lcc.stars_processing.deciders package¶
Subpackages¶
Submodules¶
lcc.stars_processing.deciders.custom_decider module¶
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class
lcc.stars_processing.deciders.custom_decider.
CustomDecider
(boundaries)[source]¶ 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
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evaluate
(star_coords)[source]¶ 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
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learn
(right_coords=[], wrong_coords=[])[source]¶ 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
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lcc.stars_processing.deciders.neuron_decider module¶
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class
lcc.stars_processing.deciders.neuron_decider.
NeuronDecider
(treshold=0.5, hidden_neurons=2, validationProportion=0.15, maxEpochs=100)[source]¶ 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
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OUTPUT_NEURONS
= 1¶
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getTrainer
()[source]¶ Returns: pybrain net instance, SupervisedDataSet
- Learned net object, empty SupervisedDataSet which can be loaded
by sample of inspected objects
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learn
(searched, others)[source]¶ 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
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lcc.stars_processing.deciders.supervised_deciders module¶
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class
lcc.stars_processing.deciders.supervised_deciders.
GMMBayesDec
(treshold=0.5)[source]¶ 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
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class
lcc.stars_processing.deciders.supervised_deciders.
GaussianNBDec
(treshold=0.5)[source]¶ 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
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class
lcc.stars_processing.deciders.supervised_deciders.
LDADec
(treshold=0.5)[source]¶ 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
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class
lcc.stars_processing.deciders.supervised_deciders.
QDADec
(treshold=0.5)[source]¶ 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
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class
lcc.stars_processing.deciders.supervised_deciders.
SVCDec
(treshold=0.5)[source]¶ Bases:
lcc.stars_processing.utilities.superv_base_decider.SupervisedBase
Sklearn implementation of Support Vector Machines
http://scikit-learn.org/stable/modules/svm.html
Methods
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class
lcc.stars_processing.deciders.supervised_deciders.
TreeDec
(treshold=0.5)[source]¶ Bases:
lcc.stars_processing.utilities.superv_base_decider.SupervisedBase
Sklearn implementation of Decision Trees
http://scikit-learn.org/stable/modules/tree.html
Methods