read_fake_chroma) from utils.calc_utils import (find_a_v_mens_va, regression, average_distance_va, nearest_dist_average_va, no_stdev_average_va, find_in_dict, valence_distance_va, arousal_distance_va) from utils.plot import (plot_all_va) import numpy as np import random import sys # y, sr = load_files('audio/101.mp3') # mfcc_v = mfcc(y, sr) # get exsisting valence and arousal data valence, arousal = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set ids, feat = read_fake_chroma('features/fakechroma') best_avg = sys.maxint best_near = sys.maxint best_std = sys.maxint best_val = sys.maxint best_aro = sys.maxint for i in range(50): train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:]
from utils.cross_validation import cross_valid ''' regression tree for each response ''' # get exsisting valence and arousal data ids, va, aro, rows = seperate_va('csv/survery2dataMin1.csv') while 101 in ids: id101 = ids.index(101) ids[id101:(id101+1)] = [] va[id101:(id101+1)] = [] aro[id101:(id101+1)] = [] va_dict, aro_dict = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set no, feat = read_fake_chroma('features/fakechroma') X = feature_matrix_by_id(ids, feat) Yva = va Yaro = aro #for i in range(100): X, Yva, Yaro, ids = shufle_same(X, Yva, Yaro, ids) print 'v: ' + str(cross_valid(10, X, Yva, ids, va_dict)) print 'A: ' + str(cross_valid(10, X, Yaro, ids, aro_dict))
import sys import numpy as np from sklearn.tree import DecisionTreeRegressor ''' regression tree for each response ''' # y, sr = load_files('audio/101.mp3') # mfcc_v = mfcc(y, sr) # get exsisting valence and arousal data all_ids, all_val, all_aro = mean_va('csv/survery2dataMin1.csv') valence, arousal = csv_2_dict_va('csv/survery2dataMin1.csv') #data by columns # 3 - age # 4 - sex # 5 - place of living # 6 - music school # 7 - medicines # 8 - drugs # 12 - amount of listening music # 13 - playing instrument or singing # 17\18 - mood in VA # 19\20 - mood color # 21:40 - preception of 10 labels # 41:57 - presence of mood # 58:77 - color perception for 10 lables
import random import sys import numpy as np from sklearn.tree import DecisionTreeRegressor ''' regression tree for each response ''' # get exsisting valence and arousal data ids, va, aro, h, s, v= uniform_va_hsv('csv/surveydatahsv.csv') va_dict, aro_dict = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set X = np.column_stack((h,s,v)) Yva = va Yaro = aro best_avg = sys.maxint best_aro = sys.maxint best_va = sys.maxint for i in range(100): X, Yva, Yaro, ids = shufle_same(X, Yva, Yaro, ids)