Nowadays, many applications use Hidden Markov Models (HMMs) to solve crucial issues such as bioinformatics, speech recognition, musical analysis, digital signal processing, data mining, financial applications, time series analysis and many others. HMMs are probabilistic models which are very useful to model sequence behaviours or discrete time series events. Formally it models Markov processes with hidden states, like an extension for Markov Chains. For computer scientists, is a state machine with probabilistic transitions where each state can emit a value with a given probability.

For better understanding HMMs, I will illustrate how it works with "The Fair Bet Casino" problem. Imagine you are in a casino where you can bet on coins tosses, tossed by a dealer. A coin toss can have two outcomes: head (H) or tail (T). Now suppose that the coin dealer has two coins, a fair (F) which outputs both H and T with 1/2 probabilities and a biased coin (B) which outputs H with probability 3/4 and T with 1/4. Using probability language we say:

- P(H|F) = 1/2
- P(T|F) = 1/2
- P(H|B) = 3/4
- P(T|B) = 1/4

Now imagine that the dealer changes the coin in a way you can't see, but you know that he does it with a 1/10 probability. So thinking the coin tosses as a sequence of events we can say:

- P(F
_{i+1}|F_{i}) = 9/10 - P(B
_{i+1}|F_{i}) = 1/10 - P(B
_{i+1}|B_{i}) = 9/10 - P(F
_{i+1}|B_{i}) = 1/10

That's a HMM! It isn't any rocket science. Is just important to add a few remarks. We call the set of all possible emissions of the Markov process as the alphabet Σ ({H, T} in our problem). For many of computational method involving HMMs you will also need a initial state distribution π. For our problem we may assume that the we have equal probability for each coin.

Now comes in our mind what we can do with the model in our hands. There are lot's of stuff to do with it, such as: given a sequence of results, when the dealer used the biased coin or even generate a random sequence with a coherent behaviour when compared to the model.

There is a nice library called ghmm (available for C and Python) which handles HMMs and already gives us the most famous and important HMM algorithms. Unfortunately the python wrapper is not pythonic. Let's model our problem in python to have some fun:

Now comes in our mind what we can do with the model in our hands. There are lot's of stuff to do with it, such as: given a sequence of results, when the dealer used the biased coin or even generate a random sequence with a coherent behaviour when compared to the model.

There is a nice library called ghmm (available for C and Python) which handles HMMs and already gives us the most famous and important HMM algorithms. Unfortunately the python wrapper is not pythonic. Let's model our problem in python to have some fun:

import ghmm

# setting 0 for Heads and 1 for Tails as our Alphabet

sigma = ghmm.IntegerRange(0, 2)

# transition matrix: rows and columns means origin and destiny states

transitions_probabilities = [

[0.9, 0.1], # 0: fair state

[0.1, 0.9], # 1: biased state

]

# emission matrix: rows and columns means states and symbols respectively

emissions_probabilities = [

[0.5, 0.5], # 0: fair state emissions probabilities

[0.75, 0.25], # 1: biased state emissions probabilities

]

# probability of initial states

pi = [0.5, 0.5] # equal probabilities for 0 and 1

hmm = ghmm.HMMFromMatrices(

` sigma,`

` # you can model HMMs with others emission probability distributions`

ghmm.DiscreteDistribution(sigma),

` transitions_probabilities,`

emissions_probabilities,

pi

)

`>>> print hmm`

`DiscreteEmissionHMM(N=2, M=2)`

state 0 (initial=0.50)

Emissions: 0.50, 0.50

Transitions: ->0 (0.90), ->1 (0.10)

state 1 (initial=0.50)

Emissions: 0.75, 0.25

Transitions: ->0 (0.10), ->1 (0.90)

Now that we have our HMM object on the hand we can play with it. Suppose you have the given sequence of coin tosses and you would like to distinguish which coin was being used at a given state:

tosses = [1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1]

The viterbi algorithm can be used to trace the most probable states at each coin toss according to the HMM distribution:

# not as pythonic is could be :-/

sequence = ghmm.EmissionSequence(sigma, tosses)

viterbi_path, _ = hmm.viterbi(sequence)

>>> print viterbi_path

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

Nice! But sometimes is interesting to have the probability of each state on the point instead of only the most probable one. To have that, you must use the posterior or forward algorithms to have more detailed information.

states_probabilities = hmm.posterior(sequence)

>>> print

` states_probabilities`

` [[0.8407944139086141, 0.1592055860913865], [0.860787703168127, 0.13921229683187356], ... ]`

The posterior method result, returns the list of probabilities at each state, for example, in the first index we have

`[0.8407944139086141, 0.1592055860913865]`

. That means that we have ~0.84 probability of chance that the dealer is using the fair coin and ~0.16 for the biased coin. We also can plot a graph to show the behaviour of the curve of the probability of the dealer being using the fair coin (I used matplotlib for the graphs).Probability of being a fair coin over time |

This is only a superficial example of what can HMMs do. It's worthy give a look at it if you want do some sequence or time series analysis in any domain. I hope this post presented and cleared what are HMM and how they can be used to analyse data.

Welcome to Wiztech Automation - Embedded System Training in Chennai. We have knowledgeable Team for Embedded Courses handling and we also are after Job Placements offer provide once your Successful Completion of Course. We are Providing on Microcontrollers such as 8051, PIC, AVR, ARM7, ARM9, ARM11 and RTOS. Free Accommodation, Individual Focus, Best Lab facilities, 100% Practical Training and Job opportunities.

ReplyDelete✔ Embedded System Training in chennai

✔ Embedded System Training Institute in chennai

✔ Embedded Training in chennai

✔ Embedded Course in chennai

✔ Best Embedded System Training in chennai

✔ Best Embedded System Training Institute in chennai

✔ Best Embedded System Training Institutes in chennai

✔ Embedded Training Institute in chennai

✔ Embedded System Course in chennai

✔ Best Embedded System Training in chennai

WIZTECH Automation, Anna Nagar, Chennai, has earned reputation offering the best automation training in Chennai in the field of industrial automation. Flexible timings, hands-on-experience, 100% practical. The candidates are given enhanced job oriented practical training in all major brands of PLCs (AB, Keyence, ABB, GE-FANUC, OMRON, DELTA, SIEMENS, MITSUBISHI, SCHNEIDER, and MESSUNG)

ReplyDeletePLC training in chennai

Automation training in chennai

Best plc training in chennai

PLC SCADA training in chennai

Process automation training in chennai

Final year eee projects in chennai

VLSI training in chennai

Embedded system training: Wiztech Automation Provides Excellent training in embedded system training in Chennai - IEEE Projects - Mechanical projects in Chennai. Wiztech provide 100% practical training, Individual focus, Free Accommodation, Placement for top companies. The study also includes standard microcontrollers such as Intel 8051, PIC, AVR, ARM, ARMCotex, Arduino, etc.

ReplyDeleteEmbedded system training in chennai

Embedded Course training in chennai

Matlab training in chennai

Android training in chennai

LabVIEW training in chennai

Robotics training in chennai

Oracle training in chennai

Final year projects in chennai

Mechanical projects in chennai

ece projects in chennai

PLC training in Cochin, Kerala

ReplyDeleteAutomation training in Cochin, Kerala

Embedded System training in Cochin, Kerala

VLSI training in Cochin, Kerala

PLC training institute in Cochin, Kerala

Embedded training in Cochin, Kerala

Best plc training in Cochin, Kerala

Best B.Tech College in Noida

ReplyDelete