Find Nearest Prototype

For each cell of the initial design, select the closest observation to its center and use it as a representative for that cell.

findNearestPrototype(feat.object, dist_meth, mink_p, fast_k, ...)

Arguments

feat.object	[`FeatureObject`] A feature object as created by `createFeatureObject`.
dist_meth	[`character(1)`] Which distance method should be used for computing the distance between two observations? All methods of `dist` are possible options with `"euclidean"` being the default.
mink_p	[`integer(1)`] Value of `p` in case `dist_meth` is `"minkowski"`. The default is `2`, i.e. the euclidean distance.
fast_k	[`numeric(1)`] Percentage of elements that should be considered within the nearest neighbour computation. The default is `0.05`.
...	[any] Further arguments, which might be used within the distance computation (`dist`).

Value

[data.frame]. A data.frame containing one prototype (i.e. a representative observation) per cell. Each prototype consists of its values from the decision space, the corresponding objective value, its own cell ID and the cell ID of the cell, which it represents.

Examples

# (1) create the initial sample and feature object:
X = createInitialSample(n.obs = 1000, dim = 2,
  control = list(init_sample.lower = -10, init_sample.upper = 10))
feat.object = createFeatureObject(X = X,
  fun = function(x) sum(x^2), blocks = 10)

# (2) find the nearest prototypes of all cells:
findNearestPrototype(feat.object)
#>             x1         x2          y represented.cell
#> 1   -9.1921242 -9.0064189 165.610730                1
#> 2   -7.1728049 -9.0344815 133.070986                2
#> 3   -4.9291467 -8.8116848 101.942277                3
#> 4   -2.8179450 -9.1780557  92.177520                4
#> 5   -1.2579016 -8.9989387  82.563214                5
#> 6    1.2783302 -8.8338316  79.670709                6
#> 7    2.6060323 -9.0667223  88.996858                7
#> 8    5.2873017 -9.4685650 117.609283                8
#> 9    6.8866042 -9.3072216 134.049691                9
#> 10   8.9597678 -9.0438228 162.068169               10
#> 11  -9.1560979 -7.1865540 135.480688               11
#> 12  -7.2181069 -7.3401710 105.979177               12
#> 13  -5.1582342 -6.8413816  73.411882               13
#> 14  -3.1509529 -6.8770223  57.221940               14
#> 15  -1.1481608 -6.9400791  49.482971               15
#> 16   1.3538582 -7.2911361  54.993597               16
#> 17   2.7582979 -7.3372707  61.443748               17
#> 18   5.1805693 -6.6796386  71.455869               18
#> 19   6.7567192 -7.2748574  98.576806               19
#> 20   8.7042268 -7.2155466 127.827677               20
#> 21  -9.3526419 -4.6569856 109.159426               21
#> 22  -7.0354358 -4.7532352  72.090601               22
#> 23  -4.6237401 -5.0273736  46.653457               23
#> 24  -2.8798218 -4.8106064  31.435308               24
#> 25  -0.8883363 -5.0430549  26.221544               25
#> 26   1.1772573 -4.7113757  23.582995               26
#> 27   2.6474628 -5.2596226  34.672689               27
#> 28   4.7561758 -4.6949086  44.663375               28
#> 29   6.7692689 -5.3448123  74.390020               29
#> 30   9.1173688 -5.5301953 113.709473               30
#> 31  -9.2865841 -3.1344369  96.065339               31
#> 32  -7.3761409 -2.8409292  62.478333               32
#> 33  -4.9584027 -2.7435960  32.113076               33
#> 34  -2.8769355 -2.8315567  16.294471               34
#> 35  -1.1061446 -3.6034843  14.208655               35
#> 36   1.0298377 -3.3927091  12.571040               36
#> 37   3.0203645 -3.1171306  18.839105               37
#> 38   4.8452362 -2.9414676  32.128545               38
#> 39   6.8538322 -3.3439898  58.157283               39
#> 40   8.6737800 -2.9304546  83.822023               40
#> 41  -8.9807381 -0.8735919  81.416820               41
#> 42  -6.7399571 -0.9872939  46.401771               42
#> 43  -4.9934631 -0.9899817  25.914737               43
#> 44  -3.2087881 -1.1622347  11.647110               44
#> 45  -0.9642806 -1.0294432   1.989590               45
#> 46   0.7948449 -1.3727095   2.516110               46
#> 47   2.5546988 -1.0679051   7.666907               47
#> 48   4.8974826 -1.0442423  25.075778               48
#> 49   6.9355689 -0.9464955  48.997970               49
#> 50   8.8099773 -1.1556138  78.951143               50
#> 51  -9.2071037  0.6173514  85.151881               51
#> 52  -7.1421628  1.2324811  52.529499               52
#> 53  -5.2130738  1.2112080  28.643163               53
#> 54  -3.0927940  1.1235515  10.827743               54
#> 55  -1.0263273  0.8444377   1.766423               55
#> 56   1.2476648  1.2671013   3.162213               56
#> 57   3.2890426  0.5365956  11.105736               57
#> 58   4.9764422  1.4293942  26.808145               58
#> 59   6.9522675  1.0167873  49.367880               59
#> 60   8.7059876  1.0241248  76.843052               60
#> 61  -8.6122848  3.3521173  85.408141               61
#> 62  -6.4571504  3.2691593  52.382194               62
#> 63  -5.0956086  2.7621242  33.594557               63
#> 64  -2.9409717  3.0983526  18.249103               64
#> 65  -1.0804413  3.0380443  10.397067               65
#> 66   1.1194335  2.8570833   9.416056               66
#> 67   2.9813953  2.8463173  16.990240               67
#> 68   5.1964850  3.0001566  36.004396               68
#> 69   7.0591262  3.2525331  60.410235               69
#> 70   9.1917454  3.0379458  93.717297               70
#> 71  -8.7293257  5.1197232 102.412692               71
#> 72  -6.9400338  4.5483678  68.851719               72
#> 73  -4.8131894  4.8619119  46.804980               73
#> 74  -2.7002896  4.9576950  31.870303               74
#> 75  -1.0944309  5.0999447  27.207215               75
#> 76   0.9636309  5.1122874  27.064067               76
#> 77   2.5148636  5.2089940  33.458157               77
#> 78   5.2399549  5.0695548  53.157513               78
#> 79   6.7238033  5.1265384  71.490927               79
#> 80   9.4176845  4.7285192 111.051674               80
#> 81  -8.9131934  6.9972659 128.406747               81
#> 82  -6.9526341  6.3917991  89.194216               82
#> 83  -4.9089749  7.3465482  78.069804               83
#> 84  -3.2090377  6.3406450  50.501702               84
#> 85  -0.1554294  6.2578979  39.185444               85
#> 86   0.9683665  7.1415053  51.938831               86
#> 87   3.2453445  6.9610383  58.988315               87
#> 88   5.0278344  6.9520946  73.610737               88
#> 89   7.1009889  7.1871096 102.078587               89
#> 90   9.2667380  7.1208039 136.578282               90
#> 91  -9.7349008  8.9697131 175.224046               91
#> 92  -6.7410766  8.7702326 122.359093               92
#> 93  -5.0130338  9.3732866 112.989009               93
#> 94  -2.6195803  9.3074199  93.490267               94
#> 95  -0.9794622  8.6101272  75.093637               95
#> 96   0.6615546  8.5392975  73.357257               96
#> 97   3.5038290  9.1084557  95.240784               97
#> 98   4.9951494  9.3496554 112.367573               98
#> 99   7.1593201  9.3944086 139.510777               99
#> 100  8.8262868  9.0277691 159.403954              100

Arguments

Value

Examples

Contents