SOM functions
Map initialization
There are two functions for initializing a codebook:
randinit_codes which produces a
random codebook and
lininit_codes which produces a
linearly initalized codebook based on the eigenvectors of the data. In
these functions xdim and ydim specify the dimensions of
the map, topology type is set to topol and neighborhood to
neigh. Vector dimension is taken from Data.
- struct entries *randinit_codes(struct entries *data, int topol, int neigh, int xdim, int ydim)
- Creates a random codebook.
Returns a ready to use codebook.
- struct entries *lininit_codes(struct entries *data, int topol, int neigh, int xdim, int ydim)
- Creates a linearly initialized codebook. Codebook vectors lie in
the plane defined by the mean and the two largest eigenvectors
of data.
Returns a ready to use codebook.
Map distances
These functions calculate distances between the units on the 2D map.
- float hexa_dist(int bx, int by, int tx, int ty)
- Calculates distance of two map units when using hexagonal topology.
- float rect_dist(int bx, int by, int tx, int ty)
- Calculates distance of two map units when using rectangular topology.
Neighborhood adaptation
In the following functions sample is the data vector, bx
and by are the coordinated of the winning codebook vector,
radius is the radius of the neighborhood and alpha is
the learning parameter. The functions to calculate distances and adapt
individual vectors, etc. are taken from teach.
- void bubble_adapt(struct teach_params *teach, struct data_entry *sample, int bx, int by, float radius, float alpha)
- Adaptation function for bubble-neighborhood.
- void gaussian_adapt(struct teach_params *teach, struct data_entry *sample, int bx, int by, float radius, float alpha)
- Adaptation function for gaussian neighborhood.
SOM training
- struct entries *som_training(struct teach_params *teach)
- Train a SOM. Radius of the neighborhood decreases linearly from
the initial value to one and the learning parameter decreases
linearly from its initial value to zero. All parameters are
taken from teach (see
Customizing SOM/LVQ_PAK
for more information).
Quantization error calculations
- float find_qerror(struct teach_params *teach)
- Calculates quantization error.
- float find_qerror2(struct teach_params *teach)
- Calculates quantization error in a different way. Instead of
only calculating the difference between the winning codebook
vector and the data vector also the quantization error with
other vectors in the neighborhood is calculated weighted with
the neighborhood function.
- float bubble_qerror(struct teach_params *teach, struct data_entry *sample, int bx, int by, float radius)
- Calculates quantization error over the neighborhood of the
winning entry.
- float gaussian_qerror(struct teach_params *teach, struct data_entry *sample, int bx, int by, float radius)
- Calculates quantization error over the neighborhood of the
winning entry.
Other functions
- void normalize(float *v, int n)
- Normalizes an array of floats (v). N is the length
of the array.
- float dotprod(float *v, float *w, int n)
- Calculated the dot product of two vectors (float arrays v
and w). Length of vectors is n.
- int gram_schmidt(float *v, int n, int e)
- Has something to do with finding the eigenvectors, I guess.
- struct data_entry *find_eigenvectors(struct entries *data)
- Finds eigenvectors of data. Returns a pointer to a list
of three data_entrys: first is the mean, second is the
first eigenvector and third is the second eigenvector. The
next-field in the data_entry-structure points to
the next entry.
- int set_som_params(struct teach_params *params)
- Sets some SOM specific stuff in the params if they aren't
set already. Sets the mapdist and neigh_adapt
functions according to the flags in params.
SOM/LVQ Programming info /
SOM/LVQ Home Page /
Neural Networks Research Centre