NEAREST_NEIGHBOR_CVIP

nearest_neighbor_cvip() - reads test and training files of feature vectors and creates output file for classification using nearest neighbor classification method.

SYNTAX
DESCRIPTION
REFERENCE
EXAMPLE 1
EXAMPLE 2
EXAMPLE 3
CREDITS

SYNTAX

nearest_neighbor_cvip(file_tt, file_tr, file_out, option_normalize, option_distance, [s_min], [s_max], [r_softmax], [r_minkowski])

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.
file_out - Name of the output file. A CSV file as the inputs with the test vectors classified based on their NN in training set.

option_normalize -

   0 – No Normalization

   1 – Range-normalize

   2 – Unit Vector normalization

   3 – Standard Normal Density normalization

   4 – Min-max normalization     ----> s_min, s_max

   5 – Softmax scaling           ----> r_softmax

option_distance -

   1 – Euclidean Distance

   2 – City block or Absolute value metric

   3 – Maximum value metric

   4 – Minkowski distance        ----> r_minkowski

   5 – Vector inner product

   6 – Tanimoto metric

If option_normalize is either 4 or 5, or option_distance is 4, then their
corresponding parameters should be given as input arguments seperated
by comma after option_distance. Look at the below examples:

DESCRIPTION

Given the training and test set with the desired methods for normalization and distance, This function classifies the vectors in the test set with Nearest Neighbor (NN) method. Depending on the normalization method and the distance used, the number of input arguments vary.

The accepted values for normalization and distance are as follows. Also the extra input arguments for the corresponding method is shown by arrows (----->).

REFERENCE

1. Scott E Umbaugh. DIGITAL IMAGE PROCESSING AND ANALYSIS: Applications with MATLAB and CVIPtools, 3rd Edition.

EXAMPLE 1

                  file_tt = 'myTestVectors.CSV';
                  file_tr = 'myTrainingVectors.CSV';
                  file_out = 'ClassifiedTestVectors.CSV';
                  option_normalize = 4;
                  option_distance = 6;
                  s_min = 0; s_max = 1;
                  nearest_neighbor_cvip(file_tt, file_tr, file_out, option_normalize, option_distance, s_min, s_max)

EXAMPLE 2

                  file_tt = 'myTestVectors.CSV';
                  file_tr = 'myTrainingVectors.CSV';
                  file_out = 'ClassifiedTestVectors.CSV';
                  option_normalize = 4;
                  option_distance = 4;
                  s_min = 0; s_max = 1;
                  r_minkowski = 3;
                  nearest_neighbor_cvip(file_tt, file_tr, file_out, option_normalize, option_distance, s_min, s_max, r_minkowski)

EXAMPLE 3

                  file_tt = 'myTestVectors.CSV';
                  file_tr = 'myTrainingVectors.CSV';
                  file_out = 'ClassifiedTestVectors.CSV';
                  option_normalize = 5;
                  option_distance = 4;
                  r_softmax = 5;
                  r_minkowski = 3;
                  nearest_neighbor_cvip(file_tt, file_tr, file_out, option_normalize, option_distance, r_softmax, r_minkowski)