python类BallTree()的实例源码

Utilities.py 文件源码 项目:BayesVP 作者: cameronliang 项目源码 文件源码 阅读 63 收藏 0 点赞 0 评论 0
def estimate_bayes_factor(traces, logp, r=0.05):
    """
    Esitmate Odds ratios in a random subsample of the chains in MCMC
    AstroML (see Eqn 5.127, pg 237)
    """

    ndim, nsteps = traces.shape # [ndim,number of steps in chain]

    # compute volume of a n-dimensional (ndim) sphere of radius r
    Vr = np.pi ** (0.5 * ndim) / gamma(0.5 * ndim + 1) * (r ** ndim)

    # use neighbor count within r as a density estimator
    bt = BallTree(traces.T)
    count = bt.query_radius(traces.T, r=r, count_only=True)

    # BF = N*p/rho
    bf = logp + np.log(nsteps) + np.log(Vr) - np.log(count) #log10(bf)

    p25, p50, p75 = np.percentile(bf, [25, 50, 75])
    return p50, 0.7413 * (p75 - p25)

########################################################################
ikdb.py 文件源码 项目:ikdb 作者: krishauser 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def buildNNDataStructure(self):
        """Builds a nearest neighbor data structure.  User doesn't need to
        call this unless the self.problems attribute was changed manually."""
        if len(self.problemFeatures)==0 or len(self.featureNames)==0:
            return
        try:
            from sklearn.neighbors import NearestNeighbors,BallTree
            from scipy.spatial import KDTree
            with self.lock:
                try:
                    farray = self.problemFeatures.array
                except AttributeError:
                    farray = np.array(self.problemFeatures.items)
                if self.metricTransform is not None:
                    farray = np.dot(farray,self.metricTransform)
                #self.nn = NearestNeighbors(n_neighbors=1,algorithm="auto").fit(farray)
                self.nn = BallTree(farray)
                #self.nn = KDTree(farray)
                self.nnBuildSize = len(self.problemFeatures)
        except ImportError:
            print "IKDatabase: Warning, scikit-learn is not installed, queries will be much slower"
            with self.lock:
                self.nn = None
                self.nnBuildSize = 0
        return
coreg.py 文件源码 项目:decoding_challenge_cortana_2016_3rd 作者: kingjr 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def _point_cloud_error_balltree(src_pts, tgt_tree):
    """Find the distance from each source point to its closest target point

    Uses sklearn.neighbors.BallTree for greater efficiency

    Parameters
    ----------
    src_pts : array, shape = (n, 3)
        Source points.
    tgt_tree : sklearn.neighbors.BallTree
        BallTree of the target points.

    Returns
    -------
    dist : array, shape = (n, )
        For each point in ``src_pts``, the distance to the closest point in
        ``tgt_pts``.
    """
    dist, _ = tgt_tree.query(src_pts)
    return dist.ravel()
test_neighbors.py 文件源码 项目:Parallel-SGD 作者: angadgill 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def test_unsupervised_inputs():
    # test the types of valid input into NearestNeighbors
    X = rng.random_sample((10, 3))

    nbrs_fid = neighbors.NearestNeighbors(n_neighbors=1)
    nbrs_fid.fit(X)

    dist1, ind1 = nbrs_fid.kneighbors(X)

    nbrs = neighbors.NearestNeighbors(n_neighbors=1)

    for input in (nbrs_fid, neighbors.BallTree(X), neighbors.KDTree(X)):
        nbrs.fit(input)
        dist2, ind2 = nbrs.kneighbors(X)

        assert_array_almost_equal(dist1, dist2)
        assert_array_almost_equal(ind1, ind2)
graphclust.py 文件源码 项目:cellranger 作者: 10XGenomics 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def build_neighbor_index(x, leaf_size):
    return sk_neighbors.BallTree(x, leaf_size=leaf_size)
VLAD.py 文件源码 项目:VLAD 作者: jorjasso 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def indexBallTree(X,leafSize):
    tree = BallTree(X, leaf_size=leafSize)              
    return tree

#typeDescriptors =SURF, SIFT, OEB
#k = number of images to be retrieved
EC_functions.py 文件源码 项目:Model-Free-Episodic-Control 作者: ShibiHe 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def update_tree(self, time):
        print 'rebuild tree'
        self.tree = BallTree(self.state[:self.items, :], leaf_size=self.size)
        self.last_tree_built_time = time
        print 'rebuild done'
boundary.py 文件源码 项目:boundary-detection 作者: marcos-sandim 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def __init__(self, points, rho, dimension):
        """Constructor

        Initializes the grid and helper structures using the provided points
        and rho parameter.

        Args:
            points: A numpy array containing the coordinates of the particles.
            rho: Needed to compute the rho-boundary of the system.
            dimension: The dimension of the particle system.
        """
        self.points = points
        self.rho = rho
        self.dimension = dimension
        self.cell_size = 2.0 * rho

        self.aabb_min = np.amin(points, axis=0)
        self.aabb_max = np.amax(points, axis=0)

        self.grid_dims = (self.aabb_max - self.aabb_min) / self.cell_size
        # Regarding the + 3: 1 for left side, 1 for right side, 1 for rounding
        # up
        self.grid_dims = np.trunc(self.grid_dims) + 3
        self.grid_dims = self.grid_dims.astype(int)

        self.grid_min = self.aabb_min - self.cell_size
        self.grid_max = self.grid_min + self.grid_dims * self.cell_size

        self.grid_count = np.zeros(self.grid_dims, dtype=int)
        self.grid_elems = np.empty(self.grid_dims, dtype=object)

        self.update_grid()
        self.tree = NeighborsTree(
            self.points, leaf_size=10, metric='euclidean')

        self.neighbor_cell_list = self.compute_neighbor_cell_list()
test_t_sne.py 文件源码 项目:Parallel-SGD 作者: angadgill 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def test_barnes_hut_angle():
    # When Barnes-Hut's angle=0 this corresponds to the exact method.
    angle = 0.0
    perplexity = 10
    n_samples = 100
    for n_components in [2, 3]:
        n_features = 5
        degrees_of_freedom = float(n_components - 1.0)

        random_state = check_random_state(0)
        distances = random_state.randn(n_samples, n_features)
        distances = distances.astype(np.float32)
        distances = distances.dot(distances.T)
        np.fill_diagonal(distances, 0.0)
        params = random_state.randn(n_samples, n_components)
        P = _joint_probabilities(distances, perplexity, False)
        kl, gradex = _kl_divergence(params, P, degrees_of_freedom, n_samples,
                                    n_components)

        k = n_samples - 1
        bt = BallTree(distances)
        distances_nn, neighbors_nn = bt.query(distances, k=k + 1)
        neighbors_nn = neighbors_nn[:, 1:]
        Pbh = _joint_probabilities_nn(distances, neighbors_nn,
                                      perplexity, False)
        kl, gradbh = _kl_divergence_bh(params, Pbh, neighbors_nn,
                                       degrees_of_freedom, n_samples,
                                       n_components, angle=angle,
                                       skip_num_points=0, verbose=False)
        assert_array_almost_equal(Pbh, P, decimal=5)
        assert_array_almost_equal(gradex, gradbh, decimal=5)


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