Features of the KohonenFeatureMap class
|Number of neuron layers||1 input layer, 1 feature map|
|Input dimension||minimum: 1, maximum: 3|
|Number of neurons in feature map||minimum: 1*1, maximum: N*M|
|Number of input values||minimum: 1, maximum: N|
|Weight matrices||Automatically created and initialized. One matrix connects input layer and feature map. The other matrix is not of the WeightMatrix type and connects the neurons of the feature map among themselves.|
|One learning step includes||Selection of a random input value, finding the most activated map neuron, changing of the weights|
Steps to create a Kohonen Feature Map
1. Object declaration
Required. Declares the object kfm to be of type KohonenFeatureMap.
2. Constructor call
kfm = new KohonenFeatureMap();
Required. Creates an instance of KohonenFeatureMap. This is the only constructor of the class and takes no arguments.
3. Create feature map
Required. Creates the net's feature map with xSize*ySize map neurons.
4. Define input matrix
Required. Creates the input matrix.
5. Connect input layer with feature map
Required. Connects each input neuron (automatically created, depending on the dimension of the input matrix im) with each neuron of the feature map. Besides, all map neurons are connected among themselves. All weight matrices are created and initialized with random values, taken from the input matrix im (created in step 4).
Set initial learning rate
Sets the net's initial learning rate to x. The default value is 0.6.
Set initial activation area
Sets the net's initial activation area to x. The default value is the greater of both map sizes divided by 2.
Set final activation area
Sets the net's final activation area to x. The default value is initActivationArea divided by 10.
Set maximum learning cycles
Sets the maximum number of learning cycles to x. The default value is -1 (no maximum).
Reset learning time
Resets the learning time.
6. Perform a learning cycle
Required. Performs one learning cycle. This method is usually called within a loop, which exits, if the finishedLearning() method returns true.
Information is available on:
Returns the current activation area of the feature map.
Returns the time that elapsed since the learning process started.
Final activation area
Returns the final activation area of the feature map.
Initial activation area
Returns the initial activation area of the feature map.
Initial learning rate
Returns the initial learning rate of the net.
Returns the current learning cycle of the net.
Returns the current learning rate of the net.
Map size in x
Returns the size of the feature map in x-dimension.
Map size in y
Returns the size of the feature map in y-dimension.
Number of weights
Returns the number of weights in the weight matrix that connects the input neurons with the neurons of the feature map.
Returns all weight values of the weight matrix that connects the input neurons with the neurons of the feature map.