我正在尝试使用Pykalman
实现卡尔曼滤波器的简化应用,但是我在Pykalman
包附带的EM算法的估计步骤中收到错误。
这是一个基于模拟数据的具有时变系数的简单线性回归。下面的代码模拟数据并启动卡尔曼滤波器,但当我尝试使用kf.em(Data)
根据观察结果估计参数时,它会返回错误:ValueError: object arrays are not supported
。
我在pykalman
做错了吗?
下面的模型和完整代码。错误发生在代码的最后一行。
State-Space representation of the model
import pandas as pd
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import pylab as pl
from pykalman import KalmanFilter
# generates the data
Data = pd.DataFrame(columns=['NoiseAR','NoiseReg', 'x', 'beta', 'y'], index=range(1000))
Data['NoiseAR'] = np.random.normal(loc=0.0, scale=1.0, size=1000)
Data['NoiseReg'] = np.random.normal(loc=0.0, scale=1.0, size=1000)
for i in range(1000):
if i==0:
Data.loc[i, 'x'] = Data.loc[i, 'NoiseAR']
else:
Data.loc[i, 'x'] = 0.95*Data.loc[i-1, 'x'] + Data.loc[i, 'NoiseAR']
for i in range(1000):
Data.loc[i, 'beta'] = np.sin(np.radians(i))
Data['y'] = Data['x']*Data['beta'] + Data['NoiseReg']
# set up the kalman filter
F = [1.]
H = Data['x'].values.reshape(1000,1,1)
Q = [2.]
R = [2.]
init_state_mean = [0.]
init_state_cov = [2.]
kf = KalmanFilter(
transition_matrices=F,
observation_matrices=H,
transition_covariance=Q,
observation_covariance=R,
initial_state_mean=init_state_mean,
initial_state_covariance=init_state_cov,
em_vars=['transition_covariance', 'observation_covariance', 'initial_state_mean', 'initial_state_covariance']
)
# estimate the parameters from em_vars using the EM algorithm
kf = kf.em(Data['y'].values)
答案 0 :(得分:2)
我明白了! Data['y'].values
是一个带有dtype=object
的numpy数组。我所要做的就是使用.astype(float)
将数组的类型更改为float。这必须使用pykalman
的kalman过滤器对象中的所有内容来完成,因此我还必须更改H
矩阵的类型。
希望这有助于将来的某个人!
以下是最终工作代码的样子:
import pandas as pd
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import pylab as pl
from pykalman import KalmanFilter
Data = pd.DataFrame(columns=['NoiseAR','NoiseReg', 'x', 'beta', 'y'], index=range(1000))
Data['NoiseAR'] = np.random.normal(loc=0.0, scale=1.0, size=1000)
Data['NoiseReg'] = np.random.normal(loc=0.0, scale=1.0, size=1000)
plt.plot(Data[['NoiseAR','NoiseReg']])
plt.show()
for i in range(1000):
if i == 0:
Data.loc[i, 'x'] = Data.loc[i, 'NoiseAR']
else:
Data.loc[i, 'x'] = 0.95 * Data.loc[i - 1, 'x'] + Data.loc[i, 'NoiseAR']
plt.plot(Data['x'])
plt.show()
for i in range(1000):
Data.loc[i, 'beta'] = np.sin(np.radians(i))
plt.plot(Data['beta'])
plt.show()
Data['y'] = Data['x']*Data['beta'] + Data['NoiseReg']
plt.plot(Data[['x', 'y']])
plt.show()
F = [1.]
H = Data['x'].values.reshape(1000,1,1).astype(float)
Q = [2.]
R = [2.]
init_state_mean = [0.]
init_state_cov = [2.]
kf = KalmanFilter(
transition_matrices=F,
observation_matrices=H,
transition_covariance=Q,
observation_covariance=R,
initial_state_mean=init_state_mean,
initial_state_covariance=init_state_cov,
em_vars=['transition_covariance', 'observation_covariance', 'initial_state_mean', 'initial_state_covariance']
)
kf = kf.em(Data['y'].values.astype(float))
filtered_state_estimates = kf.filter(Data['y'].values.astype(float))[0]
smoothed_state_estimates = kf.smooth(Data['y'].values.astype(float))[0]
pl.figure(figsize=(10, 6))
lines_true = pl.plot(Data['beta'].values, linestyle='-', color='b')
lines_filt = pl.plot(filtered_state_estimates, linestyle='--', color='g')
lines_smooth = pl.plot(smoothed_state_estimates, linestyle='-.', color='r')
pl.legend(
(lines_true[0], lines_filt[0], lines_smooth[0]),
('true', 'filtered', 'smoothed')
)
pl.xlabel('time')
pl.ylabel('state')
pl.show()