Description Usage Arguments Value See Also Examples

`baumWelch`

recursively calls this function to give a final estimate of parameters for tree HMM
Uses Parallel Processing to speed up calculations for large data. Should not be used directly.

1 2 | ```
baumWelchRecursion(hmm, observation, kn_states = NULL,
kn_verify = NULL)
``` |

`hmm` |
hmm Object of class List given as output by |

`observation` |
A list consisting "k" vectors for "k" features, each vector being a character series of discrete emmision values at different nodes serially sorted by node number |

`kn_states` |
(Optional) A (L * 2) dataframe where L is the number of training nodes where state values are known. First column should be the node number and the second column being the corresponding known state values of the nodes |

`kn_verify` |
(Optional) A (L * 2) dataframe where L is the number of validation nodes where state values are known. First column should be the node number and the second column being the corresponding known state values of the nodes |

List containing estimated Transition and Emission probability matrices

1 2 3 4 5 6 7 8 9 | ```
tmat = matrix(c(0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0),
5,5, byrow= TRUE ) #for "X" (5 nodes) shaped tree
hmmA = initHMM(c("P","N"),list(c("L","R")), tmat) #one feature with two discrete levels "L" and "R"
obsv = list(c("L","L","R","R","L")) #emissions for the one feature for the 5 nodes in order 1:5
kn_st = data.frame(node=c(2),state=c("P"),stringsAsFactors = FALSE)
#state at node 2 is known to be "P"
kn_vr = data.frame(node=c(3,4,5),state=c("P","N","P"),stringsAsFactors = FALSE)
#state at node 3,4,5 are "P","N","P" respectively
newparam= baumWelchRecursion(hmmA,obsv,kn_st, kn_vr)
``` |

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