PATTERN_MIN_MAX_NORM_CVIP
pattern_min_max_norm_cvip() - two csv files are input, training and test sets, and returns new csv files with the feature vectors normalized with min-max normalization.
Contents
SYNTAX
[normalized_file_tt, normalized_file_tr] = pattern_min_max_norm_cvip(file_tt, file_tr, s_min, s_max)
Input Parameters include :
- file_tt - Name of the test set file. A CSV file with a predefined structure.
- file_tr - Name of the training set file. A CSV file with a predefined structure.
- s_min - The min parameter in Min-Max normalization.
- s_max - The max parameter in Min-Max normalization.
Output Parameters include :
- normalized_file_tt - A string containing the name of the normalized test file. It is the same name as file_tt with the prefix min_max_.
- normalized_file_tr - A string containing the name of the normalized training file. It is the same name as file_tr with the prefix min_max_.
DESCRIPTION
This function gets the training set and the test set as input arguments and normalizes them using min-max normalization. Then the results are saved to two new CSV files and their names are returned as output.
REFERENCE
1. Scott E Umbaugh. DIGITAL IMAGE PROCESSING AND ANALYSIS: Applications with MATLAB and CVIPtools, 3rd Edition.
EXAMPLE
% Test set file file_tt = 'myTestVectors.CSV'; % Training set file file_tr = 'myTrainingVectors.CSV'; % Minimum perimeter s_min = 1; % Maximum perimeter s_max = 10; % Calling function [normalized_file_tt, normalized_file_tr] = pattern_min_max_norm_cvip(file_tt, file_tr, s_min, s_max)
normalized_file_tt = min_max_myTestVectors.CSV normalized_file_tr = min_max_myTrainingVectors.CSV
CREDITS
Author: Mehrdad Alvandipour, March 2017
Copyright © 2017-2018 Scott
E Umbaugh
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