import GPy import numpy as np from GPy.kern._src.custom_kern import RationalQuadratic as ratquadkern # ker1 = ratquadkern(1,[[0,1, 1],[1,0,1],[1,1,0]]) ker1 = ratquadkern(1,[ [0,1,1,2,1,2,2,3], [1,0,1,1,2,2,3,2], [1,1,0,1,1,2,2,2], [2,1,1,0,2,1,2,1], [1,2,1,2,0,1,1,2], [2,2,2,1,1,0,1,1], [2,3,2,2,1,1,0,1], [3,2,2,1,2,1,1,0], ]) print "Gpy version: ",GPy.__version__ #print ker1 X = np.array([[0],[1],[2],[3],[4],[5]] ,dtype = np.int32) Y = np.array([[100],[100],[50],[50],[25],[25]]) m = GPy.models.GPRegression(X,Y,ker1) # print m ##m.optimize(messages=False) # print m # for x in xrange(2,8): # print m.predict(np.array([[x]], dtype=np.int32)) print m.predict(np.array([[0],[1],[2],[3],[4],[5],[6],[7]], dtype=np.int32))
found = False current = starting graph.initialize_node_matrix() graph.all_distance() edge_matrix_structure = graph.build_edge_martix() result_prediction = np.empty((0,1), dtype = np.int32) #print graph.edge_list #print result_prediction #print len(graph.edge_list) for x in graph.edge_list: if [graph.get_edge_index(x[0],x[1])] not in result_prediction: result_prediction = np.append(result_prediction, np.array([ [graph.get_edge_index(x[0],x[1])]]), axis=0) observed_edges = np.empty((0,1), dtype = np.int32) observed_congestions = np.empty((0,1), dtype = np.int32) ker1 = ratquadkern(1,edge_matrix_structure) time = 0 #print current print graph.edge_list print ker1 print edge_matrix_structure is_pos_def(x) while not found: time = time + 1 #print time #print current #print goal #observe for n in graph.get_vertex(current).get_connections(): if [graph.get_edge_index(n.get_id(),current)] not in observed_edges: observed_edges = np.append(observed_edges, np.array([[graph.get_edge_index(n.get_id(),current)]]), axis=0)