complete HyperNEAT!

config/config.py

@@ -0,0 +1,70 @@
+import os
+import warnings
+import configparser
+
+import numpy as np
+
+
+class Configer:
+
+    @classmethod
+    def __load_default_config(cls):
+        par_dir = os.path.dirname(os.path.abspath(__file__))
+        default_config_path = os.path.join(par_dir, "default_config.ini")
+        return cls.__load_config(default_config_path)
+
+    @classmethod
+    def __load_config(cls, config_path):
+        c = configparser.ConfigParser()
+        c.read(config_path)
+        config = {}
+
+        for section in c.sections():
+            for key, value in c.items(section):
+                config[key] = eval(value)
+
+        return config
+
+    @classmethod
+    def __check_redundant_config(cls, default_config, config):
+        for key in config:
+            if key not in default_config:
+                warnings.warn(f"Redundant config: {key} in config!")
+
+    @classmethod
+    def __complete_config(cls, default_config, config):
+        for key in default_config:
+            if key not in config:
+                config[key] = default_config[key]
+
+    @classmethod
+    def load_config(cls, config_path=None):
+        default_config = cls.__load_default_config()
+        if config_path is None:
+            config = {}
+        elif not os.path.exists(config_path):
+            warnings.warn(f"config file {config_path} not exist!")
+            config = {}
+        else:
+            config = cls.__load_config(config_path)
+
+        cls.__check_redundant_config(default_config, config)
+        cls.__complete_config(default_config, config)
+
+        cls.refactor_activation(config)
+        cls.refactor_aggregation(config)
+
+        config['input_idx'] = np.arange(config['num_inputs'])
+        config['output_idx'] = np.arange(config['num_inputs'], config['num_inputs'] + config['num_outputs'])
+
+        return config
+
+    @classmethod
+    def refactor_activation(cls, config):
+        config['activation_default'] = 0
+        config['activation_options'] = np.arange(len(config['activation_option_names']))
+
+    @classmethod
+    def refactor_aggregation(cls, config):
+        config['aggregation_default'] = 0
+        config['aggregation_options'] = np.arange(len(config['aggregation_option_names']))