Longest Common Subsequences

An example plot of digitaldna.LongestCommonSubsequence

Out:

fitting...
finding cut...
predicting...
done.

from digitaldna import TwitterDDNASequencer
from digitaldna import LongestCommonSubsequence
from digitaldna import Verbosity
import numpy as np

# Generate DDNA from Twitter
model = TwitterDDNASequencer(input_file='timelines.json', alphabet='b3_type')
arr = model.fit_transform()

# Simulate bots by repeating 10 times the first timeline
nrep_arr = [10 if i == 0 else 1 for i in range(len(arr))]
arr = np.repeat(arr, nrep_arr, axis=0)
arr[:, 0] = np.random.randint(0, high=10100, size=len(arr))

# Compute Longest Common Subsequences
estimator = LongestCommonSubsequence(window=2, verbosity=Verbosity.FILE)
estimator.fit_predict(arr[:, 1])

# Plot
estimator.plot_LCS()

Total running time of the script: ( 0 minutes 3.474 seconds)