Python Dataset._convetNumericalSequence示例

示例#1

 def predict(self, seq):
     result = {}
     self.predict_data_ = seq
     for label, value in self.gmmhmms.iteritems():
         gmmhmm = value['gmmhmm']
         status_set = value['status_set']
         d = Dataset(motion_type=status_set['motion'], sound_type=status_set['sound'],
                     location_type=status_set['location'])
         seq_converted = np.array(d._convetNumericalSequence(seq))
         result[label] = gmmhmm.score(seq_converted)
     return result

示例#2

def classifyByGMMHMM(seq, models, configs):

    Y = []
    for config in configs:
        _rawdata_type = config["logType"]
        _event_type = config["eventType"]
        _motion_type = config["motionType"]
        _sound_type = config["soundType"]
        _location_type = config["locationType"]

        d = Dataset(
            rawdata_type=_rawdata_type,
            event_type=_event_type,
            motion_type=_motion_type,
            sound_type=_sound_type,
            location_type=_location_type
        )
        # Initiation of data need prediction.
        y = np.array(d._convetNumericalSequence(seq))
        Y.append(y)


    _GMMHMMs = []
    for model in models:
        _GMMs = []
        for gmm in model["gmmParams"]["params"]:
            _GMM = GMM(
                n_components=model["nMix"],
                covariance_type=model["covarianceType"]
            )
            _GMM.covars_  = np.array(gmm["covars"])
            _GMM.means_   = np.array(gmm["means"])
            _GMM.weights_ = np.array(gmm["weights"])
            _GMMs.append(_GMM)
        _GMMHMM = GMMHMM(
            n_components=model["nComponent"],
            n_mix=model["nMix"],
            startprob=np.array(model["hmmParams"]["startProb"]),
            transmat=np.array(model["hmmParams"]["transMat"]),
            gmms=_GMMs,
            covariance_type=model["covarianceType"]
        )
        _GMMHMMs.append(_GMMHMM)

    results = []
    # for _GMMHMM in _GMMHMMs:
        # res = _GMMHMM.score(Y)
        # results.append(res)
    for i in range(0, len(models)):
        res = _GMMHMMs[i].score(Y[i])
        results.append(res)

    return results

示例#3

文件： test.py 项目： petchat/senz.analyzer.behavior.event

model_dining.fit(D)
model_fitness.fit(F)
model_work.fit(W)
model_shop.fit(S)


print model_dining.startprob_.tolist()
print model_dining.transmat_.tolist()


print 'After training'

print ' - Classification for seq dining s-'

print 'dining result:'
print model_dining.score(np.array(dataset_dining._convetNumericalSequence(seq_d_s)))
print 'fitness result:'
print model_fitness.score(np.array(dataset_dining._convetNumericalSequence(seq_d_s)))
print 'shop result:'
print model_shop.score(np.array(dataset_dining._convetNumericalSequence(seq_d_s)))
print 'work result:'
print model_work.score(np.array(dataset_dining._convetNumericalSequence(seq_d_s)))

print ' - Classification for seq dining l-'

print 'dining result:'
print model_dining.score(np.array(dataset_dining._convetNumericalSequence(seq_d)))
print 'fitness result:'
print model_fitness.score(np.array(dataset_dining._convetNumericalSequence(seq_d)))
print 'work result:'
print model_work.score(np.array(dataset_dining._convetNumericalSequence(seq_d)))

示例#4

文件： test.py 项目： petchat/senz.middleware.userbehavior.event

model_dining.fit(D)
model_fitness.fit(F)
model_work.fit(W)
model_shop.fit(S)

print model_dining.startprob_.tolist()
print model_dining.transmat_.tolist()

print 'After training'

print ' - Classification for seq dining s-'

print 'dining result:'
print model_dining.score(
    np.array(dataset_dining._convetNumericalSequence(seq_d_s)))
print 'fitness result:'
print model_fitness.score(
    np.array(dataset_dining._convetNumericalSequence(seq_d_s)))
print 'shop result:'
print model_shop.score(
    np.array(dataset_dining._convetNumericalSequence(seq_d_s)))
print 'work result:'
print model_work.score(
    np.array(dataset_dining._convetNumericalSequence(seq_d_s)))

print ' - Classification for seq dining l-'

print 'dining result:'
print model_dining.score(
    np.array(dataset_dining._convetNumericalSequence(seq_d)))