I found "unk" token in the glove vector file glove.6B.50d.txt downloaded from https://nlp.stanford.edu/projects/glove/. Its value is as follows:
unk -0.79149 0.86617 0.11998 0.00092287 0.2776 -0.49185 0.50195 0.00060792 -0.25845 0.17865 0.2535 0.76572 0.50664 0.4025 -0.0021388 -0.28397 -0.50324 0.30449 0.51779 0.01509 -0.35031 -1.1278 0.33253 -0.3525 0.041326 1.0863 0.03391 0.33564 0.49745 -0.070131 -1.2192 -0.48512 -0.038512 -0.13554 -0.1638 0.52321 -0.31318 -0.1655 0.11909 -0.15115 -0.15621 -0.62655 -0.62336 -0.4215 0.41873 -0.92472 1.1049 -0.29996 -0.0063003 0.3954
Is it a token to be used for unknown words or is it some kind of abbreviation?
The unk token in the pretrained GloVe files is not an unknown token!
See this google groups thread where Jeffrey Pennington (GloVe author) writes:
The pre-trained vectors do not have an unknown token, and currently the code just ignores out-of-vocabulary words when producing the co-occurrence counts.
It's an embedding learned like any other on occurrences of "unk" in the corpus (which appears to happen occasionally!)
Instead, Pennington suggests (in the same post):
...I've found that just taking an average of all or a subset of the word vectors produces a good unknown vector.
You can do that with the following code (should work with any pretrained GloVe file):
import numpy as np
GLOVE_FILE = 'glove.6B.50d.txt'
# Get number of vectors and hidden dim
with open(GLOVE_FILE, 'r') as f:
for i, line in enumerate(f):
pass
n_vec = i + 1
hidden_dim = len(line.split(' ')) - 1
vecs = np.zeros((n_vec, hidden_dim), dtype=np.float32)
with open(GLOVE_FILE, 'r') as f:
for i, line in enumerate(f):
vecs[i] = np.array([float(n) for n in line.split(' ')[1:]], dtype=np.float32)
average_vec = np.mean(vecs, axis=0)
print(average_vec)
For glove.6B.50d.txt this gives:
[-0.12920076 -0.28866628 -0.01224866 -0.05676644 -0.20210965 -0.08389011
0.33359843 0.16045167 0.03867431 0.17833012 0.04696583 -0.00285802
0.29099807 0.04613704 -0.20923874 -0.06613114 -0.06822549 0.07665912
0.3134014 0.17848536 -0.1225775 -0.09916984 -0.07495987 0.06413227
0.14441176 0.60894334 0.17463093 0.05335403 -0.01273871 0.03474107
-0.8123879 -0.04688699 0.20193407 0.2031118 -0.03935686 0.06967544
-0.01553638 -0.03405238 -0.06528071 0.12250231 0.13991883 -0.17446303
-0.08011883 0.0849521 -0.01041659 -0.13705009 0.20127155 0.10069408
0.00653003 0.01685157]
And because it is fairly compute intensive to do this with the larger glove files, I went ahead and computed the vector for glove.840B.300d.txt for you:
0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811
Since I can't comment, writing another answer.
If anyone's having trouble using the above vector given by #jayelm because copy pasting won't work. I am writing 2 lines of code that will give you the vector ready to be used in python.
vec_string = '0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811'
import numpy as np
average_glove_vector = np.array(vec_string.split(" "))
print(average_glove_vector)
Related
Here is a snippet of the corpus I try to use for training word embedding.
news_subent_12402 news_dlsub_00322 news_dlsub_00001 news_sub_00035 news_subent_07737 news_sub_00038 news_dlsub_00925 news_subent_07934 news_sub_00057 news_dlsub_01826 news_dlsub_00437 news_sub_00037 news_sub_00050 news_dlsub_00205 news_sub_00270 news_subent_05735 news_dlsub_00143 news_subent_12439 news_sub_00051 news_subent_08446 news_dlsub_00091 news_sub_00222 news_dlsub_00009 news_dlsub_00126 news_subent_15202 news_dlsub_00019 news_sub_00076 news_dlsub_00059 news_subent_11158 news_subent_10981 news_dlsub_00634 news_dlsub_00018 news_subent_03496 news_subent_16059 news_subent_08005 news_dlsub_00020 news_subent_15460 news_dlsub_00908 news_subent_12712 news_sub_00258 news_sub_00048 news_dlsub_00022 news_dlsub_00206 news_dlsub_00106 news_sub_00248 news_sub_00047 news_subent_02476 news_subent_14554 news_dlsub_00134 news_sub_00070 news_subent_06676 news_dlsub_00306 news_subent_11635 news_dlsub_01137 news_sub_00081 news_dlsub_00024 news_dlsub_00242 news_dlsub_00920 news_dlsub_00198 news_subent_02562 news_subent_09358 news_dlsub_00101 news_subent_02696 news_subent_17124 news_sub_00244 news_dlsub_00045 news_sub_00049 news_dlsub_00575 news_dlsub_00163 news_subent_03497 news_subent_10972 news_subent_05406 news_sub_00039 news_subent_14976 news_subent_20148 news_subent_02955 news_sub_00245 news_subent_02399 news_dlsub_00669 news_subent_12423 news_dlsub_00180 news_dlsub_00013 news_dlsub_00075 news_sub_00264 news_dlsub_01833 news_sub_00040 news_sub_00257 news_dlsub_00021 news_subent_14967 news_subent_03495 news_dlsub_00035 news_subent_21377 news_sub_00059 news_dlsub_01260 news_sub_00232 news_dlsub_00316 news_dlsub_00014 news_dlsub_00023 news_dlsub_00046 news_subent_02007 news_dlsub_00458 news_dlsub_00269 news_subent_04653 news_subent_06231 news_dlsub_01751 news_dlsub_00186 news_dlsub_00043 news_dlsub_00128 news_subent_05276 news_sub_00259 news_dlsub_00102 news_sub_00268 news_dlsub_00185 news_sub_00041 news_subent_09122 news_dlsub_00116 news_subent_09210 news_subent_07733 news_subent_06393 news_dlsub_00244 news_dlsub_00622 news_sub_00226 news_sub_00043 news_dlsub_00067
news_subent_03827 news_dlsub_00065 news_sub_00251 news_dlsub_01826 news_subent_17688 news_subent_07649 news_subent_02941 news_dlsub_00100 news_subent_08198 news_subent_02990 news_dlsub_00033 news_subent_02562 news_dlsub_00043 news_dlsub_00024 news_dlsub_00015 news_subent_07628 news_subent_07045 news_dlsub_00234 news_subent_09178 news_dlsub_00458 news_subent_02923 news_sub_00226 news_dlsub_00120 news_sub_00247 news_dlsub_00014 news_dlsub_01830 news_subent_02946 news_dlsub_00086 news_dlsub_00046 news_dlsub_00038 news_subent_16554 news_subent_03073 news_dlsub_00128 news_dlsub_00098 news_subent_02905 news_subent_09117 news_dlsub_00021 news_dlsub_00143 news_subent_03054 news_dlsub_00126 news_subent_16372 news_dlsub_01833 news_subent_03495 news_sub_00245 news_dlsub_00101 news_sub_00258 news_subent_11431 news_sub_00148 news_subent_09320 news_sub_00232 news_subent_02460 news_dlsub_00032 news_dlsub_00067 news_dlsub_00064 news_dlsub_00045 news_dlsub_00116 news_subent_11663 news_subent_03501 news_subent_02030 news_dlsub_00035 news_dlsub_00476 news_dlsub_00039 news_subent_14505 news_dlsub_00091 news_sub_00244 news_sub_00268 news_dlsub_00130 news_subent_02007 news_subent_03014 news_dlsub_00022 news_dlsub_00019 news_subent_09358 news_dlsub_00270 news_subent_17124 news_dlsub_00071 news_sub_00266 news_subent_06429 news_subent_02621 news_sub_00248
news_subent_03497 news_subent_03495 news_dlsub_01326 news_sub_00151 news_sub_00070 news_dlsub_00143 news_dlsub_00012 news_dlsub_00212 news_subent_04653 news_subent_02022 news_dlsub_00101 football_club_187 news_subent_02902 news_dlsub_00116 news_dlsub_00925 news_sub_00137 news_dlsub_00120 news_sub_00036 news_subent_02889 news_subent_14976 news_dlsub_00269 news_dlsub_00687 news_subent_15202 news_dlsub_00669 news_dlsub_00126 news_sub_00248 news_dlsub_00437 news_sub_00071 news_dlsub_00177 news_dlsub_00694 news_dlsub_00618 news_sub_00051 news_sub_00043 news_subent_14997 news_subent_02411 news_subent_16059 news_sub_00245 news_subent_02923 news_dlsub_00035 news_sub_00069 news_subent_05320 news_sub_00082 news_sub_00259 news_dlsub_01035 news_dlsub_00413 news_sub_00072 news_dlsub_00020 news_sub_00052 news_dlsub_00023 news_subent_03496 news_subent_02893 news_subent_16508 news_sub_00065 news_sub_00047 news_subent_05740 news_subent_13389 news_sub_00055 news_subent_09439 news_subent_02991 news_sub_00268 news_dlsub_00003 news_subent_04609 news_subent_03509 news_subent_04069 news_dlsub_00128 news_dlsub_00099 news_dlsub_00206 news_dlsub_00582 news_sub_00037 news_dlsub_00021 news_sub_00247 news_dlsub_01179 news_sub_00057 news_dlsub_00046 news_sub_00039 news_sub_00050 news_subent_03014 news_sub_00042 news_dlsub_01826 news_sub_00038 news_dlsub_00410 news_subent_12422 news_sub_00048 news_subent_13648 news_dlsub_01807 news_subent_20148 news_sub_00084 news_sub_00049 news_dlsub_00029 news_subent_11392 news_dlsub_00412 news_sub_00246 news_sub_00244 news_subent_16385 news_dlsub_00634 news_subent_13536 news_subent_03073 news_sub_00226 news_subent_11478 news_sub_00035 news_subent_14967 football_club_192 news_sub_00232 news_sub_00054 news_subent_06587 news_dlsub_00014 news_subent_02399 news_dlsub_00013 news_dlsub_00102 news_sub_00040 news_subent_01990 news_dlsub_00007 news_subent_07675 news_subent_07719 news_sub_00041 news_subent_04655 news_dlsub_00300 news_dlsub_00019 news_subent_07756 news_dlsub_00234 news_sub_00076
While that every line is a sentence and news_dlsub_00001 is just an intact word. I do not want the fasttext to construct subword embedding and what I want is just the embeddings for the intact words like news_dlsub_01326 news_subent_12402 and so on.
There are 15354 distinct words in my corpus and about 10m rows(sentences) overall.
Here is the training script :
./fasttext skipgram -input user_profile_tags_rows.txt -output model_user_tags -lr 0.01 -epoch 50 -wordNgrams 1 -bucket 200000 -dim 128 -loss hs -thread 80 -ws 5 -minCount 1
So how can I set the training script that disable the embedding representation training for subwords for efficiency ? Thanks.
If you want to train word embeddings with no subword information, you can set the -maxn parameter to 0. This means that you only use character ngrams with a max length of 0, i.e., no character ngrams are used.
Set both options to zero: -maxn 0 -minn 0
I found "unk" token in the glove vector file glove.6B.50d.txt downloaded from https://nlp.stanford.edu/projects/glove/. Its value is as follows:
unk -0.79149 0.86617 0.11998 0.00092287 0.2776 -0.49185 0.50195 0.00060792 -0.25845 0.17865 0.2535 0.76572 0.50664 0.4025 -0.0021388 -0.28397 -0.50324 0.30449 0.51779 0.01509 -0.35031 -1.1278 0.33253 -0.3525 0.041326 1.0863 0.03391 0.33564 0.49745 -0.070131 -1.2192 -0.48512 -0.038512 -0.13554 -0.1638 0.52321 -0.31318 -0.1655 0.11909 -0.15115 -0.15621 -0.62655 -0.62336 -0.4215 0.41873 -0.92472 1.1049 -0.29996 -0.0063003 0.3954
Is it a token to be used for unknown words or is it some kind of abbreviation?
The unk token in the pretrained GloVe files is not an unknown token!
See this google groups thread where Jeffrey Pennington (GloVe author) writes:
The pre-trained vectors do not have an unknown token, and currently the code just ignores out-of-vocabulary words when producing the co-occurrence counts.
It's an embedding learned like any other on occurrences of "unk" in the corpus (which appears to happen occasionally!)
Instead, Pennington suggests (in the same post):
...I've found that just taking an average of all or a subset of the word vectors produces a good unknown vector.
You can do that with the following code (should work with any pretrained GloVe file):
import numpy as np
GLOVE_FILE = 'glove.6B.50d.txt'
# Get number of vectors and hidden dim
with open(GLOVE_FILE, 'r') as f:
for i, line in enumerate(f):
pass
n_vec = i + 1
hidden_dim = len(line.split(' ')) - 1
vecs = np.zeros((n_vec, hidden_dim), dtype=np.float32)
with open(GLOVE_FILE, 'r') as f:
for i, line in enumerate(f):
vecs[i] = np.array([float(n) for n in line.split(' ')[1:]], dtype=np.float32)
average_vec = np.mean(vecs, axis=0)
print(average_vec)
For glove.6B.50d.txt this gives:
[-0.12920076 -0.28866628 -0.01224866 -0.05676644 -0.20210965 -0.08389011
0.33359843 0.16045167 0.03867431 0.17833012 0.04696583 -0.00285802
0.29099807 0.04613704 -0.20923874 -0.06613114 -0.06822549 0.07665912
0.3134014 0.17848536 -0.1225775 -0.09916984 -0.07495987 0.06413227
0.14441176 0.60894334 0.17463093 0.05335403 -0.01273871 0.03474107
-0.8123879 -0.04688699 0.20193407 0.2031118 -0.03935686 0.06967544
-0.01553638 -0.03405238 -0.06528071 0.12250231 0.13991883 -0.17446303
-0.08011883 0.0849521 -0.01041659 -0.13705009 0.20127155 0.10069408
0.00653003 0.01685157]
And because it is fairly compute intensive to do this with the larger glove files, I went ahead and computed the vector for glove.840B.300d.txt for you:
0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811
Since I can't comment, writing another answer.
If anyone's having trouble using the above vector given by #jayelm because copy pasting won't work. I am writing 2 lines of code that will give you the vector ready to be used in python.
vec_string = '0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811'
import numpy as np
average_glove_vector = np.array(vec_string.split(" "))
print(average_glove_vector)
In matlab, say I have a vector:
a = [0.001818443 0.002518386 0.003220336 0.005121008 0.005896566 0.006067186 0.006398329 0.006398407 0.008139209 0.008179847 0.009156658 0.010554859 0.010897468 0.011446512 0.011646556 0.013025077 0.013744086 0.014617856 0.014830682 0.014941674 0.015810817 0.015953685 0.016022438 0.01662184 0.017497373 0.018051816 0.018084908 0.019634522 0.02055418 0.021050675 0.021573997 0.022819828 0.024124407 0.02499767 0.025745151 0.026366919 0.029380223 0.029534435 0.029988118 0.033517171 0.037612906 0.038975473 0.045889922 0.04598939 0.045995714 0.046398796 0.047502087 0.048214192 0.050098857 0.053184072 0.055892719 0.057670042 0.059855042 0.061763503 0.065288778 0.066057267 0.066842804 0.067799739 0.069549949 0.070151584 0.071572852 0.084351815 0.084676596 0.088092878 0.088329724 0.089958848 0.09000399 0.092272165 0.092561834 0.092888091 0.093193395 0.095380669 0.100645564 0.101506295 0.104656067 0.105057051 0.10527394 0.107024689 0.107608649 0.108699789 0.109433298 0.111805635 0.111990726 0.112119163 0.113570537 0.115032624 0.123301615 0.125012672 0.127758465 0.131636854 0.132397883 0.132910662 0.136185494 0.137039002 0.138037293 0.139764132 0.140727482 0.141044291 0.141523184 0.142202565 0.142563209 0.145192227 0.146881752 0.149407941 0.152043489 0.153144103 0.153181689 0.153795372 0.15548845 0.158134807 0.159905274 0.160042269 0.160098768 0.160483614 0.164710118 0.166850893 0.167909098 0.167986172 0.169019205 0.173698541 0.174264483 0.17696023 0.177973442 0.182348008 0.192484859 0.193880575 0.197216088 0.197939827 0.198109045 0.199497862 0.200821988 0.203783239 0.20515055 0.206573438 0.207185798 0.209268239 0.209386896 0.210951877 0.212023024 0.213363487 0.214391326 0.221532866 0.223109522 0.225343507 0.22641709 0.228343987 0.229206318 0.231114414 0.234752015 0.237026193 0.237716285 0.240721044 0.242380638 0.245407553 0.246434245 0.247559843 0.247849238 0.253027915 0.256495379 0.257901113 0.258536851 0.259306669 0.259722973 0.269672307 0.269866791 0.277255751 0.282296038 0.283236904 0.286946602 0.290243971 0.291043929 0.29129401 0.295039384 0.306007647 0.318012708 0.31816788 0.323432941 0.327635294 0.331093206 0.332249017 0.336175038 0.338095934 0.34029032 0.34317957 0.343788501 0.344121892 0.354931564 0.35667793 0.363077884 0.366887155 0.367370204 0.368269666 0.368423655 0.372253797 0.373533628 0.376022623 0.380837432 0.38261496 0.38391646 0.387548982 0.390389207 0.391454753 0.392089937 0.393426643 0.399546308 0.400245613 0.410202767 0.410776868 0.411723243 0.413456921 0.415884043 0.417789874 0.420313779 0.421429254 0.422511926 0.425439714 0.432455519 0.436032696 0.437367296 0.440152475 0.451378258 0.451755234 0.452195559 0.461256878 0.462486697 0.466794805 0.468274083 0.470176312 0.471656017 0.476690087 0.476869314 0.479601939 0.480684241 0.48433634 0.48590973 0.487749692 0.488207469 0.492988875 0.494796745 0.498156249 0.4985104 0.504612947 0.504967424 0.50679421 0.507507695 0.508506011 0.509330529 0.510055669 0.513319728 0.52891378 0.534509478 0.539047817 0.53915951 0.541296687 0.545072863 0.552167571 0.554581857 0.561492014 0.563884635 0.56426438 0.565241463 0.568908009 0.571068798 0.571510656 0.574766859 0.574855866 0.578654534 0.580464471 0.580887919 0.586141679 0.586966123 0.588709274 0.589037758 0.590368048 0.591671348 0.595978448 0.596819829 0.600253407 0.606098247 0.606571956 0.607739275 0.619011439 0.621019538 0.621584422 0.621911881 0.625158613 0.626027273 0.630605849 0.648745354 0.651696835 0.652124313 0.653617078 0.658464598 0.658753146 0.659914726 0.660454081 0.663254931 0.666213877 0.66710949 0.671102828 0.672232077 0.677146974 0.679136682 0.684108254 0.685413256 0.695469558 0.699891217 0.70086933 0.702538999 0.703841471 0.706858722 0.708526759 0.719914701 0.720515776 0.722374479 0.729898476 0.73207238 0.735491621 0.747979179 0.748009317 0.756113996 0.765310468 0.767536693 0.773459493 0.776692483 0.78337608 0.785347933 0.793150735 0.802590758 0.803742726 0.803939618 0.804124158 0.820932029 0.822075589 0.822879052 0.823691124 0.828333937 0.828903924 0.829498182 0.831744237 0.839063743 0.841337078 0.84171847 0.859784354 0.866309271 0.866320299 0.866971295 0.867950201 0.871969488 0.874637485 0.880743292 0.88827141 0.893950376 0.895879931 0.896267286 0.896848582 0.898141369 0.901481362 0.910473103 0.911477969 0.913705886 0.926522732 0.930260635 0.930701078 0.93463515 0.934808147 0.935922692 0.936764529 0.93959948 0.939793801 0.944385802 0.944693766 0.947020268 0.947097561 0.950125145 0.952711274 0.95419146 0.956369148 0.9570486 0.958475028 0.969842914 0.981551219 0.989160935 0.992674135 0.995621611 1.004946727 1.012283438 1.015692666 1.024106096 1.030556733 1.032833092 1.03935138 1.03983552 1.047370018 1.063408518 1.063839459 1.065426157 1.068160918 1.069540521 1.077658708 1.087582646 1.087760841 1.094524538 1.098800356 1.1001774 1.107885842 1.108951101 1.111112066 1.113553249 1.118698485 1.118853872 1.124506817 1.125842913 1.140696534 1.145563833 1.163177388 1.172282341 1.172495225 1.175475612 1.176864362 1.17990075 1.182195585 1.189691417 1.19404695 1.201722268 1.207506808 1.207712757 1.217276704 1.226244517 1.231233705 1.235338344 1.235488468 1.237856505 1.246471414 1.248198622 1.24847215 1.249213122 1.253941679 1.255492087 1.261797214 1.272565441 1.275217172 1.279047194 1.2941275 1.296036211 1.303184537 1.303538428 1.304022026 1.306154543 1.313038716 1.325574114 1.325811852 1.329512083 1.335354282 1.336243495 1.349582224 1.349879286 1.349900576 1.351984631 1.352752927 1.380875102 1.381652802 1.385006548 1.385551407 1.399529901 1.400418216 1.400665522 1.402213831 1.407079557 1.407902968 1.426432367 1.427988618 1.430494495 1.436726215 1.455773986 1.457812821 1.457839075 1.464415454 1.465676132 1.471725239 1.478228056 1.493616649 1.505911081 1.510317752 1.517723479 1.519844042 1.520079555 1.52266623 1.528263582 1.528956908 1.539259874 1.54540469 1.552334282 1.559284266 1.560271081 1.560397723 1.58015835 1.584258957 1.586824283 1.5901615 1.601497093 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1646.530894 1649.071555 1654.143848 1655.068151 1659.690971 1667.482004 1668.764776 1677.231907 1678.43389 1680.018626 1681.080011 1681.642036 1686.215979 1693.028329 1696.574678 1697.26473 1699.010764 1699.50461 1699.574827 1699.889703 1703.539823 1704.893495 1705.346165 1708.078973 1708.857574 1711.671005 1712.28974 1718.32623 1720.538478 1730.582461 1730.697847 1733.562219 1737.854236 1742.26224 1744.92597 1746.791975 1749.73444 1749.806068 1754.604788 1760.3735 1763.940348 1778.185828 1781.402007 1782.452126 1783.059196 1783.494646 1791.479627 1793.032542 1796.056038 1796.581084 1810.67445 1811.996046 1814.322865 1815.332067 1818.201264 1820.767308 1826.482686 1828.233819 1830.732533 1830.877894 1836.531339 1836.535165 1838.751353 1849.003077 1852.535901 1856.165601 1856.421761 1856.605105 1858.371169 1862.783262 1863.240753 1866.161551 1876.872951 1877.844603 1881.280883 1884.118732 1890.465139 1891.273181 1902.445269 1902.90357 1905.020741 1910.565364 1911.111421 1918.625541 1920.950517 1926.326659 1928.176102 1929.506964 1934.604217 1939.777123 1939.9775 1941.755309 1943.528118 1944.940437 1948.146898 1949.393129 1952.669934 1957.382105 1974.814187 1974.930052 1977.787028 1978.05484 1981.278578 1985.544084 1985.951329 1987.672333 1989.859323 1999.003701 2000.29839 2010.240822 2011.18677 2013.788559 2015.608553 2017.241148 2018.726592 2023.237331 2024.367796 2025.012118 2030.088672 2030.858289 2031.91155 2047.429465 2056.103201 2060.681174 2067.853485 2068.090404 2070.455076 2074.196306 2075.368651 2079.204847 2085.470085 2085.729643 2088.787981 2090.90702 2096.795218 2098.107784 2109.1462 2111.881475 2112.3375 2119.125894 2132.544339 2134.309156 2137.225421 2137.34022 2140.171403 2141.529288 2150.369762 2151.661726 2154.312051 2155.000962 2156.770678 2157.759589 2164.041693 2164.805817 2165.059708 2168.65522 2169.206429 2174.040541 2188.917693 2192.494984 2208.212818 2208.768799 2216.977084 2218.692144 2220.102318 2230.953269 2234.154411 2234.405244 2235.065111 2236.344979 2249.802239 2264.686865 2267.396929 2273.404114 2273.754356 2277.413755 2280.6782 2283.149105 2285.348922 2289.328316 2290.734348 2295.517396 2312.503918 2320.990391 2322.061912 2323.440401 2324.878674 2333.165823 2333.252925 2335.603857 2337.325547 2345.946966 2358.273812 2366.02324 2370.471418 2371.784879 2375.909149 2379.075768 2385.552289 2389.641257 2389.86403 2390.725414 2395.819447 2407.106186 2408.175752 2429.088981 2434.82175 2440.124058 2467.929505 2471.566345 2477.609812 2489.945091 2491.580836 2510.302527 2511.638942 2515.044217 2515.71574 2517.028608 2524.354087 2524.877922 2526.06623 2535.558951 2535.721585 2536.492167 2537.325694 2537.709872 2539.365845 2540.281295 2553.669496 2558.765222 2571.722037 2572.527088 2574.079989 2582.056893 2582.866113 2588.746023 2591.215869 2599.739431 2608.250835 2609.567659 2609.624241 2610.649666 2619.595179 2621.563198 2636.421069 2639.394226 2642.023428 2669.445731 2675.382826 2680.034969 2694.084625 2696.268191 2704.531319 2707.839333 2716.792796 2730.732111 2732.069684 2737.677226 2778.443114 2782.215594 2785.874988 2791.253987 2791.436181 2801.164946 2805.848603 2806.178239 2810.666466 2813.901345 2830.859735 2853.498852 2856.717434 2868.888741 2870.367911 2878.874877 2883.703936 2885.632474 2905.874322 2907.515485 2916.389413 2923.073962 2929.712875 2931.019447 2933.791531 2933.930571 2935.077087 2949.087886 2976.158269 2981.768596 2983.915934 2989.715087 3001.266498 3002.479299 3011.896758 3011.912842 3020.089709 3025.493187 3042.22217 3043.797986 3044.370623 3045.683447 3053.071336 3054.37784 3059.479072 3063.92801 3107.161718 3151.462292 3173.531554 3256.077437 3256.892854 3259.605488 3265.533651 3289.866956 3319.895402 3333.730546 3378.930836 3388.87019 3396.586658 3408.947451 3436.547313 3439.845643 3464.238962 3465.187872 3465.352967 3498.880213 3522.537563 3533.001912 3553.327338 3600.815901 3603.355282 3717.619142 3755.870625 3760.009184 3773.018383 3783.863253 3811.612746 3811.771546 3814.024695 3867.8375 3883.205185 3886.970119 3908.597606 3911.469436 3917.313319 3939.971862 3986.038094 3987.670344 4046.750127 4066.433675 4092.504341 4097.021961 4169.546188 4186.34036 4187.66536 4190.45858 4204.426559 4212.053484 4243.227528 4250.384263 4254.988812 4260.547742 4262.13353 4320.226864 4367.875314 4388.094236 4536.069442 4564.327703 4567.306728 4604.70907 4646.504789 4671.757915 4699.991441 4787.618259 4876.00814 4904.917446 4917.17524 4925.327908 5011.724579 5098.552384 5303.654354 5308.235953 5309.20718 5392.941707 5419.906687 5475.854044 5495.805466 5568.353474 5640.966362 5729.170599 5756.300338 5806.041122 5832.62891 5835.703258 5915.919707 5927.430005 5981.107598 6030.061892 6096.21919 6167.676768 6201.442974 6225.328106 6260.287897 6289.796644 6322.660099 6326.143981 6469.370471 6476.395452 6499.135987 6644.829529 7103.246168 7336.643496 7450.887574 7463.63355 7724.153705 8125.302957 8315.141568 8480.871611 8667.155784 8993.192971 10269.69361 11689.13634 12457.90885 12725.48318 12877.43593];
I want to split a into ~10 discrete groups based on value to make a grouped boxplot of:
data = rand(2348,1);
so that:
boxplot(data,group)
The discrete groups could be like the x-axis in this plot:
Let's say we split into 10 groups by ascending order of values. Try this:
%Note I added 2 extra zero values to start of this matrix to make it divisible by 10
a = [0.000 0.000 0.001818443 0.002518386 0.003220336 0.005121008 0.005896566 0.006067186 0.006398329 0.006398407 0.008139209 0.008179847 0.009156658 0.010554859 0.010897468 0.011446512 0.011646556 0.013025077 0.013744086 0.014617856 0.014830682 0.014941674 0.015810817 0.015953685 0.016022438 0.01662184 0.017497373 0.018051816 0.018084908 0.019634522 0.02055418 0.021050675 0.021573997 0.022819828 0.024124407 0.02499767 0.025745151 0.026366919 0.029380223 0.029534435 0.029988118 0.033517171 0.037612906 0.038975473 0.045889922 0.04598939 0.045995714 0.046398796 0.047502087 0.048214192 0.050098857 0.053184072 0.055892719 0.057670042 0.059855042 0.061763503 0.065288778 0.066057267 0.066842804 0.067799739 0.069549949 0.070151584 0.071572852 0.084351815 0.084676596 0.088092878 0.088329724 0.089958848 0.09000399 0.092272165 0.092561834 0.092888091 0.093193395 0.095380669 0.100645564 0.101506295 0.104656067 0.105057051 0.10527394 0.107024689 0.107608649 0.108699789 0.109433298 0.111805635 0.111990726 0.112119163 0.113570537 0.115032624 0.123301615 0.125012672 0.127758465 0.131636854 0.132397883 0.132910662 0.136185494 0.137039002 0.138037293 0.139764132 0.140727482 0.141044291 0.141523184 0.142202565 0.142563209 0.145192227 0.146881752 0.149407941 0.152043489 0.153144103 0.153181689 0.153795372 0.15548845 0.158134807 0.159905274 0.160042269 0.160098768 0.160483614 0.164710118 0.166850893 0.167909098 0.167986172 0.169019205 0.173698541 0.174264483 0.17696023 0.177973442 0.182348008 0.192484859 0.193880575 0.197216088 0.197939827 0.198109045 0.199497862 0.200821988 0.203783239 0.20515055 0.206573438 0.207185798 0.209268239 0.209386896 0.210951877 0.212023024 0.213363487 0.214391326 0.221532866 0.223109522 0.225343507 0.22641709 0.228343987 0.229206318 0.231114414 0.234752015 0.237026193 0.237716285 0.240721044 0.242380638 0.245407553 0.246434245 0.247559843 0.247849238 0.253027915 0.256495379 0.257901113 0.258536851 0.259306669 0.259722973 0.269672307 0.269866791 0.277255751 0.282296038 0.283236904 0.286946602 0.290243971 0.291043929 0.29129401 0.295039384 0.306007647 0.318012708 0.31816788 0.323432941 0.327635294 0.331093206 0.332249017 0.336175038 0.338095934 0.34029032 0.34317957 0.343788501 0.344121892 0.354931564 0.35667793 0.363077884 0.366887155 0.367370204 0.368269666 0.368423655 0.372253797 0.373533628 0.376022623 0.380837432 0.38261496 0.38391646 0.387548982 0.390389207 0.391454753 0.392089937 0.393426643 0.399546308 0.400245613 0.410202767 0.410776868 0.411723243 0.413456921 0.415884043 0.417789874 0.420313779 0.421429254 0.422511926 0.425439714 0.432455519 0.436032696 0.437367296 0.440152475 0.451378258 0.451755234 0.452195559 0.461256878 0.462486697 0.466794805 0.468274083 0.470176312 0.471656017 0.476690087 0.476869314 0.479601939 0.480684241 0.48433634 0.48590973 0.487749692 0.488207469 0.492988875 0.494796745 0.498156249 0.4985104 0.504612947 0.504967424 0.50679421 0.507507695 0.508506011 0.509330529 0.510055669 0.513319728 0.52891378 0.534509478 0.539047817 0.53915951 0.541296687 0.545072863 0.552167571 0.554581857 0.561492014 0.563884635 0.56426438 0.565241463 0.568908009 0.571068798 0.571510656 0.574766859 0.574855866 0.578654534 0.580464471 0.580887919 0.586141679 0.586966123 0.588709274 0.589037758 0.590368048 0.591671348 0.595978448 0.596819829 0.600253407 0.606098247 0.606571956 0.607739275 0.619011439 0.621019538 0.621584422 0.621911881 0.625158613 0.626027273 0.630605849 0.648745354 0.651696835 0.652124313 0.653617078 0.658464598 0.658753146 0.659914726 0.660454081 0.663254931 0.666213877 0.66710949 0.671102828 0.672232077 0.677146974 0.679136682 0.684108254 0.685413256 0.695469558 0.699891217 0.70086933 0.702538999 0.703841471 0.706858722 0.708526759 0.719914701 0.720515776 0.722374479 0.729898476 0.73207238 0.735491621 0.747979179 0.748009317 0.756113996 0.765310468 0.767536693 0.773459493 0.776692483 0.78337608 0.785347933 0.793150735 0.802590758 0.803742726 0.803939618 0.804124158 0.820932029 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1.172495225 1.175475612 1.176864362 1.17990075 1.182195585 1.189691417 1.19404695 1.201722268 1.207506808 1.207712757 1.217276704 1.226244517 1.231233705 1.235338344 1.235488468 1.237856505 1.246471414 1.248198622 1.24847215 1.249213122 1.253941679 1.255492087 1.261797214 1.272565441 1.275217172 1.279047194 1.2941275 1.296036211 1.303184537 1.303538428 1.304022026 1.306154543 1.313038716 1.325574114 1.325811852 1.329512083 1.335354282 1.336243495 1.349582224 1.349879286 1.349900576 1.351984631 1.352752927 1.380875102 1.381652802 1.385006548 1.385551407 1.399529901 1.400418216 1.400665522 1.402213831 1.407079557 1.407902968 1.426432367 1.427988618 1.430494495 1.436726215 1.455773986 1.457812821 1.457839075 1.464415454 1.465676132 1.471725239 1.478228056 1.493616649 1.505911081 1.510317752 1.517723479 1.519844042 1.520079555 1.52266623 1.528263582 1.528956908 1.539259874 1.54540469 1.552334282 1.559284266 1.560271081 1.560397723 1.58015835 1.584258957 1.586824283 1.5901615 1.601497093 1.616542626 1.623922568 1.655459388 1.656518418 1.658450231 1.668041767 1.677051536 1.683863037 1.685259883 1.694649161 1.698606755 1.703678803 1.713706728 1.717011705 1.723855901 1.724709626 1.730421966 1.74844195 1.749035987 1.756795943 1.763444653 1.775098962 1.779555761 1.818373597 1.827387112 1.843804423 1.84962867 1.86445448 1.867109055 1.88714409 1.889979731 1.891826361 1.892089882 1.899382375 1.902349674 1.905658074 1.914800201 1.922334951 1.927667035 1.948721828 1.954657495 1.965798927 1.966959101 1.979507708 1.980399089 2.002907363 2.005971219 2.017485761 2.025826577 2.031746032 2.038502827 2.044743806 2.054344832 2.069686195 2.097264843 2.099601236 2.110771315 2.112519282 2.119955879 2.121704608 2.127885945 2.132366059 2.138348477 2.14114747 2.141311108 2.163556408 2.164123085 2.172858142 2.199500097 2.205509788 2.219358975 2.238957728 2.249818992 2.2594448 2.269944498 2.2706867 2.280357357 2.282960593 2.295150671 2.318687227 2.321618718 2.334867032 2.335012279 2.344689038 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1920.950517 1926.326659 1928.176102 1929.506964 1934.604217 1939.777123 1939.9775 1941.755309 1943.528118 1944.940437 1948.146898 1949.393129 1952.669934 1957.382105 1974.814187 1974.930052 1977.787028 1978.05484 1981.278578 1985.544084 1985.951329 1987.672333 1989.859323 1999.003701 2000.29839 2010.240822 2011.18677 2013.788559 2015.608553 2017.241148 2018.726592 2023.237331 2024.367796 2025.012118 2030.088672 2030.858289 2031.91155 2047.429465 2056.103201 2060.681174 2067.853485 2068.090404 2070.455076 2074.196306 2075.368651 2079.204847 2085.470085 2085.729643 2088.787981 2090.90702 2096.795218 2098.107784 2109.1462 2111.881475 2112.3375 2119.125894 2132.544339 2134.309156 2137.225421 2137.34022 2140.171403 2141.529288 2150.369762 2151.661726 2154.312051 2155.000962 2156.770678 2157.759589 2164.041693 2164.805817 2165.059708 2168.65522 2169.206429 2174.040541 2188.917693 2192.494984 2208.212818 2208.768799 2216.977084 2218.692144 2220.102318 2230.953269 2234.154411 2234.405244 2235.065111 2236.344979 2249.802239 2264.686865 2267.396929 2273.404114 2273.754356 2277.413755 2280.6782 2283.149105 2285.348922 2289.328316 2290.734348 2295.517396 2312.503918 2320.990391 2322.061912 2323.440401 2324.878674 2333.165823 2333.252925 2335.603857 2337.325547 2345.946966 2358.273812 2366.02324 2370.471418 2371.784879 2375.909149 2379.075768 2385.552289 2389.641257 2389.86403 2390.725414 2395.819447 2407.106186 2408.175752 2429.088981 2434.82175 2440.124058 2467.929505 2471.566345 2477.609812 2489.945091 2491.580836 2510.302527 2511.638942 2515.044217 2515.71574 2517.028608 2524.354087 2524.877922 2526.06623 2535.558951 2535.721585 2536.492167 2537.325694 2537.709872 2539.365845 2540.281295 2553.669496 2558.765222 2571.722037 2572.527088 2574.079989 2582.056893 2582.866113 2588.746023 2591.215869 2599.739431 2608.250835 2609.567659 2609.624241 2610.649666 2619.595179 2621.563198 2636.421069 2639.394226 2642.023428 2669.445731 2675.382826 2680.034969 2694.084625 2696.268191 2704.531319 2707.839333 2716.792796 2730.732111 2732.069684 2737.677226 2778.443114 2782.215594 2785.874988 2791.253987 2791.436181 2801.164946 2805.848603 2806.178239 2810.666466 2813.901345 2830.859735 2853.498852 2856.717434 2868.888741 2870.367911 2878.874877 2883.703936 2885.632474 2905.874322 2907.515485 2916.389413 2923.073962 2929.712875 2931.019447 2933.791531 2933.930571 2935.077087 2949.087886 2976.158269 2981.768596 2983.915934 2989.715087 3001.266498 3002.479299 3011.896758 3011.912842 3020.089709 3025.493187 3042.22217 3043.797986 3044.370623 3045.683447 3053.071336 3054.37784 3059.479072 3063.92801 3107.161718 3151.462292 3173.531554 3256.077437 3256.892854 3259.605488 3265.533651 3289.866956 3319.895402 3333.730546 3378.930836 3388.87019 3396.586658 3408.947451 3436.547313 3439.845643 3464.238962 3465.187872 3465.352967 3498.880213 3522.537563 3533.001912 3553.327338 3600.815901 3603.355282 3717.619142 3755.870625 3760.009184 3773.018383 3783.863253 3811.612746 3811.771546 3814.024695 3867.8375 3883.205185 3886.970119 3908.597606 3911.469436 3917.313319 3939.971862 3986.038094 3987.670344 4046.750127 4066.433675 4092.504341 4097.021961 4169.546188 4186.34036 4187.66536 4190.45858 4204.426559 4212.053484 4243.227528 4250.384263 4254.988812 4260.547742 4262.13353 4320.226864 4367.875314 4388.094236 4536.069442 4564.327703 4567.306728 4604.70907 4646.504789 4671.757915 4699.991441 4787.618259 4876.00814 4904.917446 4917.17524 4925.327908 5011.724579 5098.552384 5303.654354 5308.235953 5309.20718 5392.941707 5419.906687 5475.854044 5495.805466 5568.353474 5640.966362 5729.170599 5756.300338 5806.041122 5832.62891 5835.703258 5915.919707 5927.430005 5981.107598 6030.061892 6096.21919 6167.676768 6201.442974 6225.328106 6260.287897 6289.796644 6322.660099 6326.143981 6469.370471 6476.395452 6499.135987 6644.829529 7103.246168 7336.643496 7450.887574 7463.63355 7724.153705 8125.302957 8315.141568 8480.871611 8667.155784 8993.192971 10269.69361 11689.13634 12457.90885 12725.48318 12877.43593];
%ascending order sort
a = sort(a);
%split into 10.
data = reshape(a,235,10);
%boxplot
boxplot(data(:,1:10))
Produces this:
You can compare the values of a with their quantiles to generate groups with similar sizes. This approch automatically handles the case when the size of a is not a multiple of the number of groups.
N = 10; % desired number of groups
thresholds = quantile(a, 0:1/N:1-1/N); % for approximately equal-size groups
group = sum(bsxfun(#ge, a, thresholds.'), 1); % this ranges from 1 to N
For your example a and data, this gives the following group sizes:
>> sum(bsxfun(#eq, group(:), 1:N), 1)
ans =
235 235 234 235 235 235 235 234 235 235
To do the plot, you probably want to use the average of each group as the x-axis value:
a_average_group = accumarray(group(:), a(:), [], #mean);
boxplot(data, a_average_group(group))
produces
Some way to group the data automatically is to use hist. The elements in a are sorted into 10 equally spaced bins along the x-axis between the minimum and maximum values of a. As hist returns the center of the bins, there is some calulation to get the right border of the bin, so one can calculate the correct bin given some value of a. In your example the 8th bin has no values though, resulting in only 9 boxplots drawn.
a = [0.001818443 0.002518386 0.003220336 0.005121008 0.005896566 0.006067186 0.006398329 0.006398407 0.008139209 0.008179847 0.009156658 0.010554859 0.010897468 0.011446512 0.011646556 0.013025077 0.013744086 0.014617856 0.014830682 0.014941674 0.015810817 0.015953685 0.016022438 0.01662184 0.017497373 0.018051816 0.018084908 0.019634522 0.02055418 0.021050675 0.021573997 0.022819828 0.024124407 0.02499767 0.025745151 0.026366919 0.029380223 0.029534435 0.029988118 0.033517171 0.037612906 0.038975473 0.045889922 0.04598939 0.045995714 0.046398796 0.047502087 0.048214192 0.050098857 0.053184072 0.055892719 0.057670042 0.059855042 0.061763503 0.065288778 0.066057267 0.066842804 0.067799739 0.069549949 0.070151584 0.071572852 0.084351815 0.084676596 0.088092878 0.088329724 0.089958848 0.09000399 0.092272165 0.092561834 0.092888091 0.093193395 0.095380669 0.100645564 0.101506295 0.104656067 0.105057051 0.10527394 0.107024689 0.107608649 0.108699789 0.109433298 0.111805635 0.111990726 0.112119163 0.113570537 0.115032624 0.123301615 0.125012672 0.127758465 0.131636854 0.132397883 0.132910662 0.136185494 0.137039002 0.138037293 0.139764132 0.140727482 0.141044291 0.141523184 0.142202565 0.142563209 0.145192227 0.146881752 0.149407941 0.152043489 0.153144103 0.153181689 0.153795372 0.15548845 0.158134807 0.159905274 0.160042269 0.160098768 0.160483614 0.164710118 0.166850893 0.167909098 0.167986172 0.169019205 0.173698541 0.174264483 0.17696023 0.177973442 0.182348008 0.192484859 0.193880575 0.197216088 0.197939827 0.198109045 0.199497862 0.200821988 0.203783239 0.20515055 0.206573438 0.207185798 0.209268239 0.209386896 0.210951877 0.212023024 0.213363487 0.214391326 0.221532866 0.223109522 0.225343507 0.22641709 0.228343987 0.229206318 0.231114414 0.234752015 0.237026193 0.237716285 0.240721044 0.242380638 0.245407553 0.246434245 0.247559843 0.247849238 0.253027915 0.256495379 0.257901113 0.258536851 0.259306669 0.259722973 0.269672307 0.269866791 0.277255751 0.282296038 0.283236904 0.286946602 0.290243971 0.291043929 0.29129401 0.295039384 0.306007647 0.318012708 0.31816788 0.323432941 0.327635294 0.331093206 0.332249017 0.336175038 0.338095934 0.34029032 0.34317957 0.343788501 0.344121892 0.354931564 0.35667793 0.363077884 0.366887155 0.367370204 0.368269666 0.368423655 0.372253797 0.373533628 0.376022623 0.380837432 0.38261496 0.38391646 0.387548982 0.390389207 0.391454753 0.392089937 0.393426643 0.399546308 0.400245613 0.410202767 0.410776868 0.411723243 0.413456921 0.415884043 0.417789874 0.420313779 0.421429254 0.422511926 0.425439714 0.432455519 0.436032696 0.437367296 0.440152475 0.451378258 0.451755234 0.452195559 0.461256878 0.462486697 0.466794805 0.468274083 0.470176312 0.471656017 0.476690087 0.476869314 0.479601939 0.480684241 0.48433634 0.48590973 0.487749692 0.488207469 0.492988875 0.494796745 0.498156249 0.4985104 0.504612947 0.504967424 0.50679421 0.507507695 0.508506011 0.509330529 0.510055669 0.513319728 0.52891378 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1.172495225 1.175475612 1.176864362 1.17990075 1.182195585 1.189691417 1.19404695 1.201722268 1.207506808 1.207712757 1.217276704 1.226244517 1.231233705 1.235338344 1.235488468 1.237856505 1.246471414 1.248198622 1.24847215 1.249213122 1.253941679 1.255492087 1.261797214 1.272565441 1.275217172 1.279047194 1.2941275 1.296036211 1.303184537 1.303538428 1.304022026 1.306154543 1.313038716 1.325574114 1.325811852 1.329512083 1.335354282 1.336243495 1.349582224 1.349879286 1.349900576 1.351984631 1.352752927 1.380875102 1.381652802 1.385006548 1.385551407 1.399529901 1.400418216 1.400665522 1.402213831 1.407079557 1.407902968 1.426432367 1.427988618 1.430494495 1.436726215 1.455773986 1.457812821 1.457839075 1.464415454 1.465676132 1.471725239 1.478228056 1.493616649 1.505911081 1.510317752 1.517723479 1.519844042 1.520079555 1.52266623 1.528263582 1.528956908 1.539259874 1.54540469 1.552334282 1.559284266 1.560271081 1.560397723 1.58015835 1.584258957 1.586824283 1.5901615 1.601497093 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2.362974205 2.390145632 2.39280766 2.39488483 2.396397248 2.396786702 2.41576171 2.419119918 2.441157923 2.444805269 2.464446901 2.464904486 2.467566697 2.472567888 2.478733367 2.478770886 2.487350756 2.49321811 2.495309064 2.503098264 2.508249701 2.515845321 2.536509644 2.550922635 2.551167925 2.554758102 2.560023347 2.570208707 2.586874641 2.591255858 2.621816341 2.628169732 2.631001714 2.632092126 2.633379668 2.642666194 2.655607509 2.682555117 2.688667321 2.692033844 2.699468672 2.705095951 2.708973091 2.737148617 2.749372431 2.750397407 2.756591892 2.792700138 2.796508678 2.81091224 2.81340476 2.818119651 2.819068516 2.840763732 2.855245731 2.867534546 2.882410894 2.906305385 2.921292046 2.921449315 2.923724053 2.936059643 2.941498451 2.942915417 2.94633052 2.988440975 3.000337153 3.01308498 3.020820735 3.035323578 3.035939809 3.042578546 3.052730332 3.054474708 3.057013298 3.057530131 3.067704681 3.068800023 3.076034658 3.089870019 3.099800517 3.101610913 3.119631497 3.124341072 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2235.065111 2236.344979 2249.802239 2264.686865 2267.396929 2273.404114 2273.754356 2277.413755 2280.6782 2283.149105 2285.348922 2289.328316 2290.734348 2295.517396 2312.503918 2320.990391 2322.061912 2323.440401 2324.878674 2333.165823 2333.252925 2335.603857 2337.325547 2345.946966 2358.273812 2366.02324 2370.471418 2371.784879 2375.909149 2379.075768 2385.552289 2389.641257 2389.86403 2390.725414 2395.819447 2407.106186 2408.175752 2429.088981 2434.82175 2440.124058 2467.929505 2471.566345 2477.609812 2489.945091 2491.580836 2510.302527 2511.638942 2515.044217 2515.71574 2517.028608 2524.354087 2524.877922 2526.06623 2535.558951 2535.721585 2536.492167 2537.325694 2537.709872 2539.365845 2540.281295 2553.669496 2558.765222 2571.722037 2572.527088 2574.079989 2582.056893 2582.866113 2588.746023 2591.215869 2599.739431 2608.250835 2609.567659 2609.624241 2610.649666 2619.595179 2621.563198 2636.421069 2639.394226 2642.023428 2669.445731 2675.382826 2680.034969 2694.084625 2696.268191 2704.531319 2707.839333 2716.792796 2730.732111 2732.069684 2737.677226 2778.443114 2782.215594 2785.874988 2791.253987 2791.436181 2801.164946 2805.848603 2806.178239 2810.666466 2813.901345 2830.859735 2853.498852 2856.717434 2868.888741 2870.367911 2878.874877 2883.703936 2885.632474 2905.874322 2907.515485 2916.389413 2923.073962 2929.712875 2931.019447 2933.791531 2933.930571 2935.077087 2949.087886 2976.158269 2981.768596 2983.915934 2989.715087 3001.266498 3002.479299 3011.896758 3011.912842 3020.089709 3025.493187 3042.22217 3043.797986 3044.370623 3045.683447 3053.071336 3054.37784 3059.479072 3063.92801 3107.161718 3151.462292 3173.531554 3256.077437 3256.892854 3259.605488 3265.533651 3289.866956 3319.895402 3333.730546 3378.930836 3388.87019 3396.586658 3408.947451 3436.547313 3439.845643 3464.238962 3465.187872 3465.352967 3498.880213 3522.537563 3533.001912 3553.327338 3600.815901 3603.355282 3717.619142 3755.870625 3760.009184 3773.018383 3783.863253 3811.612746 3811.771546 3814.024695 3867.8375 3883.205185 3886.970119 3908.597606 3911.469436 3917.313319 3939.971862 3986.038094 3987.670344 4046.750127 4066.433675 4092.504341 4097.021961 4169.546188 4186.34036 4187.66536 4190.45858 4204.426559 4212.053484 4243.227528 4250.384263 4254.988812 4260.547742 4262.13353 4320.226864 4367.875314 4388.094236 4536.069442 4564.327703 4567.306728 4604.70907 4646.504789 4671.757915 4699.991441 4787.618259 4876.00814 4904.917446 4917.17524 4925.327908 5011.724579 5098.552384 5303.654354 5308.235953 5309.20718 5392.941707 5419.906687 5475.854044 5495.805466 5568.353474 5640.966362 5729.170599 5756.300338 5806.041122 5832.62891 5835.703258 5915.919707 5927.430005 5981.107598 6030.061892 6096.21919 6167.676768 6201.442974 6225.328106 6260.287897 6289.796644 6322.660099 6326.143981 6469.370471 6476.395452 6499.135987 6644.829529 7103.246168 7336.643496 7450.887574 7463.63355 7724.153705 8125.302957 8315.141568 8480.871611 8667.155784 8993.192971 10269.69361 11689.13634 12457.90885 12725.48318 12877.43593];
[counts,centers] = hist(a,10);
borders = centers+(centers(2)-centers(1))/2
b = arrayfun(#(x) sum(x>=borders),a);
boxplot(a,b)
I'm using MATLAB to plot a colormap of the following data matrix:
1010.89914200000 1006.07847500000 1013.91775300000 1016.37012500000 1012.64447500000 1005.15384200000 998.323644400000 1007.09643600000 1010.39007800000 1010.71070000000 1007.75920300000 1003.43986900000 1001.77407500000 1000.93290600000 1009.19935000000 1010.79651100000 1006.15733600000 1001.08001400000 1006.62765600000 1008.98760600000 1012.59690300000 1014.13669400000 1012.41850000000 1002.83265600000
1010.20939200000 1006.22354400000 1014.13985000000 1016.01628600000 1012.16700600000 1004.47184200000 1000.64279200000 1006.93063300000 1009.98950000000 1010.53409400000 1007.46695300000 1003.07056100000 1001.31949400000 1001.61572800000 1009.12864700000 1010.45935000000 1006.24603900000 1001.40907500000 1006.31782500000 1009.00493300000 1012.13883300000 1013.93106700000 1011.88797800000 1003.01553600000
1009.70062500000 1006.35064700000 1014.37573900000 1015.70891100000 1011.51357800000 1003.79413100000 1001.69951400000 1006.79389200000 1009.72253100000 1010.41560600000 1007.01026100000 1002.91738300000 1000.86708100000 1002.11736400000 1009.09675300000 1009.98591400000 1005.46058600000 1000.94756700000 1006.18716400000 1008.73213100000 1012.14396700000 1013.51734400000 1010.97627200000 1003.29271400000
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1011.16811700000 1009.12379400000 1016.69230000000 1017.09807500000 1011.42089200000 1002.90685800000 1003.45940000000 1008.64450600000 1010.42875300000 1011.61208900000 1006.92791100000 1004.99500600000 1001.85114700000 1007.48185600000 1010.74164700000 1009.37652200000 1005.22774400000 1005.74404200000 1008.04547500000 1011.20978900000 1013.70315600000 1014.90081400000 1009.46251700000 1007.25164700000
1010.77128100000 1009.35747200000 1016.79156100000 1016.94504400000 1011.34709700000 1002.68326400000 1004.21865300000 1008.90464200000 1010.65171700000 1011.66855800000 1006.76375000000 1004.84766700000 1001.64799400000 1007.95280800000 1011.15295600000 1008.96945800000 1004.99150800000 1006.73475300000 1008.30754700000 1011.69105600000 1014.00060300000 1015.01862800000 1009.08597500000 1007.85208300000
1010.13805300000 1009.61241400000 1016.74593600000 1016.61859700000 1011.07230300000 1002.35865300000 1004.26001400000 1008.88295800000 1010.48621700000 1011.41217200000 1006.14741900000 1004.82872800000 1001.26854200000 1008.04161100000 1011.11477800000 1008.65341400000 1004.63091100000 1007.43303300000 1008.40434700000 1011.74642800000 1014.01016900000 1014.80443600000 1008.33869400000 1007.93218300000
1009.50911900000 1009.62698100000 1016.46591900000 1015.96478900000 1010.07972200000 1001.85613100000 1004.44510800000 1008.68885000000 1009.90488600000 1010.78808600000 1005.41067200000 1004.68175000000 1000.73121900000 1007.96492200000 1011.01123900000 1008.22990300000 1003.85685000000 1007.24613600000 1008.27287800000 1012.14434200000 1013.92860000000 1014.61665800000 1007.55034400000 1008.09293300000
1008.70381100000 1009.61384700000 1015.81295800000 1015.06458300000 1008.88961700000 1000.62531700000 1004.21893300000 1008.20705000000 1009.58456400000 1010.16761700000 1004.43428100000 1004.27616900000 1000.27990000000 1007.68135000000 1010.75977200000 1007.51076700000 1002.70041700000 1006.61412500000 1008.12120300000 1012.10416700000 1013.70059700000 1014.09623100000 1006.09171400000 1008.05956900000
1008.02154700000 1009.64861100000 1015.14255300000 1014.23098600000 1008.14283900000 999.389411100000 1004.11294400000 1008.03047200000 1009.17975600000 1009.66578900000 1003.65600000000 1003.73793600000 999.649786100000 1007.55710300000 1010.73914200000 1006.48525600000 1001.60153900000 1006.51005600000 1007.86194700000 1011.76237200000 1013.33892200000 1013.76143300000 1004.98931400000 1007.96544700000
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1007.69477800000 1010.25384700000 1014.99210800000 1013.30303900000 1007.45265800000 998.250572200000 1004.45173600000 1008.31279400000 1009.06058100000 1008.82283300000 1002.55153300000 1003.10945300000 999.019630600000 1007.24711700000 1010.81812500000 1006.09366700000 1000.98814200000 1006.25510800000 1008.09595800000 1011.79827200000 1013.07516400000 1013.10823300000 1003.03183900000 1008.06458600000
1007.77405000000 1010.73199400000 1015.15546900000 1013.15583900000 1007.25787500000 997.884352800000 1004.75770600000 1008.85976700000 1009.25861700000 1008.55503600000 1002.43732200000 1002.93201900000 998.928380600000 1007.53712200000 1010.76891400000 1006.20278600000 1000.95717200000 1006.24851400000 1008.26927500000 1011.92516700000 1013.04778600000 1012.83340000000 1002.36345600000 1008.45907200000
1008.10153900000 1011.22165600000 1015.49348900000 1013.28646400000 1007.37516700000 997.841966700000 1005.30818900000 1009.36494400000 1009.81374200000 1008.59688100000 1002.46048900000 1003.12547200000 998.873250000000 1008.09001100000 1010.74253600000 1006.00015800000 1001.09626700000 1006.44443300000 1008.61596400000 1012.13335000000 1013.32301100000 1012.72540800000 1001.82905800000 1008.71992200000
1008.42932800000 1012.08600000000 1015.93560300000 1013.55619200000 1007.18396400000 997.877988900000 1005.97368300000 1009.70238900000 1010.35090600000 1008.72040000000 1002.82235000000 1003.50095000000 998.968997200000 1008.71467200000 1011.18516100000 1006.09722800000 1001.00105000000 1006.51150600000 1008.80134200000 1012.38964200000 1013.64155000000 1012.74316400000 1001.41934400000 1008.87543300000
1008.61755800000 1012.61210000000 1016.07700000000 1013.93586700000 1007.27577800000 997.712516700000 1006.77127200000 1010.00605300000 1010.65426700000 1008.88601400000 1003.20735600000 1003.72689700000 999.271758300000 1009.20620300000 1011.16123600000 1006.69687200000 1000.70609700000 1006.79953300000 1009.01541700000 1012.91482800000 1014.02131100000 1012.95065300000 1001.67870000000 1009.32203600000
1008.56708100000 1012.99515000000 1016.33867800000 1013.68418100000 1007.24658600000 997.281041700000 1007.01185000000 1010.30151400000 1010.94030600000 1008.70953300000 1003.34373100000 1004.04526700000 999.558302800000 1009.36840300000 1011.30540300000 1006.56111700000 1000.80576400000 1007.06003900000 1009.30639400000 1013.20931400000 1014.27723600000 1013.05665800000 1001.85187500000 1009.70395300000
1008.29791700000 1013.28623100000 1016.51517200000 1014.03927800000 1006.84327200000 997.159275000000 1007.09736400000 1010.48451100000 1010.96097800000 1008.46591400000 1003.29630300000 1003.91275300000 1000.09343100000 1009.50430800000 1011.42889700000 1006.72751400000 1000.51083900000 1007.10306100000 1009.37503100000 1013.39047200000 1014.59432200000 1013.03755800000 1002.26636900000 1010.06034200000
1008.21371700000 1013.59691400000 1016.65878900000 1013.99910800000 1006.30900300000 997.142713900000 1007.04279200000 1010.75180300000 1011.00974400000 1008.16542800000 1003.24316900000 1003.42274700000 1000.31632800000 1009.49489200000 1011.19880800000 1007.23048900000 1000.05682200000 1007.13264700000 1009.40641100000 1013.34435800000 1014.59523900000 1012.99997500000 1002.45651400000 1010.21388600000
1007.63143900000 1013.69220800000 1016.52085600000 1013.28099200000 1005.84439200000 997.479972200000 1007.22611700000 1010.59871900000 1010.82017800000 1007.92970300000 1003.35441900000 1002.36780800000 1000.54393300000 1009.14068600000 1011.02388300000 1007.05475600000 1000.35642800000 1006.99240300000 1009.16368100000 1013.13141400000 1014.49421400000 1012.86652200000 1002.60169400000 1010.12598900000
using:
imagesc(data)
Now I need to normalize each column separately in the following way: finding the maximum value of each column and set it's color, and finding the lowest value of each column and set it's color as well.
So in the end I need a colormap which have the same color for every local maximum and minimum of a column.
I'm quite new to plotting, so I tried looking for normalized colormaps here and on the web, but didn't find anything that can help me.
You're not going to be able to do this strictly with a colormap because the colormap is applied globally to the image and the color used for the local max in one column would be applied to all elements with that value regardless of whether they were the local max of their respective column.
Instead, you can normalize each column of your data and then display that.
%// Subtract off the minimum of each column
D = bsxfun(#minus, data, min(data, [], 1));
%// Divide by the maximum
D = bsxfun(#rdivide, D, max(D, [], 1));
%// Display the result
imagesc(D);
I have a vector of 810 doubles, which have a very good reason (not relevant, so reason left out here) to roughly follow a Gaussian distribution. I now wish to verify this.
-1425.35483258195
-2005.85887636008
1560.31387221920
422.221432204087
-396.336462872091
1028.04845220438
-126.818743685290
482.602336878657
-351.945829219904
-408.071209112604
-251.839429417447
-1325.82167938863
-1304.65143253464
984.905373772623
-866.213152797951
73.6149979242073
3834.52647065066
917.976216226379
815.189065312880
-96.0747513429396
-319.662630897599
-1221.53710722367
1190.72857085035
2144.87935230603
143.558912788403
-167.475218992091
84.4066585851642
604.944484054070
1509.18911810685
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143.669853748808
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46.2128162171030
-141.952303144073
292.716350945286
-1952.97174976434
-391.978769841029
-573.922085615045
-1301.25783316103
-645.990154917124
-934.774832747395
116.250933690360
544.823087571245
160.470631259077
2602.37582436213
534.434360410673
989.338067269714
-447.272873139365
1118.95219395721
-898.345257943601
-681.176900874247
213.211488587428
-785.169609773158
-430.621935639622
-61.2013695948126
-377.605278281317
1497.99884427522
404.777865468637
-1504.27516707823
-789.565967187437
-1118.50469245357
665.045393010892
28.5870825819056
-1045.44580718356
409.712490593987
-772.727898866153
252.289204820367
-905.257023890462
-300.707805837961
-74.4828651478892
-1087.13018005821
275.455585388661
-947.372403119386
-348.098011249108
1645.59654581296
517.906791768635
322.688565166583
331.939567925477
-612.937714892369
1302.63905136005
-153.606350780020
962.124237023736
-627.507634881584
-226.761116511015
463.132204629023
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1532.46771710064
222.343444164121
303.799291944722
1084.75952392675
856.849727349139
514.671849059982
-1655.41677124587
-127.246214481867
1734.57538636725
56.2555496894129
297.184996955808
386.823038887674
1353.32907458661
-2307.42128989437
654.140638203676
-1392.55190147038
-607.378457188426
2384.44806882885
2293.43321702051
47.3551805244424
746.478352537506
-938.994569192230
192.721071163348
-4.29989698278951
100.289121179456
812.825042468773
103.758682367813
-490.446548621378
-1339.81881565035
-595.379417250646
499.458821146142
765.412270843621
532.095358987890
-247.000541594816
-480.064780362965
316.263039414615
449.159830642379
1933.73501675200
52.4933985187945
3107.07754310984
218.560847122495
-575.905775870265
2124.73813469493
1272.05488997025
-777.848735713576
-1981.23363300488
-1677.42292095700
605.970193322080
-759.457775207884
-84.6880200736514
-825.486129072864
739.859927410010
781.960329365344
-77.4149957958771
57.2191392940895
-500.299306284041
-575.933520504403
-1402.37563293800
-164.380079314064
232.496199533151
-1150.91949913415
-76.7244722653677
-47.1854688325975
3140.15003755460
-1330.34731058125
145.959328309073
1600.84592274169
-24.9232437067194
1395.72597116652
1621.21780908032
85.7934267467826
3279.93389739169
1516.36196103567
-1039.08123306694
-26.8801938594925
1492.40185616989
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Since the vector is not ordered, directly plotting them in current order gives a messy plot like this.
What is a smart way of easily reordering the data so that I can verify whether they roughly follow a normal distribution or not?
To see if your data follow a Gaussian distribution you can use normplot. It draws the empirical distribution function of the data on a scale such that a Gaussian corresponds to a straight line. The more straight the line is, the more similar to a Gaussian.
If often happens that the curve is straight in the central part, and deviates on its ends. That's the case with your data; see figure below.
The explanation is that the Gaussian distribution usually arises from a sum of random variables, by virtue of the Central Limit Theorem. The distribution of the sum is Gaussian in the limit as the number of summands tends to infinity. For a finite number of terms the distribution is only approximately Gaussian; and the approximation is better at the center than on the tails (in fact, that's why the theorem is called central).
If you have the Statistics Toolbox, you could also run one of the hypothesis tests, like the Jarque-Bera Test, which will tell you (within a confidence limit) how likely it is that your points were drawn from a normal distribution.
[h, p] = jbtest(X, 0.05);
if h
disp(sprintf('There is less than %.3f%% chance that your data is abnormal', 100 * p));
else
disp(sprintf('Your data is not normal within your confidence limit'));
end
This doesn't give you a pretty plot, but it will give you a less biased answer than just eyeballing it.
In this case, there is better than 99.9% chance that your data set was drawn from a normal distribution.
Simply use the histogram function:
histogram(X)
To view the PDF you can use:
if exist('histogram','func')
histogram(data)
else
hist(data)
end
This looks pretty normal to me. You can always try modeling it with a gaussian.