R = np.dot(A, np.dot(B, C))计算顺序
时间: 2023-09-14 15:10:26 浏览: 41
要计算 R = np.dot(A, np.dot(B, C)) 的顺序,我们需要先计算 np.dot(B, C),然后再计算 np.dot(A, np.dot(B, C))。具体步骤如下:
1. 首先,计算 np.dot(B, C):
- 将矩阵 B 与矩阵 C 相乘,得到一个中间结果 X。
2. 然后,计算 np.dot(A, np.dot(B, C)):
- 将矩阵 A 与中间结果 X 相乘,得到最终结果 R。
所以,计算顺序是先计算 np.dot(B, C),再计算 np.dot(A, np.dot(B, C))。
相关问题
dwxh,dwhh,dwhy = np.zeros_like(wxh), np.zeros_like(Whh),np.zeros_like(Why) dbh, dby = np.zeros_like(bh),np.zeros_like(by) dhnext = np.zeros_like(hs[0]) for t in reversed(range(len(inputs))) : dy = np.copy(ps[t]) for b in range(0,B): dy[targets[t][b],b] -= 1 dwhy += np.dot(dy, hs[t].T) dby += np.expand_dims(np.sum(dy,axis=1),axis=1) dh = np.dot(Why.T, dy)+ dhnext dh = dh * (1-hs[t]*hs[t] ) dbh += np.expand_dims(np.sum(dh,axis=1),axis=1) dWxh += np.dot(dh,xs[t].T) dWhh += np.dot(dh,hs[t - 1].T) dhnext = np.dot(Whh.T,dh) return loss,dWxh,dWhh,dwhy,dbh,dby, hs[len(inputs) - 1] 给这段代码加上注释
```python
# 初始化各个参数的梯度为0
dwxh, dwhh, dwhy = np.zeros_like(wxh), np.zeros_like(Whh), np.zeros_like(Why)
dbh, dby = np.zeros_like(bh), np.zeros_like(by)
dhnext = np.zeros_like(hs[0])
# 反向传播过程
for t in reversed(range(len(inputs))):
# 计算当前时刻的输出值
dy = np.copy(ps[t])
for b in range(0,B):
dy[targets[t][b], b] -= 1
# 计算各个参数的梯度
dwhy += np.dot(dy, hs[t].T)
dby += np.expand_dims(np.sum(dy, axis=1), axis=1)
dh = np.dot(Why.T, dy) + dhnext
dh = dh * (1 - hs[t] * hs[t])
dbh += np.expand_dims(np.sum(dh, axis=1), axis=1)
dWxh += np.dot(dh, xs[t].T)
dWhh += np.dot(dh, hs[t - 1].T)
dhnext = np.dot(Whh.T, dh)
# 返回损失函数值以及各个参数的梯度和最后一个时刻的隐藏状态
return loss, dwxh, dwhh, dwhy, dbh, dby, hs[len(inputs) - 1]
```
dt = 1.0 # 采样时间 A = np.array([[1, dt, 0.5*dt*dt], [0, 1, dt], [0, 0, 1]]) # 状态转移矩阵 H = np.array([1, 0, 0]).reshape(1, 3) # 观测矩阵 Q = np.array([[0.05, 0.05, 0.0], [0.05, 0.1, 0.01], [0.0, 0.01, 0.01]]) # 过程噪声协方差矩阵 R = np.array([0.5]).reshape(1, 1) # 观测噪声协方差矩阵 P = np.eye(3) # 状态协方差矩阵 # 定义卡尔曼滤波函数 def kalman_filter(z): x_hat = np.zeros((3, 1)) # 初始状态向量 for i in range(len(z)): # 预测 x_hat_minus = np.dot(A, x_hat) P_minus = np.dot(np.dot(A, P), A.T) + Q # 更新 K = np.dot(np.dot(P_minus, H.T), np.linalg.inv(np.dot(np.dot(H, P_minus), H.T) + R)) x_hat = x_hat_minus + np.dot(K, (z[i] - np.dot(H, x_hat_minus))) P = np.dot((np.eye(3) - np.dot(K, H)), P_minus) return x_hat # 对每个方向的时序信号进行卡尔曼滤波 x_filt = kalman_filter(x)报错File "G:\project2\KSVD.py", line 36, in <module> x_filt = kalman_filter(x) File "G:\project2\KSVD.py", line 26, in kalman_filter P_minus = np.dot(np.dot(A, P), A.T) + Q UnboundLocalError: local variable 'P' referenced before assignment
这个错误是因为在函数内部使用了变量P,但没有将其定义为全局变量或者传递给函数。解决方法是在函数内部将P定义为全局变量,或者将其作为函数的参数传递进来。例如:
```python
def kalman_filter(z, P):
x_hat = np.zeros((3, 1)) # 初始状态向量
for i in range(len(z)):
# 预测
x_hat_minus = np.dot(A, x_hat)
P_minus = np.dot(np.dot(A, P), A.T) + Q
# 更新
K = np.dot(np.dot(P_minus, H.T), np.linalg.inv(np.dot(np.dot(H, P_minus), H.T) + R))
x_hat = x_hat_minus + np.dot(K, (z[i] - np.dot(H, x_hat_minus)))
P = np.dot((np.eye(3) - np.dot(K, H)), P_minus)
return x_hat, P
P = np.eye(3) # 初始状态协方差矩阵
x_filt, P = kalman_filter(x, P)
```