Override to return labels when not calling {@link #BaseLabels(String)}.

Computing the Gram matrix as described here: https://harishnarayanan.org/writing/artistic-style-transfer/ Permuting dimensions is not needed because D

scale x,y points

Make sure the color values are >=0 and <=1

Used to create the different time series for ploting purposes

Extract a "tar.gz" file into a local folder. @param inputPath Input file path. @param outputPath Output directory path. @throws IOException IO error.

Gradient function. Compute the gradient of the cost function and how much error each parameter puts into the result. @param x features @param y labe

Plot the training data. Assume 2d input, classification output @param features Training data features @param labels Training data labels (one-hot repr

Plot the training data. Assume 2d input, classification output @param model Model to use to get predictions @param testIter Test Iterato

Plot the training data. Assume 2d input, classification output @param features Training data features @param labels Training data labels (one-hot repr

Plot the training data. Assume 2d input, classification output @param model Model to use to get predictions @param trainIter DataSet Iter

Logistic function. Computes a number between 0 and 1 for each element. Note that ND4J comes with its own sigmoid function. @param y input values @re

Training algorithm. Gradient descent optimization. The logistic cost function (or max-entropy) is convex, and thus we are guaranteed to find the glob

This is to collect all the data and return it as one minibatch. Obviously only for use here with small datasets @param iter @return

Returns column names this op is meant to run on @return

Precondition: notes is sorted by startTick @param notes @return the number of notes which overlap in time with another note (of higher index)

Create data for the background data set

Create data for the background data set

Creates an IndArray from a list of strings Used for plotting purposes

Make the Neural network draw the image.