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How do I label each vector of each input component, say u$_1$..., d$_1$..., on my graph at each vector's respective starting point. I want to see if there is a clustering of certain vectors like u$_30$-u$_50$ are all near each other, for example. Here is my code:
fx = [1.01, 1.0165376460248143, 1.016585505356985,...
1.0166786731186357, 1.0166445649045002, 1.01684528204491,...
1.0168363943981442, 1.0169505828006045, 1.0169903647693619,...
1.0170588800755562, 1.0170214556321182, 1.0171007103163394,...
1.0170611565299144, 1.0171737504115423, 1.0171325089936156,...
1.0173884633568437, 1.0173821295549914, 1.017540453473392,...
1.0176091468862674, 1.0177647297690604, 1.017711866139699,...
1.0177536635811828, 1.0178254876275734, 1.0173994306983212,...
1.0200331803664529, 1.0232411092365432, 1.0232773133875106,...
1.023383936276914, 1.0233275057530007, 1.023510835824228,...
1.0234461433923472, 1.023507118352957, 1.0237210297124908,...
1.0236390252916325, 1.0237007559499636, 1.0239084387662698,...
1.0238131746118633, 1.024266374303865, 1.024212732428539,...
1.02440393427416, 1.0245390401237269, 1.0252178000353167,...
1.0252021019242434, 1.0275875709904758, 1.0275871039342042];
fy = [0.99, 0.99, 0.9899194500620327, 0.9897134368447225,...
0.9899339650105077, 0.9895259027418399, 0.9898115223446341,...
0.9896762515189842, 0.9896129792784014, 0.9894766621994305,...
0.9896189382715079, 0.9894614440540032, 0.9896292673356496,...
0.9894095770062209, 0.989655005387203, 0.9892019096930893,...
0.9894189058876284, 0.9892732425545386, 0.9891916768216495,...
0.9889512723219249, 0.9892071461243063, 0.9891515372181835,...
0.9890346980816267, 0.9901802532401042, 0.9892771992437573,...
0.9881487558751526, 0.9880037699743045, 0.9875669935217211,...
0.9878502051001951, 0.9872010568874899, 0.9875329139453003,...
0.9873775054641964, 0.9868251990627905, 0.9871082986923524,...
0.9869819983991632, 0.9865548473263468, 0.9867867860622922,...
0.9859765136441385, 0.9861731333993694, 0.9859212446482857,...
0.9857475603282838, 0.9848759880952044, 0.9850648602644492,...
0.9821891156159342, 0.9822254068452594];
fz =[0.01, -0.0014683388934621459, -0.0028093690242636917,...
-0.006255424514110392, -0.002405171080788649, -0.009167776104980133,...
0.0003750210183572269, -0.001823375333180016, -0.002906415137850454,...
-0.005227263048381278, -0.0028662319950483552, -0.0055329993182467365,...
-0.0027458980004112996, -0.00644276568444028, -0.00226410433801184,...
-0.009832266892691467, 0.0012478354917326469, -0.001163969711179093,...
-0.0026270200357900887, -0.006946260715800828, -0.00188587841967576,...
-0.002880843788516535, -0.0049636661241180685, 0.015586949435911355,...
0.010368914010693711, -0.0010649331940053245, -0.002328942248654949,...
-0.006634620630021168, -0.0020052485893380344, -0.008543368794125199,...
-0.00044976575279103564, -0.0019790036016751333, -0.008330963679008077,...
-0.0006481277669472506, -0.0020539789179887767, -0.0075781311330381336,...
-0.001294365366809558, -0.011629381859506432, 0.003447063734076782,...
0.0011256038145771368, -0.0008637305140054806, -0.012865086502170518,...
0.005283762238371167, -0.016926299226379265, 0.011993515880473204];
x = [1.01, 1.0165376460248143, 1.016585505356985, 1.0166786731186357,...
1.0166445649045002, 1.01684528204491, 1.0168363943981442,...
1.0169505828006045, 1.0169903647693619, 1.0170588800755562,...
1.0170214556321182, 1.0171007103163394, 1.0170611565299144,...
1.0171737504115423, 1.0171325089936156, 1.0173884633568437,...
1.0173821295549914, 1.017540453473392, 1.0176091468862674,...
1.0177647297690604, 1.017711866139699, 1.0177536635811828,...
1.0178254876275734, 1.0173994306983212, 1.0200331803664529,...
1.0232411092365432, 1.0232773133875106, 1.023383936276914,...
1.0233275057530007, 1.023510835824228, 1.0234461433923472,...
1.023507118352957, 1.0237210297124908, 1.0236390252916325,...
1.0237007559499636, 1.0239084387662698, 1.0238131746118633,...
1.024266374303865, 1.024212732428539, 1.02440393427416,...
1.0245390401237269, 1.0252178000353167, 1.0252021019242434,...
1.0275875709904758, 1.0275871039342042];
y = [0.99, 0.99, 0.9899194500620327, 0.9897134368447225,...
0.9899339650105077, 0.9895259027418399, 0.9898115223446341,...
0.9896762515189842, 0.9896129792784014, 0.9894766621994305,...
0.9896189382715079, 0.9894614440540032, 0.9896292673356496,...
0.9894095770062209, 0.989655005387203, 0.9892019096930893,...
0.9894189058876284, 0.9892732425545386, 0.9891916768216495,...
0.9889512723219249, 0.9892071461243063, 0.9891515372181835,...
0.9890346980816267, 0.9901802532401042, 0.9892771992437573,...
0.9881487558751526, 0.9880037699743045, 0.9875669935217211,...
0.9878502051001951, 0.9872010568874899, 0.9875329139453003,...
0.9873775054641964, 0.9868251990627905, 0.9871082986923524,...
0.9869819983991632, 0.9865548473263468, 0.9867867860622922,...
0.9859765136441385, 0.9861731333993694, 0.9859212446482857,...
0.9857475603282838, 0.9848759880952044, 0.9850648602644492,...
0.9821891156159342, 0.9822254068452594];
z =[0.01, -0.0014683388934621459, -0.0028093690242636917,...
-0.006255424514110392, -0.002405171080788649, -0.009167776104980133,...
0.0003750210183572269, -0.001823375333180016, -0.002906415137850454,...
-0.005227263048381278, -0.0028662319950483552, -0.0055329993182467365,...
-0.0027458980004112996, -0.00644276568444028, -0.00226410433801184,...
-0.009832266892691467, 0.0012478354917326469, -0.001163969711179093,...
-0.0026270200357900887, -0.006946260715800828, -0.00188587841967576,...
-0.002880843788516535, -0.0049636661241180685, 0.015586949435911355,...
0.010368914010693711, -0.0010649331940053245, -0.002328942248654949,...
-0.006634620630021168, -0.0020052485893380344, -0.008543368794125199,...
-0.00044976575279103564, -0.0019790036016751333, -0.008330963679008077,...
-0.0006481277669472506, -0.0020539789179887767, -0.0075781311330381336,...
-0.001294365366809558, -0.011629381859506432, 0.003447063734076782,...
0.0011256038145771368, -0.0008637305140054806, -0.012865086502170518,...
0.005283762238371167, -0.016926299226379265, 0.011993515880473204];
figure
q = quiver3(fx,fy,fz,x,y,z)
The problem is that I want to label the starting point of each vector:
You can use text:
q = quiver3(fx,fy,fz,x,y,z)
text(fx,fy,fz,num2str((1:numel(fx)).'))
The first 3 inputs are the coordinates of the label and the next input is a list (column character array or a cell-array) of the labels. I don't understand how your labeling is working (i.e. what is u$_1$ or d$_1$), so I just numbered the vectors from 1 to 45.
I have the following matrix which rows are points sampled from a function
f = [ -3.7850 -11.5240
-3.7753 -11.4822
-3.7680 -11.5427
-3.7592 -11.5607
-3.7576 -11.5461
-3.7454 -11.5887
-3.7386 -11.4070
-3.7358 -11.4450
-3.7289 -11.5511
-3.7254 -11.3713
-3.7122 -11.4515
-3.6820 -11.5582
-3.6758 -11.5946
-3.6732 -11.5823
-3.6679 -11.6365
-3.6487 -11.3525
-3.6424 -11.2745
-3.6322 -11.3478
-3.6235 -11.6379
-3.6159 -11.6308
-3.5619 -11.1980
-3.5550 -11.2284
-3.5544 -11.5925
-3.5147 -11.6578
-3.5041 -11.6756
-3.4860 -11.1550
-3.4654 -11.6341
-3.4550 -11.1329
-3.3802 -11.6701
-3.3691 -11.1083
-3.3541 -11.0790
-3.3485 -11.5887
-3.3006 -11.6384
-3.2481 -11.5570
-3.2459 -11.0268
-3.2441 -10.9314
-3.2301 -11.5225
-3.2270 -10.8832
-3.1543 -10.8612
-3.1528 -11.5490
-3.1167 -11.5021
-3.1102 -10.8255
-3.0645 -11.5618
-2.9967 -11.5420
-2.9898 -10.8136
-2.9645 -10.7107
-2.9211 -11.4197
-2.9175 -10.6389
-2.8558 -10.6015
-2.8327 -11.5108
-2.7768 -11.4501
-2.7392 -10.5492
-2.7217 -11.4230
-2.6988 -10.4724
-2.6235 -11.3226
-2.6196 -11.3806
-2.5772 -10.4518
-2.5458 -10.4317
-2.5014 -10.3176
-2.4832 -11.3822
-2.4778 -10.2456
-2.4029 -11.2907
-2.3723 -10.3002
-2.3590 -11.2911
-2.3491 -10.2110
-2.2756 -11.2318
-2.2554 -10.1204
-2.2542 -10.1411
-2.2181 -11.2300
-2.1982 -9.9584
-2.1645 -9.7938
-2.1541 -11.1682
-2.1476 -9.8235
-2.1451 -9.9205
-2.1280 -10.0064
-2.1269 -9.8947
-2.0898 -9.7926
-2.0781 -11.1293
-1.9985 -11.0985
-1.9249 -11.0443
-1.8220 -11.0419
-1.7359 -11.0043
-1.6924 -10.9775
-1.6049 -10.9579
-1.5275 -10.9339
-1.4757 -10.9113
-1.4122 -10.8854
-1.3245 -10.8908
-1.2936 -10.7893
-1.2091 -10.8121
-1.1575 -10.8064
-1.1237 -10.7105
-1.0571 -10.7724
-1.0217 -10.7096
-0.9717 -10.6984
-0.9447 -10.7103
-0.9120 -10.6687
-0.8908 -10.6670]
Plotting by plot(f(:,1),f(:,2),'+') it is clear that the function has a V-shape. However, I need to plot it continuously, but doing plot(f(:,1),f(:,2)) results in a zig-zag function. How can I plot the points as I want to? (beside sorting them manually)
You could try rotating your data, sorting it and rotating it back. e.g:
theta = -1;
R = [cos(theta) -sin(theta);sin(theta) cos(theta)];
f2 = f*R;
f3 = sortrows(f2);
f4 = f3*R';
plot(f4(:,1),f4(:,2),'-',f(:,1),f(:,2),'+')
You can tweak theta to change the angle, which affects the sort order, I just took a guess that -1 is about right.
(Using Octacve) I have a text file with triangle vertices defined in this way:
((x11, y11, z11), (x12, y12, z12), (x13, y13, z13))((x21, y21, z21), (x22, y22, z22), (x23, y23, z23))...((xn1, yn1, zn1), (xn2, yn2, zn2), (xn3, yn3, zn3))
This is a list of triangles in 3D space, with every triangle defined as ((xn1, yn1, zn1)(xn2, yn2, zn2)(xn3, yn3, zn3)).
How can I import this file in Octave in order to see the generated mesh? I know I can use trimesh, but I'm not be able to parse this file in order to retrieve point coordinates.
EDIT: This is a real file with some triangle:
((-0.780869, -1.56174, 4.68521), (-0.776988, -1.08169, 4.81936), (-0.776988, -2.02627, 4.5045))((-0.776988, -1.08169, 4.81936), (-0.765386, -0.590883, 4.90561), (-0.776988, -2.02627, 4.5045))((-0.776988, -2.02627, 4.5045), (-0.765386, -0.590883, 4.90561), (-0.765386, -2.47066, 4.27902))((-0.765386, -0.590883, 4.90561), (-0.746177, -0.0942074, 4.94311), (-0.765386, -2.47066, 4.27902))((-0.765386, -2.47066, 4.27902), (-0.746177, -0.0942074, 4.94311), (-0.746177, -2.8905, 4.01101))((-0.746177, -0.0942074, 4.94311), (-0.719552, 0.403404, 4.93148), (-0.746177, -2.8905, 4.01101))((-0.746177, -2.8905, 4.01101), (-0.719552, 0.403404, 4.93148), (-0.719552, -3.28161, 3.70314))((-0.719552, 0.403404, 4.93148), (-0.685776, 0.897006, 4.87084), (-0.719552, -3.28161, 3.70314))((-0.719552, -3.28161, 3.70314), (-0.685776, 0.897006, 4.87084), (-0.685776, -3.64011, 3.35847))((-0.685776, 0.897006, 4.87084), (-0.645184, 1.38169, 4.76179), (-0.685776, -3.64011, 3.35847))((-0.685776, -3.64011, 3.35847), (-0.645184, 1.38169, 4.76179), (-0.645184, -3.96243, 2.98042))((-0.645184, 1.38169, 4.76179), (-0.59818, 1.85265, 4.60542), (-0.645184, -3.96243, 2.98042))((-0.645184, -3.96243, 2.98042), (-0.59818, 1.85265, 4.60542), (-0.59818, -4.24537, 2.57274))((-0.59818, 1.85265, 4.60542), (-0.545231, 2.30519, 4.40327), (-0.59818, -4.24537, 2.57274))((-0.59818, -4.24537, 2.57274), (-0.545231, 2.30519, 4.40327), (-0.545231, -4.48612, 2.1395))((-0.545231, 2.30519, 4.40327), (-0.486864, 2.73482, 4.15737), (-0.545231, -4.48612, 2.1395))((-0.545231, -4.48612, 2.1395), (-0.486864, 2.73482, 4.15737), (-0.486864, -4.68228, 1.685))((-0.486864, 2.73482, 4.15737), (-0.423657, 3.13728, 3.87014), (-0.486864, -4.68228, 1.685))((-0.486864, -4.68228, 1.685), (-0.423657, 3.13728, 3.87014), (-0.423657, -4.83191, 1.21375))((-0.423657, 3.13728, 3.87014), (-0.356241, 3.50855, 3.54445), (-0.423657, -4.83191, 1.21375))((-0.423657, -4.83191, 1.21375), (-0.356241, 3.50855, 3.54445), (-0.356241, -4.93351, 0.730433))((-0.356241, 3.50855, 3.54445), (-0.285283, 3.84496, 3.18354), (-0.356241, -4.93351, 0.730433))((-0.356241, -4.93351, 0.730433), (-0.285283, 3.84496, 3.18354), (-0.285283, -4.98609, 0.23986))((-0.285283, 3.84496, 3.18354), (-0.211491, 4.14315, 2.79099), (-0.285283, -4.98609, 0.23986))((-0.285283, -4.98609, 0.23986), (-0.211491, 4.14315, 2.79099), (-0.211491, -4.98911, -0.253097))((-0.211491, 4.14315, 2.79099), (-0.135596, 4.40016, 2.3707), (-0.211491, -4.98911, -0.253097))((-0.211491, -4.98911, -0.253097), (-0.135596, 4.40016, 2.3707), (-0.135596, -4.94255, -0.743539))((-0.135596, 4.40016, 2.3707), (-0.0583544, 4.61344, 1.92684), (-0.135596, -4.94255, -0.743539))((-0.135596, -4.94255, -0.743539), (-0.0583544, 4.61344, 1.92684), (-0.0583544, -4.84686, -1.22659))
(This was tested with MATLAB, but should work fine in Octave too)
To parse the file you may use the following function:
function A = fparse_triangle(fname)
f = fopen(fname, 'r');
A = reshape(fscanf(f, '((%f, %f, %f)(%f, %f, %f)(%f, %f, %f))'),3,3,[]);
fclose(f);
end
The result will be a 3×3×n matrix, on first index having coordinates of vertices (X, Y, Z), on second index having vertices of a triangle (1st, 2nd, 3rd), and on third index having triangles.
Later edit
This alternative will take in account vertices that are separated by commas, as in the recently added example:
function A = fparse_triangle_alt(fname)
f = fopen(fname, 'r');
A = reshape(fscanf(f, '((%f, %f, %f), (%f, %f, %f), (%f, %f, %f))'),3,3,[]);
fclose(f);
end
NB
Mind that from this to a trimesh call there's a bit more processing involved. Let me know if you'd need help with that.
Suppose that we have following array:
0.196238259763928
0.0886250228175519
0.417543614272817
0.182403230538167
0.136500793051860
0.389922187581014
0.0344012946153299
0.381603315802419
0.0997542838649466
0.274807632628596
0.601652859233616
0.209431489000677
0.396925294300794
0.0351587496999554
0.177321874549738
0.369200511917405
0.287108838007101
0.477076452316346
0.127558716868438
0.792431584110476
0.0459982776925879
0.612598437936600
0.228340227044324
0.190267907472804
0.564751537228850
0.00269368929400299
0.940538666131177
0.101588565140294
0.426175626669060
0.600215481734847
0.127859067121782
0.985881201195063
0.0945679498528667
0.950077461673118
0.415212985598547
0.467423473845033
1.24336273213410
0.0848695928658021
1.84522775800633
0.289288949281834
1.38792131632743
1.73186592736729
0.554254947026916
3.46075557122590
0.0872957577705428
4.93259798197976
2.03544238985229
3.71059303259615
8.47095716918618
0.422940369071662
25.2287636895831
4.14535369056670
63.7312173032838
152.080907190007
1422.19492782494
832.134744027851
0.0220089962114756
60.8238733887811
7.71053463387430
10.4151913932115
11.3141744831953
0.988978595613829
8.65598040591953
0.219820300144944
3.92785491164888
2.28370963778411
1.60232807621444
2.51086405960291
0.0181622519984990
2.27469230188760
0.487809730727909
0.961063613990814
1.90435488292485
0.515640996120482
1.25933693517960
0.0953200831348589
1.52851575480462
0.582109930768162
0.933543409438383
0.717947488528521
0.0445235241119612
1.21157308704582
0.0942421028083462
0.536069075206508
0.821400666720535
0.308956823975938
1.28706199713640
0.0339217632187507
1.19575886464231
0.0853733920496230
0.736744959694641
0.635218502184121
0.262305581223588
0.986899895695809
0.0398800891449550
0.758792061180657
0.134279188964854
0.442531129290843
0.542782326712391
0.377221037448628
0.704787750202814
0.224180325609783
0.998785634315287
0.408055416702400
0.329684702125840
0.522384453408780
0.154542718256493
0.602294251721841
0.240357912028348
0.359040779285709
0.525224294805813
0.427539247203335
0.624034405807298
0.298184846094056
0.498659616687732
0.0962076792277457
0.430092706132805
0.656212420735658
0.278310520474744
0.866037361133916
0.184971060800812
0.481149730712771
0.624405636807668
0.382388147099945
0.435350646037440
0.216499523971397
1.22960953802959
0.330841706900755
0.891793067878849
0.628241046456751
0.278687691121678
1.06358076764171
0.365652714373067
1.34921178081181
0.652888708375276
0.861138633227739
1.02878577330537
0.591174450919664
1.93594290806582
0.497631035062465
1.14486512201656
0.978067581547298
0.948931658572253
2.01004088022982
0.917415940349743
2.24124811810385
1.42691656876436
2.15636037453584
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In MATLAB it is very easy to find peaks using findpeaks, like so:
[pxx_peaks,location] = findpeaks(Pxx);
If we plot pxx_peaks, we get
plot(pxx_peaks)
Of course, besides these peaks, there are smaller peaks which are not shown on the picture, but my goal is to find all peaks which are 95-96% above all other peaks.
I have tried like this:
>> average = mean(pxx_peaks);
>> stand = std(pxx_peaks);
>> final_peaks = pxx_peaks( pxx_peaks > average + 3*stand );
The result of this is
>> final_peaks
final_peaks =
1.0e+03 *
1.4222
1.4900
1.5104
1.1200
but how to return their corresponding locations? I want to write it as one m-file, so please help me
EDIT
also please help me in this question: can I parameterize the confidence interval? For instance instead of 95%, I want to find peaks that are 60% above then other peaks, is it possible?
Note that 3σ ≈ 99.73%
As for your first question, it's easy, you just have to keep track of the locations in the same way as you do for the peaks:
inds = pxx_peaks > mean(pxx_peaks) + 3*std(pxx_peaks);
final_peaks = pxx_peaks(inds);
final_locations = location(inds);
plot(Pxx), hold on
plot(final_locations, final_peaks, 'r.')
As for your second question, that's a little more complicated. If you want to formulate it like you say, you'll have to convert a desired percentage to the correct number of σ. That involves an integration of the standard normal, and a root finding:
%// Convert confidence interval percentage to number-of-sigmas
F = #(P) fzero(#(sig) quadgk(#(x) exp(-x.^2/2),-sig,+sig)/sqrt(2*pi) - P/100, 1);
% // Repeat with the desired percentage
inds = pxx_peaks > mean(pxx_peaks) + F(63)*std(pxx_peaks); %// 63%
final_peaks = pxx_peaks(inds);
final_locations = location(inds);
plot(final_locations, final_peaks, 'r.')
I'm new to Matlab and doing a signal processing project(Speech Recognition). After doing some calculations, I get some values known as MFCC (Mel-Frequency Cepstral Coefficient) in a matrix. I'm now supposed to apply a Gaussian Mixture Model (GMM) distribution using the function gmdistribution.fit(X,k). But I keep getting the error,
X must have more rows than columns.
I don't understand, how can I fix this? I tried doing a transpose of the matrix, but then, I get other errors.
??? Error using ==> gmcluster at 181
Ill-conditioned covariance created at iteration 3.
Error in ==> gmdistribution.fit at 199
[S,NlogL,optimInfo] =...
My MFCC matrix generally has 13 rows and about 50-80 columns.
Any ideas on how to fix this? Should I be using only upto 12 cols at a time? OR what could be an alternate expectation-maximization (EM) algorithm to obtain a maximum likelihood (ML)estimate in Speech recognition?
Here's a sample matrix that I get after extracting the mfcc feature vectors from the speech:
53.19162380493035 53.04536473593154 52.52404588266867 52.76558091790412 53.63907256262721 53.357790132994836 52.73205096524416 52.902995065027056 52.61096061282659 54.15474467851871 53.67444472478125 52.64177726437717 52.51697384592561 52.71137919365186 53.092851922453896 53.16427640450918 54.43019514688636 60.79640902129941 59.84919922646779 63.15389910551327 61.88723594060794 64.74826830389657 64.8349874832628 64.86278444375218 65.76126193531795 65.64589407152897 65.46920375829764 65.69178734432299 65.28831375816117 64.56074008418904 63.4966945660873 63.81859800557705 63.72800219675504 62.48994205815299 62.170438508902436 61.06563184036766 59.13583014975035 58.81335869501639 56.32130498897641 55.13711899166046 54.013505531107796 54.15759852717166 53.44176740036524 53.13219768600348 53.03407270007307 52.88271825256845 53.822163186509016 53.53892778841879 54.04538463287215 59.485371756367954 58.48009762761471 54.643413468895346 52.808848460884654 52.87392859698496 52.42111841679119 53.2365666558251 53.30622484832905 53.1799318016215 53.784807994410315 53.248067707554924 52.69122098296521 52.50131276155125 53.43030515391315 53.902384536061604 54.029570128176985 52.842675820980034 52.79731975873874 53.18695701339912
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I don't understand, how can I fix this?
You need to transpose the matrix, you got it right. The vectors must be on the raws
Any ideas on how to fix this?
GMDISTRIBUTION implements the standard Expectation-Maximization (EM) algorithm. In some cases, it may converge to a solution which contains singular or close-singular covariance matrix for one or more components. Those components usually contains a few data points almost lying in a lower-dimensional subspace. A solution with singular covariance matrix is
usually considered as spurious. Sometimes, this problem may go away if you try another set of initial values; Sometimes, this problem will always occur because of any of the following reasons:
The number of dimension of data is relatively high, but there are not enough observations.
Some of the features(variables) of your data are highly correlated.
Some or all the features are discrete.
You try to fit the data to too many components.
In your case, it seems that the number of components that you used, 8, is too big. you can try to reduce the number of components. Generally, there are also other ways that you can use to avoid getting "Ill-conditioned covariance matrix" error message"
If you don't mind to get solutions with ill-conditioned covariance matrix, you can use option 'Regularize' in the GMDISTRIBUTION/FIT function to add a very small positive number to the diagonal of every covariance matrix.
You can specify the value of 'SharedCov' to be true to use equal covariance matrix for every component.
You can specify the value of 'CovType' to be 'diagonal' .
See also
http://www.mathworks.com/matlabcentral/newsreader/view_thread/168289
Should I be using only upto 12 cols at a time?
No
#Shark
I had the same problem, trying to generate an Gaussian MM object from a set of data.
I solved it by specifing the type of covariance:
GMM1=gmdistribution.fit(X,k,'CovType','diagonal')
GMM1 is the object name. You can find the meaning of X and k in
help gmdistribution.fit
If this doesn't work for you, try specifying the initial values of the EM algorithm
that gmdistribution already uses to generate the GMM.
Elios
first of you should make it linear because it is too big for matlab to do it, after that its better to just take 7-10 features (i think you get more than this).
after you did your work then use reshape function in order to make it what you want