def train():
# ----------- Load data -----------
# load data
dict = cPickle.load(open('pre_load.p', 'rb'))
tr_X, tr_y, tr_na_list, te_X, te_y, te_na_list = dict['tr_X'], dict[
'tr_y'], dict['tr_na_list'], dict['te_X'], dict['te_y'], dict[
'te_na_list']
tr_positive = np.take(tr_X, np.where(tr_y == 1)[0])
tr_positive = [t / np.max(t) for t in tr_positive]
tr_positive = [
librosa.feature.stack_memory(t.transpose(), n_steps=sh_order)
for t in tr_positive
]
tr_negative = np.take(tr_X, np.where(tr_y == 0)[0])
tr_negative = [t / np.max(t) for t in tr_negative]
tr_negative = [
librosa.feature.stack_memory(t.transpose(), n_steps=sh_order)
for t in tr_negative
]
# # # ----------- Do training seperate bases for each file-----------
# nmf_model=NMF(rank_p, norm_W=1, iterations=500, update_func = "kl", verbose=True)
# W_positive=[]
# for f in tr_positive:
# [W,H,error]=nmf_model.process(f.transpose())
# W_positive.append(W)
#
# nmf_model=NMF(rank_p, norm_W=1, iterations=500, update_func = "kl", verbose=True)
# W_negative=[]
# for f in tr_negative:
# [W,H,error]=nmf_model.process(f.transpose())
# W_negative.append(W)
tr_positive = np.hstack(tr_positive)
tr_negative = np.hstack(tr_negative)
train_data = np.hstack((tr_positive, tr_negative))
print >> sys.stderr, train_data.shape
#
# # # ----------- Do training overcomplete dictionary -----------
# p = decomposition.PCA(whiten=True, n_components= 0.99)
# pca_data=p.fit_transform(train_data)
# # num=500
# num_dim=pca_data.shape[1]
# num_training_samples=pca_data.shape[0]
# km = spherical_kmeans.OSKmeans(num,num_dim)
# print "Learning k-means: "+ str(num)
# for _ in range(1000):
# print _
# for index in range(num_training_samples):
# km.update(pca_data[index,:])
# codebook=km.centroids
# cPickle.dump( [codebook, p], open( W_name, 'wb' ), protocol=cPickle.HIGHEST_PROTOCOL )
# # # ----------- Do training -----------
if type == '0_1':
print >> sys.stderr, "NMF on positive examples"
nmf_model = NMF(rank_p,
norm_W=1,
iterations=200,
update_func="kls",
verbose=False)
[W_positive, H, error] = nmf_model.process(tr_positive, lam=lam)
# # # a_H=np.ones(rank_n+rank_p)
# # # b_H=np.ones(rank_n+rank_p)
# # # [error, W_positive, H_gap] = gap_vbem(tr_positive, rank_n+rank_p, a_H, b_H, iterations=100, verbose=True)
# #
print >> sys.stderr, "NMF on negative examples"
nmf_model = NMF(rank_n,
norm_W=1,
iterations=200,
update_func="kls",
verbose=False)
[W_negative, H, error] = nmf_model.process(tr_negative, lam=lam)
# # # [error, W_negative, H_gap] = gap_vbem(tr_negative, rank_n+rank_p, a_H, b_H, iterations=100, verbose=True)
cPickle.dump([W_positive, W_negative],
open(W_name, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
elif type == '01':
# # -------- Train with masking ----------
print >> sys.stderr, "masked NMF on training files"
mask = np.zeros((rank_p, tr_negative.shape[1]))
V = np.hstack((tr_negative, tr_positive))
H0 = np.random.rand(rank_n + rank_p, V.shape[1]) + eps
H0[-mask.shape[0]:, :mask.shape[1]] = mask
nmf_model = NMF(rank_n + rank_p,
norm_W=1,
iterations=200,
update_func="kls",
verbose=False)
[W, H, error] = nmf_model.process(V, H0=H0, lam=lam)
print >> sys.stderr, error
# # a_H=np.ones(rank_n+rankwork/bird_backup/W/W_mel_01_kl_50p_50_9folds.n.p_p)
# # b_H=np.ones(rank_n+rank_p)
# # [error, W_gap, H_gap] = gap_vbem(V, rank_n+rank_p, a_H, b_H, H0, iterations=100, verbose=False)
#
cPickle.dump(W, open(W_name, 'wb'), protocol=cPickle.HIGHEST_PROTOCOL)
else:
raise ValueError('Dictionary type not recognized')
print >> sys.stderr, "Dictionary " + W_name + " finished!"
def run_nmf(self, tr_positive, tr_negative):
'''Extract a dictionary via NMF given a method chosen in config file
Args:
tr_positive: a numpy array containing all the positive examples
tr_negative: a numpy array containing all the negative examples
Output:
NONE, the dictionary is saved in a file
'''
print(tr_positive.shape)
if self.type == '0_1':
print("NMF on positive examples")
nmf_model = NMF(self.rank_1,
norm_W=1,
iterations=self.iterations,
update_func=self.update_func,
verbose=True)
[W_positive, H, error] = nmf_model.process(tr_positive)
print("NMF on negative examples")
nmf_model = NMF(self.rank_0,
norm_W=1,
iterations=self.iterations,
update_func=self.update_func,
verbose=True)
[W_negative, H, error] = nmf_model.process(tr_negative)
print("Saved dictionary to " + self.W_name)
W = np.hstack((W_positive, W_negative))
cPickle.dump([W_positive, W_negative],
open(self.W_name, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
elif self.type == 'unsupervised':
print("Unsupervised NMF")
V = np.hstack((tr_negative, tr_positive))
nmf_model = NMF(self.rank_0 + self.rank_1,
norm_W=1,
iterations=self.iterations,
update_func=self.update_func,
verbose=True)
[W, H, error] = nmf_model.process(V)
print("Saved dictionary to " + self.W_name)
cPickle.dump(W,
open(self.W_name, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
elif self.type == '01':
# # -------- Train with masking ----------
print("Masked NMF on training files")
V = np.hstack((tr_negative, tr_positive))
mask = np.zeros((self.rank_1, tr_negative.shape[1]))
H0 = np.random.rand(self.rank_0 + self.rank_1, V.shape[1]) + eps
H0[-mask.shape[0]:, :mask.shape[1]] = mask
nmf_model = NMF(self.rank_0 + self.rank_1,
norm_W=1,
iterations=self.iterations,
update_func=self.update_func,
verbose=True)
[W, H, error] = nmf_model.process(V, H0=H0)
print("Saved dictionary to " + self.W_name)
cPickle.dump(W,
open(self.W_name, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
elif self.type == '01_orth':
# # -------- Train with masking ----------
print("masked NMF on training files")
V = np.hstack((tr_negative, tr_positive))
mask = np.zeros((self.rank_1, tr_negative.shape[1]))
H0 = np.random.rand(self.rank_0 + self.rank_1, V.shape[1]) + eps
H0[-mask.shape[0]:, :mask.shape[1]] = mask
nmf_model = NMF(self.rank_0 + self.rank_1,
norm_W=1,
rankW0=self.rank_0,
rankW1=self.rank_1,
len_V0=tr_negative.shape[1],
iterations=self.iterations,
update_func=self.update_func,
verbose=False)
print(self.lam_orth)
[W, H, error] = nmf_model.process(V, H0=H0, lam_orth=self.lam_orth)
print("Saved dictionary to " + self.W_name)
cPickle.dump(W,
open(self.W_name, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
else:
raise ValueError('Dictionary type not recognized')
return W
