在Matlab中对序列进行分类的隐马尔可夫模型 [英] Hidden Markov model classifying a sequence in Matlab

问题描述

我是机器学习的新手，我了解了用于隐藏的Markov模型的Matlab统计工具箱，我想使用它对给定的信号序列进行分类.我在矩阵P中使用3D坐标，即[501x3]，我想以此为基础训练模型.转换完整轨迹的终点是一组特定的点，即达到目标的(0,0,0).

I'm very new to machine learning, I'v read about Matlab's Statistics toolbox for hidden Markov model, I want to classify a given sequence of signals using it. I'v 3D co-ordinates in matrix P i.e [501x3] and I want to train model based on that. Evert complete trajectory ends on a specfic set of points, i.e at (0,0,0) where it achieves its target.

根据我的情况，合适的伪代码/方法是什么.

What is the appropriate Pseudocode/approach according to my scenario.

我的伪代码:

1. 501x3矩阵P是发射矩阵，其中每个坐标均为状态
2. 随机NxN个转换矩阵值(但我对此感到困惑)
3. 使用功能hmmgenerate
生成测试序列
4. 使用hmmtrain(sequence,old_transition,old_emission)
进行训练
5. 将最终跃迁和发射矩阵赋予未知序列的hmmdecode，以给出概率(也令人困惑)

1. 501x3 matrix P is Emission matrix where each co-ordinate is state
2. random NxN transition matrix values (but i'm confused in it)
3. generating test sequence using the function hmmgenerate
4. train using hmmtrain(sequence,old_transition,old_emission)
5. give final transition and emission matrix to hmmdecode with an unknown sequence to give the probability (confusing also)

简而言之，我想用HMM对每个具有[501x3]的10类轨迹进行分类.我想为每个轨迹采样50行，即[50x3]以建立模型.但是，我使用HMM的murphyk's工具箱来处理此类随机序列.

EDIT 1: In a nutshell, I want to classify 10 classes of trajectories having each of [501x3] with HMM. I want to sampled 50 rows i.e [50x3] for each trajectory in order to build model. However, I'v murphyk's toolbox of HMM for such random sequences.

推荐答案

该语句/案例告诉您构建和训练具有以下组成部分的隐马尔可夫模型，该模型具有以下组成部分，具体取决于选择，使用murphyk's HMM工具箱:

The statement/case tells to build and train a hidden Markov's model having following components specially using murphyk's toolbox for HMM as per the choice:

1. O =观测的向量
2. Q =状态向量
3. T =向量序列
4. nex =序列数
5. M =混合物数量

演示代码(来自murphyk's工具箱):

Demo Code (from murphyk's toolbox):

    O = 8;          %Number of coefficients in a vector
    T = 420;         %Number of vectors in a sequence
    nex = 1;        %Number of sequences
    M = 1;          %Number of mixtures
    Q = 6;          %Number of states



data = randn(O,T,nex);

% initial guess of parameters
prior0 = normalise(rand(Q,1));
transmat0 = mk_stochastic(rand(Q,Q));

if 0
    Sigma0 = repmat(eye(O), [1 1 Q M]);
    % Initialize each mean to a random data point
    indices = randperm(T*nex);
    mu0 = reshape(data(:,indices(1:(Q*M))), [O Q M]);
    mixmat0 = mk_stochastic(rand(Q,M));
else
    [mu0, Sigma0] = mixgauss_init(Q*M, data, 'full');
    mu0 = reshape(mu0, [O Q M]);
    Sigma0 = reshape(Sigma0, [O O Q M]);
    mixmat0 = mk_stochastic(rand(Q,M));
end

[LL, prior1, transmat1, mu1, Sigma1, mixmat1] = ...
    mhmm_em(data, prior0, transmat0, mu0, Sigma0, mixmat0, 'max_iter', 5);


loglik = mhmm_logprob(data, prior1, transmat1, mu1, Sigma1, mixmat1);

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