def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("-f", "--file-path", help="Path to the audio file") parser.add_argument("-a", "--artist", help="Name of the artist of the song") parser.add_argument("-t", "--title", help="title of the song") args = parser.parse_args() if args.file_path is None or not os.path.exists(args.file_path): print("invalid file path") return if args.artist is None: print("no artist provided") return if args.title is None: print("no title provided") return md = MusicData(args.file_path, title=args.title, artist=args.artist) md.detect_beats() md.detect_onsets() md.save_beat_diagram() md.save_onsets_diagram() upload_files_s3(args.file_path) save_to_dynamodb(md)
from gradient_descent import plot_history, plot_history_train_validation from music_data import MusicData from normal_equation import solve_normal_equation from normalize import z_norm import numpy as np from compute_accuracy import compute_accuracy, compute_accuracy_year if __name__ == '__main__': ''' Read music data. ''' print "Loading data." #music_train = MusicData("resources/YearPredictionMSD_samples_train.txt") #music_test = MusicData("resources/YearPredictionMSD_samples_test.txt") music_train = MusicData("resources/YearPredictionMSD_train.txt") #music_train.add_features(2) #music_test.add_features(max_degree) #music_validation.add_features(max_degree) # Normalize data. print "Normalize data." #music_train.X = z_norm(music_train.X) #music_validation = MusicData("resources/YearPredictionMSD_validation.txt") #music_test = MusicData("resources/YearPredictionMSD_test.txt") year1 = 1985 year2 = 2000 for year1 in range(1980, 2010):
from compute_cost import compute_cost from gradient_descent import gradient_descent, gradient_descent_with_J_history, \ plot_history from music_data import MusicData from normal_equation import solve_normal_equation from normalize import z_norm, z_norm_by_feature if __name__ == '__main__': ''' Read music data. ''' print "Loading data." # music_train = MusicData("resources/YearPredictionMSD_samples_train.txt") # music_test = MusicData("resources/YearPredictionMSD_samples_test.txt") music_train = MusicData("resources/YearPredictionMSD_train.txt") music_validation = MusicData("resources/YearPredictionMSD_validation.txt") music_test = MusicData("resources/YearPredictionMSD_test.txt") # Normalize data. print "Normalize data." # music_train.X = z_norm(music_train.X) # music_validation.X = z_norm(music_validation.X) # music_test.X = z_norm(music_test.X) music_train.balance_data_oversampling_smote_regular() music_train.X, mean_X, std_X = z_norm_by_feature(music_train.X) #music_train.balance_data_undersampling_custom() music_validation.X = z_norm_by_feature(music_validation.X, mean_X, std_X) music_test.X = z_norm_by_feature(music_test.X, mean_X, std_X) # Balacing train data.
from gradient_descent import plot_history, plot_history_train_validation from music_data import MusicData from normal_equation import solve_normal_equation from normalize import z_norm import numpy as np if __name__ == '__main__': ''' Read music data. ''' print "Loading data." #music_train = MusicData("resources/YearPredictionMSD_samples_train.txt") #music_test = MusicData("resources/YearPredictionMSD_samples_test.txt") music_train = MusicData("resources/YearPredictionMSD_train.txt") music_validation = MusicData("resources/YearPredictionMSD_validation.txt") music_test = MusicData("resources/YearPredictionMSD_test.txt") max_degree = 3 # Add polynomial features. print "Adding polynomial features." music_train.add_features(max_degree) music_test.add_features(max_degree) music_validation.add_features(max_degree) ## Normalize data. print "Normalize data." #music_train.X = z_norm(music_train.X) #music_validation.X = z_norm(music_validation.X) #music_test.X = z_norm(music_test.X)
from compute_cost import compute_cost from gradient_descent import plot_history, plot_history_train_validation from music_data import MusicData from normal_equation import solve_normal_equation from normalize import z_norm, z_norm_by_feature import numpy as np if __name__ == '__main__': ''' Read music data. ''' print "Loading data." # music_train = MusicData("resources/YearPredictionMSD_samples_train.txt") # music_test = MusicData("resources/YearPredictionMSD_samples_test.txt") music_train = MusicData("resources/YearPredictionMSD_train.txt") music_validation = MusicData("resources/YearPredictionMSD_validation.txt") music_test = MusicData("resources/YearPredictionMSD_test.txt") max_degree = 3 # Add polynomial features. print "Adding polynomial features." music_train.add_features(max_degree) music_test.add_features(max_degree) music_validation.add_features(max_degree) # Normalize data. print "Normalize data." # music_train.X = z_norm(music_train.X) # music_validation.X = z_norm(music_validation.X) # music_test.X = z_norm(music_test.X)