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, tg_error=False, qt_error=False)

Return the best match units for data.

Parameters:

• data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single)
• tg_error (bool) – indicates whether to calculate the topographic error (default: False)
• qt_error (bool) – indicates whether to calculate the quantization error (default: False)

Returns:

an array of the bmus coordinates (y,x)

Return type:

2D numpy.ndarray

activation_map(data)

Return the distance between each data sample and each codebook unit.

Parameters:data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single) Returns:an array with shape (nsample, nunits) 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

umatrix

Returns:the network U-matrix. Return type:2D numpy.ndarray

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, tg_error=False, qt_error=False)

Return the best match units for data.

Parameters:

• data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single)
• tg_error (bool) – indicates whether to calculate the topographic error (default: False)
• qt_error (bool) – indicates whether to calculate the quantization error (default: False)

Returns:

an array of the bmus coordinates (y,x)

Return type:

2D numpy.ndarray

activation_map(data)

Return the distance between each data sample and each codebook unit.

Parameters:data (scipy.sparse.spmatrix) – sparse input matrix (ideally csr_matrix of numpy.single) Returns:an array with shape (nsample, nunits) 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

umatrix

Returns:the network U-matrix. Return type:2D numpy.ndarray

Submodules

sparse_som.classifier module

class sparse_som.SomClassifier(cls=<type 'sparse_som.som.BSom'>, height=10, width=10, dim=None, **kwargs)

__init__(cls=<type 'sparse_som.som.BSom'>, height=10, width=10, dim=None, **kwargs)

Parameters:

• cls (Som or BSom) – SOM constructor
• height (int) – SOM height (default 10)
• width (int) – SOM width (default 10)
• dim (int) – SOM nodes nb of dimensions (if None, detected when fit called)
• **kwargs – named parameters for the constructor

setup(**kwargs)

Add / Change SOM constructor parameters.

params()

Get the SOM constructor parameters.

fit(data, labels, **kwargs)

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()

predict(data, unkown=None)

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)

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()

Returns:the ratio part of the dominant label for each unit. Return type:2D numpy.ndarray

histogram(bmus=None)

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

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