prepare for experiment

examples/enhane_xor.py

@@ -0,0 +1,44 @@
+import numpy as np
+import jax
+from utils import Configer
+from algorithms.neat import Pipeline
+from time_utils import using_cprofile
+from algorithms.neat.function_factory import FunctionFactory
+from problems import EnhanceLogic
+import time
+
+
+def evaluate(problem, func):
+    inputs = problem.ask_for_inputs()
+    pop_predict = jax.device_get(func(inputs))
+    # print(pop_predict)
+    fitnesses = []
+    for predict in pop_predict:
+        f = problem.evaluate_predict(predict)
+        fitnesses.append(f)
+    return np.array(fitnesses)
+
+# @using_cprofile
+# @partial(using_cprofile, root_abs_path='/mnt/e/neatax/', replace_pattern="/mnt/e/neat-jax/")
+def main():
+    tic = time.time()
+    config = Configer.load_config()
+    problem = EnhanceLogic("xor", n=3)
+    problem.refactor_config(config)
+    function_factory = FunctionFactory(config)
+    evaluate_func = lambda func: evaluate(problem, func)
+    pipeline = Pipeline(config, function_factory, seed=33413)
+    print("start run")
+    pipeline.auto_run(evaluate_func)
+
+    total_time = time.time() - tic
+    compile_time = pipeline.function_factory.compile_time
+    total_it = pipeline.generation
+    mean_time_per_it = (total_time - compile_time) / total_it
+    evaluate_time = pipeline.evaluate_time
+    print(f"total time: {total_time:.2f}s, compile time: {compile_time:.2f}s, real_time: {total_time - compile_time:.2f}s, evaluate time: {evaluate_time:.2f}s")
+    print(f"total it: {total_it}, mean time per it: {mean_time_per_it:.2f}s")
+
+
+if __name__ == '__main__':
+    main()