Python distance.cdist方法代碼示例

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def kernel_matrix_xX(svm_model, original_x, original_X):

        if (svm_model.svm_kernel == 'polynomial_kernel' or svm_model.svm_kernel == 'soft_polynomial_kernel'):
            K = (svm_model.zeta + svm_model.gamma * np.dot(original_x, original_X.T)) ** svm_model.Q
        elif (svm_model.svm_kernel == 'gaussian_kernel' or svm_model.svm_kernel == 'soft_gaussian_kernel'):
            K = np.exp(-svm_model.gamma * (cdist(original_X, np.atleast_2d(original_x), 'euclidean').T ** 2)).ravel()

        '''
        K = np.zeros((svm_model.data_num, svm_model.data_num))

        for i in range(svm_model.data_num):
            for j in range(svm_model.data_num):
                if (svm_model.svm_kernel == 'polynomial_kernel' or svm_model.svm_kernel == 'soft_polynomial_kernel'):
                    K[i, j] = Kernel.polynomial_kernel(svm_model, original_x, original_X[j])
                elif (svm_model.svm_kernel == 'gaussian_kernel' or svm_model.svm_kernel == 'soft_gaussian_kernel'):
                    K[i, j] = Kernel.gaussian_kernel(svm_model, original_x, original_X[j])
        '''

        return K 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def __init__(self, cities, **kwargs):
        """
        A two-dimensional traveling salesman problem (TSP)

        Parameters
        ----------
        cities : numpy.array
            The cities with 2-dimensional coordinates provided by a matrix where where city is represented by a row.

        """
        n_cities, _ = cities.shape

        self.cities = cities
        self.D = cdist(cities, cities)

        super(TravelingSalesman, self).__init__(
            n_var=n_cities,
            n_obj=1,
            xl=0,
            xu=n_cities,
            type_var=np.int,
            elementwise_evaluation=True,
            **kwargs
        ) 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def vna(self, coords, **kw):
      """
      Compute the neutral-atom potential V_NA(coords) for a set of Cartesian
      coordinates coords.
      The subroutine could be also used for computing the non-linear core
      corrections or some other atom-centered fields.
      """

      sp2v = kw['sp2v'] if 'sp2v' in kw else self.ao_log.sp2vna
      sp2rcut = kw['sp2rcut'] if 'sp2rcut' in kw else self.ao_log.sp2rcut_vna
      atom2coord = kw['atom2coord'] if 'atom2coord' in kw else self.atom2coord

      nc = coords.shape[0]
      vna = np.zeros(nc)
      for ia,(R,sp) in enumerate(zip(atom2coord, self.atom2sp)):
          if sp2v[sp] is None: # This can be done better via preparation of a special atom2sp excluding ghost atoms
              continue
          #print(__name__, ia, sp, sp2rcut[sp])
          dd = cdist(R.reshape((1,3)), coords).reshape(nc)
          vnaa = self.ao_log.interp_rr(sp2v[sp], dd, rcut=sp2rcut[sp])
          vna = vna + vnaa
      return vna 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def _similarity_smooth(presented, recalled, features, distance):
    lists = presented.index.get_values()
    res = np.empty((len(lists), len(features), recalled.iloc[0].shape[0], presented.iloc[0].shape[0]))*np.nan
    for li, l in enumerate(lists):
        p_list = presented.loc[l]
        r_list = recalled.loc[l]
        for i, feature in enumerate(features):
            get_feature = lambda x: np.array(x[feature]) if np.array(pd.notna(x['item'])).any() else np.nan
            p = np.vstack(p_list.apply(get_feature).get_values())
            r = r_list.dropna().apply(get_feature).get_values()
            r = np.vstack(list(filter(lambda x: x is not np.nan, r)))
            tmp = 1 - cdist(r, p, distance)
            res[li, i, :tmp.shape[0], :] =  tmp
    if distance == 'correlation':
        return np.nanmean(res, 1)
    else:
        return np.mean(res, 1) 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def _cold_start_initialize(self):

        # Initialize the models for the untrained arms with the data from the most similar arm
        for arm in self.untrained_arms:

            distances = {}
            for n in self.arms:
                if n not in self.untrained_arms:
                    distances[n] = cdist(np.asarray([self.features[n]]), np.asarray([self.features[arm]]),
                                         metric='cosine')

            # Identify the closest arm
            closest_arm = min(distances, key=distances.get)
            print('Cold Start Arm:', arm, 'Closest arm:', closest_arm)

            # Set the arm to use the values of the closets arm
            self.arm_to_model[arm].beta = self.arm_to_model[closest_arm].beta.copy()
            self.arm_to_model[arm].A = self.arm_to_model[closest_arm].A.copy()
            self.arm_to_model[arm].A_inv = self.arm_to_model[closest_arm].A_inv.copy()
            self.arm_to_model[arm].Xty = self.arm_to_model[closest_arm].Xty.copy()

            self.untrained_arms.remove(arm) 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def compute_pairwise_distances(first_xyz, second_xyz):
  """Computes pairwise distances between two molecules.

  Takes an input (m, 3) and (n, 3) numpy arrays of 3D coords of
  two molecules respectively, and outputs an m x n numpy
  array of pairwise distances in Angstroms between the first and
  second molecule. entry (i,j) is dist between the i"th 
  atom of first molecule and the j"th atom of second molecule.

  Parameters
  ----------
  first_xyz: np.ndarray
    Of shape (m, 3)
  seocnd_xyz: np.ndarray
    Of shape (n, 3)

  Returns
  -------
  np.ndarray of shape (m, n)
  """

  pairwise_distances = cdist(first_xyz, second_xyz, metric='euclidean')
  return pairwise_distances 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def execute(cls, ctx, op):
        from scipy.spatial.distance import cdist

        inputs, device_id, xp = as_same_device(
            [ctx[inp.key] for inp in op.inputs], device=op.device, ret_extra=True)

        if xp is cp:  # pragma: no cover
            raise NotImplementedError('`cdist` does not support running on GPU yet')

        with device(device_id):
            inputs_iter = iter(inputs)
            xa = next(inputs_iter)
            xb = next(inputs_iter)
            kw = dict()
            if op.p is not None:
                kw['p'] = op.p
            if op.w is not None:
                kw['w'] = next(inputs_iter)
            if op.v is not None:
                kw['V'] = next(inputs_iter)
            if op.vi is not None:
                kw['VI'] = next(inputs_iter)

        ctx[op.outputs[0].key] = cdist(xa, xb, metric=op.metric, **kw) 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def tracklet_classify(A, pca, D, knn, clf_coding):
    encode_fea = np.zeros((len(A),len(D)))
    for n in range(len(A)):
        pca_fea = pca.transform(A[n])
        dist = distance.cdist(pca_fea, D, 'euclidean')
        x = np.zeros((len(pca_fea),len(D)))
        for k in range(len(dist)):
            sort_idx = np.argsort(dist[k,:])
            temp_D = D[sort_idx[0:knn],:]
            temp_coder = SparseCoder(dictionary=temp_D, transform_n_nonzero_coefs=10, 
                                 transform_alpha=0.05, transform_algorithm='lasso_lars')
            #import pdb; pdb.set_trace()
            xx = np.zeros((1,D.shape[1]))
            xx[:,:] = pca_fea[k,:]
            temp_x = temp_coder.transform(xx)
            x[k,sort_idx[0:knn]] = temp_x

        encode_fea[n,:] = np.max(x, axis=0)
    pred_set_label = clf_coding.predict(encode_fea)
    return pred_set_label 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def gen_gausprocess_se(ntrain, ntest, noise=1., lenscale=1., scale=1.,
                       xmin=-10, xmax=10):
    """
    Generate a random (noisy) draw from a Gaussian Process with a RBF kernel.
    """

    # Xtrain = np.linspace(xmin, xmax, ntrain)[:, np.newaxis]
    Xtrain = np.random.rand(ntrain)[:, np.newaxis] * (xmin - xmax) - xmin
    Xtest = np.linspace(xmin, xmax, ntest)[:, np.newaxis]
    Xcat = np.vstack((Xtrain, Xtest))

    K = scale * np.exp(-cdist(Xcat, Xcat, metric='sqeuclidean') /
                       (2 * lenscale**2))
    U, S, V = np.linalg.svd(K)
    L = U.dot(np.diag(np.sqrt(S))).dot(V)
    f = np.random.randn(ntrain + ntest).dot(L)

    ytrain = f[0:ntrain] + np.random.randn(ntrain) * noise
    ftest = f[ntrain:]

    return Xtrain, ytrain, Xtest, ftest 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def transform(self, X, lenscale=None):
        """
        Apply the RBF to X.

        Parameters
        ----------
        X: ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        lenscale: scalar or ndarray, optional
            scalar or array of shape (d,) length scales (one for each dimension
            of X). If not input, this uses the value of the initial length
            scale.

        Returns
        -------
        ndarray:
            of shape (N, D) where D is number of RBF centres.
        """
        N, d = X.shape
        lenscale = self._check_dim(d, lenscale)

        den = (2 * lenscale**2)
        return np.exp(- cdist(X / den, self.C / den, 'sqeuclidean')) 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def _spatial_sort(glyph):
  from scipy.spatial.distance import cdist
  from numpy import argsort
  from numpy import argmin

  curr = argmin(glyph[:,0])
  visited = set([curr])
  order = [curr]

  dd = cdist(glyph, glyph)

  while len(visited)<len(glyph):
    row = dd[curr,:]

    for i in argsort(row):
      if row[i]<=0.0 or i==curr or i in visited:
        continue
      order.append(i)
      visited.add(i)
      break
  glyph[:,:] = glyph[order,:] 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def analysis_KMeans():
	mean_distortions = []
	K = len(labels_idx)
	K_range = range(320, 1000)
	for k in K_range:
		print("Cluster k is {}".format(k))
		kmeans_model = KMeans(n_clusters=k, init="k-means++", n_jobs=-1)
		kmeans_model.fit(np_features)
		t_distortions = sum(
			np.min(cdist(np_features, kmeans_model.cluster_centers_, 'euclidean'), axis=1)) / np_features.shape[0]
		mean_distortions.append(t_distortions)

	with open("./kmeans_cluster.csv", "a+") as wh:
		for idx in range(len(K_range)):
			wh.write("{},{}\n".format(K_range[idx], mean_distortions[idx]))

	# plt.plot(K_range, mean_distortions, 'bx-')
	# plt.xlabel('k')
	# plt.ylabel(u'Avgerage distortion degree')
	# plt.title(u'Elbows rule to select the best K value')
	# plt.savefig("kmeans_cluster.png") 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def _find_site_neighbors(site_locs, n_neighbors, shank_map):
    from scipy.spatial.distance import cdist

    if np.unique(shank_map).size <= 1:
        pass

    n_sites = site_locs.shape[0]
    n_neighbors = int(min(n_neighbors, n_sites))

    neighbors = np.zeros((n_sites, n_neighbors), dtype=np.int)
    for i in range(n_sites):
        i_loc = site_locs[i, :][np.newaxis, :]
        dists = cdist(i_loc, site_locs).ravel()
        neighbors[i, :] = dists.argsort()[:n_neighbors]

    return neighbors 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def tsne_to_grid(X_2d):
    from lapjv import lapjv
    from scipy.spatial.distance import cdist

    out_dim = np.sqrt(len(X_2d))
    out_dim = int(out_dim)
    to_plot = np.square(out_dim)
    grid = np.dstack(np.meshgrid(np.linspace(0, 1, out_dim), np.linspace(0, 1, out_dim))).reshape(-1, 2)
    cost_matrix = cdist(grid, X_2d[:to_plot], "sqeuclidean").astype(np.float32)
    cost_matrix = cost_matrix * (100000 / cost_matrix.max())
    row_asses, col_asses, _ = lapjv(cost_matrix)
    grid_jv = grid[col_asses]

    return grid_jv, to_plot
    # out = np.ones((out_dim*out_res, out_dim*out_res, 3))

    # to_plot = np.square(out_dim)
    # for pos, img in zip(grid_jv, img_collection[0:to_plot]):
    #     h_range = int(np.floor(pos[0]* (out_dim - 1) * out_res))
    #     w_range = int(np.floor(pos[1]* (out_dim - 1) * out_res))
    #     out[h_range:h_range + out_res, w_range:w_range + out_res]  = image.img_to_array(img)

    # im = image.array_to_img(out)
    # im.save(out_dir + out_name, quality=100) 

# 需要導入模塊: from scipy.spatial import distance [as 別名]
# 或者: from scipy.spatial.distance import cdist [as 別名]
def test_euclidean_distances(dtype, x_array_constr, y_array_constr):
    # check that euclidean distances gives same result as scipy cdist
    # when X and Y != X are provided
    rng = np.random.RandomState(0)
    X = rng.random_sample((100, 10)).astype(dtype, copy=False)
    X[X < 0.8] = 0
    Y = rng.random_sample((10, 10)).astype(dtype, copy=False)
    Y[Y < 0.8] = 0

    expected = cdist(X, Y)

    X = x_array_constr(X)
    Y = y_array_constr(Y)
    distances = euclidean_distances(X, Y)

    # the default rtol=1e-7 is too close to the float32 precision
    # and fails due too rounding errors.
    assert_allclose(distances, expected, rtol=1e-6)
    assert distances.dtype == dtype