Home | Trees | Indices | Help |
|
---|
|
Learn the topological structure of the input data by building a corresponding graph approximation (original Neural Gas algorithm). The Neural Gas algorithm was originally published in Martinetz, T. and Schulten, K.: A "Neural-Gas" Network Learns Topologies. In Kohonen, T., Maekisara, K., Simula, O., and Kangas, J. (eds.), Artificial Neural Networks. Elsevier, North-Holland., 1991. **Attributes and methods of interest** - graph -- The corresponding `mdp.graph.Graph` object - max_epochs - maximum number of epochs until which to train.
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Inherited from Inherited from |
|||
Inherited from GrowingNeuralGasNode | |||
---|---|---|---|
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Inherited from Node | |||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|
|||
Inherited from Node | |||
---|---|---|---|
|
|||
|
|
|||
Inherited from |
|||
Inherited from Node | |||
---|---|---|---|
_train_seq List of tuples:: |
|||
dtype dtype |
|||
input_dim Input dimensions |
|||
output_dim Output dimensions |
|||
supported_dtypes Supported dtypes |
|
Neural Gas algorithm. Default parameters taken from the original publication. :Parameters: start_poss sequence of two arrays containing the position of the first two nodes in the GNG graph. In unspecified, the initial nodes are chosen with a random position generated from a gaussian distribution with zero mean and unit variance. num_nodes number of nodes to use. Ignored if start_poss is given. epsilon_i, epsilon_f initial and final values of epsilon. Fraction of the distance between the closest node and the presented data point by which the node moves towards the data point in an adaptation step. Epsilon decays during training by e(t) = e_i(e_f/e_i)^(t/t_max) with t being the epoch. lambda_i, lambda_f initial and final values of lambda. Lambda influences how the weight change of nodes in the ranking decreases with lower rank. It is sometimes called the "neighborhood factor". Lambda decays during training in the same manner as epsilon does. max_age_i, max_age_f Initial and final lifetime, after which an edge will be removed. Lifetime is measured in terms of adaptation steps, i.e., presentations of data points. It decays during training like epsilon does. max_epochs number of epochs to train. One epoch has passed when all data points from the input have been presented once. The default in the original publication was 40000, but since this has proven to be impractically high too high for many real-world data sets, we adopted a default value of 100. n_epochs_to_train number of epochs to train on each call. Useful for batch learning and for visualization of the training process. Default is to train once until max_epochs is reached.
|
Return the nodes in the graph in a list ranked by their squared distance to x. |
Remove edges with age > max_age.
|
|
Update the internal structures according to the input data `x`. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_train` to implement their training phase. The docstring of the `_train` method overwrites this docstring. Note: a subclass supporting multiple training phases should implement the *same* signature for all the training phases and document the meaning of the arguments in the `_train` method doc-string. Having consistent signatures is a requirement to use the node in a flow.
|
Home | Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Thu Mar 10 15:28:21 2016 | http://epydoc.sourceforge.net |