diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py index 37fbe93ddd08eee3165c4d7f17964b4d55a6d6f7..0a9bebc857aeffa3e9ff305e903291bbfaa757be 100644 --- a/ANN_Data_Generator.py +++ b/ANN_Data_Generator.py @@ -8,6 +8,7 @@ TODO: Adapt code to generate both normalized and non-normalized data -> Done TODO: Improve verbose output -> Done TODO: Change order of methods -> Done TODO: Fix bug in initialization of input matrix -> Done +TODO: Improve function selection (more even distribution) -> Done """ @@ -88,13 +89,13 @@ class TrainingDataGenerator(object): print('Samples to complete:', num_samples) tic = timeit.default_timer() - num_function_samples = num_samples//len(initial_conditions) - function_id = 0 input_data = np.zeros((num_samples, self._stencil_length+2)) + num_init_cond = len(initial_conditions) count = 0 for i in range(num_samples): # Pick a Function here + function_id = i % num_init_cond initial_condition = initial_conditions[function_id]['function'] initial_condition.randomize(initial_conditions[function_id]['config']) @@ -122,11 +123,6 @@ class TrainingDataGenerator(object): input_data[i] = dg_scheme.build_training_data( centers[self._stencil_length//2], self._stencil_length, initial_condition) - # Update Function ID - if (i % num_function_samples == num_function_samples - 1) \ - and (function_id != len(initial_conditions)-1): - function_id = function_id + 1 - count += 1 if count % 100 == 0: print(str(count) + ' samples completed.')