* Assing `value` to a cluster. * @param {array || object} value

* addData * @param {Array | Object} xInputs * @param {Array | Object} yInputs * @param {*} options

* Retrain the model with the provided images and labels using the * models original features as starting point. * @param {function} onProgres

* .detect() - classifies multiple features by default * @param {*} optionsOrCallback * @param {*} configOrCallback * @param {*} cb

* Generate a random result. * @param {String} label - A label of the feature your want to generate * @param {function} callback - A function t

* Predict the next character based on the model's current state. * @param {number} temp * @param {function} callback - Optional. A function to b

* normalizeData * @param {*} _dataRaw * @param {*} _meta

* Load dataset, find initial centroids, and run model. * @param {string || array || object} dataset

* Given an canvas element, applies image-to-image translation using the provided model. Returns an image. * @param {HTMLCanvasElement} inputElement

* synchronous classify * @param {*} _input

* Check if the given _param is undefined, otherwise return the _default * @param {*} _param * @param {*} _default

* Adding an example to a class. * @param {*} input - An example to add to the dataset, usually an activation from another model. * @param {numbe

* This function will check if the p5 is in the environment * Either it is in the p5Instance mode OR it is in the window * @returns {boolean} if

* Get the example count for each label. It returns an object that maps class label to example count for each class. * @returns {Number}

* It returns the total number of labels. * @returns {String}

* Adds a new image element to Mobilenet * @param {HTMLVideoElement || p5.Video || String} inputOrLabel * @param {String || function} labelOrCal

* Reset the model state.

* Clear all examples in a label. * @param {number||number} labelIndex - The class index or label, a number or a string.

* dispose and release the memory for the model

* Returns the pitch from the model attempting to predict the pitch. * @param {function} callback - Optional. A function to be called when the model

* Returns a p5Image * @param {*} tfBrowserPixelImage

* add custom layers in options

* Detect objects that are in video, returns bounding box, label, and confidence scores * @param {HTMLVideoElement|HTMLImageElement|HTMLCanvasElemen

* dispose and release memory for a model

* Will be deprecated soon in favor of ".classify()" - does the same as .classify() * @param {HTMLImageElement | HTMLCanvasElement | object | functi

* Convert Blob to P5.Image * @param {Blob} blob * Note: may want to reject instead of returning null. * @returns {Promise<p5.Image | null>}

* Feed a string of characters to the model state. * @param {String} inputSeed - A string to feed the charRNN model state. * @param {function} ca

* formatInputsForPredictionAll * @param {*} _input * @param {*} meta * @param {*} inputHeaders

* convert raw bytes to blob object * @param {number[] | Uint8ClampedArray | ArrayLike<number>} raws * @param {number} width * @param {number}

* Download the whole dataset as a JSON file. It's useful for saving state. * @param {String} name - Optional. The name of the JSON file that will b

* Segments the image with partSegmentation * @param {HTMLImageElement | HTMLCanvasElement | object | function | number} optionsOrCallback -

* addLayer * @param {*} options

* compile the model * @param {*} _options

* create the metadata from the data * this covers: * 1. getting the datatype from the data * 2. getting the min and max from the data *

* create a new neural network with crossover * @param {*} other

* .detecSinglet() - classifies a single feature with higher accuracy * @param {*} optionsOrCallback * @param {*} configOrCallback * @param {*

* Run K-Means algorithm.

* format the inputs for prediction * this means applying onehot or normalization * so that the user can use original data units rather * than

* loadJSON * @param {*} _dataUrlOrJson * @param {*} _inputLabelsArray * @param {*} _outputLabelsArray

* Copies the stream from the source video into a new video element. * The copied element is set to property `this.video` and is also returned by th

* Prepare data for training by applying oneHot to raw * @param {*} dataRaw

* search though the xInputs and format for adding to data.raws * @param {*} input

* addDefaultLayers * @param {*} _task