plot-01-demo=init_methods-model=mix+gauss.py 文件源码

def show_many_random_initial_models(
        obsPriorArgsDict,
        initArgsDict,
        nrows=1, ncols=6):
    ''' Create plot of many different random initializations
    '''
    fig_handle, ax_handle_list = pylab.subplots(
        figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows),
        nrows=nrows, ncols=ncols, sharex=True, sharey=True)
    for trial_id in range(nrows * ncols):
        cur_model = bnpy.make_initialized_model(
            dataset,
            allocModelName='FiniteMixtureModel',
            obsModelName='Gauss',
            algName='VB',
            allocPriorArgsDict=dict(gamma=10.0),
            obsPriorArgsDict=obsPriorArgsDict,
            initArgsDict=initArgsDict,
            seed=int(trial_id),
            )
        # Plot the current model
        cur_ax_handle = ax_handle_list.flatten()[trial_id]
        bnpy.viz.PlotComps.plotCompsFromHModel(
            cur_model, Data=dataset, ax_handle=cur_ax_handle)
        cur_ax_handle.set_xticks([-2, -1, 0, 1, 2])
        cur_ax_handle.set_yticks([-2, -1, 0, 1, 2])
    pylab.tight_layout()



###############################################################################
# initname: 'randexamples'
# ------------------------
# This procedure selects K examples uniformly at random.
# Each cluster is then initialized from one selected example,
# using a standard global step update. 
#
# **Example 1**:
# Initialize with 8 clusters, with prior biased towards small covariances
#
# .. math::
#
#   \E_{\mbox{prior}}[ \Sigma_k ] = 0.01 I_D