultant kernel just

NewPerceptron takes in a learning rate alpha, the number of features (not including the constant term) being evaluated by the model, the update callba

Normalize takes in an array of arrays of inputs as well as the corresponding array of solutions and normalizes each 'row' of data to unit vector lengt

OnlineLearn runs off of the datastream within the Perceptron structure. Whenever the model makes a wrong prediction the parameter vector theta is upda

OnlineLearn lets the NaiveBayes model learn from the datastream, waiting for new data to come into the stream from a separate goroutine

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ

Probability takes in a small document, returns the estimated class of the document based on the model as well as the probability that the model is par

OnlineLearn runs similar to using a fixed dataset with Stochastic Gradient Descent, but it handles data by passing it as a channal, and returns errors

insertSorted takes a array v, and inserts u into the list in the position such that the list is sorted inversely. The function will not change the len

GaussianKernel takes in a parameter for sigma (σ) and returns a valid (Gaussian) Radial Basis Function Kernel. If the input dimensions aren't valid, t

Get looks up a word from h's Word map and should be used in place of a direct map lookup. The only caveat is that it will always return the 'success'

NewKMeans returns a pointer to the k-means model, which clusters given inputs in an unsupervised manner. The algorithm only has one optimization metho

NewTriangleKMeans returns a pointer to the k-means model, which clusters given inputs in an unsupervised manner. The differences between this algorith

OnlineLearn runs similar to using a fixed dataset with Stochastic Gradient Descent, but it handles data by passing it as a channel, and returns errors

Learn takes the struct's dataset and expected results and runs batch gradient descent on them, optimizing theta so you can predict based on those resu

Learn takes the struct's dataset and expected results and runs batch gradient descent on them, optimizing theta so you can predict based on those resu

PolynomialKernel takes in an optional constant (where any extra args passed will be added and count as the constant,) and a main arg of the degree of

TFIDF returns the TermFrequency- InverseDocumentFrequency of a word within a corpus given by the trained NaiveBayes model Look at the TFIDF docs to s

TanhKernel takes in a required Kappa modifier parameter (defaults to 1.0 if 0.0 given,) and optional float64 args afterwords which will be added toget

Tokenize splits input sentences into a lowecase slice of strings. The tokenizer's SlitOn string is used as a delimiter and it

LoadDataFromCSV takes in a path to a CSV file and loads that data into a Golang 2D array of 'X' values and a Golang 1D array of 'Y', or expected resul

NewKNN returns a pointer to the k-means model, which clusters given inputs in an unsupervised manner. The algorithm only has one optimization method (

Predict takes in a variable x (an array of floats,) and finds the value of the hypothesis function given the current parameter vector θ if normalize

EuclideanDistance returns the distance betweek two float64 vectors. NOTE that this function does not check that the vectors are different lengths (to

GradientAscent operates on a Ascendable model and further optimizes the parameter vector Theta of the model, which is then used within the Predict fun

InverseDocumentFrequency returns the 'uniqueness' of a word within the corpus defined within a trained NaiveBayes model. Look at the TFIDF docs to se

LearningRate returns the learning rate α to be used in Gradient Descent as the modifier term

LoadDataFromCSVToStream loads a CSV data file just like LoadDataFromCSV, but it pushes each row into a data channel as it scans. This is useful for ve

ManhattanDistance returns the manhattan distance between teo float64 vectors. This is the sum of the differences between each value Example Points: .

MaxIterations returns the maximum number of iterations to try using gradient ascent. Might return after less if strong convergance is detected, but it

MostImportantWords runs TFIDF on a whole document, returning the n most important words in the document. If n is greater than the number of words then

NewLocalLinear returns a pointer to the linear model initialized with the learning rate alpha, the training set trainingSet, and the expected results

OnlineLearn runs similar to using a fixed dataset with Stochastic Gradient Descent, but it handles data by passing it as a channal, and returns errors

PersistToFile takes in an absolute filepath and saves the parameter vector θ to the file, which can be restored later. The function will take paths fr

Restore takes the bytes of a NaiveBayes model and restores a model to it. It defaults the sanitizer to base.OnlyWordsAndNumbers and the tokenizer to t

RestoreFromFile takes in a path to a parameter vector theta and assigns the model it's operating on's parameter vector to that. The only parameters no

Set sets word k's value to v in h's Word map

StochasticGradientAscent operates on a StochasticAscendable model and further optimizes the parameter vector Theta of the model, which is then used wi

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

computeCentroidDistanceMatrix, as said in the function name, computes the centroid distance matrix, saving it to the model. Note that because we only

weight corresponds to the weight given between two datapoints (based on how 'far apart' they are.) w[i] = exp(-1 * |x[i] - x|^2 / 2σ^2)

Dij returns the derivative of the cost function J(θ) with respect to the j-th parameter of the hypothesis, θ[j], for the training example x[i]. Used i

Dij returns the derivative of the cost function J(θ) with respect to the j-th parameter of the hypothesis, θ[j], for the training example x[i]. Used i

Dj returns the derivative of the cost function J(θ) with respect to the j-th parameter of the hypothesis, θ[j], for the training example x[i]. Called

Dj returns the partial derivative of the cost function J(θ) with respect to theta[k] where theta is the parameter vector associated with our hypothesi

Dj returns the partial derivative of the cost function J(θ) with respect to theta[j] where theta is the parameter vector associated with our hypothesi

Dj returns the derivative of the cost function J(θ) with respect to the j-th parameter of the hypothesis, θ[j]. Called as Dj(j)

Examples returns the number of examples in the training set the model is using

* OnlineLearn implements a variant of the K-Means learning algorithm to work with streams of data. The basis of the model is discusses within this (ht

PersistToFile takes in an absolute filepath and saves the parameter vector θ to the file, which can be restored later. The function will take paths fr

PersistToFile takes in an absolute filepath and saves the parameter vector θ to the file, which can be restored later. The function will take paths fr

PersistToFile takes in an absolute filepath and saves the parameter vector θ to the file, which can be restored later. The function will take paths fr

PersistToFile takes in an absolute filepath and saves the parameter vector θ to the file, which can be restored later. The function will take paths fr

PersistToFile takes in an absolute filepath and saves the parameter vector θ to the file, which can be restored later. The function will take paths fr

PersistToFile takes in an absolute filepath and saves the centroid vector to the file, which can be restored later. The function will take paths from

PersistToFile takes in an absolute filepath and saves the centroid vector to the file, which can be restored later. The function will take paths from

RestoreFromFile takes in a path to a parameter vector theta and assigns the model it's operating on's parameter vector to that. The path must ba an a

RestoreFromFile takes in a path to a parameter vector theta and assigns the model it's operating on's parameter vector to that. The path must ba an a

RestoreFromFile takes in a path to a parameter vector theta and assigns the model it's operating on's parameter vector to that. The path must ba an a

RestoreFromFile takes in a path to a parameter vector theta and assigns the model it's operating on's parameter vector to that. The path must ba an a

RestoreFromFile takes in a path to a parameter vector theta and assigns the model it's operating on's parameter vector to that. The path must ba an a

RestoreFromFile takes in a path to a centroid vector and assigns the model it's operating on's parameter vector to that. The path must ba an absolute

RestoreFromFile takes in a path to a centroid vector and assigns the model it's operating on's parameter vector to that. The path must ba an absolute

RestoreWithFuncs takes raw JSON data of a model and restores a model from it. The tokenizer and sanitizer passed in will be assigned to the restored m

SaveClusteredData takes operates on a k-means model, concatenating the given dataset with the assigned class from clustering and saving it to file. B

SaveClusteredData takes operates on a k-means model, concatenating the given dataset with the assigned class from clustering and saving it to file. B

TermFrequencies gives the TermFrequency of all words in a document, and is more efficient at doing so than calling that function multiple times

TermFrequency returns the term frequency of a word within a corpus defined by the trained NaiveBayes model Look at the TFIDF docs to see more about h

recalculateCentroids assigns each centroid to the mean of all points assigned to it. This method is abstracted within the Triangle accelerated KMeans

round rounds a float64

* Benchmarks */