コード例 #1
0
ファイル: openTets.py プロジェクト: AraiKensuke/EnDe
def openTets(fn, tets, t0=None, t1=None, lm=None, detectHash=True, chooseSpikes=_motion_):
    """
    Open a file with marks and positions
    stets   if I give 
    """
    if lm is None:
        with open(fn, "rb") as f:
            lm = _pkl.load(f)
        f.close()

    if type(tets) is int:
        tets = [tets]
    elif type(tets[0]) is str:
        inds = []
        for i in xrange(len(tets)):
            inds.append(lm.tetlist.index(tets[i]))
        tets = inds

    t0 = 0 if (t0 is None) else t0
    t1 = lm.marks.shape[0] if (t1 is None) else t1

    if chooseSpikes == _motion_:
        minds = lm.minds
    elif chooseSpikes == _all_:
        minds = _N.array([[t0, t1]])
    else:
        lminds = []
        if lm.minds[0, 0] > 0:
            lminds.append([0, lm.minds[0, 0]])
        for i in xrange(lm.minds.shape[0] - 1):
            lminds.append([lm.minds[i, 1], lm.minds[i+1, 0]])
        if lm.minds[-1, 1] > 0:
            lminds.append([lm.minds[-1, 1], t1])
        minds = _N.array(lminds)

    inds = _N.where((minds[:, 0] >= t0) & (minds[:, 0] <=  t1))[0]

    if detectHash:
        allnhmks = []
        allhmks  = []
    else:
        allmarks = []

    for tet in tets:
        marks = []
        pos   = []
        for t in inds:
            mksl = lm.marks[minds[t, 0]:minds[t, 1], tet]
            shsl = lm.pos[minds[t, 0]:minds[t, 1]]
            nn = _N.where(_N.equal(mksl, None) == False)[0]

            for n in nn:
                for l in xrange(len(mksl[n])):
                    marks.append(mksl[n][l])
                    pos.append(shsl[n])

        posmarks = _N.empty((len(marks), 5))
        posmarks[:, 1:] = _N.array(marks)
        posmarks[:, 0]  = _N.array(pos)

        if detectHash:
            #  separate the hash
            nhid, hid, gmms = _fu.sepHashEM(posmarks)

            nhmks     = posmarks[nhid]
            hmks      = posmarks[hid]

            allnhmks.append(nhmks)
            allhmks.append(hmks)
        else:
            allmarks.append(posmarks)

    if detectHash:
        return allnhmks, allhmks, lm, gmms
    else:
        return allmarks, lm
コード例 #2
0
ファイル: fitMvNormTet.py プロジェクト: AraiKensuke/EnDe
    def init0(self, pos, mk, n1, n2):
        """
        M       total number of clusters

        MS      number of clusters assigned to signal
        MF      number of clusters used for initial fit
        M - MF  If doing touchup, number of clusters to assign to this
        """
        print "init0"

        oo = self

        k  = oo.k
        MF = oo.M 

        ###  
        #  
        #  parameters of cluster covariance.  Becomes prior
        #  priors
        oo.PR_cov_PSI = _N.tile(_N.eye(k)*0.5, oo.M).T.reshape(oo.M, k, k)
        oo.PR_cov_nu  = _N.ones(oo.M, dtype=_N.int)*(k+1)
        #  parameters of cluster mean.  Becomes prior
        oo.PR_mu_mu = _N.zeros((oo.M, k))
        oo.PR_mu_sg = _N.tile(_N.eye(k)*30, oo.M).T.reshape(oo.M, k, k)
        oo.iPR_mu_sg= _N.linalg.inv(oo.PR_mu_sg)

        #  parameters of cluster weights
        oo.PR_m_alp = _N.ones(oo.M) * (1./oo.M)

        _x   = _N.empty((n2-n1, k))
        _x[:, 0]    = pos
        _x[:, 1:]   = mk
        N   = n2-n1

        #  Gibbs sampling 
        ################  init cluster centers

        ##########################
        BINS    = 20
        bins    = _N.linspace(-6, 6, BINS+1)
        blksz   = 20
        
        unonhash, hashsp, gmms = _fu.sepHashEM(_x)
        #unonhash, hashsp, hashThr = _fu.sepHash(_x)
        if (len(unonhash) > 0) and (len(hashsp) > 0):
            labS, labH, clstrs = _fu.emMKPOS_sep(_x[unonhash], _x[hashsp])
        elif len(unonhash) == 0:
            labS, labH, clstrs = _fu.emMKPOS_sep(None, _x[hashsp])
        else:
            labS, labH, clstrs = _fu.emMKPOS_sep(_x[unonhash], None)

        print "****   clusters sig:  %(1)d   hash:  %(2)d" % {"1" : clstrs[0], "2" : clstrs[1]}

        MS     = 0
        if len(labS) > 0:
            MS = _N.max(_N.unique(labS)) + 1
        ##################
        lab        = _N.array(labS.tolist() + (labH + clstrs[0]).tolist())
        x          = _N.empty((n2-n1, k))

        strt = 0
        if len(unonhash) > 0:
            x[0:len(unonhash)] = _x[unonhash]
            strt = len(unonhash)
        if len(hashsp) > 0:
            x[strt:]  = _x[hashsp]

        #  now assign the cluster we've found to Gaussian mixtures
        covAll = _N.cov(x.T)
        dcovMag= _N.diagonal(covAll)*0.005

        print "look at the covariances"
        for im in xrange(MF):  #  if lab < 0, these marks not used for init
            kinds = _N.where(lab == im)[0]  #  inds

            if len(kinds) >= 6:   # problem when cov is not positive def.
                oo.smu[0, im]  = _N.mean(x[kinds], axis=0)
                oo.scov[0, im] = _N.cov(x[kinds], rowvar=0)
                #print "im %(im)d  %(c).3e" % {"im" : im, "c" : _N.linalg.det(oo.scov[0, im])}
                if _N.linalg.det(oo.scov[0, im]) < 0:
                    print len(kinds)
                    print x[kinds]
                oo.sm[0, im]   = float(len(kinds)+1) / (N+MF)
            else:
                if len(kinds) > 2:
                    oo.smu[0, im]  = _N.mean(x[kinds], axis=0)
                else:
                    oo.smu[0, im]  = _N.mean(x, axis=0)
                oo.scov[0, im] = covAll*0.125
                oo.sm[0, im]   = float(len(kinds)+1) / (N+MF)
コード例 #3
0
ファイル: openTets.py プロジェクト: Eden-Kramer-Lab/sumojam
def openTets(fn,
             tets,
             t0=None,
             t1=None,
             lm=None,
             detectHash=True,
             chooseSpikes=_motion_):
    """
    Open a file with marks and positions
    stets   if I give 
    """
    if lm is None:
        with open(fn, "rb") as f:
            lm = _pkl.load(f)
        f.close()

    if type(tets) is int:
        tets = [tets]
    elif type(tets[0]) is str:
        inds = []
        for i in range(len(tets)):
            inds.append(lm.tetlist.index(tets[i]))
        tets = inds

    t0 = 0 if (t0 is None) else t0
    t1 = lm.marks.shape[0] if (t1 is None) else t1

    if chooseSpikes == _motion_:
        minds = lm.minds
    elif chooseSpikes == _all_:
        minds = _N.array([[t0, t1]])
    else:
        lminds = []
        if lm.minds[0, 0] > 0:
            lminds.append([0, lm.minds[0, 0]])
        for i in range(lm.minds.shape[0] - 1):
            lminds.append([lm.minds[i, 1], lm.minds[i + 1, 0]])
        if lm.minds[-1, 1] > 0:
            lminds.append([lm.minds[-1, 1], t1])
        minds = _N.array(lminds)

    inds = _N.where((minds[:, 0] >= t0) & (minds[:, 0] <= t1))[0]

    if detectHash:
        allnhmks = []
        allhmks = []
    else:
        allmarks = []

    for tet in tets:
        marks = []
        pos = []
        for t in inds:
            mksl = lm.marks[minds[t, 0]:minds[t, 1], tet]
            shsl = lm.pos[minds[t, 0]:minds[t, 1]]
            nn = _N.where(_N.equal(mksl, None) == False)[0]

            for n in nn:
                for l in range(len(mksl[n])):
                    marks.append(mksl[n][l])
                    pos.append(shsl[n])

        posmarks = _N.empty((len(marks), 5))
        posmarks[:, 1:] = _N.array(marks)
        posmarks[:, 0] = _N.array(pos)

        if detectHash:
            #  separate the hash
            nhid, hid, gmms = _fu.sepHashEM(posmarks)

            nhmks = posmarks[nhid]
            hmks = posmarks[hid]

            allnhmks.append(nhmks)
            allhmks.append(hmks)
        else:
            allmarks.append(posmarks)

    if detectHash:
        return allnhmks, allhmks, lm, gmms
    else:
        return allmarks, lm