diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py index e858ad33da207b361604d56d1236e01190c475ed..37f5b44675ddd671fc625273f78ca34561c13060 100644 --- a/ANN_Data_Generator.py +++ b/ANN_Data_Generator.py @@ -11,13 +11,13 @@ TODO: Fix bug in initialization of input matrix -> Done TODO: Improve function selection (more even distribution) -> Done TODO: Add documentation -> Done TODO: Improve comments -> Done +TODO: Remove unnecessary lines -> Done """ import numpy as np import os -import Initial_Condition import DG_Approximation import timeit @@ -237,10 +237,6 @@ class TrainingDataGenerator(object): Length of cell in grid. """ - # Calculating Cell centers for a given 1D domain with n elements, and - # Calculating Corresponding Legendre Basis Coefficients for given polynomial_degree - # Create stencil and basis_coefficients for smooth_function mapped onto stencil - # Select random cell length grid_spacing = 2 / (2 ** np.random.randint(3, high=9, size=1)) @@ -287,13 +283,3 @@ class TrainingDataGenerator(object): for key in data.keys(): name = self._data_dir + '/' + key + '_data.npy' np.save(name, data[key]) - - -# Get Training/Validation Datasets -np.random.seed(1234) -# generator = TrainingDataGenerator(functions, left_bound=boundary[0], right_bound=boundary[1]) -# generator = TrainingDataGenerator(functions, left_bound=boundary[0], right_bound=boundary[1]) - -sample_number = 1000 -# data_1 = generator.build_training_data(sample_number, 0) -# data_2 = generator.build_training_data(sample_number, 1)