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Improvement: New function highorder_fuzzy_markov_chain that return the fuzzy markov chain (prior probability vector and transition matrix) of a PWFTS model

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Petrônio Cândido de Lima e Silva 2022-01-27 10:24:32 -03:00 committed by GitHub
parent d3aaa8a692
commit c8f4513f5e
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1 changed files with 17 additions and 8 deletions
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pyFTS/models/pwfts.py
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@ -661,22 +661,29 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
return tmp
def visualize_distributions(model, **kwargs):
import matplotlib.pyplot as plt
from matplotlib import gridspec
import seaborn as sns
def highorder_fuzzy_markov_chain(model):
ordered_sets = model.partitioner.ordered_sets
ftpg_keys = sorted(model.flrgs.keys(), key=lambda x: model.flrgs[x].get_midpoint(model.sets))
lhs_probs = [model.flrg_lhs_unconditional_probability(model.flrgs[k])
for k in ftpg_keys]
lhs_probs = np.array([model.flrg_lhs_unconditional_probability(model.flrgs[k])
for k in ftpg_keys])
mat = np.zeros((len(ftpg_keys), len(ordered_sets)))
for row, w in enumerate(ftpg_keys):
for col, k in enumerate(ordered_sets):
if k in model.flrgs[w].RHS:
mat[row, col] = model.flrgs[w].rhs_unconditional_probability(k)
return ftpg_keys, ordered_sets, lhs_probs, mat
def visualize_distributions(model, **kwargs):
import matplotlib.pyplot as plt
from matplotlib import gridspec
import seaborn as sns
ftpg_keys, ordered_sets, lhs_probs, mat = highorder_fuzzy_markov_chain(model)
size = kwargs.get('size', (5,10))
@ -695,3 +702,5 @@ def visualize_distributions(model, **kwargs):
ax.set_xticklabels(ordered_sets)
ax.grid(True)
ax.xaxis.set_tick_params(rotation=90)