<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 DM_08_04
Import packages
We'll create a hidden Markov model to examine the state-shifting in the dataset.
Step1:  Import data
Read CSV file into "df."
Step2:  Drop the row number and "corr" so we can focus on the influence of "prev" and "Pacc" on "rt." Also define "prev" as a factor.
Step3:  Create model
Make an HMM with 2 states. (The choice of 2 is based on theory.)
Step4:  Predict the hidden state for each record and get count of predicted states.
Step5:  Get the mean reaction time (rt) for each of the two states.
Step6:  Visualize results
Make a graph to show the change of states.
<ASSISTANT_TASK:>
Python Code:
% matplotlib inline
import pylab
import numpy as np
import pandas as pd
from hmmlearn.hmm import GaussianHMM
Explanation: DM_08_04
Import packages
We'll create a hidden Markov model to examine the state-shifting in the dataset.
End of explanation
df = pd.read_csv("speed.csv", sep = ",")
df.head(5)
Explanation: Import data
Read CSV file into "df."
End of explanation
x = df.drop(["row", "corr"], axis = 1)
x["prev"] = pd.factorize(x["prev"])[0]
Explanation: Drop the row number and "corr" so we can focus on the influence of "prev" and "Pacc" on "rt." Also define "prev" as a factor.
End of explanation
model = GaussianHMM(n_components=2, n_iter=10000, random_state=1).fit(x)
model.monitor_
Explanation: Create model
Make an HMM with 2 states. (The choice of 2 is based on theory.)
End of explanation
states = model.predict(x)
pd.Series(states).value_counts()
Explanation: Predict the hidden state for each record and get count of predicted states.
End of explanation
model.means_[:, 0]
Explanation: Get the mean reaction time (rt) for each of the two states.
End of explanation
fig = pylab.figure(figsize=(20, 1))
ax = fig.add_subplot(111)
ax.grid(True)
ax.set_xlabel("Record number")
ax.set_ylabel("State")
ax.plot(states)
Explanation: Visualize results
Make a graph to show the change of states.
End of explanation
<END_TASK>