libcity.model.utils¶
-
libcity.model.utils.build_sparse_matrix(device, lap)[source]¶ 构建稀疏矩阵(tensor)
- Parameters
device –
lap – 拉普拉斯
Returns:
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libcity.model.utils.calculate_normalized_laplacian(adj)[source]¶ L = D^-1/2 (D-A) D^-1/2 = I - D^-1/2 A D^-1/2 对称归一化的拉普拉斯
- Parameters
adj – adj matrix
- Returns
L
- Return type
np.ndarray
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libcity.model.utils.calculate_random_walk_matrix(adj_mx)[source]¶ L = D^-1 * A 随机游走拉普拉斯
- Parameters
adj_mx – adj matrix
- Returns
L
- Return type
np.ndarray
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libcity.model.utils.calculate_scaled_laplacian(adj_mx, lambda_max=2, undirected=True)[source]¶ 计算近似后的拉普莱斯矩阵~L
- Parameters
adj_mx –
lambda_max –
undirected –
- Returns
~L = 2 * L / lambda_max - I
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libcity.model.utils.get_cheb_polynomial(l_tilde, k)[source]¶ compute a list of chebyshev polynomials from T_0 to T_{K-1}
- Parameters
l_tilde (scipy.sparse.coo.coo_matrix) – scaled Laplacian, shape (N, N)
k (int) – the maximum order of chebyshev polynomials
- Returns
cheb_polynomials, length: K, from T_0 to T_{K-1}
- Return type
list(np.ndarray)