# -*- coding: utf-8 -*-
"""
@author: Soraya Terrab (sorayaterrab), Laura C. Kühle
TODO: Improve '_generate_cell_data'
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
"""
import numpy as np
import os
import Initial_Condition
import DG_Approximation
class TrainingDataGenerator(object):
def __init__(self, initial_conditions, left_bound=-1, right_bound=1, balance=0.5,
stencil_length=3, directory=None):
self._balance = balance
self._left_bound = left_bound
self._right_bound = right_bound
# Set stencil length
if stencil_length % 2 == 0:
raise ValueError('Invalid stencil length (even value): "%d"' % 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 = 'test_data'
if directory is not None:
self._data_dir = directory
if not os.path.exists(self._data_dir):
os.makedirs(self._data_dir)
def build_training_data(self, num_samples):
print('Calculating training data...')
data_dict = self._calculate_data_set(num_samples)
print('Finished calculating training data!')
self._save_data(data_dict)
return data_dict
def _save_data(self, data):
for key in data.keys():
name = self._data_dir + '/' + key + '_data.npy'
np.save(name, data[key])
def _calculate_data_set(self, num_samples):
num_smooth_samples = round(num_samples * self._balance)
smooth_input, smooth_output = self._generate_cell_data(num_smooth_samples,
self._smooth_functions, True)
num_troubled_samples = num_samples - num_smooth_samples
troubled_input, troubled_output = self._generate_cell_data(num_troubled_samples,
self._troubled_functions, False)
# Merge Data
input_matrix = np.concatenate((smooth_input, troubled_input), axis=0)
output_matrix = np.concatenate((smooth_output, troubled_output), axis=0)
# Shuffle data while keeping correct input/output matches
order = np.random.permutation(num_smooth_samples + num_troubled_samples)
input_matrix = input_matrix[order]
output_matrix = output_matrix[order]
# Create normalized input data
norm_input_matrix = self._normalize_data(input_matrix)
return {'input': input_matrix, 'output': output_matrix,
'normalized_input': norm_input_matrix}
def _generate_cell_data(self, num_samples, initial_conditions, is_smooth):
num_function_samples = num_samples//len(initial_conditions)
function_id = 0
input_data = np.zeros((num_samples, 5))
count = 0
for i in range(num_samples):
# Pick a Function here
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()
centers = [center[0] for center in centers]
initial_condition.induce_adjustment(-h[0]/3)
left_bound, right_bound = interval
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)
# 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.')
# Shuffle input data
order = np.random.permutation(num_samples)
input_data = input_data[order]
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)
return input_data, output_data
def _build_stencil(self):
# Determining grid_spacing
grid_spacing = 2 / (2 ** np.random.randint(3, high=9, size=1))
# Pick a Random point between the left and right bound
point = np.random.random(1) * (self._right_bound-self._left_bound) + self._left_bound
# Ensure Bounds of x-point stencil are within the left and right bound
while point - self._stencil_length/2 * grid_spacing < self._left_bound\
or point + self._stencil_length/2 * grid_spacing > self._right_bound:
grid_spacing = grid_spacing / 2
# x-point stencil
interval = np.array([point - self._stencil_length/2 * grid_spacing,
point + self._stencil_length/2 * grid_spacing])
stencil = np.array([point + factor * grid_spacing
for factor in range(-(self._stencil_length//2),
self._stencil_length//2 + 1)])
return interval, stencil, grid_spacing
@staticmethod
def _normalize_data(input_data):
normalized_input_data = input_data
for i in range(len(input_data)):
max_function_value = max(max(np.absolute(input_data[i])), 1)
normalized_input_data[i] = input_data[i] / max_function_value
return normalized_input_data
# 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)