annoviko /
pyclustering
| Total Complexity | 18 |
| Total Lines | 131 |
| Duplicated Lines | 20.61 % |
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
| 1 | """! |
||
| 32 | class kmedoids: |
||
| 33 | """! |
||
| 34 | @brief Class represents clustering algorithm K-Medoids (another one title is PAM - Parti). |
||
| 35 | @details The algorithm is less sensitive to outliers tham K-Means. The principle difference between K-Medoids and K-Medians is that |
||
| 36 | K-Medoids uses existed points from input data space as medoids, but median in K-Medians can be unreal object (not from |
||
| 37 | input data space). |
||
| 38 | |||
| 39 | Example: |
||
| 40 | @code |
||
| 41 | # load list of points for cluster analysis |
||
| 42 | sample = read_sample(path); |
||
| 43 | |||
| 44 | # create instance of K-Medoids algorithm |
||
| 45 | kmedians_instance = kmedians(sample, [1, 10]); |
||
| 46 | |||
| 47 | # run cluster analysis and obtain results |
||
| 48 | kmedians_instance.process(); |
||
| 49 | kmedians_instance.get_clusters(); |
||
| 50 | @endcode |
||
| 51 | |||
| 52 | """ |
||
| 53 | |||
| 54 | |||
| 55 | def __init__(self, data, initial_index_medoids, tolerance = 0.25): |
||
| 56 | """! |
||
| 57 | @brief Constructor of clustering algorithm K-Medoids. |
||
| 58 | |||
| 59 | @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple. |
||
| 60 | @param[in] initial_index_medoids (list): Indexes of intial medoids (indexes of points in input data). |
||
| 61 | @param[in] tolerance (double): Stop condition: if maximum value of distance change of medoids of clusters is less than tolerance than algorithm will stop processing |
||
| 62 | |||
| 63 | """ |
||
| 64 | self.__pointer_data = data; |
||
| 65 | self.__clusters = []; |
||
| 66 | self.__medoids = [ self.__pointer_data[medoid_index] for medoid_index in initial_index_medoids ]; |
||
| 67 | self.__tolerance = tolerance; |
||
| 68 | |||
| 69 | |||
| 70 | def process(self): |
||
| 71 | """! |
||
| 72 | @brief Performs cluster analysis in line with rules of K-Medoids algorithm. |
||
| 73 | |||
| 74 | @remark Results of clustering can be obtained using corresponding get methods. |
||
| 75 | |||
| 76 | @see get_clusters() |
||
| 77 | @see get_medoids() |
||
| 78 | |||
| 79 | """ |
||
| 80 | |||
| 81 | changes = float('inf');
|
||
| 82 | |||
| 83 | stop_condition = self.__tolerance * self.__tolerance; # Fast solution |
||
| 84 | #stop_condition = self.__tolerance; # Slow solution |
||
| 85 | |||
| 86 | while (changes > stop_condition): |
||
| 87 | self.__clusters = self.__update_clusters(); |
||
| 88 | updated_medoids = self.__update_medoids(); # changes should be calculated before asignment |
||
| 89 | |||
| 90 | changes = max([euclidean_distance_sqrt(self.__medoids[index], updated_medoids[index]) for index in range(len(updated_medoids))]); # Fast solution |
||
| 91 | |||
| 92 | self.__medoids = updated_medoids; |
||
| 93 | |||
| 94 | |||
| 95 | def get_clusters(self): |
||
| 96 | """! |
||
| 97 | @brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data. |
||
| 98 | |||
| 99 | @see process() |
||
| 100 | @see get_medoids() |
||
| 101 | |||
| 102 | """ |
||
| 103 | |||
| 104 | return self.__clusters; |
||
| 105 | |||
| 106 | |||
| 107 | def get_medoids(self): |
||
| 108 | """! |
||
| 109 | @brief Returns list of medoids of allocated clusters. |
||
| 110 | |||
| 111 | @see process() |
||
| 112 | @see get_clusters() |
||
| 113 | |||
| 114 | """ |
||
| 115 | |||
| 116 | return self.__medoids; |
||
| 117 | |||
| 118 | |||
| 119 | def __update_clusters(self): |
||
| 120 | """! |
||
| 121 | @brief Calculate distance to each point from the each cluster. |
||
| 122 | @details Nearest points are captured by according clusters and as a result clusters are updated. |
||
| 123 | View Code Duplication | ||
|
Duplication
introduced
by
|
|||
| 124 | @return (list) updated clusters as list of clusters where each cluster contains indexes of objects from data. |
||
| 125 | |||
| 126 | """ |
||
| 127 | |||
| 128 | clusters = [[] for i in range(len(self.__medoids))]; |
||
| 129 | for index_point in range(len(self.__pointer_data)): |
||
| 130 | index_optim = -1; |
||
| 131 | dist_optim = 0.0; |
||
| 132 | |||
| 133 | for index in range(len(self.__medoids)): |
||
| 134 | dist = euclidean_distance_sqrt(self.__pointer_data[index_point], self.__medoids[index]); |
||
| 135 | |||
| 136 | if ( (dist < dist_optim) or (index is 0)): |
||
| 137 | index_optim = index; |
||
| 138 | dist_optim = dist; |
||
| 139 | |||
| 140 | clusters[index_optim].append(index_point); |
||
| 141 | |||
| 142 | # If cluster is not able to capture object it should be removed |
||
| 143 | clusters = [cluster for cluster in clusters if len(cluster) > 0]; |
||
| 144 | |||
| 145 | return clusters; |
||
| 146 | |||
| 147 | |||
| 148 | def __update_medoids(self): |
||
| 149 | """! |
||
| 150 | @brief Find medoids of clusters in line with contained objects. |
||
| 151 | |||
| 152 | @return (list) list of medoids for current number of clusters. |
||
| 153 | |||
| 154 | """ |
||
| 155 | |||
| 156 | medoids = [[] for i in range(len(self.__clusters))]; |
||
| 157 | |||
| 158 | for index in range(len(self.__clusters)): |
||
| 159 | medoid_index = median(self.__pointer_data, self.__clusters[index]); |
||
| 160 | medoids[index] = self.__pointer_data[medoid_index]; |
||
| 161 | |||
| 162 | return medoids; |
Loading history...