May 18 2007 4:48:56.600 AM SPAETH2_PRB FORTRAN90 version A set of test programs for SPAETH2. TEST01 DATA_SIZE reports the size of a data set. DATA_R_READ reads a real data set. DATA_R_SHOW makes a plot of them. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 .................................................... . * . . * . . * . . . . * . . . . . . . . * . . * * . . . . * . . * . . * . .* . . . . . . * . . . . * . . * * . . * . . * * . . . . * * * *. .................................................... TEST01 DATA_SIZE reports the size of a data set. DATA_R_READ reads a real data set. DATA_R_SHOW makes a plot of them. Data set is in file spaeth2_04.txt The number of data items is M = 10 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 29 .................................................... . * . . . . * . . . . . . * * . . . . . . . . . . *. . . . . . * . . . . . . * . . . . * . . . . . .* . . . . . . * . .................................................... TEST01 DATA_SIZE reports the size of a data set. DATA_R_READ reads a real data set. DATA_R_SHOW makes a plot of them. Data set is in file spaeth2_05.txt The number of data items is M = 59 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 139 .................................................... . * * . . . . * . . * . . * * . . . . . . * . . * * . . * * . . . . * *** * * . . ** * . . * * . .* * * * . . * . . * * . . * @ . . * * * * * . . * . . * * @** . . * * . . * * *. . * * . . * * * * * * * *. .................................................... TEST02 LEADER uses a simple clustering algorithm. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 Using a cluster parameter of RHO = 10.0000 Number of clusters created was 22 Using a cluster parameter of RHO = 20.0000 Number of clusters created was 18 Using a cluster parameter of RHO = 30.0000 Number of clusters created was 15 Using a cluster parameter of RHO = 40.0000 Number of clusters created was 11 TEST03 LEADER uses a simple clustering algorithm. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 Using a cluster parameter of RHO = 10.0000 Number of clusters created was 21 Using a cluster parameter of RHO = 20.0000 Number of clusters created was 17 Using a cluster parameter of RHO = 30.0000 Number of clusters created was 14 Using a cluster parameter of RHO = 40.0000 Number of clusters created was 9 TEST04 ZWEIGO groups data into TWO clusters. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 Number of clusters created was 2 I Cluster 1 2 2 1 3 2 4 2 5 2 6 1 7 2 8 1 9 1 10 2 11 2 12 2 13 1 14 1 15 1 16 1 17 1 18 1 19 1 20 2 21 2 22 2 TEST05 HMEANS uses a variance diminishing procedure. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 For each cluster size from 1 to 10, we carry out the experiment 3 times. DATA_D_READ: Number of lines read was 65 NC Initial Final Variance Variance 1 153509. 153509. 1 153509. 153509. 1 153509. 153509. 2 151287. 64629.5 2 135538. 64629.5 2 152835. 64629.5 3 143006. 40840.8 3 140338. 39791.7 3 143729. 42869.9 4 128967. 21167.6 4 135992. 39791.7 4 142864. 21167.6 5 136029. 18322.2 5 103547. 16464.3 5 126937. 16464.3 6 137266. 28286.4 6 125460. 13618.9 6 109009. 13809.5 7 92601.0 11420.8 7 89417.5 12005.5 7 122455. 11372.2 8 84404.9 9526.92 8 91256.3 10064.2 8 128977. 17879.5 9 106536. 14866.4 9 72171.1 14240.0 9 106232. 12630.3 10 108720. 17981.7 10 77452.7 10421.9 10 70531.0 7412.92 TEST06 KMEANS uses a variance diminishing procedure. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 NC Initial Final Variance Variance 1 153509. 0.108694-321 1 153509. 0.108694-321 1 153509. 0.108694-321 2 151287. 64629.5 2 135538. 64629.5 2 152835. 64629.5 3 143006. 40412.7 3 140338. 39099.0 3 143729. 39099.0 4 128967. 34149.9 4 135992. 21167.6 4 142864. 21167.6 5 136029. 16464.3 5 103547. 17132.5 5 126937. 17132.6 6 137266. 14634.0 6 125460. 12429.2 6 109009. 12429.2 7 92601.0 10490.8 7 89417.5 10490.8 7 122455. 10764.3 8 84404.9 8825.75 8 91256.3 8933.92 8 128977. 9181.25 9 106536. 7516.25 9 72171.1 7408.00 9 106232. 7408.00 10 108720. 5743.00 10 77452.7 6763.42 10 70531.0 5743.00 TEST07 CLUDIA carries out the KMEANS procedure for a generalized distance function that is stored in a matrix. For this example, we simply wish to repeat the particular case of standard distance in the L2 norm. But CLUDIA can handle more interesting cases as well. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 NC Initial Final Variance Variance 1 25140.2 1142.74 1 25140.2 1142.74 1 25140.2 1142.74 2 12267.3 723.724 2 13604.8 723.724 2 12531.6 723.724 3 7993.97 555.291 3 7977.65 557.654 3 10289.2 557.654 4 5511.83 405.580 4 6072.86 405.580 4 6105.22 405.580 5 5015.10 346.581 5 3846.13 346.581 5 4882.41 357.272 6 3591.45 312.614 6 3798.74 312.335 6 4482.54 298.272 7 2617.61 264.305 7 2684.39 266.141 7 3584.48 278.367 8 2229.16 243.280 8 2384.09 233.822 8 2934.09 236.757 9 2641.53 206.273 9 1956.99 204.625 9 2441.83 224.652 10 2163.56 190.098 10 1597.92 180.641 10 1708.77 180.641 TEST08 DMAT_DET computes the determinant of a symmetric matrix. The matrix to be analyzed: 1 2 3 4 5 1 .833333 .666667 .500000 .333333 .166667 2 .666667 1.33333 1.00000 .666667 .333333 3 .500000 1.00000 1.50000 1.00000 .500000 4 .333333 .666667 1.00000 1.33333 .666667 5 .166667 .333333 .500000 .666667 .833333 The computed determinant is .166667 The correct determinant is .166667 TEST09 WMEANS uses a determinant diminishing procedure. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 NC Determinant 1 .166667 1 .166667 1 .166667 2 -0.163803E+09 2 -0.716852E+09 2 -0.156564E+09 3 -0.102820E+10 3 -0.108889E+10 3 -0.420690E+09 4 -0.901441E+09 4 -0.477692E+09 4 -0.408632E+09 5 -0.956310E+09 5 -0.168810E+10 5 -0.931522E+09 6 -0.124759E+10 6 -0.890691E+09 6 -0.169378E+10 7 -0.126072E+10 7 -0.134555E+10 7 -0.905442E+09 8 -0.189793E+10 8 -0.146844E+10 8 -0.732490E+09 9 -0.955236E+09 9 -0.673727E+09 9 -0.121980E+10 10 -0.137496E+10 10 -0.148706E+10 10 -0.521151E+09 TEST10 ORDERED clusters an ordered set of 1D data. Data set is in file spaeth2_02.txt DATA_D_READ: Number of lines read was 34 The sorted 1D dataset: The number of data items is M = 15 The dimension of the data items is N = 1 1 1 .000000 2 .100000 3 .200000 4 .500000 5 2.00000 6 2.10000 7 2.50000 8 3.00000 9 3.20000 10 4.00000 11 5.00000 12 6.00000 13 8.00000 14 10.0000 15 11.0000 Cluster First Value 1 1 2 5 3 10 4 13 5 14 Point Variance 1 .000000 2 .000000 3 .000000 4 .000000 5 .000000 6 0.500000E-02 7 0.100000E-01 8 0.250000E-01 9 0.450000E-01 10 .165000 11 .300000 12 .800000 13 1.34000 14 3.27200 15 3.77200 TEST11 CLUSTA uses a multiple location allocation approach. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 NC Initial Final Variance Variance 1 153509. 1722.13 1 153509. 1722.13 1 153509. 1722.13 2 151287. 1102.81 2 135538. 1102.81 2 152835. 1102.81 3 143006. 863.305 3 140338. 868.953 3 143729. 894.403 4 128967. 610.616 4 135992. 610.616 4 142864. 610.616 5 136029. 528.446 5 103547. 528.446 5 126937. 549.900 6 137266. 473.957 6 125460. 476.157 6 109009. 476.123 7 92601.0 430.234 7 89417.5 486.980 7 122455. 629.137 8 84404.9 399.193 8 91256.3 387.634 8 128977. 404.987 9 106536. 359.209 9 72171.1 398.471 9 106232. 329.787 10 108720. 309.895 10 77452.7 298.389 10 70531.0 302.204 TEST12 I4VEC_PERML returns permutations of a vector. 0 1 2 3 4 1 1 2 4 3 2 1 3 2 4 3 1 3 4 2 4 1 4 2 3 5 1 4 3 2 6 2 1 3 4 7 2 1 4 3 8 2 3 1 4 9 2 3 4 1 10 2 4 1 3 11 2 4 3 1 12 3 1 2 4 13 3 1 4 2 14 3 2 1 4 15 3 2 4 1 16 3 4 1 2 17 3 4 2 1 18 4 1 2 3 19 4 1 3 2 20 4 2 1 3 21 4 2 3 1 22 4 3 1 2 23 4 3 2 1 24 1 2 3 4 TEST13 I4VEC_PERMS returns the first half of the set of all permutations of a vector. 0 1 2 3 4 1 1 2 4 3 2 1 2 3 4 3 1 2 4 3 4 1 2 3 4 5 1 2 4 3 6 1 2 3 4 7 1 2 4 3 8 1 2 3 4 9 1 2 4 3 10 1 2 3 4 11 1 2 4 3 12 1 2 4 3 TEST14 EMEANS uses a sum of absolute distances to the median. Data set is in file spaeth2_03.txt The number of data items is M = 22 The dimension of the data items is N = 2 DATA_D_READ: Number of lines read was 65 NC Initial Final Median Median Distance Distance 1 3053.00 3053.00 1 3053.00 3053.00 1 3053.00 3053.00 2 2112.00 1403.00 2 2001.00 1493.00 2 2971.00 1403.00 3 2293.00 1574.00 3 2149.00 1420.00 3 2540.00 1470.00 4 1783.00 1360.00 4 2398.00 2036.00 4 2378.00 1281.00 5 2554.00 1471.00 5 1925.00 1261.00 5 2230.00 1391.00 6 1915.00 1205.00 6 1730.00 977.000 6 2098.00 1229.00 7 1702.00 1006.00 7 1735.00 1225.00 7 2131.00 1307.00 8 1596.00 880.000 8 1454.00 906.000 8 1860.00 971.000 9 1487.00 1185.00 9 1443.00 1019.00 9 1515.00 1342.00 10 1590.00 723.000 10 1385.00 824.000 10 1352.00 827.000 SPAETH2_PRB Normal end of execution. May 18 2007 4:48:57.717 AM