From 70dd7b5e25267e49e9ce849e48e2c693a9843db2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
<laura.kuehle@uni-duesseldorf.de>
Date: Wed, 24 Nov 2021 16:08:38 +0100
Subject: [PATCH] Restructured '_generate_cell_data()' and improved comments
for it.
---
ANN_Data_Generator.py | 47 +++++++++++++++++--------------------------
1 file changed, 19 insertions(+), 28 deletions(-)
diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 0a9bebc..c8a4e91 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -2,7 +2,7 @@
"""
@author: Soraya Terrab (sorayaterrab), Laura C. Kühle
-TODO: Improve '_generate_cell_data'
+TODO: Improve '_generate_cell_data' -> Done
TODO: Extract normalization (Combine smooth and troubled before normalizing) -> Done
TODO: Adapt code to generate both normalized and non-normalized data -> Done
TODO: Improve verbose output -> Done
@@ -91,59 +91,50 @@ class TrainingDataGenerator(object):
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
+ # Select and initialize initial condition
function_id = i % num_init_cond
initial_condition = initial_conditions[function_id]['function']
initial_condition.randomize(initial_conditions[function_id]['config'])
- # Create basis_coefficients for function mapped onto stencil
- polynomial_degree = np.random.randint(1, high=5)
-
- # 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
- interval, centers, h = self._build_stencil()
+ # Build random stencil of given length
+ interval, centers, spacing = self._build_stencil()
+ left_bound, right_bound = interval
centers = [center[0] for center in centers]
- initial_condition.induce_adjustment(-h[0]/3)
+ # Induce adjustment to capture troubled cells
+ adjustment = 0 if initial_condition.is_smooth else centers[self._stencil_length//2]
+ initial_condition.induce_adjustment(-spacing[0]/3)
- left_bound, right_bound = interval
+ # Calculate basis coefficients for stencil
+ polynomial_degree = np.random.randint(1, high=5)
dg_scheme = DG_Approximation.DGScheme(
'NoDetection', polynomial_degree=polynomial_degree,
num_grid_cells=self._stencil_length, left_bound=left_bound, right_bound=right_bound,
quadrature='Gauss', quadrature_config={'num_eval_points': polynomial_degree+1})
-
- if initial_condition.is_smooth():
- input_data[i] = dg_scheme.build_training_data(
- 0, self._stencil_length, initial_condition)
- else:
- input_data[i] = dg_scheme.build_training_data(
- centers[self._stencil_length//2], self._stencil_length, initial_condition)
+ input_data[i] = dg_scheme.build_training_data(adjustment, initial_condition)
count += 1
- if count % 100 == 0:
+ if count % 1000 == 0:
print(str(count) + ' samples completed.')
toc = timeit.default_timer()
print('Finished calculating data ' + troubled_indicator + ' troubled cells!')
print('Calculation time:', toc-tic, '\n')
- # Shuffle input data
- order = np.random.permutation(num_samples)
- input_data = input_data[order]
-
+ # Set output data
output_data = np.zeros((num_samples, 2))
- if is_smooth:
- output_data[:, 1] = np.ones(num_samples)
- else:
- output_data[:, 0] = np.ones(num_samples)
+ output_index = 1 if is_smooth else 0
+ output_data[:, output_index] = np.ones(num_samples)
return input_data, output_data
def _build_stencil(self):
+ # 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
+
# Determining grid_spacing
grid_spacing = 2 / (2 ** np.random.randint(3, high=9, size=1))
--
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