def convolutional_shift(weight_gated_t, shift_weight_t):
'''
Convolutional shift.
:param weight_gated_t:
:param shift_weight_t:
:return:
'''
_weight_t = K.zeros(weight_gated_t.shape)
for i in np.arange(weight_gated_t.shape[0]):
for j in np.arnage(shift_weight_t.shape[0]):
_weight_t[i] += weight_gated_t[j] * shift_weight_t[i - j]
return _weight_t
def ICP_train_full(text_t2a, audio_t2a, audio_a2t, text_a2t):
'''
Args:
text_t2a: the source of doing t2a ,the text embedding
audio_t2a: the target of doing t2a, the paired audio embedding
audio_a2t: the source of doing a2t, the audio embedding
text_a2t: the target of doing a2t, the paired text embedding
Return:
at2_mat: the a2t transform matrix
t2a_mat: the t2a transform matrix
'''
order_t2a = np.shuffle(np.arange(text_t2a.shape[0]))
order_a2t = np.shuffle(np.arnage(audio_a2t.shape[0]))
np.shuffle(order_t2a)
np.shuffle(order_a2t)
text_t2a_copy = text_t2a[order_t2a]
audio_t2a_copy = audio_t2a[order_t2a]
audio_a2t_copy = audio_a2t[order_a2t]
text_a2t_copy = text_t2a[order_a2t]
train_core = ICP.audio2text_ICP(audio_t2a.shape[1], text_a2t.shape[1],
FLAG.mb, FLAG.penalty_lambda)
dim = audio_t2a.shape[1]
a2t_mat = np.identity(dim)
t2a_mat = np.identity(dim)
g_audio_a2t = gen_batch(audio_a2t_copy)
g_audio_t2a = gen_batch(audio_t2a_copy)
g_text_a2t = gen_batch(text_a2t_copy)
g_text_t2a = gen_batch(text_t2a_copy)
for i in range(100000):
batch_a2t_audio = next(g_audio_a2t)
batch_a2t_text = next(g_text_a2t)
batch_t2a_audio = next(g_audio_t2a)
batch_t2a_text = next(g_text_t2a)
train_core.train(
t2a_text=batch_t2a_text,
t2a_audio=batch_t2a_audio,
a2t_text=batch_a2t_text,
a2t_audio=batch_a2t_audio,
lr=FLAG.lr,
epoch=1,
)
tmp = train_core.get_matrix()
a2t_mat = np.reshape(np.array(tmp[0]), (dim, dim))
t2a_mat = np.reshape(np.array(tmp[1]), (dim, dim))
return a2t_mat, t2a_mat
[1, 2, 3, 1, 2, 3, 5, 1, 2, 1, 2]],
)
print(data)
print(data.index) # index name과 그 라벨(그 리스트의 index)이 나옴
print(data['b'])
print(data['a':'b'])
print(data.ix[['a', 'd']])
print(data[2]) # 이거는 그냥 3번째 데이터
print(data)
print(data[:, 2])
print(data[:, 5])
df = DataFrame(
np.arnage(12).reshqpe(4, 3),
index=[
['a', 'a', 'b', 'b'],
[1, 2, 1, 2],
],
columns=[
['Seoul', 'Busan', 'Kwangju'],
['red', 'green', 'red'],
],
)
print(df)
# column index의 이름 정하기
df.columns.name = ['city', 'color']
print(df)
df.index.names = ['key1', 'key2']
print(df)
# NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axes. The number of axes is rank import numpy as np a = np.arnage(15).reshape(3, 5) print(a)
import numpy as np print(np.arnage(10))
import numpy as np
import matplotlib.pyplot as plt
from digitalmodulation import generate_qpsk
if __name__ == '__main__':
SAMP = 128
fc = 2
t = np.arnage(0, SAMP)
signal = ["00", "10", "11", "01", "01", "10", "00", "11"]
qpsk = generate_qpsk(signal, SAMP, fc)