cnvsToStateIs0=cnvsToStateIs;
stateIsToCNVs0 = stateIsToCNVs;
if len(IDs)==1:
stateCovs = np.expand_dims(stateCovs,1)
#model = GaussianHMM(len(states),covariance_type="full",n_iter=1);
model = GaussianHMM(numStates,covariance_type="full", n_iter=1);
###insert my own params
model.means_ = stateMeans;
model.covars_ = stateCovs;
### make transmat
if args.transition <= -100:
transitionMatrix = (1-np.eye(numStates))*args.transition*np.log(10);
model._log_transmat =transitionMatrix;
else:
transitionMatrix = np.add(np.eye(numStates)*(1-(numStates-1)*10**args.transition),(1-np.eye(numStates))*10**args.transition);
model._set_transmat(transitionMatrix);
if args.verbose>0: sys.stderr.write(np.array_str(model._log_transmat)+"\n");
#exit;
meanNormal = meanAll;
normalState = cnvsToStateIs[args.ploidy];
lastClass = {};
for chr in chrOrder:
lastClass[chr] = np.tile(args.ploidy,allData[chr].shape[0]);
for i in range(0,args.iterations):
