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Reading/Plotting the values of different parameter sets

ReadPlot_convergence.Rd

This function reads a file containing different parameter sets and ther corresponfing goodness-of-fit values

Usage

read_convergence(file="ConvergenceMeasures.txt", MinMax=NULL, beh.thr=NA, 
     verbose=TRUE, plot=TRUE, col=c("black", "darkolivegreen"), lty=c(1,3), 
     lwd=c(2,2), main="Global Optimum & Normalized Swarm Radius vs Iteration Number", 
     xlab="Iteration Number", ylab=c("Global Optimum", expression(delta[norm])), 
     pch=c(15, 18), cex=1, cex.main=1.4, cex.axis=1.2, cex.lab=1.2, 
     legend.pos="topright", ..., do.png=FALSE, png.width=1500, png.height=900, 
     png.res=90,png.fname="ConvergenceMeasures.png")
           
plot_convergence(x, verbose=TRUE, col=c("black", "darkolivegreen"), lty=c(1,3),
     lwd=c(2,2), main="Global Optimum & Normalized Swarm Radius vs Iteration Number", 
     xlab="Iteration Number", ylab=c("Global Optimum", expression(delta[norm])), 
     pch=c(15, 18), cex=1, cex.main=1.4, cex.axis=1.2, cex.lab=1.2, 
     legend.pos="topright", ...,  do.png=FALSE, png.width=1500, png.height=900, 
     png.res=90, png.fname="ConvergenceMeasures.png")

Arguments

file

character, name (including path) of the file to be read

verbose

logical; if TRUE, progress messages are printed

x

data.frame with the convergence outputs obtained with read_convergence.

MinMax

OPTIONAL
character, indicates if the optimum value in params corresponds to the minimum or maximum of the the objective function. Valid values are in: c('min', 'max')

beh.thr

numeric, used for selecting only the behavioural parameter sets, i.e., those with a goodness-of-fit value larger/lowervalue than beh.th, depending on the value of MinMax.
It is only used for drawing a horizontal line used for separating behavioural from non behavioural parameter sets.

plot

logical, indicates if a plot with the convergence measures has to be produced

col

OPTIONAL. Only used when plot=TRUE
character, colour to be used for drawing the lines

lty

OPTIONAL. Only used when plot=TRUE
numeric, line type to be used

lwd

OPTIONAL. Only used when plot=TRUE
numeric, line width

xlab

OPTIONAL. Only used when plot=TRUE
character, label for the 'x' axis

ylab

OPTIONAL. Only used when plot=TRUE
character, label for the 'y' axis

main

OPTIONAL. Only used when plot=TRUE
character, title for the plot

pch

OPTIONAL. Only used when plot=TRUE
numeric, type of symbol for drawing the points of the dotty plots (e.g., 1: white circle)

cex

OPTIONAL. Only used when plot=TRUE
numeric, values controlling the size of text and points with respect to the default

cex.main

OPTIONAL. Only used when plot=TRUE
numeric, magnification to be used for main titles relative to the current setting of cex

cex.axis

OPTIONAL. Only used when plot=TRUE
numeric, magnification to be used for axis annotation relative to the current setting of cex

cex.lab

OPTIONAL. Only used when plot=TRUE
numeric, magnification to be used for x and y labels relative to the current setting of cex

legend.pos

OPTIONAL. Only used when plot=TRUE
character, position of the legend. Valid values are in c("bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", "center"). See legend

...

OPTIONAL. Only used when plot=TRUE
further arguments passed to the plot command or from other methods

do.png

logical, indicates if the plot with the convergence measures has to be saved into a PNG file instead of the screen device

png.width

OPTIONAL. Only used when do.png=TRUE
numeric, width of the device. See png

png.height

OPTIONAL. Only used when do.png=TRUE
numeric, height of the device. See png

png.res

OPTIONAL. Only used when do.png=TRUE
numeric, nominal resolution in ppi which will be recorded in the PNG file, if a positive integer of the device. See png

png.fname

OPTIONAL. Only used when do.png=TRUE
character, name of the output PNG file. See png

Value

A list with the following elements:

Iter

iteration number

.

Gbest

global optimum for each iteration

.

GbestRate

rate of change of the global optimum (current iter/previous iter)

.

IterBestFit

best performance for the current iteration

.

normSwarmRadius

normalised swarm radious

.

[gbest-mean(pbest)]/mean(pbest)

gbest: global optimum, mean(pbest): mean values of the personal best of all the particles

Author

Mauricio Zambrano-Bigiarini, mzb.devel@gmail.com

See also

read_results, plot_results

Examples

# \donttest{
local({
  
# Setting the user temporal directory as working directory
oldwd <- getwd()      
on.exit(setwd(oldwd), add = TRUE) 
setwd(tempdir())

# Number of dimensions to be optimised
D <- 4

# Boundaries of the search space (Sphere function)
lower <- rep(-100, D)
upper <- rep(100, D)

# Setting the seed
set.seed(100)

# Runing PSO with the 'sphere' test function, writting the results to text files
hydroPSO(fn=sphere, lower=lower, upper=upper,
        control=list(MinMax="min", write2disk=TRUE, plot=TRUE)  
        )
  
# Reading the convergence measures got by running hydroPSO
read_convergence(file="./PSO.out/ConvergenceMeasures.txt")

}) # local END
#>                                                                                 
#> ================================================================================
#> [                                Initialising  ...                             ]
#> ================================================================================
#>                                                                                 
#> [npart=40 ; maxit=1000 ; method=spso2011 ; topology=random ; boundary.wall=absorbing2011]
#>          
#> [ user-definitions in control: MinMax=min ; write2disk=TRUE ; plot=TRUE ]
#>          
#>                                                                                 
#> ================================================================================
#> [ Writing the 'PSO_logfile.txt' file ...                                       ]
#> ================================================================================
#>                                                                                 
#> ================================================================================
#> [                                 Running  PSO ...                             ]
#> ================================================================================
#>                                                                                 




































































































#> iter: 100  Gbest: 6.143E-08  Gbest_rate: 66.13%  Iter_best_fit: 6.143E-08  nSwarm_Radius: 3.14E-06  |g-mean(p)|/mean(p): 98.08%


#>                            
#> [ Writing output files... ]
#>                            
#>                                     |                                           
#> ================================================================================
#> [                          Creating the R output ...                           ]
#> ================================================================================
#>                                                      
#> [ Reading the file 'ConvergenceMeasures.txt' ... ]
#> [ Total number of iterations: 102 ]
#> [ Plotting convergence measures' ... ]

#>     Iter        Gbest GbestRate  IterBestFit normSwarmRadius GbestPbestRatio
#> 1      1 2.496037e+03   100.000 2.496037e+03    3.172621e-01          80.803
#> 2      2 3.089613e+02    87.622 3.089613e+02    1.952015e-01          95.008
#> 3      3 3.089613e+02     0.000 1.373981e+03    2.889168e-01          94.882
#> 4      4 3.089613e+02     0.000 2.075614e+03    2.632173e-01          94.630
#> 5      5 3.089613e+02     0.000 1.233861e+03    2.124345e-01          93.653
#> 6      6 3.089613e+02     0.000 9.065310e+02    1.646323e-01          91.288
#> 7      7 8.134900e+01    73.670 8.134900e+01    1.583449e-01          97.263
#> 8      8 8.134900e+01     0.000 7.774286e+02    1.650119e-01          96.915
#> 9      9 8.134900e+01     0.000 5.422436e+02    1.471361e-01          96.285
#> 10    10 8.134900e+01     0.000 2.068215e+02    1.187738e-01          95.143
#> 11    11 8.134900e+01     0.000 1.551307e+02    1.002909e-01          93.886
#> 12    12 8.134900e+01     0.000 1.892053e+02    9.612793e-02          92.767
#> 13    13 8.134900e+01     0.000 1.398016e+02    1.035551e-01          91.633
#> 14    14 5.554823e+01    31.716 5.554823e+01    8.237369e-02          93.576
#> 15    15 9.647942e+00    82.631 9.647942e+00    7.007312e-02          98.702
#> 16    16 9.647942e+00     0.000 2.329054e+01    6.603689e-02          98.545
#> 17    17 9.647942e+00     0.000 3.492954e+01    6.294083e-02          98.244
#> 18    18 9.647942e+00     0.000 3.519265e+01    6.226520e-02          98.129
#> 19    19 9.647942e+00     0.000 2.785130e+01    5.352650e-02          97.485
#> 20    20 9.647942e+00     0.000 2.846986e+01    4.183406e-02          96.781
#> 21    21 9.023023e+00     6.477 9.023023e+00    3.460949e-02          96.346
#> 22    22 7.303407e+00    19.058 7.303407e+00    3.724562e-02          96.352
#> 23    23 3.008332e+00    58.809 3.008332e+00    2.944322e-02          98.182
#> 24    24 6.933120e-01    76.954 6.933120e-01    2.454574e-02          99.378
#> 25    25 6.933120e-01     0.000 2.813179e+00    2.254574e-02          99.227
#> 26    26 6.933120e-01     0.000 4.441790e+00    1.950986e-02          99.043
#> 27    27 6.933120e-01     0.000 1.749271e+00    1.627126e-02          98.790
#> 28    28 6.716446e-01     3.125 6.716446e-01    1.449753e-02          98.538
#> 29    29 6.716446e-01     0.000 6.754569e-01    1.154765e-02          98.070
#> 30    30 6.716446e-01     0.000 8.211323e-01    1.083901e-02          96.974
#> 31    31 6.716446e-01     0.000 6.767017e-01    9.583346e-03          96.319
#> 32    32 9.146959e-02    86.381 9.146959e-02    9.386729e-03          99.393
#> 33    33 9.146959e-02     0.000 1.115529e+00    7.737459e-03          99.317
#> 34    34 9.146959e-02     0.000 1.019398e-01    6.407650e-03          99.196
#> 35    35 9.146959e-02     0.000 2.000663e-01    5.495661e-03          99.067
#> 36    36 9.146959e-02     0.000 3.070983e-01    6.685656e-03          98.738
#> 37    37 9.146959e-02     0.000 2.832960e-01    5.199535e-03          98.384
#> 38    38 9.146959e-02     0.000 3.110369e-01    4.891508e-03          97.939
#> 39    39 9.146959e-02     0.000 2.456618e-01    4.476079e-03          97.714
#> 40    40 9.146959e-02     0.000 1.361169e-01    3.520348e-03          97.448
#> 41    41 9.112837e-02     0.373 9.112837e-02    3.243466e-03          96.511
#> 42    42 9.112837e-02     0.000 1.108540e-01    3.288174e-03          95.666
#> 43    43 8.390113e-02     7.931 8.390113e-02    2.722801e-03          94.947
#> 44    44 3.161287e-02    62.321 3.161287e-02    2.128737e-03          97.719
#> 45    45 3.161287e-02     0.000 4.607031e-02    2.132200e-03          97.506
#> 46    46 3.161287e-02     0.000 4.610374e-02    1.982863e-03          96.761
#> 47    47 4.207934e-03    86.689 4.207934e-03    1.757924e-03          99.427
#> 48    48 4.207934e-03     0.000 1.069408e-02    1.358194e-03          99.318
#> 49    49 4.207934e-03     0.000 2.019614e-02    1.366197e-03          99.149
#> 50    50 4.207934e-03     0.000 1.002616e-02    1.259148e-03          98.974
#> 51    51 4.207934e-03     0.000 6.585332e-03    1.175506e-03          98.839
#> 52    52 1.835719e-03    56.375 1.835719e-03    9.926934e-04          99.398
#> 53    53 1.835719e-03     0.000 5.948079e-03    8.758056e-04          99.204
#> 54    54 1.835719e-03     0.000 7.329408e-03    7.011485e-04          98.785
#> 55    55 1.835719e-03     0.000 1.028233e-02    7.229435e-04          98.525
#> 56    56 1.398915e-03    23.795 1.398915e-03    5.991501e-04          98.511
#> 57    57 1.398915e-03     0.000 2.302913e-03    4.940016e-04          97.773
#> 58    58 1.398915e-03     0.000 1.854917e-03    4.581696e-04          96.907
#> 59    59 1.146212e-03    18.064 1.146212e-03    3.905583e-04          97.064
#> 60    60 1.146212e-03     0.000 1.503596e-03    3.649289e-04          96.817
#> 61    61 1.146212e-03     0.000 1.559456e-03    3.174378e-04          95.825
#> 62    62 3.068545e-04    73.229 3.068545e-04    2.760338e-04          98.472
#> 63    63 3.068545e-04     0.000 1.000044e-03    2.521666e-04          98.060
#> 64    64 3.068545e-04     0.000 6.712116e-04    2.536867e-04          97.674
#> 65    65 3.068545e-04     0.000 3.913582e-04    2.162801e-04          97.344
#> 66    66 3.068545e-04     0.000 1.094820e-03    2.133923e-04          96.929
#> 67    67 3.068545e-04     0.000 7.063522e-04    2.070140e-04          95.726
#> 68    68 2.049828e-04    33.199 2.049828e-04    1.651334e-04          96.399
#> 69    69 4.168625e-05    79.664 4.168625e-05    1.443711e-04          99.147
#> 70    70 4.168625e-05     0.000 8.322529e-05    1.415024e-04          99.008
#> 71    71 4.168625e-05     0.000 1.722213e-04    1.370242e-04          98.743
#> 72    72 4.168625e-05     0.000 1.902369e-04    1.164545e-04          98.413
#> 73    73 4.168625e-05     0.000 6.447493e-05    9.929215e-05          98.012
#> 74    74 3.072050e-05    26.305 3.072050e-05    7.914288e-05          98.216
#> 75    75 3.072050e-05     0.000 5.209704e-05    7.493874e-05          97.941
#> 76    76 3.072050e-05     0.000 7.213335e-05    6.704383e-05          97.707
#> 77    77 2.140574e-05    30.321 2.140574e-05    6.165041e-05          97.864
#> 78    78 9.882768e-06    53.831 9.882768e-06    4.987594e-05          98.810
#> 79    79 9.882768e-06     0.000 1.086764e-05    4.043539e-05          98.448
#> 80    80 3.405683e-06    65.539 3.405683e-06    3.562276e-05          99.285
#> 81    81 3.405683e-06     0.000 7.110449e-06    3.070367e-05          99.097
#> 82    82 3.405683e-06     0.000 1.083147e-05    2.764154e-05          98.768
#> 83    83 2.496317e-06    26.701 2.496317e-06    2.395479e-05          98.726
#> 84    84 2.496317e-06     0.000 6.148388e-06    2.251955e-05          98.503
#> 85    85 1.211539e-06    51.467 1.211539e-06    1.947720e-05          99.203
#> 86    86 1.040015e-06    14.158 1.040015e-06    1.749146e-05          99.283
#> 87    87 1.040015e-06     0.000 2.782354e-06    1.653610e-05          98.930
#> 88    88 1.040015e-06     0.000 1.790423e-06    1.410923e-05          98.074
#> 89    89 1.033346e-06     0.641 1.033346e-06    1.216543e-05          97.967
#> 90    90 1.033346e-06     0.000 1.724961e-06    1.128046e-05          97.823
#> 91    91 9.932600e-07     3.879 9.932600e-07    1.086178e-05          97.848
#> 92    92 9.932600e-07     0.000 1.387926e-06    8.944578e-06          97.554
#> 93    93 9.932600e-07     0.000 1.138679e-06    7.392670e-06          93.970
#> 94    94 4.354435e-07    56.160 4.354435e-07    6.297021e-06          97.101
#> 95    95 3.636878e-07    16.479 3.636878e-07    5.858944e-06          97.401
#> 96    96 1.813750e-07    50.129 1.813750e-07    6.007477e-06          98.639
#> 97    97 1.813750e-07     0.000 3.349881e-07    4.841607e-06          96.863
#> 98    98 1.813750e-07     0.000 2.747090e-07    3.825328e-06          95.703
#> 99    99 1.813750e-07     0.000 2.339099e-07    3.693049e-06          95.115
#> 100  100 6.142730e-08    66.132 6.142730e-08    3.142231e-06          98.079
#> 101  101 6.142730e-08     0.000 1.441196e-07    2.783280e-06          97.522
#> 102  102 6.142730e-08     0.000 1.585029e-07    2.303783e-06          96.585
# } # donttest END