""""""""""""""""""""""""""""""""" 
This file is for running.
Do not modify this file.

For running: DiffEqnSolver.py
For modifying: settings.py
"""""""""""""""""""""""""""""""""

import os
import time

import DataUtils
import Settings as settings
from SymbolicFunctionLearner import SFL

settings.mode = "de"

current_model = SFL()
if not os.path.exists('images'):
    os.makedirs('images')

print('\nBeginning experiment: {}'.format(current_model.name))

print("{} tree layers.".format(settings.n_tree_layers))
print("{} features of {} component(s) each.".format(settings.num_features, settings.num_dims_per_feature))
print("{} components in output.".format(settings.n_dims_in_output))
print("{} operators: {}.".format(len(current_model.function_set),
                                 current_model.function_set))

train_errors = []
valid_errors = []
test_errors = []
true_eqns = []

train_X = DataUtils.generate_data(settings.train_N, n_vars=current_model.n_input_variables,
                                  avoid_zero=settings.avoid_zero)
valid_X = DataUtils.generate_data(settings.train_N, n_vars=current_model.n_input_variables,
                                  avoid_zero=settings.avoid_zero)
test_X = DataUtils.generate_data(settings.test_N, n_vars=current_model.n_input_variables,
                                 min_x=settings.test_scope[0],
                                 max_x=settings.test_scope[1])

print("\n========================")
print("Starting Solver.")
print("==========================\n")

# Train the model from scratch several times, keeping the best one.
start_time = time.time()
best_model, best_iter, best_err = current_model.repeat_train(train_X,
                                                             num_repeats=settings.num_train_repeat_processes,
                                                             test_x=test_X)
running_time = time.time() - start_time

print("best_model: {}".format(best_model))
print("----------------------")
print("Finished DE. Took {:.2f} minutes.\n".format(running_time / 60))


print("Final solution found at attempt {}:".format(best_iter))
print("y = {}".format(best_model))
print("Test error:  {}".format(best_err))
if best_err < 0.02:
    print("Attained error less than 0.02 - great!")
print()