covars[:] = 10
gmm_cpu = mixture.GMM(nComp)
gmm_cpu.dtype = np.float32
gmm_cpu.init_params = ''
gmm_cpu.means_ = means
gmm_cpu.weights_ = weights
gmm_cpu.covars_ = covars
gmm_cpu.fit(samples)
gmm = GMM(context, nIter, nComp, nSamples)
a = calcA_cpu(weights, means, covars)
cl.enqueue_copy(queue, gmm.dA, a).wait()
gmm.has_preset_wmc = True
w,m,c = gmm.fit(dSamples, nSamples, retParams=True)
print 'converged: {0}'.format(gmm.has_converged)
print gmm_cpu.weights_
print w
print
print gmm_cpu.means_
print m
print
print gmm_cpu.covars_
print c
gmm_cpu.init_params = 'wmc'
iter = 10
