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使用 HMMLEARN 0.2.1，我得到了这个错误

Traceback (most recent call last):
File "hmmlearn_ex.py", line 48, in <module>
hidden_states = model.predict(X)
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-         linux-x86_64.egg/hmmlearn/base.py", line 336, in predict
_, state_sequence = self.decode(X, lengths)
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/base.py", line 296, in decode
self._check()
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/hmm.py", line 180, in _check
super(GaussianHMM, self)._check()
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/base.py", line 527, in  _check.    format(self.transmat_.sum(axis=1)))
ValueError: rows of transmat_ must sum to 1.0 (got 
[ 1.  1.  1.  1.    1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.      1. 1.  1.  1.  1.  0.  1.  1.  0.  1.  1.  1.  1.  0.  1.  1.  1.  1.  1.    0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  1.  1.  0.  1. 1.  1.  1.  0.  1.  1.  1.  1.  1.  1.  0.  1.  1.  1.  0.  1.  1.  1.])

下面是我的代码：

import numpy as np
import speech_data_conso
import hmmlearn
from hmmlearn.hmm import GaussianHMM, MultinomialHMM

#Parameters
speech_data_conso.DELTA = False
SET = 'A'
load_savemodel = True
batch_size_1 = 200 # 9600
validate_batch_size_1 = 20 # 2400
learning_rate = 0.001 # 0.001
training_iter = 30000000 # 300000
n_states = 7

if speech_data_conso.DELTA :
MODEL_PATH = '/home/jirayu/Desktop/MFCC/DELTA/MODEL/SET_' + SET +     '/MODEL.tfl' # for save model
RESULT_PATH = '/home/jirayu/Desktop/MFCC/DELTA/MODEL/SET_' + SET + '/result.log'
#DATA_DIR='/home/jirayu/Desktop/MFCC/SET_A/MISS6/'
TRAINING_DIR_1 = '/home/jirayu/Desktop/MFCC/DELTA/SET_' + SET + '/TRAIN/'
VALIDATION_DIR_1 = '/home/jirayu/Desktop/MFCC/DELTA/SET_' + SET + '/INPUT/'

else :
MODEL_PATH = '/home/jirayu/Desktop/MFCC/NO-DELTA/MODEL/SET_' + SET + '/MODEL.tfl' # for save model
RESULT_PATH = '/home/jirayu/Desktop/MFCC/NO-DELTA/MODEL/SET_' + SET + '/result.log'
#DATA_DIR='/home/jirayu/Desktop/MFCC/SET_A/MISS6/'
TRAINING_DIR_1 = '/home/jirayu/Desktop/MFCC/NO-DELTA/SET_' + SET + '/TRAIN/'
VALIDATION_DIR_1 = '/home/jirayu/Desktop/MFCC/NO-DELTA/SET_' + SET + '/INPUT/'

# training batch - 1 
speech_data_conso.DATA_DIR = TRAINING_DIR_1
batch=speech_data_conso.spectro_batch_generator(batch_size_1)
X,Y,Z=next(batch)

# validation batch - 1
speech_data_conso.DATA_DIR = VALIDATION_DIR_1
batch=speech_data_conso.spectro_batch_generator(validate_batch_size_1)
P,Q,R=next(batch)

diff = np.diff(X)
input_x = np.column_stack([diff, Z])

model = GaussianHMM(n_components=120, covariance_type="diag",     n_iter=10, init_params="st").fit(input_x)
#model = MultinomialHMM(n_components=n_states).fit(input_x)
hidden_states = model.predict(P)

print("done")

    ##########################################################################    #####
# Print trained parameters and plot
print("Transition matrix")
print(model.transmat_)
print()

print("Means and vars of each hidden state")
for i in range(model.n_components):
    print("{0}th hidden state".format(i))
    print("mean = ", model.means_[i])
    print("var = ", np.diag(model.covars_[i]))
    print()

请帮助我将需要使用 HMM 进行研究，我确信我以正确的方式实现了这段代码，并且还在互联网上研究了我做对了，许多人在 hmmlearn 最新版本中仍然没有解决同样的问题

同样在预测时（我切换到使用 GMMHMM 进行训练）：

warnings.warn(msg, category=DeprecationWarning)
Traceback (most recent call last):
File "hmmlearn_ex.py", line 49, in <module>
hidden_states = model.predict(P)
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-  linux-x86_64.egg/hmmlearn/base.py", line 336, in predict
_, state_sequence = self.decode(X, lengths)
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/base.py", line 296, in decode
self._check()
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/hmm.py", line 713, in _check
super(GMMHMM, self)._check()
File "/usr/local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/base.py", line 519, in _check
.format(self.startprob_.sum()))

ValueError: startprob_ 总和必须为 1.0（得到 nan）

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