# Paths
dataset_folder_path = os.path.join("files", "dataset")
#%% Prepare Data
# Imports
from utils.preprocessing import *
from data.DataSet import DataSet
from functools import partial
import numpy as np
dataset = DataSet()
dataset.load(dataset_folder_path, test_set_percentage=0.333, validation_set_percentage=0)
dataset.apply(apply_mean_centering)
dataset.apply(apply_unit_distance_normalization)
#dataset.apply(partial(normalize_pressure_value, max_pressure_val=512))
dataset.apply(partial(spline_interpolate_and_resample, num_samples=NUM_SAMPLES))
dataset.expand_many(partial(rotate_digit, degrees=[5, 10, 15, 45, -5, -10, -15, -45]))
dataset.expand(reverse_digit_sequence)
# dataset.apply(lambda digit: convert_xy_to_derivative(digit, normalize=False))
#dataset.apply(partial(convert_xy_to_derivative, normalize=True))
#%% Split Train, Valid, Test
# Imports
import numpy as np
from sklearn.model_selection import train_test_split
X_train_valid = np.array(dataset.train_data)
X_test = np.array(dataset.test_data)
# Convert labels to numpy array and OneHot encode them
encoder, train_valid_labels, _, Y_test = dataset.onehot_encode_labels()
train_valid_labels = train_valid_labels.astype('float32').todense()
Y_test = Y_test.astype('float32').todense()
print("Training Data Len:", len(dataset.train_data))
print("Validation Data Len:", len(dataset.valid_data))
print("Test Data Len:", len(dataset.test_data))
#%%
NUM_SAMPLES = 50
ANGLES_TO_ROTATE = [5, 10, 15, 45, -5, -10, -15, -45]
from utils.preprocessing import *
from functools import partial
dataset.apply(apply_mean_centering)
#dataset.apply(apply_unit_distance_normalization)
dataset.apply(partial(spline_interpolate_and_resample,
num_samples=NUM_SAMPLES))
dataset.expand_many(partial(rotate_digit, degrees=ANGLES_TO_ROTATE))
dataset.expand(reverse_digit_sequence)
print("Training Data Len:", len(dataset.train_data))
print("Validation Data Len:", len(dataset.valid_data))
print("Test Data Len:", len(dataset.test_data))
#%%
import numpy as np
X_train = np.array(dataset.train_data)
X_valid = np.array(dataset.valid_data)
X_test = np.array(dataset.test_data)
# Convert labels to numpy array and OneHot encode them
encoder, Y_train, Y_valid, Y_test = dataset.onehot_encode_labels()