Update gitignore
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Jakob Lass
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.gitignore
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3
.gitignore
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tests/__pycache__/*
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*.pyc
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build/*
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src/AMBER.egg-info/*
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@ -1,218 +0,0 @@
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# tools
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import numpy as np
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import scipy
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import torch
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from scipy.sparse import lil_matrix, block_diag, csr_array, diags, csr_matrix
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def create_laplacian_matrix(nx, ny=None):
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"""
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Helper method to create the laplacian matrix for the laplacian
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regularization
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Parameters
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----------
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:param nx: height of the original image
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:param ny: width of the original image
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Returns
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-------
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:rtype: scipy.sparse.csr_matrix
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:return:the n x n laplacian matrix, where n = nx*ny
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"""
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if ny is None:
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ny = nx
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assert (nx > 1)
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assert (ny > 1)
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# Blocks corresponding to the corner of the image (linking row elements)
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top_block = lil_matrix((ny, ny), dtype=np.float32)
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top_block.setdiag([2] + [3] * (ny - 2) + [2])
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top_block.setdiag(-1, k=1)
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top_block.setdiag(-1, k=-1)
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# Blocks corresponding to the middle of the image (linking row elements)
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mid_block = lil_matrix((ny, ny), dtype=np.float32)
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mid_block.setdiag([3] + [4]*(ny - 2) + [3])
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mid_block.setdiag(-1, k=1)
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mid_block.setdiag(-1, k=-1)
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# Construction of the diagonal of blocks
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list_blocks = [top_block] + [mid_block]*(nx-2) + [top_block]
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blocks = block_diag(list_blocks)
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# Diagonals linking different rows
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blocks.setdiag(-1, k=ny)
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return blocks
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def delete_from_csr(mat, row_indices=[], col_indices=[]):
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"""
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Remove the rows (denoted by ``row_indices``) and columns (denoted by ``col_indices``) from the CSR sparse matrix ``mat``.
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WARNING: Indices of altered axes are reset in the returned matrix
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"""
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if not isinstance(mat, csr_matrix):
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raise ValueError("works only for CSR format -- use .tocsr() first")
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rows = []
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cols = []
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if row_indices:
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rows = list(row_indices)
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if col_indices:
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cols = list(col_indices)
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if len(rows) > 0 and len(cols) > 0:
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row_mask = np.ones(mat.shape[0], dtype=bool)
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row_mask[rows] = False
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col_mask = np.ones(mat.shape[1], dtype=bool)
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col_mask[cols] = False
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return mat[row_mask][:, col_mask]
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elif len(rows) > 0:
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mask = np.ones(mat.shape[0], dtype=bool)
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mask[rows] = False
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return mat[mask]
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elif len(cols) > 0:
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mask = np.ones(mat.shape[1], dtype=bool)
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mask[cols] = False
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return mat[:, mask]
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else:
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return mat
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def remove_vertex(L, lst_rows=[], lst_cols=[]):
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"""
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Function that removes a vertex and adjust the graph laplacian matrix.
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"""
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L_cut = delete_from_csr(L.tocsr(), row_indices=lst_rows, col_indices=lst_cols)
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L_cut = L_cut - diags(L_cut.diagonal())
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L_cut = L_cut - diags(L_cut.sum(axis=1).A1)
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assert (L_cut.sum(axis=1).A1 == np.zeros(L_cut.shape[0])).all()
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return L_cut
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def laplacian(Y, nx, ny=None):
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"""
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Function that removes the vertices corresponding to Nan locations of tensor Y.
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Args:
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- Y: torch.tensor of the observations (Float64)
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- nx,ny: dimensions of the image (Int64)
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"""
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# find Nan indices
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Nan_indices = torch.where(torch.isnan(Y.ravel()) is True)[0]
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# get list of indices
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list_idx = list(Nan_indices.detach().numpy())
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# create Laplacian
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L = create_laplacian_matrix(nx, ny=ny)
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L = remove_vertex(L, list_idx, list_idx)
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return L
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def unnormalized_laplacian(y, nx, ny=None, method='inverse'):
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"""Construct numpy array with non zeros weights and non zeros indices.
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Args:
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- y: np.array for observations of size (size_x*size_y)
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- method: str indicating how to compute the weight of the unnormalized laplacian
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"""
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# create laplacian matrix
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lapl_tmp = laplacian(y, nx, ny)
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lapl_tmp.setdiag(np.zeros(nx*ny))
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# select the non nan indices
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y_tmp = y[torch.isnan(y) is False]
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# store non zero indices
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idx_rows = np.array(lapl_tmp.nonzero()[0])
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idx_cols = np.array(lapl_tmp.nonzero()[1])
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# construct the set of weights
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nnz_w = np.zeros_like(idx_rows, dtype=np.float32)
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# construction of the non zeros weights
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if method == 'inverse':
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nnz_w = 1/(np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]) + 1e-4)
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else:
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nnz_w = np.exp(-np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]))
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# construct the non diagonal terms of the Laplacian
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lapl_nondiag = csr_array((nnz_w, (idx_rows, idx_cols)), shape=(lapl_tmp.shape[0], lapl_tmp.shape[0]), dtype=np.float32)
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# construct the diagonal terms of the Laplacian
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lapl_diag = diags(lapl_nondiag.sum(axis=0))
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# construct the Laplacian
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L = lapl_diag - lapl_nondiag
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return L
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def laplacian_chain(nb_vertices):
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"""
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Construct the Laplacian matrix of a chain.
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"""
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L = np.zeros((nb_vertices, nb_vertices))
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# First vertex
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L[0, 0] = 1
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L[0, 1] = -1
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# Toeplitz matrix
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if nb_vertices > 2:
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first_row = torch.zeros(nb_vertices)
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first_row[0] = -1
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first_row[1] = 2
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first_row[2] = -1
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first_col = torch.zeros(nb_vertices-2)
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first_col[0] = -1
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D = scipy.linalg.toeplitz(first_col, r=first_row)
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L[1:nb_vertices-1, :] = D
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# Last vertex
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L[-1, -2] = -1
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L[-1, -1] = 1
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return L
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def unnormalized_laplacian_chain(y, nx, method='inverse'):
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"""Construct numpy array with non zeros weights and non zeros indices.
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Args:
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- y: np.array for aggregated observations of size (nb_bins)
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- method: str indicating how to compute the weight of the unnormalized laplacian
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"""
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# create laplacian matrix
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lapl_tmp = csr_matrix(laplacian_chain(nx))
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lapl_tmp.setdiag(np.zeros(nx*nx))
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# select the non nan indices
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y_tmp = np.nan_to_num(y)
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# store non zero indices
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idx_rows = np.array(lapl_tmp.nonzero()[0])
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idx_cols = np.array(lapl_tmp.nonzero()[1])
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# construct the set of weights
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nnz_w = np.zeros_like(idx_rows, dtype=np.float32)
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# construction of the non zeros weights
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if method == 'inverse':
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nnz_w = 1/(np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]))
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else:
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nnz_w = np.exp(-np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]))
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# construct the non diagonal terms of the Laplacian
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lapl_nondiag = csr_array((nnz_w, (idx_rows, idx_cols)), shape=(lapl_tmp.shape[0], lapl_tmp.shape[0]), dtype=np.float32)
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# construct the diagonal terms of the Laplacian
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lapl_diag = diags(lapl_nondiag.sum(axis=0))
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# construct the Laplacian
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L = lapl_diag - lapl_nondiag
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return L
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