[mlpack-git] [mlpack] hmm_train number of gaussians (#479)

davudadiguezel notifications at github.com
Tue Nov 24 03:57:40 EST 2015


Hi Ryan,
I got some more output with -v and with gdb:

[INFO ] GMM::Estimate(): log-likelihood of trained GMM is 9248.34.
[INFO ] Cluster 1 is empty.
[DEBUG] Point 63 assigned to empty cluster 1.
[INFO ] Cluster 2 is empty.
[DEBUG] Point 42 assigned to empty cluster 2.
[INFO ] Cluster 4 is empty.
[DEBUG] Point 43 assigned to empty cluster 4.
[INFO ] Cluster 5 is empty.

error: Mat::col(): index out of bounds

terminate called after throwing an instance of 'std::logic_error'
  what():  Mat::col(): index out of bounds

Program received signal SIGABRT, Aborted.
0x00007ffff651fcc9 in __GI_raise (sig=sig at entry=6)
    at ../nptl/sysdeps/unix/sysv/linux/raise.c:56
56      ../nptl/sysdeps/unix/sysv/linux/raise.c: No such file or directory.
(gdb) bt
#0  0x00007ffff651fcc9 in __GI_raise (sig=sig at entry=6)
    at ../nptl/sysdeps/unix/sysv/linux/raise.c:56
#1  0x00007ffff65230d8 in __GI_abort () at abort.c:89
#2  0x00007ffff6e2a535 in __gnu_cxx::__verbose_terminate_handler() ()
   from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#3  0x00007ffff6e286d6 in ?? () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#4  0x00007ffff6e28703 in std::terminate() () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#5  0x00007ffff6e28922 in __cxa_throw () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#6  0x00000000005c39f7 in arma::arma_stop<char const*> (
    x=@0x7fffffffabf8: 0x677e20 "Mat::col(): index out of bounds")
    at /usr/include/armadillo_bits/debug.hpp:113
#7  0x00000000005e5e52 in arma::arma_check<char [32]> (state=true, x=...)
    at /usr/include/armadillo_bits/debug.hpp:358
#8  0x0000000000617ddd in col (col_num=77, this=0x7fffffffc8d0)
    at /usr/include/armadillo_bits/Mat_meat.hpp:2588
#9  mlpack::kmeans::MaxVarianceNewCluster::EmptyCluster<mlpack::metric::LMetric<2, true>, arma::Mat<double> > (this=0xb72460, data=..., emptyCluster=5, oldCentroids=..., newCentroids=..., 
    clusterCounts=..., metric=..., iteration=0)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/kmeans/max_variance_new_cluster_impl.hpp:58
#10 0x0000000000610b56 in mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >::Cluster (this=0xb72450, data=..., clusters=10, centroids=..., 
    initialGuess=false)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/kmeans/kmeans_impl.hpp:160
#11 0x0000000000609bc0 in mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >::Cluster (this=0xb72450, data=..., clusters=10, assignments=..., centroids=..., 
    initialAssignmentGuess=false, initialCentroidGuess=false)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/kmeans/kmeans_impl.hpp:241
#12 0x0000000000602135 in mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >::Cluster (this=0xb72450, data=..., clusters=10, assignments=..., 
    initialGuess=false)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/kmeans/kmeans_impl.hpp:64
#13 0x00000000005fcb75 in mlpack::gmm::EMFit<mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >, mlpack::gmm::PositiveDefiniteConstraint>::InitialClustering
    (this=0xb72440, observations=..., dists=..., weights=...)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/gmm/em_fit_impl.hpp:214
#14 0x00000000005f3524 in mlpack::gmm::EMFit<mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >, mlpack::gmm::PositiveDefiniteConstraint>::Estimate (
    this=0xb72440, observations=..., dists=..., weights=..., useInitialModel=false)
---Type <return> to continue, or q <return> to quit---
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/gmm/em_fit_impl.hpp:39
#15 0x00000000005e81d5 in mlpack::gmm::GMM<mlpack::gmm::EMFit<mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >, mlpack::gmm::PositiveDefiniteConstraint> >::Estimate (this=0x9a3ed0, observations=..., trials=1, useExistingModel=false)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/../mlpack/methods/gmm/gmm_impl.hpp:190
#16 0x00000000005dbdbf in mlpack::hmm::HMM<mlpack::gmm::GMM<mlpack::gmm::EMFit<mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >, mlpack::gmm::PositiveDefiniteConstraint> > >::Train (this=0x7fffffffd790, dataSeq=..., stateSeq=...)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/methods/hmm/hmm_impl.hpp:274
#17 0x00000000005d0b53 in Train::Apply<mlpack::hmm::HMM<mlpack::gmm::GMM<mlpack::gmm::EMFit<mlpack::kmeans::KMeans<mlpack::metric::LMetric<2, true>, mlpack::kmeans::RandomPartition, mlpack::kmeans::MaxVarianceNewCluster, mlpack::kmeans::NaiveKMeans, arma::Mat<double> >, mlpack::gmm::PositiveDefiniteConstraint> > > > (hmm=..., trainSeqPtr=0x7fffffffd420)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/methods/hmm/hmm_train_main.cpp:146
#18 0x00000000005c3335 in main (argc=15, argv=0x7fffffffe148)
    at /org/share/home/adigueze/mlpack-master/src/mlpack/methods/hmm/hmm_train_main.cpp:320

There is a "no such file" - error, but I don't think it comes from me. I am pretty that I have labels for every state and I don't get invalid label errors.
Here are my files:
[observationPKM.csv.txt](https://github.com/mlpack/mlpack/files/42512/observationPKM.csv.txt)
[labels.csv.txt](https://github.com/mlpack/mlpack/files/42513/labels.csv.txt)

(you will have to delete the .txt extension)

How many samples are about "enough" samples? I just run with 4000. I will get some more but I don't think I will have more than 50k. Will that be enough to do some more gaussians?
Greetings
Davud


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https://github.com/mlpack/mlpack/issues/479#issuecomment-159199593
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