PATTERN_UNIT_VECTOR_NORM_CVIP
pattern_unit_vector_norm_cvip() - two csv files are input,training and test sets, and returns new csv files with the feature vectors normalized with unit vector normalization.
Contents
SYNTAX
[normalized_file_tt, normalized_file_tr] = pattern_unit_vector_norm_cvip(file_tt, file_tr)
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.
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 unit_vec_normalized_.
- normalized_file_tr - A string containing the name of the normalized training file. It is the same name as file_tr with the prefix unit_vec_normalized_.
DESCRIPTION
Using unit vector normalization, the function calculates the similarity between the vectors in training set and the test set.The distances are returned in a matrix form whose rows represent the test vectors and columns represent the training vector.
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'; % Calling function [normalized_file_tt, normalized_file_tr] = pattern_unit_vector_norm_cvip(file_tt, file_tr)
normalized_file_tt = unit_vec_normalized_myTestVectors.CSV normalized_file_tr = unit_vec_normalized_myTrainingVectors.CSV
CREDITS
Author: Mehrdad Alvandipour, March 2017
Copyright © 2017-2018 Scott
E Umbaugh
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