plot_mhmm()

plot_mhmm() visualizes mixture weights and per-cluster transition or emission matrices for a fitted MHMM.

Function Usage

plot_mhmm(
    model,
    which="clusters",
    figsize=None,
    ax=None
)

seqHMM Parameter Mapping

Sequenzo	seqHMM plot.mhmm()
model	Fitted mhmm object
which="clusters"	Mixture weight bar chart
which="transition" / "emission"	Per-cluster parameter panels

Entry Parameters

Parameter	Required	Type	Description
model	✓	MHMM	Fitted mixture model.
which	✗	str	"clusters", "transition", "emission", or "all". Default "clusters".
figsize	✗	tuple / None	Figure size.
ax	✗	Axes / None	Existing matplotlib axes.

Returns

A matplotlib Figure.

Example

Assume seq is a prepared SequenceData object with the observed sequences you want to model.

from sequenzo.seqhmm import build_mhmm, fit_mhmm, plot_mhmm
import matplotlib.pyplot as plt

mhmm = build_mhmm(seq, n_clusters=3, n_states=4, random_state=42)
mhmm = fit_mhmm(mhmm)

plot_mhmm(mhmm, which="all")
plt.show()

R Counterpart

• Closest R function: seqHMM plot.mhmm()
• Mapping note: Sequenzo uses matplotlib heatmaps; R uses igraph-based layouts per cluster.

Notes

• Model must be fitted before plotting.
• For single HMM graphs, use plot_hmm().

See Also

• Markov Chain Models Introduction maps the full HMM-family workflow.
• Model Comparison helps choose between fitted models.
• Sequenzo and seqHMM Mapping gives the R correspondence.

Authors

Code: Yuqi Liang

Documentation: Yuqi Liang

References

Helske, S., & Helske, J. (2019). Mixture hidden Markov models for sequence data: The seqHMM package in R. Journal of Statistical Software, 88(3), 1–32.