PATTERN_MINKOWSKI_CVIP

pattern_minkowski_cvip() - takes two csv files as input, test and training set, then calculates the minkowski distance metric by comparing each vector in the test set to each in the training set.

SYNTAX
DESCRIPTION
REFERENCE
EXAMPLE
CREDITS

SYNTAX

d = pattern_minkowski_cvip(file_tt, file_tr, r)

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.
r - The parameter for mikowski distance metric. A real number greater than or equal to 1.

Output Parameters include :

d - A matrix containing the distances between each vector in the test set to each vector in the training set. Each column represents a vector in training set. Each row represents a vector in test set.

DESCRIPTION

Using minkowski metric, the function calculates the distance between the vectors in training set and the test set.The minkowski metric has an extra argument r which needs to be given as input.The distances are returned in a matrix form whose rows represent the test vectors and columns represent the training vectors.

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

 d = pattern_minkowski_cvip(file_tt, file_tr,3)

d =

  Columns 1 through 4

  12.5000 -21.6506i  10.0266 -17.3666i  10.6900 -18.5157i   7.4941 -12.9801i
  12.6047 -21.8320i  11.3742 -19.7007i  12.9219 -22.3813i   7.5471 -13.0720i
  10.5241 -18.2283i   8.3203 -14.4112i   9.3983 -16.2783i   5.8517 -10.1354i
  13.0604 -22.6212i  11.7304 -20.3176i  13.2292 -22.9136i   8.0415 -13.9282i
  10.0518 -17.4103i   8.0052 -13.8654i   9.2312 -15.9888i   5.4104 - 9.3711i
  11.5003 -19.9191i   9.0638 -15.6990i   9.8253 -17.0179i   6.5343 -11.3178i
   0.0000 + 0.0000i   3.9365 + 0.0000i   3.8259 - 6.6266i  11.3393 + 0.0000i
   1.9682 - 3.4091i   0.0000 + 0.0000i   2.0104 - 3.4821i  13.1250 + 0.0000i
   7.6517 + 0.0000i   4.0207 + 0.0000i   0.0000 + 0.0000i  17.1430 + 0.0000i
   5.6696 - 9.8201i   6.5625 -11.3666i   8.5715 -14.8462i   0.0000 + 0.0000i
   7.1047 -12.3057i   5.6696 - 9.8201i   7.3658 -12.7579i   1.9682 - 3.4091i
   6.5010 -11.2600i   4.3021 - 7.4515i   5.6696 - 9.8201i   7.6517 + 0.0000i

  Columns 5 through 7

   5.3221 - 9.2182i   5.9988 -10.3903i   5.9988 -10.3903i
   5.7388 - 9.9399i   7.2582 -12.5716i   7.2582 -12.5716i
   3.4966 - 6.0563i   4.0691 - 7.0479i   4.0691 - 7.0479i
   6.1303 -10.6179i   7.5595 -13.0935i   7.5595 -13.0935i
   3.0000 - 5.1962i   3.7054 - 6.4179i   3.7054 - 6.4179i
   4.3643 - 7.5591i   5.0000 - 8.6603i   5.0000 - 8.6603i
  14.2094 + 0.0000i  13.0020 + 0.0000i  13.0020 + 0.0000i
  11.3393 + 0.0000i   8.6043 + 0.0000i   8.6043 + 0.0000i
  14.7315 + 0.0000i  11.3393 + 0.0000i  11.3393 + 0.0000i
   3.9365 + 0.0000i   3.8259 - 6.6266i   3.8259 - 6.6266i
   0.0000 + 0.0000i   2.0104 - 3.4821i   2.0104 - 3.4821i
   4.0207 + 0.0000i   0.0000 + 0.0000i   0.0000 + 0.0000i

PATTERN_MINKOWSKI_CVIP

Contents

SYNTAX

DESCRIPTION

REFERENCE

EXAMPLE

CREDITS