diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index b7600bbf24f304a395831fea42a7f7b6a3579a00..c5f07896b924a2dc6fefb95a9189c8a2540e473a 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -33,7 +33,7 @@ class TrainingDataGenerator:
Builds random training data.
"""
- def __init__(self, left_bound=-1, right_bound=1, stencil_length=3):
+ def __init__(self, left_bound=-1, right_bound=1):
"""Initializes TrainingDataGenerator.
Parameters
@@ -42,8 +42,6 @@ class TrainingDataGenerator:
Left boundary of interval. Default: -1.
right_bound : float, optional
Right boundary of interval. Default: 1.
- stencil_length : int, optional
- Size of training data array. Default: 3.
"""
self._basis_list = [OrthonormalLegendre(pol_deg)
@@ -54,14 +52,9 @@ class TrainingDataGenerator:
num_ghost_cells=0, num_grid_cells=2**exp)
for exp in range(3, 9)]
- # Set stencil length
- if stencil_length % 2 == 0:
- raise ValueError('Invalid stencil length (even value): "%d"'
- % stencil_length)
- self._stencil_length = stencil_length
-
def build_training_data(self, initial_conditions, num_samples, balance=0.5,
- directory='test_data', add_reconstructions=True):
+ directory='test_data', add_reconstructions=True,
+ stencil_length=3):
"""Builds random training data.
Creates training data consisting of random ANN input and saves it.
@@ -80,6 +73,8 @@ class TrainingDataGenerator:
add_reconstructions : bool, optional
Flag whether reconstructions of the middle cell are included.
Default: True.
+ stencil_length : int, optional
+ Size of training data array. Default: 3.
Returns
-------
@@ -89,10 +84,17 @@ class TrainingDataGenerator:
"""
tic = time.perf_counter()
+
+ # Set stencil length
+ if stencil_length % 2 == 0:
+ raise ValueError('Invalid stencil length (even value): "%d"'
+ % stencil_length)
+
print('Calculating training data...\n')
data_dict = self._calculate_data_set(initial_conditions,
num_samples, balance,
- add_reconstructions)
+ add_reconstructions,
+ stencil_length)
print('Finished calculating training data!')
self._save_data(directory=directory, data=data_dict)
@@ -101,7 +103,7 @@ class TrainingDataGenerator:
return data_dict
def _calculate_data_set(self, initial_conditions, num_samples, balance,
- add_reconstructions):
+ add_reconstructions, stencil_length):
"""Calculates random training data of given stencil length.
Creates training data with a given ratio between smooth and
@@ -117,6 +119,8 @@ class TrainingDataGenerator:
Ratio between smooth and discontinuous training data.
add_reconstructions : bool
Flag whether reconstructions of the middle cell are included.
+ stencil_length : int
+ Size of training data array.
Returns
-------
@@ -137,12 +141,13 @@ class TrainingDataGenerator:
num_smooth_samples = round(num_samples * balance)
smooth_input, smooth_output = self._generate_cell_data(
- num_smooth_samples, smooth_functions, add_reconstructions, True)
+ num_smooth_samples, smooth_functions, add_reconstructions,
+ stencil_length, True)
num_troubled_samples = num_samples - num_smooth_samples
troubled_input, troubled_output = self._generate_cell_data(
num_troubled_samples, troubled_functions, add_reconstructions,
- False)
+ stencil_length, False)
# Merge Data
input_matrix = np.concatenate((smooth_input, troubled_input), axis=0)
@@ -162,7 +167,7 @@ class TrainingDataGenerator:
'input_data.normalized': norm_input_matrix}
def _generate_cell_data(self, num_samples, initial_conditions,
- add_reconstructions, is_smooth):
+ add_reconstructions, stencil_length, is_smooth):
"""Generates random training input and output.
Generates random training input and output for either smooth or
@@ -177,6 +182,8 @@ class TrainingDataGenerator:
List of names of initial conditions for training.
add_reconstructions : bool
Flag whether reconstructions of the middle cell are included.
+ stencil_length : int
+ Size of training data array.
is_smooth : bool
Flag whether initial conditions are smooth.
@@ -194,7 +201,7 @@ class TrainingDataGenerator:
print('Samples to complete:', num_samples)
tic = time.perf_counter()
- num_datapoints = self._stencil_length
+ num_datapoints = stencil_length
if add_reconstructions:
num_datapoints += 2
input_data = np.zeros((num_samples, num_datapoints))
@@ -209,11 +216,11 @@ class TrainingDataGenerator:
# Build mesh for random stencil of given length
mesh = self._mesh_list[int(np.random.randint(
- 3, high=9, size=1))-3].random_stencil(self._stencil_length)
+ 3, high=9, size=1))-3].random_stencil(stencil_length)
# Induce adjustment to capture troubled cells
adjustment = 0 if initial_condition.is_smooth() \
- else mesh.non_ghost_cells[self._stencil_length//2]
+ else mesh.non_ghost_cells[stencil_length//2]
initial_condition.induce_adjustment(-mesh.cell_len/3)
# Calculate basis coefficients for stencil
@@ -226,7 +233,7 @@ class TrainingDataGenerator:
input_data[i] = self._basis_list[
polynomial_degree].calculate_cell_average(
projection=projection[:, 1:-1],
- stencil_length=self._stencil_length,
+ stencil_length=stencil_length,
add_reconstructions=add_reconstructions)
count += 1
diff --git a/workflows/ANN_data.smk b/workflows/ANN_data.smk
index e4a33c101d522460a3f192fdf6a658ce59292db3..1e2bf9061c9a729e8272be9d6248a6c739a77985 100644
--- a/workflows/ANN_data.smk
+++ b/workflows/ANN_data.smk
@@ -38,9 +38,9 @@ rule generate_data:
with open(str(log), 'w') as logfile:
sys.stdout = logfile
generator = ANN_Data_Generator.TrainingDataGenerator(
- left_bound=params.left_bound, right_bound=params.right_bound,
- stencil_length=params.stencil_length)
+ left_bound=params.left_bound, right_bound=params.right_bound)
data = generator.build_training_data(balance=params.balance,
initial_conditions=initial_conditions, directory=DIR,
num_samples=params.sample_number,
- add_reconstructions=params.reconstruction_flag)
\ No newline at end of file
+ add_reconstructions=params.reconstruction_flag,
+ stencil_length=params.stencil_length)
\ No newline at end of file