|
@@ 123-145 (lines=23) @@
|
| 120 |
|
|
| 121 |
|
if not self._need_mean_ff: |
| 122 |
|
return; |
| 123 |
|
|
| 124 |
|
self._mean_ff_result.append(np.mean(fitness_functions)); |
| 125 |
|
|
| 126 |
|
|
| 127 |
|
def get_global_best(self): |
| 128 |
|
"""! |
| 129 |
|
@return (dict) Returns dictionary with keys 'chromosome' and 'fitness_function' where evolution of the best chromosome |
| 130 |
|
and its fitness function's value (evolution of global optimum) are stored in lists. |
| 131 |
|
|
| 132 |
|
""" |
| 133 |
|
return self._global_best_result; |
| 134 |
|
|
| 135 |
|
|
| 136 |
|
def get_population_best(self): |
| 137 |
|
"""! |
| 138 |
|
@brief (dict) Returns dictionary with keys 'chromosome' and 'fitness_function' where evolution of the current best chromosome |
| 139 |
|
and its fitness function's value (evolution of local optimum) are stored in lists. |
| 140 |
|
|
| 141 |
|
""" |
| 142 |
|
return self._best_population_result; |
| 143 |
|
|
| 144 |
|
|
| 145 |
|
def get_mean_fitness_function(self): |
| 146 |
|
"""! |
| 147 |
|
@brief (list) Returns fitness function's values on each iteration. |
| 148 |
|
|
|
@@ 147-168 (lines=22) @@
|
| 144 |
|
|
| 145 |
|
def get_mean_fitness_function(self): |
| 146 |
|
"""! |
| 147 |
|
@brief (list) Returns fitness function's values on each iteration. |
| 148 |
|
|
| 149 |
|
""" |
| 150 |
|
return self._mean_ff_result; |
| 151 |
|
|
| 152 |
|
|
| 153 |
|
|
| 154 |
|
class ga_visualizer: |
| 155 |
|
"""! |
| 156 |
|
@brief Genetic algorithm visualizer is used to show clustering results that are specific for |
| 157 |
|
this particular algorithm: clusters, evolution of global and local optimum. |
| 158 |
|
@details The visualizer requires 'ga_observer' that collects evolution of clustering process in |
| 159 |
|
genetic algorithm. The observer is created by user and passed to genetic algorithm. There |
| 160 |
|
is usage example of the visualizer using the observer: |
| 161 |
|
@code |
| 162 |
|
# Read data for clustering |
| 163 |
|
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1); |
| 164 |
|
|
| 165 |
|
# Create instance of observer that will collect all information: |
| 166 |
|
observer_instance = ga_observer(True, True, True); |
| 167 |
|
|
| 168 |
|
# Create genetic algorithm where observer will collect information: |
| 169 |
|
ga_instance = genetic_algorithm(data=sample, |
| 170 |
|
count_clusters=2, |
| 171 |
|
chromosome_count=20, |
annoviko /
pyclustering
