replicating XMM crash

crashing_xmm.py

import xmm
import numpy as np

class GMM_regression(object):
    
    def __init__(self):
        self.clear(3,8)

    def clear(self, idim = None, odim = None):
        self.input_dimensions = idim or self.input_dimensions
        self.output_dimensions = odim or self.output_dimensions
        self.dimensions = self.input_dimensions + self.output_dimensions
        self.labels = set()
        self.now_training = None
        self.now_performing = False
        self.training_set = xmm.TrainingSet(xmm.BIMODAL)
        self.training_set.set_dimension(self.dimensions)
        self.training_set.set_dimension_input(self.input_dimensions)
        self.gmm = xmm.GMMGroup(xmm.BIMODAL)
        self.gmm.set_trainingSet(self.training_set)
        self.output_vec = None

    def record(self,n):
        self.now_training = int(n)

    def input(self,*l):
        if self.now_training is not None:
            self.labels.add(self.now_training)
            if self.output_vec:
                print "training", l+self.output_vec
                self.training_set.recordPhrase(self.now_training,l+self.output_vec)
        elif self.now_performing:
            self.gmm.performance_update(l)
            self.send('/instant',*self.gmm.results_instant_likelihoods)
            self.send('/normalized',*self.gmm.results_normalized_likelihoods)
            self.send('/log',*self.gmm.results_log_likelihoods)
            self.send('/predicted',*self.gmm.results_predicted_output)

    def output(self,*l):
        self.output_vec = l

    def train(self, nbmc=10, vo1=1., vo2=0.01):
        for n in self.labels:
            self.training_set.setPhraseLabel(n,xmm.Label(n+1))

        self.gmm.set_nbMixtureComponents(nbmc)
        self.gmm.set_varianceOffset(vo1, vo2)
        self.gmm.train()
        print "number of models: ", self.gmm.size()
        for label in self.gmm.models.keys():
            print "model", label.getInt(), ": trained in ", self.gmm.models[label].trainingNbIterations, \
                "iterations, loglikelihood = ", self.gmm.models[label].trainingLogLikelihood

    def perform(self, lw=40):
        self.gmm.set_likelihoodwindow(40)
        self.gmm.performance_init()
        self.now_training = None
        self.now_performing = True

    def send(self, addr, *args):
        print addr, args


gmm = GMM_regression()
gmm.clear(3,8)
gmm.record(0)
gmm.output(1,2,3,4,5,6,7,8)
for n in range(10):
    gmm.input(2,3,4)
gmm.record(1)
gmm.output(8,7,6,5,4,3,2,1)
for n in range(10):
    gmm.input(3,4,5)
gmm.train()