Python GaussianMixture.score方法代码示例

# 需要导入模块: from sklearn.mixture import GaussianMixture [as 别名]
# 或者: from sklearn.mixture.GaussianMixture import score [as 别名]
def fit_mixtures(X,mag,mbins,binwidth=0.2,seed=None,
                 keepscore=False,keepbic=False,**kwargs):
    kwargs.setdefault('n_components',25)
    kwargs.setdefault('covariance_type','full')
    fits = []
    if keepscore:
        scores = []
    if keepbic:
        bics = []
    if seed:
        np.random.seed(seed)
    for bincenter in mbins:
        # this is not an efficient way to assign bins, but the time
        # is negligible compared to the GMM fitting anyway
        ii = np.where( np.abs(mag-bincenter) < binwidth )[0]
        if False:
            print('{:.2f}: {} qsos'.format(bincenter,len(ii)))
        gmm = GaussianMixture(**kwargs)
        gmm.fit(X[ii])
        fits.append(gmm)
        if keepscore:
            scores.append(gmm.score(X[ii]))
        if keepbic:
            bics.append(gmm.bic(X[ii]))
    rv = (fits,)
    if keepscore:
        rv += (scores,)
    if keepbic:
        rv += (bics,)
    return rv

# 需要导入模块: from sklearn.mixture import GaussianMixture [as 别名]
# 或者: from sklearn.mixture.GaussianMixture import score [as 别名]
def fit_gmm_to_points(points,
                      n_components,
                      mdl,
                      ps=[],
                      num_iter=100,
                      covariance_type='full',
                      min_covar=0.001,
                      init_centers=[],
                      force_radii=-1.0,
                      force_weight=-1.0,
                      mass_multiplier=1.0):
    """fit a GMM to some points. Will return the score and the Akaike score.
    Akaike information criterion for the current model fit. It is a measure
    of the relative quality of the GMM that takes into account the
    parsimony and the goodness of the fit.
    if no particles are provided, they will be created

    points:            list of coordinates (python)
    n_components:      number of gaussians to create
    mdl:               IMP Model
    ps:                list of particles to be decorated. if empty, will add
    num_iter:          number of EM iterations
    covariance_type:   covar type for the gaussians. options: 'full', 'diagonal', 'spherical'
    min_covar:         assign a minimum value to covariance term. That is used to have more spherical
                       shaped gaussians
    init_centers:      initial coordinates of the GMM
    force_radii:       fix the radii (spheres only)
    force_weight:      fix the weights
    mass_multiplier:   multiply the weights of all the gaussians by this value
    dirichlet:         use the DGMM fitting (can reduce number of components, takes longer)
    """


    new_sklearn = False
    try:
        from sklearn.mixture import GMM
    except ImportError:
        from sklearn.mixture import GaussianMixture
        new_sklearn = True

    print('creating GMM with n_components',n_components,'n_iter',num_iter,'covar type',covariance_type)
    if new_sklearn:
        # aic() calls size() on points, so it needs to a numpy array, not a list
        points = np.array(points)
        weights_init = precisions_init = None
        if force_radii != -1.0:
            print('warning: radii can no longer be forced, but setting '
                  'initial values to ', force_radii)
            precisions_init = np.array([[1./force_radii]*3
                                       for i in range(n_components)])
        if force_weight != -1.0:
            print('warning: weights can no longer be forced, but setting '
                  'initial values to ', force_weight)
            weights_init = np.array([force_weight]*n_components)

        gmm = GaussianMixture(n_components=n_components,
                              max_iter=num_iter,
                              covariance_type=covariance_type,
                              weights_init=weights_init,
                              precisions_init=precisions_init,
                              means_init=None if init_centers==[]
                                              else init_centers)
    else:
        params='m'
        init_params='m'
        if force_radii==-1.0:
            params+='c'
            init_params+='c'
        else:
            covariance_type='spherical'
            print('forcing spherical with radii',force_radii)

        if force_weight==-1.0:
            params+='w'
            init_params+='w'
        else:
            print('forcing weights to be',force_weight)

        gmm = GMM(n_components=n_components, n_iter=num_iter,
                  covariance_type=covariance_type, min_covar=min_covar,
                  params=params, init_params=init_params)
        if force_weight!=-1.0:
            gmm.weights_=np.array([force_weight]*n_components)
        if force_radii!=-1.0:
            gmm.covars_=np.array([[force_radii]*3 for i in range(n_components)])
        if init_centers!=[]:
            gmm.means_=init_centers
    print('fitting')
    model=gmm.fit(points)
    score=gmm.score(points)
    akaikescore=model.aic(points)
    #print('>>> GMM score',gmm.score(points))

    ### convert format to core::Gaussian
    if new_sklearn:
        covars = gmm.covariances_
    else:
        covars = gmm.covars_
    for ng in range(n_components):
        covar=covars[ng]
#.........这里部分代码省略.........