コード例 #1
0
    def get_parameters(self):
        """
        This function creates some parameters to be used with DBScan.
        @return: A tuple with the generated parameters and an empty list corresponding to the clusterings.
        """
        run_parameters = []

        # (minpts, eps tuples)
        if "max" in self.parameters["clustering"]["algorithms"]["dbscan"]:
            max_eps_tries = self.parameters["clustering"]["algorithms"]["dbscan"]["max"]
        else:
            max_eps_tries = 10

        num_elements = self.distance_matrix.row_length
        klist = k_scale_gen(math.log(num_elements))
        buffer = numpy.empty(num_elements)
        kdist_matrix = k_dist(klist, buffer, self.distance_matrix)

        dbscan_param_pairs = dbscanTools.dbscan_param_space_search(self.parameters["clustering"]["evaluation"]["maximum_noise"],
                                                                   max_eps_tries,
                                                                   num_elements,
                                                                   klist,
                                                                   kdist_matrix) +\
                            zhou_adaptative_determination(kdist_matrix, self.distance_matrix)

        for (minpts, eps) in dbscan_param_pairs:
            run_parameter = ParametersGenerator.get_base_parameters()
            run_parameter["minpts"] = minpts
            run_parameter["eps"] = eps
            run_parameters.append(run_parameter)

        return run_parameters, []
コード例 #2
0
ファイル: TestDBSCAN.py プロジェクト: migonsu/pyProCT
 def test_zhou_adaptative_determination(self):
     distances = CondensedMatrix([ 0., 0., 2., 2., 
                                       0., 2., 2.,
                                           2., 2.,
                                               0.])
     buffer = numpy.empty(distances.row_length)
     k_dist_matrix = k_dist(numpy.array([2,4]), buffer, distances)
     zhou_params =  zhou_adaptative_determination(k_dist_matrix, distances)
     self.assertItemsEqual(zhou_params,[(1.0, 0.8), (4.0, 2.0)])
コード例 #3
0
 def test_k_dist(self):
     distances = CondensedMatrix([
         17.46, 9.21, 4.47, 3.16, 4.47, 5.65, 12.36, 5.83, 9.43, 12.52,
         15.65, 5., 8.06, 11.18, 13.15, 3.16, 6.35, 8.24, 3.16, 5.10, 2.
     ])
     buffer = numpy.empty(distances.row_length)
     result = k_dist(numpy.array([2, 4]), buffer, distances)
     # [[  4.47   5.65]
     # [  9.43  12.52]
     # [  8.06  11.18]
     # [  4.47   5.83]
     # [  3.16   5.1 ]
     # [  3.16   6.35]
     # [  5.1    8.24]]
     expected = [
         sorted([4.47, 9.43, 8.06, 4.47, 3.16, 3.16, 5.1]),
         sorted([5.65, 12.52, 11.18, 5.83, 5.1, 6.35, 8.24])
     ]
     numpy.testing.assert_array_almost_equal(result, expected, decimal=2)
コード例 #4
0
ファイル: TestDBSCAN.py プロジェクト: migonsu/pyProCT
 def test_k_dist(self):
     distances = CondensedMatrix([17.46,   9.21,  4.47,  3.16,   4.47,   5.65,   
                                          12.36,  5.83,  9.43,  12.52,  15.65,
                                                  5.,    8.06,  11.18,  13.15,
                                                         3.16,   6.35,   8.24,   
                                                                 3.16,   5.10,
                                                                         2.  ])
     buffer = numpy.empty(distances.row_length)
     result = k_dist(numpy.array([2,4]), buffer, distances)
     # [[  4.47   5.65]
     # [  9.43  12.52]
     # [  8.06  11.18]
     # [  4.47   5.83]
     # [  3.16   5.1 ]
     # [  3.16   6.35]
     # [  5.1    8.24]]
     expected = [sorted([  4.47,   9.43,  8.06,    4.47,   3.16,   3.16,   5.1]),
                 sorted([  5.65,  12.52, 11.18,    5.83,   5.1,    6.35,   8.24])]
     numpy.testing.assert_array_almost_equal(result,expected,decimal = 2)
コード例 #5
0
 def test_zhou_adaptative_determination(self):
     distances = CondensedMatrix([0., 0., 2., 2., 0., 2., 2., 2., 2., 0.])
     buffer = numpy.empty(distances.row_length)
     k_dist_matrix = k_dist(numpy.array([2, 4]), buffer, distances)
     zhou_params = zhou_adaptative_determination(k_dist_matrix, distances)
     self.assertItemsEqual(zhou_params, [(1.0, 0.8), (4.0, 2.0)])