self-organizing map (SOM) / Kohonen network
$ npm install ml-som
new SOM(x, y, [options])
Creates a new SOM instance with x * y dimensions.
x- Dimension of the x axis
y- Dimension of the y axis
options- Object with options for the algorithm
fields- Either a number (size of input vectors) or a map of field descriptions (to convert them to vectors)
iterations- Number of iterations over the training set for the training phase (default: 10). The total number of training steps will be
learningRate- Multiplication coefficient for the learning algorithm (default: 0.1)
method- Iteration method of the learning algorithm (default: random)
random- Pick an object of the training set randomly
traverse- Go sequentially through the training set
randomizer- Function that must give numbers between 0 and 1 (default: Math.random)
distance- Function that computes the distance between two vectors of the same length (default: square euclidean distance)
gridType- Shape of the grid (default: rect)
rect- Rectangular grid
hexa- Hexagonal grid
torus- Boolean indicating if the grid should be considered a torus for the selection of the neighbors (default: true)
var SOM = ;var options =fields:r: 0 255g: 0 255b: 0 255;var som = 20 20 options;
Train the SOM with the provided
trainingSet- Array of training elements. If the
fieldswas a number, each array element must be a normalized vector. If it was an object, each array element must be an object with at least the described properties, within the described ranges
var trainingSet =r: 0 g: 0 b: 0r: 255 g: 0 b: 0r: 0 g: 255 b: 0r: 0 g: 0 b: 255r: 255 g: 255 b: 255;som;
Returns a 2D array containing the nodes of the grid, in the structure described by the
Set the training set for use with the next method
Executes the next training iteration and returns true. Returns false if the training is over. Useful to draw the grid or compute some things after each learning step.
som;whilesomvar nodes = som;// do something with the nodes
Returns for each data point the coordinates of the corresponding best matching unit (BMU) on the grid
data- Data point or array of data points (default: training set).
computePosition- True if you want to compute the position of the point in the cell, using the direct neighbors (default: false). This option is currently only implemented for rectangular grids.
// create and train the somvar result1 = som;// result1 = [ 2, 26 ]var result2 = som;// result2 = [ [ 2, 26, [ 0.236, 0.694 ] ], [ 33, 12, [ 0.354, 0.152 ] ] ]
Returns an array of fit values which are the square root of the distance between the input vector and its corresponding BMU.
dataset- Array of vectors to for which to calculate fit values. Defaults to the training set.
Returns the mean of the fit values for the training set. This number can be used to compare several runs of the same SOM.
Exports the model to a JSON object that can be written to disk and reloaded
includeDistance- Boolean indicating if the distance function should be included in the model as a String (not recommended). Note that there is no need to include the default function and that it cannot work if the function depends on variables that are out of its scope (default: false).
Returns a new SOM instance based on the
model. If the model was created with a custom distance function, the
distance argument should be this function.
model- JSON object generated with
distanceFunction- Optionally provide the distance function used to create the model.