Welcome to sparse_som’s documentation!

Module contents

enums

class sparse_som.cooling
LINEAR = <cooling.LINEAR: 0>
EXPONENTIAL = <cooling.EXPONENTIAL: 1>
class sparse_som.topology
CIRC = <topology.CIRC: 4>
HEXA = <topology.HEXA: 6>
RECT = <topology.RECT: 8>

classes

Self-Organizing Maps wrappers for python, intended for sparse input data.

class sparse_som.BSom

Uses the batch algorithm and can take advantage from multi-core processors to learn efficiently.

Parameters:
  • h (int) – the network height
  • w (int) – the network width
  • d (int) – the dimension of input vectors
  • topol (topology.RECT or topology.HEXA) – the network topology
  • verbose (int (0..2)) – verbosity parameter
train(data, epochs=10, r0=0, rN=0.5, std=0.3, cool=cooling.LINEAR)

Train the network with data.

Parameters:
  • data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single)
  • epochs (int) – number of epochs
  • r0 (float) – radius at the first iteration
  • rN (float) – radius at the last iteration
  • cool (cooling.LINEAR or cooling.EXPONENTIAL) – cooling strategy
bmus(data)

Return the best match units for data.

Parameters:data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single)
Returns:an array of the bmus coordinates (y,x)
Return type:2D numpy.ndarray
codebook
Returns:a view of the internal codebook.
Return type:3D numpy.ndarray
dim
Returns:the dimension of the input vectors.
Return type:int
ncols
Returns:the number of columns in the network.
Return type:int
nrows
Returns:the number of rows in the network.
Return type:int
class sparse_som.Som

Uses the SD-SOM algorithm (online learning).

Parameters:
  • h (int) – the network height
  • w (int) – the network width
  • d (int) – the dimension of input vectors
  • topol (topology.RECT or topology.HEXA) – the network topology
  • verbose (int (0..2)) – verbosity parameter
train(data, tmax, r0=0, a0=0.5, rN=0.5, aN=0., std=0.3, rcool=cooling.LINEAR, acool=cooling.LINEAR)

Train the network with data.

Parameters:
  • data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single)
  • tmax (int) – number of iterations
  • r0 (float) – radius at the first iteration
  • a0 (float) – learning-rate at the first iteration
  • rN (float) – radius at the last iteration
  • aN (float) – learning-rate at the last iteration
  • rcool (cooling.LINEAR or cooling.EXPONENTIAL) – radius cooling strategy
  • acool (cooling.LINEAR or cooling.EXPONENTIAL) – alpha cooling strategy
bmus(data)

Return the best match units for data.

Parameters:data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single)
Returns:an array of the bmus coordinates (y,x)
Return type:2D numpy.ndarray
codebook
Returns:a view of the internal codebook.
Return type:3D numpy.ndarray
dim
Returns:the dimension of the input vectors.
Return type:int
ncols
Returns:the number of columns in the network.
Return type:int
nrows
Returns:the number of rows in the network.
Return type:int

Submodules

sparse_som.classifier module

class sparse_som.SomClassifier(cls=<type 'sparse_som.som.BSom'>, *args, **kwargs)[source]
__init__(cls=<type 'sparse_som.som.BSom'>, *args, **kwargs)[source]
Parameters:
  • cls (Som or BSom) – SOM constructor
  • *args – positional parameters for the constructor
  • **kwargs – named parameters for the constructor
fit(data, labels, **kwargs)[source]

Training the SOM on the the data and calibrate itself.

After the training, self.quant_error and self.topog_error are respectively set.

Parameters:
  • data (scipy.sparse.csr_matrix) – sparse input matrix (ideal dtype is numpy.float32)
  • labels (iterable) – the labels associated with data
  • **kwargs – optional parameters for train()
bmus_with_errors(data)[source]

Compute common error metrics (Quantization err. and Topographic err.) for this data.

Parameters:data (scipy.sparse.csr_matrix) – sparse input matrix (ideal dtype is numpy.float32)
Returns:the BMUs, the QE and the TE
Return type:tuple
predict(data, unkown=None)[source]

Classify data according to previous calibration.

Parameters:
  • data (scipy.sparse.csr_matrix) – sparse input matrix (ideal dtype is numpy.float32)
  • unkown – the label to attribute if no label is known
Returns:

the labels guessed for data
Return type:

numpy.array
fit_predict(data, labels, unkown=None)[source]

Fit and classify data efficiently.

Parameters:
  • data (scipy.sparse.csr_matrix) – sparse input matrix (ideal dtype is numpy.float32)
  • labels (iterable) – the labels associated with data
  • unkown – the label to attribute if no label is known
Returns:

the labels guessed for data
Return type:

numpy.array
get_precision()[source]
Returns:the ratio part of the dominant label for each unit.
Return type:2D numpy.ndarray
histogram(bmus=None)[source]

Return a 2D histogram of bmus.

Parameters:bmus (numpy.ndarray) – the best-match units indexes for underlying data.
Returns:the computed 2D histogram of bmus.
Return type:numpy.ndarray

Indices and tables