diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 4f753dd53ae456b10929797c08a3a8e5d2af3e89..acf0d9535462909648045dc09893b0251b6bb79e 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -22,50 +22,27 @@ class TrainingDataGenerator:
Generates random training data for given initial conditions.
- Attributes
- ----------
- smooth_functions : list
- List of smooth initial/continuous conditions.
- troubled_functions : list
- List of discontinuous initial conditions.
- data_dir : str
- Path to directory in which training data is saved.
-
Methods
-------
build_training_data(num_samples)
Builds random training data.
"""
- def __init__(self, initial_conditions, left_bound=-1, right_bound=1,
- balance=0.5, stencil_length=3, directory='test_data',
- add_reconstructions=True):
+ def __init__(self, left_bound=-1, right_bound=1, stencil_length=3):
"""Initializes TrainingDataGenerator.
Parameters
----------
- initial_conditions : list
- List of names of initial conditions for training.
left_bound : float, optional
Left boundary of interval. Default: -1.
right_bound : float, optional
Right boundary of interval. Default: 1.
- balance: float, optional
- Ratio between smooth and discontinuous training data. Default: 0.5.
stencil_length : int, optional
Size of training data array. Default: 3.
- directory : str, optional
- Path to directory in which training data is saved.
- Default: 'test_data'.
- add_reconstructions: bool, optional
- Flag whether reconstructions of the middle cell are included.
- Default: True.
"""
- self._balance = balance
self._left_bound = left_bound
self._right_bound = right_bound
- self._add_reconstructions = add_reconstructions
# Set stencil length
if stencil_length % 2 == 0:
@@ -73,29 +50,26 @@ class TrainingDataGenerator:
% stencil_length)
self._stencil_length = stencil_length
- # Separate smooth and discontinuous initial conditions
- self._smooth_functions = []
- self._troubled_functions = []
- for function in initial_conditions:
- if function['function'].is_smooth():
- self._smooth_functions.append(function)
- else:
- self._troubled_functions.append(function)
-
- # Set directory
- self._data_dir = directory
- if not os.path.exists(self._data_dir):
- os.makedirs(self._data_dir)
-
- def build_training_data(self, num_samples):
+ def build_training_data(self, initial_conditions, num_samples, balance=0.5,
+ directory='test_data', add_reconstructions=True):
"""Builds random training data.
Creates training data consisting of random ANN input and saves it.
Parameters
----------
+ initial_conditions : list
+ List of names of initial conditions for training.
num_samples : int
Number of training data samples to generate.
+ balance : float, optional
+ Ratio between smooth and discontinuous training data. Default: 0.5.
+ directory : str, optional
+ Path to directory in which training data is saved.
+ Default: 'test_data'.
+ add_reconstructions : bool, optional
+ Flag whether reconstructions of the middle cell are included.
+ Default: True.
Returns
-------
@@ -106,15 +80,18 @@ class TrainingDataGenerator:
"""
tic = time.perf_counter()
print('Calculating training data...\n')
- data_dict = self._calculate_data_set(num_samples)
+ data_dict = self._calculate_data_set(initial_conditions,
+ num_samples, balance,
+ add_reconstructions)
print('Finished calculating training data!')
- self._save_data(data_dict)
+ self._save_data(directory=directory, data=data_dict)
toc = time.perf_counter()
print(f'Total runtime: {toc - tic:0.4f}s')
return data_dict
- def _calculate_data_set(self, num_samples):
+ def _calculate_data_set(self, initial_conditions, num_samples, balance,
+ add_reconstructions):
"""Calculates random training data of given stencil length.
Creates training data with a given ratio between smooth and
@@ -122,8 +99,14 @@ class TrainingDataGenerator:
Parameters
----------
+ initial_conditions : list
+ List of names of initial conditions for training.
num_samples : int
Number of training data samples to generate.
+ balance : float
+ Ratio between smooth and discontinuous training data.
+ add_reconstructions : bool
+ Flag whether reconstructions of the middle cell are included.
Returns
-------
@@ -132,13 +115,24 @@ class TrainingDataGenerator:
output data.
"""
- num_smooth_samples = round(num_samples * self._balance)
+ # print(type(initial_conditions))
+ # Separate smooth and discontinuous initial conditions
+ smooth_functions = []
+ troubled_functions = []
+ for function in initial_conditions:
+ if function['function'].is_smooth():
+ smooth_functions.append(function)
+ else:
+ troubled_functions.append(function)
+
+ num_smooth_samples = round(num_samples * balance)
smooth_input, smooth_output = self._generate_cell_data(
- num_smooth_samples, self._smooth_functions, True)
+ num_smooth_samples, smooth_functions, add_reconstructions, True)
num_troubled_samples = num_samples - num_smooth_samples
troubled_input, troubled_output = self._generate_cell_data(
- num_troubled_samples, self._troubled_functions, False)
+ num_troubled_samples, troubled_functions, add_reconstructions,
+ False)
# Merge Data
input_matrix = np.concatenate((smooth_input, troubled_input), axis=0)
@@ -157,7 +151,8 @@ class TrainingDataGenerator:
return {'input_data.raw': input_matrix, 'output_data': output_matrix,
'input_data.normalized': norm_input_matrix}
- def _generate_cell_data(self, num_samples, initial_conditions, is_smooth):
+ def _generate_cell_data(self, num_samples, initial_conditions,
+ add_reconstructions, is_smooth):
"""Generates random training input and output.
Generates random training input and output for either smooth or
@@ -170,6 +165,8 @@ class TrainingDataGenerator:
Number of training data samples to generate.
initial_conditions : list
List of names of initial conditions for training.
+ add_reconstructions : bool
+ Flag whether reconstructions of the middle cell are included.
is_smooth : bool
Flag whether initial conditions are smooth.
@@ -181,13 +178,14 @@ class TrainingDataGenerator:
Array containing output data.
"""
+ # print(type(initial_conditions))
troubled_indicator = 'without' if is_smooth else 'with'
print('Calculating data ' + troubled_indicator + ' troubled cells...')
print('Samples to complete:', num_samples)
tic = time.perf_counter()
num_datapoints = self._stencil_length
- if self._add_reconstructions:
+ if add_reconstructions:
num_datapoints += 2
input_data = np.zeros((num_samples, num_datapoints))
num_init_cond = len(initial_conditions)
@@ -212,19 +210,19 @@ class TrainingDataGenerator:
# Calculate basis coefficients for stencil
polynomial_degree = np.random.randint(1, high=5)
- basis = basis_list[polynomial_degree]
mesh = Mesh(num_grid_cells=self._stencil_length, num_ghost_cells=2,
left_bound=left_bound, right_bound=right_bound)
projection = do_initial_projection(
initial_condition=initial_condition, mesh=mesh,
- basis=basis,
+ basis=basis_list[polynomial_degree],
quadrature=quadrature_list[polynomial_degree],
adjustment=adjustment)
- input_data[i] = basis.calculate_cell_average(
+ input_data[i] = basis_list[
+ polynomial_degree].calculate_cell_average(
projection=projection[:, 1:-1],
stencil_length=self._stencil_length,
- add_reconstructions=self._add_reconstructions)
+ add_reconstructions=add_reconstructions)
count += 1
if count % 1000 == 0:
@@ -298,9 +296,14 @@ class TrainingDataGenerator:
normalized_input_data.append(entry / max_function_value)
return np.array(normalized_input_data)
- def _save_data(self, data):
+ @staticmethod
+ def _save_data(directory, data):
"""Saves data."""
+ # Set directory
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
print('Saving training data.')
for key in data.keys():
- name = self._data_dir + '/' + key + '.npy'
+ name = directory + '/' + key + '.npy'
np.save(name, data[key])
diff --git a/workflows/ANN_data.smk b/workflows/ANN_data.smk
index df966e1354d4b253d156514afa2c382c71bbd529..e4a33c101d522460a3f192fdf6a658ce59292db3 100644
--- a/workflows/ANN_data.smk
+++ b/workflows/ANN_data.smk
@@ -38,10 +38,9 @@ rule generate_data:
with open(str(log), 'w') as logfile:
sys.stdout = logfile
generator = ANN_Data_Generator.TrainingDataGenerator(
- initial_conditions=initial_conditions,
left_bound=params.left_bound, right_bound=params.right_bound,
- balance=params.balance,
- stencil_length=params.stencil_length, directory=DIR,
- add_reconstructions=params.reconstruction_flag)
- data = generator.build_training_data(
- num_samples=params.sample_number)
\ No newline at end of file
+ stencil_length=params.stencil_length)
+ 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