DataPreprocessing Namespace Reference

Functions
def	main (x_data, y_data)
def	data_refiner (x_data, y_data)
	DATA EXTRACTION #########. More...
def	manipulate_super_regr (x_data, y_data, NDatasetsNew)
	DATA MANIPULATION FOR SUPERVISED REGRESSION #########. More...
def	manipulate_super_class ()
	DATA MANIPULATION FOR SUPERVISED CLASSIFICATION #########. More...
def	manipulate_unsuper_regr ()
	DATA MANIPULATION FOR UNSUPERVISED REGRESSION #########. More...
def	manipulate_unsuper_class ()
	DATA MANIPULATION FOR UNSUPERVISED CLASSIFICATION #######. More...

Function Documentation

data_refiner()

def DataPreprocessing.data_refiner	(		x_data,
			y_data
	)

DATA EXTRACTION #########.

Refines the data to exclude values not within the provided bounds.

Parameters
----------
NONE
   

Returns
-------
x_data: numpy array, dimension depending on input
    array of data ("x data") on which to perform machine learning

y_data: numpy array, dimension depending on input
    array of labels ("y data") to use as classifiers

NDatasetsNew: numpy array, dimension depending on input
    full dataset reshaped depending on possible bounds imposed on the value of y

References
----------

See Also
--------

Notes
-----

Examples
--------

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main()

def DataPreprocessing.main	(		x_data,
			y_data
	)

Main routine to perform all the data preprocessing tasks.

Parameters
----------
x_data: numpy array
    independent data for the machine learning task

y_data: numpy array
    labels for the machine learning task

Returns
-------
NONE, calls subroutines to exectue runtime checks and manipulate the data.


References
----------

See Also
--------

Notes
-----

Examples
--------

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manipulate_super_class()

def DataPreprocessing.manipulate_super_class

(

)

DATA MANIPULATION FOR SUPERVISED CLASSIFICATION #########.

Not yet implemented.

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manipulate_super_regr()

def DataPreprocessing.manipulate_super_regr	(		x_data,
			y_data,
			NDatasetsNew
	)

DATA MANIPULATION FOR SUPERVISED REGRESSION #########.

Reshape data differently depending on which quantity is to be learned for SUPERVISED REGRESSION.
    
Parameters
----------
x_data: numpy array, dimension depending on input
    array of data ("x data") on which to perform machine learning

y_data: numpy array, dimension depending on input
    array of labels ("y data") to use as classifiers

NDatasetsNew: numpy array, dimension depending on input
    full dataset reshaped depending on possible bounds imposed on the value of y
    
Returns
-------
x_data_preprocessed: numpy array, dimension depending on input
    array of data ("x data") used to train the neural network

y_data_preprocessed: numpy array, dimension depending on input
    array of labels ("y data") to use as classifiers in the training of the neural network
                
References
----------

See Also
--------

Notes
-----

Examples
--------

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manipulate_unsuper_class()

def DataPreprocessing.manipulate_unsuper_class

(

)

DATA MANIPULATION FOR UNSUPERVISED CLASSIFICATION #######.

Not yet implemented.

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manipulate_unsuper_regr()

def DataPreprocessing.manipulate_unsuper_regr

(

)

DATA MANIPULATION FOR UNSUPERVISED REGRESSION #########.

Not yet implemented.

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