def slim_sqrtm(V):
    u, s = spm_svd(V, 0)
    s = np.sqrt(np.abs(np.diag(s)))
    m = len(s)
    s = np.sparse(np.arange(0, m), np.arange(0, m), s)
    K = u * s * u.T
    return K
Esempio n. 2
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File: data.py Progetto: PimW/adm
    def create_matrix(self, movies, users, sset):
        movie_count = int(self.movies[-1][0]) + 1
        user_count = len(self.users) + 1

        matrix = np.sparse((user_count, movie_count), 0)

        for split in sset:
            for rating in split:
                user_id = int(rating[0])
                movie_id = int(rating[1])
                u_rating = int(rating[2])
                matrix[user_id, movie_id] = u_rating

        return matrix
Esempio n. 3
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def sample_zs_init(data_struct,dist_struct,obsModelType):

    #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    # Define and initialize parameters %
    #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    Kz = np.shape(dist_struct[1]['pi_z'][1])
    Ks = np.shape(dist_struct[1]['pi_s'][1])

    # Preallocate INDS
    for ii in range(0,len(data_struct)):
      T = len(data_struct[ii]['blockSize'])
      INDS[ii][obsIndzs[0:Kz,0:Ks]] = {'inds':np.sparse(1,T),'tot':0}



    for ii in range(0,len(data_struct)):
        
        N_init = np.zeros((Kz+1,Kz))
        Ns_init = np.zeros((Kz,Ks))
        
        if 'z_init' in data_struct[ii].keys():
            z_init_ii = data_struct[ii]['z_init']
        else:
            z_init_ii = data_struct[ii]['true_labels']

        
        stateSeq[ii]['z'],stateSeq[ii]['s'],totSeq,indSeq,N[:,:,ii],Ns[:,:,ii] = setZtoFixedSeq(data_struct[ii],dist_struct[ii],N_init,Ns_init,z_init_ii,0)
        
        for jj in range(0,Kz):
            for kk in range(0,Ks):
                INDS[ii][obsIndzs[jj,kk]]['tot']  = totSeq[jj,kk]
                INDS[ii][obsIndzs[jj,kk]]['inds'] = scipy.sparse(indSeq[:,jj,kk].T)


    stateCounts['N'] = N
    stateCounts['Ns'] = Ns

    return (stateSeq,INDS,stateCounts)
Esempio n. 4
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def sample_zs_init(data_struct, dist_struct, obsModelType):

    #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    # Define and initialize parameters %
    #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    Kz = np.shape(dist_struct[1]['pi_z'][1])
    Ks = np.shape(dist_struct[1]['pi_s'][1])

    # Preallocate INDS
    for ii in range(0, len(data_struct)):
        T = len(data_struct[ii]['blockSize'])
        INDS[ii][obsIndzs[0:Kz, 0:Ks]] = {'inds': np.sparse(1, T), 'tot': 0}

    for ii in range(0, len(data_struct)):

        N_init = np.zeros((Kz + 1, Kz))
        Ns_init = np.zeros((Kz, Ks))

        if 'z_init' in data_struct[ii].keys():
            z_init_ii = data_struct[ii]['z_init']
        else:
            z_init_ii = data_struct[ii]['true_labels']

        stateSeq[ii]['z'], stateSeq[ii][
            's'], totSeq, indSeq, N[:, :, ii], Ns[:, :, ii] = setZtoFixedSeq(
                data_struct[ii], dist_struct[ii], N_init, Ns_init, z_init_ii,
                0)

        for jj in range(0, Kz):
            for kk in range(0, Ks):
                INDS[ii][obsIndzs[jj, kk]]['tot'] = totSeq[jj, kk]
                INDS[ii][obsIndzs[jj, kk]]['inds'] = scipy.sparse(indSeq[:, jj,
                                                                         kk].T)

    stateCounts['N'] = N
    stateCounts['Ns'] = Ns

    return (stateSeq, INDS, stateCounts)