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

Distortion returns the distortion of the clustering currently given by the k-means model. This is the function the learning algorithm tries to minimiz

Distortion returns the distortion of the clustering currently given by the k-means model. This is the function the learning algorithm tries to minimiz

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

Examples returns the number of training examples (m) that the model currently is training from.

Examples returns the number of training examples (m) that the model currently is training from.

Examples returns the number of training examples (m) that the model currently is training from.

Examples returns the number of training examples (m) that the model currently is training from.

Examples returns the number of training examples (m) that the model currently is training from.

Examples returns the number of training examples (m) that the model currently is holding

Examples returns the number of training examples (m) that the model currently is training from.

Guesses returns the hidden parameter for the unsupervised classification assigned during learning. model.Guesses[i] = E[k.trainingSet[i]]

Guesses returns the hidden parameter for the unsupervised classification assigned during learning. model.Guesses[i] = E[k.trainingSet[i]]

J returns the Least Squares cost function of the given linear model. Could be useful in testing convergence

J returns the Least Squares cost function of the given linear model. Could be usefull in testing convergance

LNorm returns a DistanceMeasure of the l-p norm. L norms are a generalized family of the Euclidean and Manhattan distance. https://en.wikipedia.org/w

LearningRate returns the learning rate α for gradient descent to optimize the model. Could vary as a function of something else later, potentially.

LearningRate returns the learning rate α for gradient descent to optimize the model. Could vary as a function of something else later, potentially.

LearningRate returns the learning rate α for gradient descent to optimize the model. Could vary as a function of something else later, potentially.

LearningRate returns the learning rate α for gradient descent to optimize the model. Could vary as a function of something else later, potentially.

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

LearningRate returns the learning rate α for gradient descent to optimize the model. Could vary as a function of something else later, potentially.

Less gives whether the ith element of a frequency list has is lesser than the jth element by comparing their TFIDF values

MaxIterations returns the number of maximum iterations the model will go through in GradientAscent, in the worst case

MaxIterations returns the number of maximum iterations the model will go through in GradientAscent, in the worst case

MaxIterations returns the number of maximum iterations the model will go through in GradientAscent, in the worst case

MaxIterations returns the number of maximum iterations the model will go through in GradientAscent, in the worst case

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

MaxIterations returns the number of maximum iterations the model will go through in GradientAscent, in the worst case

MaxIterations returns the number of maximum iterations the model will go through

OnlineLearn has no outputs so you can run the data within a separate goroutine! A channel of errors is passed so you know when there's been an error i

OnlineLearn has no outputs so you can run the data within a separate goroutine! A channel of errors is passed so you know when there's been an error i

OnlyAsciiLetters is a transform function that will only let a-zA-Z through

OnlyAsciiWords is a transform function that will only let a-zA-Z, and spaces through

OnlyAsciiWordsAndNumbers is a transform function that will only let 0-9a-zA-Z, and spaces through

OnlyLetters is a transform function that lets any unicode letter through

OnlyWords is a transform function that lets any unicode letter through as well as spaces

OnlyWordsAndNumbers is a transform function that lets any unicode letter or digit through as well as spaces

PersistToFile and RestoreFromFile both take in paths (absolute paths!) to files and persists the necessary data to the filepath such that you can Rest

PersistToFile and RestoreFromFile both take in paths (absolute paths!) to files and persists the necessary data to the filepath such that you can Rest

PersistToFile and RestoreFromFile both take in paths (absolute paths!) to files and persists the necessary data to the filepath such that you can Rest

The variadic argument in Predict is an optional arg which (if true) tells the function to first normalize the input to vector unit length. Use (and on

Predict takes a document and returns the expected class found by the model

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 linear hypothes

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 softmax hypothe

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 logistic hypoth

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 linear hypothes

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

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

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 k-means hypothe

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 k-means hypothe

Swap swaps two indexed values in a frequency slice

make sure that calling predict while the model is still training does not cause a runtime panic because of concurrent map reads & writes

test y=3 but don't have enough iterations

same as above but with StochasticGA

test y=3 but include an invalid data set

same as above but with StochasticGA

test y=3 but include an invalid data set

invalid optimization method

test y=3

same as above but with StochasticGA

test ( 10*i + j/20 + k ) > 0 but don't have enough iterations

same as above but with StochasticGA

test ( 10*i + j/20 + k ) > 0 but include an invalid data set

same as above but with StochasticGA

test ( 10*i + j/20 + k ) > 0 but include an invalid data set

same as above but with StochasticGA

invalid method

test ( 10*i + j/20 + k ) > 0

same as above but with StochasticGA

test ( 10*i + j/20 + k ) > 0 but don't have enough iterations

same as above but with StochasticGA

test ( 10*i + j/20 + k ) > 0 but include an invalid data set

same as above but with StochasticGA

test ( 10*i + j/20 + k ) > 0 but include an invalid data set

same as above but with StochasticGA