1. 2. 3. 4. Training of Discrete Models Turn-in Your Python code: def Baum Welch(N,A,B,Pi,T) : Where: N: Number of model states A: Transition probability matrix B: Output probability matrix T: Training data Pi: Initial state probability matrix Return re-estimated A, B and Pi matrices return [A,B,Pi] # Trained Model 1. 2. 3. Notes: Your code must be original work – That is: it must not be a copy of code found online. Submit your python code in a separate file named “viterbi_.py" As Outlook filters out executable files, submit your work in a .zip file A write-up describing the Baum-Welch algorithm and your implementation of it Note: Be sure to list all references Your hand calculations for the first observation vector A sample run for each of the three observation vectors Note: Your sample run for the 1st two observation vectors must match your hand calculations.
1. 2. 3. 4. Training of Discrete Models Turn-in Your Python code: def Baum Welch(N,A,B,Pi,T) : Where: N: Number of model states A: Transition probability matrix B: Output probability matrix T: Training data Pi: Initial state probability matrix Return re-estimated A, B and Pi matrices return [A,B,Pi] # Trained Model 1. 2. 3. Notes: Your code must be original work – That is: it must not be a copy of code found online. Submit your python code in a separate file named “viterbi_.py" As Outlook filters out executable files, submit your work in a .zip file A write-up describing the Baum-Welch algorithm and your implementation of it Note: Be sure to list all references Your hand calculations for the first observation vector A sample run for each of the three observation vectors Note: Your sample run for the 1st two observation vectors must match your hand calculations.
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Question
![1.
2.
Training of Discrete Models
Turn-in
Your Python code: def BaumWelch(N,A,B,Pi,T):
Where:
N: Number of model states A: Transition probability matrix
Pi: Initial state probability matrix
Return re-estimated A, B and Pi matrices
B: Output probability matrix
T: Training data
3.
4.
<Calculate Viterbi score and best path here>
return [A,B,Pi] # Trained Model
1.
2.
3.
Notes:
Your code must be original work – That is: it must not be a copy of code found online.
Submit your python code in a separate file named “viterbi_<LastName>.py"
As Outlook filters out executable files, submit your work in a .zip file
A write-up describing the Baum-Welch algorithm and your implementation of it
Note: Be sure to list all references
Your hand calculations for the first observation vector
A sample run for each of the three observation vectors
Note: Your sample run for the 1st two observation vectors must match your hand calculations.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2ad64212-e120-4fe2-97f2-e4e87c5c792a%2F5778cf01-d1a1-4ad1-81ba-142bd46c617c%2Fmefdy3_processed.png&w=3840&q=75)
Transcribed Image Text:1.
2.
Training of Discrete Models
Turn-in
Your Python code: def BaumWelch(N,A,B,Pi,T):
Where:
N: Number of model states A: Transition probability matrix
Pi: Initial state probability matrix
Return re-estimated A, B and Pi matrices
B: Output probability matrix
T: Training data
3.
4.
<Calculate Viterbi score and best path here>
return [A,B,Pi] # Trained Model
1.
2.
3.
Notes:
Your code must be original work – That is: it must not be a copy of code found online.
Submit your python code in a separate file named “viterbi_<LastName>.py"
As Outlook filters out executable files, submit your work in a .zip file
A write-up describing the Baum-Welch algorithm and your implementation of it
Note: Be sure to list all references
Your hand calculations for the first observation vector
A sample run for each of the three observation vectors
Note: Your sample run for the 1st two observation vectors must match your hand calculations.
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