def GPGL2_seg(data, current_sample_seg):
data = data + np.random.rand(len(data), len(data[0])) * 1e-6
dist_mat = kmeans_solver.fit_transform(data)
node_top, labels = graph_cut(data, dist_mat, NUM_POINTS, NUM_CUTS)
aij_mat = fun_graph_cosntruct(node_top)
# aij_mat = squareform(pdist(node_top),checks=False)
H = nx.from_numpy_matrix(aij_mat)
pos_spring = nx.spring_layout(H)
pos_spring = np.array([pos for idx, pos in sorted(pos_spring.items())])
# pos_spring = np.array(forceatlas2.forceatlas2(aij_mat, None, 10))
pos = fun_GPGL_layout_push(pos_spring, SIZE_SUB)
pos_top = fun_GPGL_layout_push(pos_spring, SIZE_TOP)
##%%
pos_cuts = []
for i_cut in range(NUM_CUTS):
pos_cut_3D = data[labels == i_cut, :]
if (len(pos_cut_3D) < 5):
pos_raw = [[0, 0], [0, 1], [1, 1], [1, 0]]
pos = pos_raw[:len(pos_cut_3D)]
# H = nx.from_numpy_matrix(np.ones([len(pos_cut_3D),len(pos_cut_3D)]))
pos_cuts.append(pos)
continue
aij_mat = fun_graph_cosntruct(pos_cut_3D)
# aij_mat = squareform(pdist(pos_cut_3D),checks=False)
H = nx.from_numpy_matrix(aij_mat)
pos_spring = nx.spring_layout(H)
pos_spring = np.array([pos for idx, pos in sorted(pos_spring.items())])
# pos_spring = np.array(forceatlas2.forceatlas2(aij_mat, None, 10))
pos = fun_GPGL_layout_push(pos_spring, SIZE_SUB)
pos_cuts.append(pos)
##%% combine all layout positions
cuts_count = np.zeros(NUM_CUTS).astype(np.int)
pos_all = []
for idx in range(NUM_POINTS):
label = labels[idx]
pos_all.append(pos_cuts[label][cuts_count[label]] +
pos_top[label] * SIZE_SUB)
cuts_count[label] += 1
pos_all = np.array(pos_all)
##%% assign all features into the grid map
mat = np.zeros([SIZE_SUB * SIZE_TOP, SIZE_SUB * SIZE_TOP, 3])
seg = np.zeros([SIZE_SUB * SIZE_TOP, SIZE_SUB * SIZE_TOP, 51])
seg[:, :, -1] = 1
for data1, seg1, pos in zip(data, current_sample_seg, pos_all):
mat[pos[0], pos[1]] = data1
seg[pos[0], pos[1], int(seg1)] = 1
seg[pos[0], pos[1], -1] = 0
return mat, seg
def GPGL2_seg(data, current_sample_seg):
data = data + np.random.rand(len(data), len(data[0])) * 1e-6
dist_mat = kmeans_solver.fit_transform(data)
node_top, labels = graph_cut(data, dist_mat, NUM_POINTS, NUM_CUTS)
aij_mat = squareform(pdist(node_top), checks=False)
H = nx.from_numpy_matrix(aij_mat)
pos_spring = nx.spring_layout(H)
pos_spring = np.array([pos for idx, pos in sorted(pos_spring.items())])
pos = fun_GPGL_layout_push(pos_spring, SIZE_SUB)
pos_top = fun_GPGL_layout_push(pos_spring, SIZE_TOP)
##%%
pos_cuts = []
for i_cut in range(NUM_CUTS):
pos_cut_3D = data[labels == i_cut, :]
if (len(pos_cut_3D) < 5):
pos_raw = [[0, 0], [0, 1], [1, 1], [1, 0]]
pos = pos_raw[:len(pos_cut_3D)]
pos_cuts.append(pos)
continue
aij_mat = squareform(pdist(pos_cut_3D), checks=False)
H = nx.from_numpy_matrix(aij_mat)
pos_spring = nx.spring_layout(H)
pos_spring = np.array([pos for idx, pos in sorted(pos_spring.items())])
pos = fun_GPGL_layout_push(pos_spring, SIZE_SUB)
pos_cuts.append(pos)
##%% combine all layout positions
cuts_count = np.zeros(NUM_CUTS).astype(np.int)
pos_all = []
for idx in range(NUM_POINTS):
label = labels[idx]
pos_all.append(pos_cuts[label][cuts_count[label]] +
pos_top[label] * SIZE_SUB)
cuts_count[label] += 1
pos_all = np.array(pos_all)
num_nodes_m = len(np.unique(pos_all, axis=0))
node_loss_rate = (1 - num_nodes_m / NUM_POINTS)
return pos_all, node_loss_rate