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fir_class.py
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fir_class.py
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# -*- coding: utf_8 -*-
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, utils, Variable
from chainer import optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
'''
1. FIR 0, IIR 1, LSTM 2
2. EEG-ECoG間でフィルター共通 0, バラバラ 1
3. 予測対象 EEG 0, ECoG 1
4. 予測の仕方 共通 0, NNをわける（フォルター特性が変わる） 1
'''
# FIR フィルター共通 EEG予測 共通
class TimeSpacePerceptron0000(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =16
self.ecog_num = 256
super(TimeSpacePerceptron0000, self).__init__(
transformECoG = L.Linear(self.ecog_num, self.eeg_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0000, self).add_link('FIR', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
# EEG_t は 16 * time * time_delay
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
ecog_space = self.transformECoG(ECoG_filtered)
ecog_space_dropout = F.dropout(ecog_space, train=train, ratio=0.5)
eeg = EEG_filtered
return ecog_space_dropout, eeg, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルターバラバラ EEG予測 共通
class TimeSpacePerceptron0100(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =16
self.ecog_num = 256
super(TimeSpacePerceptron0100, self).__init__(
transformECoG = L.Linear(self.ecog_num, self.eeg_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0100, self).add_link('FIR_EEG', L.Linear(10, 1))
super(TimeSpacePerceptron0100, self).add_link('FIR_ECoG', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
# EEG_t は 16 * time * time_delay
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_EEG'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_ECoG'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
ecog_space = self.transformECoG(ECoG_filtered)
ecog_space_dropout = F.dropout(ecog_space, train=train, ratio=0.5)
eeg = EEG_filtered
return ecog_space_dropout, eeg, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルター共通 ECoG予測 共通
class TimeSpacePerceptron0010(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =16
self.ecog_num = 256
super(TimeSpacePerceptron0010, self).__init__(
transformECoG = L.Linear(self.eeg_num, self.ecog_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0010, self).add_link('FIR', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
x1 = self.transformECoG(EEG_filtered)
x1_dropout = F.dropout(x1, train=train, ratio=0.5)
x2 = ECoG_filtered
return x1_dropout, x2, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルターバラバラ ECoG予測 共通
class TimeSpacePerceptron0110(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =16
self.ecog_num = 256
super(TimeSpacePerceptron0110, self).__init__(
transformECoG = L.Linear(self.eeg_num, self.ecog_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0110, self).add_link('FIR_EEG', L.Linear(10, 1))
super(TimeSpacePerceptron0110, self).add_link('FIR_ECoG', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_EEG'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_ECoG'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
x1 = self.transformECoG(EEG_filtered)
x1_dropout = F.dropout(x1, train=train, ratio=0.5)
x2 = ECoG_filtered
return x1_dropout, x2, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルター共通 EEG予測 perCh
class TimeSpacePerceptron0001(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =1
self.ecog_num = 256
super(TimeSpacePerceptron0001, self).__init__(
transformECoG = L.Linear(self.ecog_num, self.eeg_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0001, self).add_link('FIR', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
# EEG_t は 16 * time * time_delay
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t, pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
ecog_space = self.transformECoG(ECoG_filtered)
ecog_space_dropout = F.dropout(ecog_space, train=train, ratio=0.5)
eeg = EEG_filtered
return ecog_space_dropout, eeg, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルターバラバラ EEG予測 perCh
class TimeSpacePerceptron0101(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =1
self.ecog_num = 256
super(TimeSpacePerceptron0101, self).__init__(
transformECoG = L.Linear(self.ecog_num, self.eeg_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0101, self).add_link('FIR_EEG', L.Linear(10, 1))
super(TimeSpacePerceptron0101, self).add_link('FIR_ECoG', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
# EEG_t は 16 * time * time_delay
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t, pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_EEG'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_ECoG'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
ecog_space = self.transformECoG(ECoG_filtered)
ecog_space_dropout = F.dropout(ecog_space, train=train, ratio=0.5)
eeg = EEG_filtered
return ecog_space_dropout, eeg, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルター共通 ECoG予測 perCh
class TimeSpacePerceptron0011(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =16
self.ecog_num = 1
super(TimeSpacePerceptron0011, self).__init__(
transformECoG = L.Linear(self.eeg_num, self.ecog_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0011, self).add_link('FIR', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t, pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
x1 = self.transformECoG(EEG_filtered)
x1_dropout = F.dropout(x1, train=train, ratio=0.5)
x2 = ECoG_filtered
return x1_dropout, x2, EEG_filtered_prev, ECoG_filtered_prev
# FIR フィルターバラバラ ECoG予測 perCh
class TimeSpacePerceptron0111(Chain):
def __init__(self, time_range):
self.tt = time_range
self.eeg_num =16
self.ecog_num = 1
super(TimeSpacePerceptron0111, self).__init__(
transformECoG = L.Linear(self.eeg_num, self.ecog_num)# ECoG to EEG 空間
)
super(TimeSpacePerceptron0111, self).add_link('FIR_EEG', L.Linear(10, 1))
super(TimeSpacePerceptron0111, self).add_link('FIR_ECoG', L.Linear(10, 1))
def __call__(self, EEG_t, ECoG_t, EEG_filtered_prev, ECoG_filtered_prev, train=False):
#EEG FIR
EEG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.eeg_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = EEG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((EEG_t[:, ch, :], pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_EEG'](x)
EEG_filtered.append(y)
EEG_filtered_prev[ch] = y
EEG_filtered = F.concat(tuple(EEG_filtered))
#ECoG FIR
ECoG_filtered = [] # y0, y1, y2, y3, y4 が入る ch の数だけ配列
for ch in range(self.ecog_num):
pre_y = [np.zeros((self.tt, 1)).astype('float32') for i in range(5)]
for t in range(5):
pre_y[t][t:] = ECoG_filtered_prev[ch].data[:len(pre_y[t])-t]
pre_y[t] = Variable(pre_y[t])
x = F.concat((ECoG_t, pre_y[0], pre_y[1], pre_y[2], pre_y[3], pre_y[4]))
y = self.__dict__['FIR_ECoG'](x)
ECoG_filtered.append(y)
ECoG_filtered_prev[ch] = y
ECoG_filtered = F.concat(tuple(ECoG_filtered))
# ここまで FIR
x1 = self.transformECoG(EEG_filtered)
x1_dropout = F.dropout(x1, train=train, ratio=0.5)
x2 = ECoG_filtered
return x1_dropout, x2, EEG_filtered_prev, ECoG_filtered_prev