def test_full_run(self):
        condensed_matrix = CondensedMatrix(matrix)
        cmax = condensed_matrix.calculateMax()
        alg = RandomAlgorithm.RandomClusteringAlgorithm(condensed_matrix)
        values = []
        calculator =  MeanMinimumDistanceCalculator(10)
        for i in range(2,20):
            clustering = alg.perform_clustering({
                                                 "max_num_of_clusters":-1, 
                                                 "num_clusters":i
                                                 })
            values.append( calculator.evaluate(clustering, condensed_matrix, 30))
        self.assertTrue(max(values) < cmax)
dataset/ETHPy150Open victor-gil-sepulveda/pyProCT/pyproct/clustering/evaluation/metrics/test/TestMetrics.py/TestMetrics.test_full_run/original