CODEWITHSUNDEEP
pythonpython-3.xpandasnumpymatplotlib
user10553396
user10553396

Reputation:

ValueError: The shape of all parameters

For my experiment, I have a very small time series data ready with three columns formatted as follows. The full dataset is attached here for reproduciability since I can't attach a file on stackoverflow:

http://www.mediafire.com/file/el1tkrdun0j2dk4/testdata.csv/file

  time        X      Y
 0.040662  1.041667  1
 0.139757  1.760417  2
 0.144357  1.190104  1
 0.145341  1.047526  1
 0.145401  1.011882  1
 0.148465  1.002970  1
 ....      .....     .

We wanted to do smoothing and here is my code

import matplotlib.pyplot as plt 
from pykalman import KalmanFilter 
import numpy as np
import pandas as pd

df = pd.read_csv('testdata.csv')
print(df)
pd.set_option('use_inf_as_null', True)

df.dropna(inplace=True)

X = df.drop('Y', axis=1)
y = df['Y']


d1= np.array(X)
d2 = np.array(y)

measurements = np.asarray(d1)

kf = KalmanFilter(transition_matrices=[1],
                  observation_matrices=[1],
                  initial_state_mean=measurements[0],
                  initial_state_covariance=1,
                  observation_covariance=5,
                  transition_covariance=1) 
state_means, state_covariances = kf.filter(measurements) 
state_std = np.sqrt(state_covariances[:,0]) 
print (state_std) 
print (state_means) 
print (state_covariances)

plt.plot(measurements, '-r', label='measurment') 
plt.plot(state_means, '-g', label='kalman-filter output') 
plt.legend(loc='upper left') 
plt.show()

If we consider only the X and Y columns as I did in my code above, it gives the following plot output

enter image description here

I wanted to have the first column ('time) (1-10) on the x-axis) and the values of the second column (X) on the y-axis.

However, when I try to add the first column in my dataset (time), I am getting the following error

ValueError: The shape of all parameters is not consistent. Please re-check their values.

How can I solve this problem? Any help would be appreciated.

Upvotes: 2

Views: 786

Answers (1)

Bruno Lubascher
Bruno Lubascher

Reputation: 2121

You can do the following changes:

1 Add measurements[0,1], so the input to intial_state_mean is a single value. This should get rid of the error you were seeing.

kf = KalmanFilter(transition_matrices=[1],
                  observation_matrices=[1],
                  initial_state_mean=measurements[0,1], # Change here
                  initial_state_covariance=1,
                  observation_covariance=5,
                  transition_covariance=1)

2 Change the input to kf.filter to include only the X column: measurements[:,1].

state_means, state_covariances = kf.filter(measurements[:,1]) # Change here
state_std = np.sqrt(state_covariances[:,0])
print (state_std)
print (state_means)
print (state_covariances)

3 Plot the time in the x-axis.

plt.plot(measurements[:,0], measurements[:,1], '-r', label='measurment') # Change here
plt.plot(measurements[:,0], state_means, '-g', label='kalman-filter output') # Change here
plt.legend(loc='upper left')
plt.show()

Upvotes: 2

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