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)
