Hidden Markov Models – Utility functions
AUTHOR:
- William Stein, 2010-03
Bases: object
A class used in order to share cdef’s methods between different files.
This function is used internally by the __init__ methods of various Hidden Markov Models.
INPUT:
- pi – vector, list, or TimeSeries
- normalize – if True, replace negative entries by 0 and rescale to ensure that the sum of the entries in each row is equal to 1. If the sum of the entries in a row is 0, replace them all by 1/N.
EXAMPLES:
sage: import sage.stats.hmm.util
sage: u = sage.stats.hmm.util.HMM_Util()
sage: u.initial_probs_to_TimeSeries([0.1,0.2,0.9], True)
[0.0833, 0.1667, 0.7500]
sage: u.initial_probs_to_TimeSeries([0.1,0.2,0.9], False)
[0.1000, 0.2000, 0.9000]
This function is used internally by the Hidden Markov Models code.
Replace entries of T[i:j] in place so that they are all nonnegative and sum to 1. Negative entries are replaced by 0 and T[i:j] is then rescaled to ensure that the sum of the entries in each row is equal to 1. If all entries are 0, replace them by 1/(j-i).
INPUT:
- T – a TimeSeries
- i – nonnegative integer
- j – nonnegative integer
OUTPUT:
- T is modified
EXAMPLES:
sage: import sage.stats.hmm.util
sage: T = stats.TimeSeries([.1, .3, .7, .5])
sage: u = sage.stats.hmm.util.HMM_Util()
sage: u.normalize_probability_TimeSeries(T,0,3)
sage: T
[0.0909, 0.2727, 0.6364, 0.5000]
sage: u.normalize_probability_TimeSeries(T,0,4)
sage: T
[0.0606, 0.1818, 0.4242, 0.3333]
sage: abs(T.sum()-1) < 1e-8 # might not exactly equal 1 due to rounding
True
This function is used internally by the __init__ methods of Hidden Markov Models to make a transition matrix from A.
INPUT:
- A – matrix, list, list of lists, or TimeSeries
- N – number of states
- normalize – if True, replace negative entries by 0 and rescale to ensure that the sum of the entries in each row is equal to 1. If the sum of the entries in a row is 0, replace them all by 1/N.
OUTPUT:
- a TimeSeries
EXAMPLES:
sage: import sage.stats.hmm.util
sage: u = sage.stats.hmm.util.HMM_Util()
sage: u.state_matrix_to_TimeSeries([[.1,.7],[3/7,4/7]], 2, True)
[0.1250, 0.8750, 0.4286, 0.5714]
sage: u.state_matrix_to_TimeSeries([[.1,.7],[3/7,4/7]], 2, False)
[0.1000, 0.7000, 0.4286, 0.5714]
Distributions used in implementing Hidden Markov Models
Discrete Gaussian Samplers over the Integers
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