[GRASS-user] v.class.mlR Error
Jamille Haarloo
j.r.haarloo at gmail.com
Tue Jun 19 10:32:51 PDT 2018
Hi Moritz,
Thanx for the advice! Will check it out. From the publications I read, I
inferred it was better to use an alternative method (data mining/
object-based) on high resolution imagery than pixel-based. I also read that
texture metrics (e.g. standard deviation of spectral bands) may capture the
unique “patterns” of wetlands and other ecosystems (given the appropriate
spatial scale). Will continue testing and checking for improvements.
I was running directy from GRASS GIS, but recently created and used a new
training map with the lower case column name. My apologies for the
inconsistency, but good to know that the previous training map wasn't the
issue.
Best,
Jamille
On Tue, Jun 19, 2018 at 6:58 AM, Moritz Lennert <
mlennert at club.worldonline.be> wrote:
> P.S. I'm not sure that for the landscape you are working on (mainly forest
> areas) OBIA is the best approach. You might want to also try a pixel-based
> approach with i.smap:
>
> #create raster training areas
> v.to.rast Training_Ben5 out=training use=attr attrcol=Ecosystem
> #create a subgroup with all four bands
> i.group WV_Benatimofo subg=sub in=WV_Benatimofo.1 at haarlooj_Be
> n_Test,WV_Benatimofo.2 at haarlooj_Ben_Test,WV_Benatimofo.3@
> haarlooj_Ben_Test,WV_Benatimofo.4 at haarlooj_Ben_Test
> #create signature file
> i.gensigset training group=WV_Benatimofo sub=sub
> #classify
> i.smap group=WV_Benatimofo sub=sub sig=sig out=smap_classified
> goodness=smap_goodness
>
> Moritz
>
>
> On 19/06/18 11:33, Moritz Lennert wrote:
>
>> Hi Jamille,
>>
>> Testing with the data you sent me offlist (which BTW was a mapset, not a
>> location. A location contains at least one mapset named PERMANENT which
>> contains the projection info - I just assumed that you are working in
>> UTM Zone 21N, created my own location and copied your mapset into that
>> location.) and with the following command:
>>
>> v.class.mlR -i --overwrite
>> segments_map=Segments_vector_Stats_Ben_test at haarlooj_Ben_Test
>> training_map=Training_Ben5 at haarlooj_Ben_Test
>> raster_segments_map=best5_myregion1_at_haarlooj_Ben_Test_
>> rank1 at haarlooj_Ben_Test
>> train_class_column=Ecosystem output_class_column=vote
>> output_prob_column=prob classifiers=svmRadial,rf,C5.0 folds=5
>> partitions=10 tunelength=10 weighting_metric=accuracy
>> r_script_file=R-script processes=3
>>
>> the module runs perfectly fine for me here on GNU/Linux. Note the fact
>> that the train_class_column=Ecosystem, not ecosystem. Case matters here.
>>
>> Could you try with the training class column in the right case ?
>>
>> Moritz
>>
>> On 18/06/18 21:51, Jamille Haarloo wrote:
>>
>>> Hello Moritz,
>>>
>>> It looks like I got some results, but I suspect there are still some
>>> issues due to the warning messages.
>>>
>>> I either kept getting that a file couldn't be found or that it had
>>> trouble running the R-script.
>>> My actions:
>>>
>>> 1. I found that it was looking in "C:\Program Files\QGIS
>>> 2.16.0\apps\Python27\Lib" for certain scripts but I also had another
>>> location of the Python27 library "C:\Program Files\GRASS GIS
>>> 7.4.0\Python27\Lib". So I tried adding the second location via the
>>> black terminal because I figured it needed the GRASS
>>> versions/formats (with my lack of experience I am not sure if I
>>> succeeded).
>>> 2. It was still failing, and I suspected this had to do with the qbwwv
>>> voting method (see output). So I unchecked that option and got
>>> results. I'am also sending a screenshot of the attribute table with
>>> results.
>>>
>>> Do you have any suggestions for improvement?
>>> Soon I will test with a much bigger area for a land-use planning project.
>>>
>>>
>>> /The last 2 command outputs:/
>>>
>>> Running R now. Following output is R output.
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> Loading required package: caret
>>> Loading required package: lattice
>>> Loading required package: ggplot2
>>> Loading required package: foreach
>>> Loading required package: iterators
>>> Loading required package: parallel
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> Warning message:
>>> In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
>>> There were missing values in resampled performance measures.
>>> Error in `$<-.data.frame`(`*tmp*`, vote_qbwwv, value = numeric(0)) :
>>> replacement has 0 rows, data has 1965
>>> Calls: $<- -> $<-.data.frame
>>> Execution halted
>>> ERROR: There was an error in the execution of the R script.
>>> Please check the R output.
>>> (Mon Jun 18 15:36:47 2018) Command finished (1 min 24 sec)
>>> (Mon Jun 18 15:50:30 2018)
>>> v.class.mlR -i --overwrite
>>> segments_map=Segments_vector_Stats_Ben_test at haarlooj_Ben_Test
>>> training_map=Training_Ben_Grass at haarlooj_Ben_Test
>>> raster_segments_map=best5_myregion1_at_haarlooj_Ben_Test_
>>> rank1 at haarlooj_Ben_Test
>>> train_class_column=ecosystem output_class_column=vote
>>> output_prob_column=prob classifiers=svmRadial,rf,C5.0 folds=5
>>> partitions=10 tunelength=10 weighting_metric=accuracy
>>> r_script_file=C:\Users\haarlooj\Documents\CELOS\v.class.
>>> mIRR_optional_output\Ben_test_mlR-script3
>>> processes=2
>>> Running R now. Following output is R output.
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> Loading required package: caret
>>> Loading required package: lattice
>>> Loading required package: ggplot2
>>> Loading required package: foreach
>>> Loading required package: iterators
>>> Loading required package: parallel
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> Warning message:
>>> In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
>>> There were missing values in resampled performance measures.
>>> Finished running R.
>>> Loading results into attribute table
>>> (Mon Jun 18 15:51:58 2018) Command finished (1 min 27 sec)
>>>
>>>
>>> Best,
>>> Jamille Haarloo
>>> Department of Natural Resources and Environmental Assessment (NARENA)
>>> Centre for Agricultural Research in Suriname (CELOS)
>>> <http://www.celos.sr.org/>
>>>
>>> Prof. Dr. Ir. Jan Ruinardlaan
>>> AdeKUS University campus
>>> Phone: 439982
>>>
>>>
>>> On Thu, Jun 14, 2018 at 11:02 AM, Jamille Haarloo <j.r.haarloo at gmail.com
>>> <mailto:j.r.haarloo at gmail.com>> wrote:
>>>
>>> Hello Moritz,
>>>
>>> Sorry about that. I am sending the zip of the location. The output
>>> at the other machine (system cannot find the file specified) was due
>>> to location/ directory issues and has been resolved, but also stops
>>> executing the R-script saying there were more than 50 errors. Been
>>> looking for similar cases online but haven't found many.
>>>
>>>
>>> haarlooj_Ben_Test.zip
>>> <https://drive.google.com/file/d/1wQqmreMbbd3oRfoVXWIIGJOG9
>>> L63miV6/view?usp=drive_web>
>>>
>>>
>>> On Thu, Jun 14, 2018 at 5:19 AM, Moritz Lennert
>>> <mlennert at club.worldonline.be <mailto:mlennert at club.worldonline.be
>>> >>
>>> wrote:
>>>
>>> P.S. Your vector data also does not contain any attribute
>>> information as this is contained in a separate sqlite file. Just
>>> another reason never to touch the contents of a GRASS database
>>> directly, but only using dedicated tools. :-)
>>>
>>>
>>> On 14/06/18 10:11, Moritz Lennert wrote:
>>>
>>> Hi Jamille,
>>>
>>> Sorry, but I've been hopping from meeting to meeting these
>>> days and
>>> haven't had the opportunity to look at your data.
>>>
>>> Actually, the form you transmit them in is very
>>> inconvenient. If you
>>> want to transmit GRASS data to someone, you should either
>>> zip an entire
>>> location directory and send that, or export the data with
>>> r.pack/v.pack.
>>>
>>> In this form it is difficult to know which projection is
>>> used and I have
>>> to handcraft the data by copying the files into different
>>> directories.
>>>
>>> So, please resend your data in a format that I can easily
>>> use.
>>>
>>> Best wishes,
>>> Moritz
>>>
>>> On 11/06/18 20:55, Jamille Haarloo wrote:
>>>
>>> Hello Moritz,
>>>
>>> I managed to edit my copy of v.class.mlR with the lines
>>> you sent. But I
>>> am still getting errors on two different machines.
>>> I am sending you the data (hope I didn't miss anything)
>>> and the command
>>> outputs. I think there is sth wrong with my computer,
>>> but also with one
>>> of the files because of the error on the other machine.
>>>
>>> _Computer I normally use:_
>>>
>>> v.class.mlR -i --overwrite
>>> segments_map=Segments_vector_
>>> Stats_Ben_test at haarlooj_Ben_Test
>>> training_map=Training_Ben5 at haarlooj_Ben_Test
>>> raster_segments_map=best5_myr
>>> egion1_at_haarlooj_Ben_Test_rank1 at haarlooj_Ben_Test
>>> train_class_column=Ecosystem output_class_column=vote
>>> output_prob_column=prob classifiers=svmRadial,rf,C5.0
>>> folds=5
>>> partitions=10 tunelength=10 weighting_modes=smv,qbwwv
>>> weighting_metric=accuracy
>>> Running R now. Following output is R output.
>>> During startup - Warning messages:
>>> 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> 3: Setting LC_TIME=en_US.cp1252 failed
>>> 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> Loading required package: caret
>>> Loading required package: lattice
>>> Loading required package: ggplot2
>>> There were 50 or more warnings (use warnings() to see
>>> the first 50)
>>> There were 50 or more warnings (use warnings() to see
>>> the first 50)
>>> Error in `$<-.data.frame`(`*tmp*`, vote_qbwwv, value =
>>> numeric(0)) :
>>> replacement has 0 rows, data has 1965
>>> Calls: $<- -> $<-.data.frame
>>> Execution halted
>>> ERROR: There was an error in the execution of the R
>>> script.
>>> Please check the R output.
>>>
>>>
>>> _The output from the other machine:_
>>> (Mon Jun 11 13:27:39 2018)
>>> v.class.mlR -i --overwrite
>>> segments_map=Segments_vector_
>>> Stats_Ben_test at haarlooj_Ben_Test
>>> training_map=Training_Ben5 at haarlooj_Ben_Test
>>> raster_segments_map=best5_myr
>>> egion1_at_haarlooj_Ben_Test_rank1 at haarlooj_Ben_Test
>>> train_class_column=Ecosystem output_class_column=vote
>>> output_prob_column=prob classifiers=svmRadial,rf,C5.0
>>> folds=5
>>> partitions=10 tunelength=10 weighting_metric=accuracy
>>> Running R now. Following output is R output.
>>> Traceback (most recent call last):
>>> File
>>> "C:\Users\HaarlooJ\AppData\Roaming\GRASS7\addons/scri
>>> pts/v.class.mlR.py <http://v.class.mlR.py>
>>> <http://v.class.mlR.py>", line 639, in <module>
>>> File
>>> "C:\Users\HaarlooJ\AppData\Roaming\GRASS7\addons/scri
>>> pts/v.class.mlR.py <http://v.class.mlR.py>
>>> <http://v.class.mlR.py>", line 576, in main
>>> shutil.copy(r_commands, r_script_file)
>>> File "C:\Program Files\GRASS GIS
>>> 7.4.0\Python27\lib\subprocess.py", line 537, in
>>> check_call
>>> retcode = call(*popenargs, **kwargs)
>>> File "C:\Program Files\GRASS GIS
>>> 7.4.0\Python27\lib\subprocess.py", line 524, in call
>>> return Popen(*popenargs, **kwargs).wait()
>>> File "C:\Program Files\GRASS GIS
>>> 7.4.0\Python27\lib\subprocess.py", line 711, in
>>> __init__
>>> errread, errwrite)
>>> File "C:\Program Files\GRASS GIS
>>> 7.4.0\Python27\lib\subprocess.py", line 948, in
>>> _execute_child
>>> startupinfo)
>>> WindowsError: [Error 2] The system cannot find the file
>>> specified
>>> (Mon Jun 11 13:27:42 2018) Command finished (3 sec)
>>>
>>>
>>> On Mon, Jun 11, 2018 at 5:47 AM, Moritz Lennert
>>> <mlennert at club.worldonline.be
>>> <mailto:mlennert at club.worldonline.be>
>>> <mailto:mlennert at club.worldonline.be
>>> <mailto:mlennert at club.worldonline.be>>> wrote:
>>>
>>> Hi Jamille,
>>>
>>> Le Fri, 8 Jun 2018 16:14:45 -0300,
>>> Jamille Haarloo <j.r.haarloo at gmail.com
>>> <mailto:j.r.haarloo at gmail.com>
>>> <mailto:j.r.haarloo at gmail.com
>>> <mailto:j.r.haarloo at gmail.com>>> a écrit :
>>>
>>> > Hello Moritz,
>>> >
>>> > This time I asked a vector to be created with
>>> the stats and used this
>>> > to extract training polygons in QGIS and
>>> imported the training map in
>>> > GRASS. I had to do some interventions regarding
>>> the column names to
>>> > make sure they are the same except for the
>>> class.
>>> > I still get an error, and the only thing I could
>>> trace is the fact
>>> > that values are missing in some rows for both
>>> vectors. I am not sure
>>> > if I should correct this/ retry it all.
>>>
>>> I haven't seen this before, so yes, please try to
>>> eliminate the rows
>>> with missing values. How did you get the feature
>>> variables and how come
>>> there are missing values ?
>>>
>>> I don't have the time to test this right now, so I
>>> prefer not to commit
>>> as is, but you could try to edit your copy of
>>> v.class.mlR to add the
>>> four lines marked with a plus:
>>>
>>> r_file.write('features <- read.csv("%s",
>>> sep="%s", header=TRUE,
>>> row.names=1)' % (feature_vars, separator))
>>> r_file.write("\n")
>>> + r_file.write("features <- na.omit(features)")
>>> + r_file.write("\n")
>>> r_file.write('training <- read.csv("%s",
>>> sep="%s", header=TRUE,
>>> row.names=1)' % (training_vars, separator))
>>> r_file.write("\n")
>>> + r_file.write("training <- na.omit(training)")
>>> + r_file.write("\n")
>>>
>>> This would eliminate all lines that have at least
>>> one missing value.
>>>
>>> Another option would be for you to send me the
>>> data (segments
>>> and training) privately, so that I can test.
>>>
>>> Moritz
>>>
>>>
>>> >
>>> > This is the command output:
>>> >
>>> > (Fri Jun 08 15:48:28 2018)
>>> >
>>> > v.class.mlR -i --overwrite
>>> >
>>> segments_map=Segments_vector_
>>> Stats_Ben_test at haarlooj_Ben_Test
>>> > training_map=Training_Ben5 at haarlooj_Ben_Test
>>> >
>>> raster_segments_map=best5_my
>>> region1_at_haarlooj_Ben_Test_rank1 at haarlooj_Ben_Test
>>> > train_class_column=Ecosystem
>>> output_class_column=vote
>>> > output_prob_column=prob
>>> classifiers=svmRadial,rf,C5.0 folds=5
>>> > partitions=10 tunelength=10
>>> weighting_modes=smv,qbwwv
>>> > weighting_metric=accuracy
>>> >
>>> classification_results=C:\Us
>>> ers\haarlooj\Documents\CELOS\v.class.mIRR_optional_output\
>>> Ben_test_Classifier-results
>>> >
>>> accuracy_file=C:\Users\haarl
>>> ooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_
>>> Classifier-accuracy
>>> >
>>> model_details=C:\Users\haarl
>>> ooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_
>>> Classifier-module-runs
>>> >
>>> bw_plot_file=C:\Users\haarlo
>>> oj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_
>>> Classifier-performance
>>> >
>>> r_script_file=C:\Users\haarl
>>> ooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_R_script
>>> > processes=3 Running R now. Following output is
>>> R output.
>>> > During startup - Warning messages:
>>> > 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> > 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> > 3: Setting LC_TIME=en_US.cp1252 failed
>>> > 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> > Loading required package: caret
>>> > Loading required package: lattice
>>> > Loading required package: ggplot2
>>> > Loading required package: foreach
>>> > Loading required package: iterators
>>> > Loading required package: parallel
>>> > During startup - Warning messages:
>>> > 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> > 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> > 3: Setting LC_TIME=en_US.cp1252 failed
>>> > 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> > During startup - Warning messages:
>>> > 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> > 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> > 3: Setting LC_TIME=en_US.cp1252 failed
>>> > 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> > During startup - Warning messages:
>>> > 1: Setting LC_CTYPE=en_US.cp1252 failed
>>> > 2: Setting LC_COLLATE=en_US.cp1252 failed
>>> > 3: Setting LC_TIME=en_US.cp1252 failed
>>> > 4: Setting LC_MONETARY=en_US.cp1252 failed
>>> > Warning message:
>>> > In nominalTrainWorkflow(x = x, y = y, wts =
>>> weights, info =
>>> > trainInfo, : There were missing values in
>>> resampled performance
>>> > measures. Error in `$<-.data.frame`(`*tmp*`,
>>> vote_qbwwv, value =
>>> > numeric(0)) : replacement has 0 rows, data has
>>> 1965
>>> > Calls: $<- -> $<-.data.frame
>>> > Execution halted
>>> > ERROR: There was an error in the execution of
>>> the R script.
>>> > Please check the R output.
>>> > (Fri Jun 08 15:49:32 2018) Command finished (1
>>> min 4 sec)
>>> >
>>> >
>>> >
>>> > Best,
>>> > Jamille
>>> >
>>> >
>>> >
>>> >
>>> > On Thu, Jun 7, 2018 at 11:09 AM, Jamille
>>> Haarloo
>>> > <j.r.haarloo at gmail.com
>>> <mailto:j.r.haarloo at gmail.com>
>>> <mailto:j.r.haarloo at gmail.com
>>> <mailto:j.r.haarloo at gmail.com>>> wrote:
>>> >
>>> > > Hello Moritz,
>>> > >
>>> > > No worries. Thankful these modules are made
>>> available for newbies
>>> > > in RS like me and also happy these
>>> interactions are possible for
>>> > > learning. Hope to get back soon after some
>>> adjustments.
>>> > >
>>> > > Best,
>>> > > Jamille
>>> > >
>>> > > On Thu, Jun 7, 2018 at 10:44 AM, Moritz
>>> Lennert <
>>> > > mlennert at club.worldonline.be
>>> <mailto:mlennert at club.worldonline.be>
>>> <mailto:mlennert at club.worldonline.be
>>> <mailto:mlennert at club.worldonline.be>>> wrote:
>>> > >
>>> > >> Thanks
>>> > >>
>>> > >> On 07/06/18 15:17, Jamille Haarloo wrote:
>>> > >>
>>> > >>> The first 20+ lines of
>>> Stats_Training_Ben_test:
>>> > >>>
>>> > >>>
>>> cat,area,perimeter,compact_ci
>>> rcle,compact_square,fd,WV_Benat
>>> > >>>
>>> imofo_1_min,WV_Benatimofo_1_m
>>> ax,WV_Benatimofo_1_range,WV_Ben
>>> > >>>
>>> atimofo_1_mean,WV_Benatimofo_
>>> 1_stddev,WV_Benatimofo_1_varia
>>> > >>>
>>> nce,WV_Benatimofo_1_coeff_var,WV_Benatimofo_1_sum,WV_
>>> > >>>
>>> Benatimofo_1_first_quart,WV_B
>>> enatimofo_1_median,WV_Benatim
>>> > >>>
>>> ofo_1_third_quart,WV_Benatimo
>>> fo_2_min,WV_Benatimofo_2_max,
>>> > >>>
>>> WV_Benatimofo_2_range,WV_Bena
>>> timofo_2_mean,WV_Benatimofo_2_
>>> > >>>
>>> stddev,WV_Benatimofo_2_varian
>>> ce,WV_Benatimofo_2_coeff_var,
>>> > >>>
>>> WV_Benatimofo_2_sum,WV_Benatimofo_2_first_quart,WV_
>>> > >>>
>>> Benatimofo_2_median,WV_Benati
>>> mofo_2_third_quart,WV_Benatimof
>>> > >>>
>>> o_3_min,WV_Benatimofo_3_max,W
>>> V_Benatimofo_3_range,WV_Benat
>>> > >>>
>>> imofo_3_mean,WV_Benatimofo_3_
>>> stddev,WV_Benatimofo_3_varianc
>>> > >>>
>>> e,WV_Benatimofo_3_coeff_var,WV_Benatimofo_3_sum,WV_
>>> > >>>
>>> Benatimofo_3_first_quart,WV_B
>>> enatimofo_3_median,WV_Benatim
>>> > >>>
>>> ofo_3_third_quart,WV_Benatimo
>>> fo_4_min,WV_Benatimofo_4_max,
>>> > >>>
>>> WV_Benatimofo_4_range,WV_Bena
>>> timofo_4_mean,WV_Benatimofo_4_
>>> > >>>
>>> stddev,WV_Benatimofo_4_varian
>>> ce,WV_Benatimofo_4_coeff_var,
>>> > >>>
>>> WV_Benatimofo_4_sum,WV_Benatimofo_4_first_quart,WV_
>>> > >>> Benatimofo_4_median,WV_Benatim
>>> ofo_4_third_quart
>>> > >>>
>>> 1144,3832.000000,1256.000000,
>>> 5.723635,0.197144,1.729624,13,7
>>> > >>>
>>> 6,63,46.4097077244259,9.98454
>>> 911351384,99.69122100017,21.513
>>> > >>>
>>> 9237092391,177842,40,47,53,
>>> 40,138,98,90.2687891440501,15.250
>>> > >>>
>>> 0825418009,232.565017531741,1
>>> 6.8940812061464,345910,81,92,
>>> > >>>
>>> 100,15,61,46,40.8582985386221,7.
>>> 82663897784868,61.2562776895
>>> > >>>
>>> 802,19.1555675536767,156569,3
>>> 6,42,47,28,124,96,68.42536534
>>> > >>>
>>> 44676,13.5774536655369,184.3
>>> 47248039801,19.8427200164517,262206,59,68,77
>>> > >>>
>>> 1145,12092.000000,2282.000000,5.854120,0.192750,1.
>>> 645226,13,
>>> > >>>
>>> 94,81,51.386288455177,10.5294
>>> 376761475,110.869057775874,20.4
>>> > >>>
>>> 907534532914,621363,45,52,59,
>>> 21,220,199,114.230731061859,23.
>>> > >>>
>>> 3590328249442,545.64441451682
>>> 2,20.4489917973953,1381278,101,
>>> > >>>
>>> 114,128,7,76,69,46.4219318557724,8.42747122371732,71.
>>> > >>>
>>> 0222712265835,18.154072626491
>>> 5,561334,42,48,52,17,198,181,
>>> > >>>
>>> 97.2732385047966,22.492313569247,505.904169697333,23.
>>> > >>> 1228176577445,1176228,84,97,110
>>> > >>>
>>> > >>> [...]
>>> > >>
>>> > >> ---------------------
>>> > >>> All the lines of the output of v.db.select
>>> > >>> Training_Ben2 at haarlooj_Ben_Tes t:
>>> > >>>
>>> > >>> cat|id|Type|code
>>> > >>> 1|4|B29|18
>>> > >>> 2|5|B31|19
>>> > >>> 3|3|B28|17
>>> > >>>
>>> > >>
>>> > >>
>>> > >> Again a lack of clear documentation on my
>>> side: both the training
>>> > >> and the segment info should contain the same
>>> attributes, with only
>>> > >> additional one column ('code' in your case)
>>> present in the
>>> > >> training data.
>>> > >>
>>> > >> It should be possible to do this
>>> differently, i.e. provide the
>>> > >> module with the features of all segments,
>>> and only the id/cat of
>>> > >> each training segment with the relevant
>>> class and have the module
>>> > >> merge the two, but this is not implemented,
>>> yet.
>>> > >>
>>> > >> I also just notice that you have the word
>>> 'Training' in both
>>> names.
>>> > >>
>>> > >> The segment_file/segment_map contains the
>>> info (cat + all feature
>>> > >> variables) of all segments you wish to
>>> classify, either in the
>>> > >> form of a csv file or in the form of a
>>> vector map with the info in
>>> > >> the attribute table.
>>> > >>
>>> > >> The training_file/training_map contains the
>>> info (cat + all
>>> feature
>>> > >> variables + class) of the training data.
>>> Often this is an extract
>>> > >> of the former, but not necessarily.
>>> > >>
>>> > >> All columns in the training file have to be
>>> present in the segment
>>> > >> file, except for the class column (your
>>> 'code').
>>> > >>
>>> > >> Sorry for the lack of docs. This module has
>>> mostly been used
>>> > >> internally here and so we are not always
>>> aware of the unclear and
>>> > >> missing parts. Having your feedback has been
>>> very useful !
>>> > >>
>>> > >> Moritz
>>> > >>
>>> > >>
>>> > >
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>> _______________________________________________
>> grass-user mailing list
>> grass-user at lists.osgeo.org
>> https://lists.osgeo.org/mailman/listinfo/grass-user
>>
>>
>
>
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