lcc.stars_processing.deciders package

Subpackages

• lcc.stars_processing.deciders.unsupervised package
  • Submodules
  • lcc.stars_processing.deciders.unsupervised.k_means_decider module
  • Module contents

Submodules

lcc.stars_processing.deciders.custom_decider module

class lcc.stars_processing.deciders.custom_decider.CustomDecider(boundaries)

Bases: lcc.stars_processing.utilities.base_decider.BaseDecider

This decider allows to specify ranges of coordinates got from descriptors. So there is no need to run learn method. Anyway it is implemented to be consistent with other deciders. Also it checks if boundaries and given coordinates match.

Attributes

boundaries	(list, iterable) List of tuples of two values - lower and higher border value
treshold	(float) Treshold value for evaluating

Methods

evaluate(star_coords)

Parameters:

star_coords : list

Coordinates of inspected star got from sub-filters

Returns:

list of lists

Probability that inspected star belongs to the searched group of objects

learn(right_coords=[], wrong_coords=[])

No need to learn this decider. Anyway it is implemented to be consistent with other deciders. Also it checks if boundaries and given coordinates match.

Parameters:

right_coords : list

“Coordinates” of searched objects

wrong_coords : list

“Coordinates” of other objects

Returns:

NoneType

None

lcc.stars_processing.deciders.neuron_decider module

class lcc.stars_processing.deciders.neuron_decider.NeuronDecider(treshold=0.5, hidden_neurons=2, validationProportion=0.15, maxEpochs=100)

Bases: lcc.stars_processing.utilities.base_decider.BaseDecider

The class is responsible for learning to recognize certain group of objects.

Attributes

hiden_neurons	(int) Number of hiden neurons.
OUTPUT_NEURONS	(int) Number of output neurons.
input_neuron	(int) Number of input neurons.
X_train	(numpy array of array of floats) Each item of the array contains specific “coordinates” of the train object in array.
X_test	(numpy array of array of floats) Each item of the array contains specific “coordinates” of the test object in array.
y_train	(numpy array of ints) Each item of the array contains a number of the group which the train object belongs. Position in the array corresponds to item in X_train.
y_test	(numpy array of ints) Each item of the array contains a number of the group which the test object belongs. Position in the array corresponds to item in X_test.
validationProportion	(float) It is the ratio of the dataset that is used for the validation dataset
maxEpochs	(int) Maximum number of epochs for training

Methods

OUTPUT_NEURONS = 1

evaluate(coords)

Find if inspected parameter-space coordinates belongs to searched object

getTrainer()

Returns:

pybrain net instance, SupervisedDataSet

Learned net object, empty SupervisedDataSet which can be loaded

by sample of inspected objects

learn(searched, others)

This method loads lists of specific values of searched objects and others. Then the sample will be divided into train and test samples according to user.

Parameters:

searched : iterable

List of searched objects values (their “coordinates”)

others : iterable

List of other objects values (their “coordinates”)

Returns:

NoneType

None

train()

Train neuron grid by training sample

lcc.stars_processing.deciders.supervised_deciders module

class lcc.stars_processing.deciders.supervised_deciders.GMMBayesDec(treshold=0.5)

Bases: lcc.stars_processing.utilities.superv_base_decider.SupervisedBase

Sklearn implementation of Bayesian Regression

http://scikit-learn.org/stable/modules/linear_model.html#bayesian-regression

Methods

class lcc.stars_processing.deciders.supervised_deciders.GaussianNBDec(treshold=0.5)

Bases: lcc.stars_processing.utilities.superv_base_decider.SupervisedBase

Sklearn implementation of Gaussian Naive Bayes

http://scikit-learn.org/stable/modules/naive_bayes.html#gaussian-naive-bayes

Methods

class lcc.stars_processing.deciders.supervised_deciders.LDADec(treshold=0.5)

Bases: lcc.stars_processing.utilities.superv_base_decider.SupervisedBase

Sklearn implementation of Linear Discriminant Analysis

http://scikit-learn.org/stable/modules/lda_qda.html

Methods

class lcc.stars_processing.deciders.supervised_deciders.QDADec(treshold=0.5)

Bases: lcc.stars_processing.utilities.superv_base_decider.SupervisedBase

Sklearn implementation of Quadratic Discriminant Analysis

http://scikit-learn.org/stable/modules/lda_qda.html

Methods

class lcc.stars_processing.deciders.supervised_deciders.SVCDec(treshold=0.5)

Bases: lcc.stars_processing.utilities.superv_base_decider.SupervisedBase

Sklearn implementation of Support Vector Machines

http://scikit-learn.org/stable/modules/svm.html

Methods

class lcc.stars_processing.deciders.supervised_deciders.TreeDec(treshold=0.5)

Bases: lcc.stars_processing.utilities.superv_base_decider.SupervisedBase

Sklearn implementation of Decision Trees

http://scikit-learn.org/stable/modules/tree.html

Methods

evaluate(coords)

Get probability of membership

Parameters:

coords : list of lists

List of prameter space coordinates

Returns:

list of floats

List of probabilities

Module contents