Define your maze, including the layout, traps, exit points, and any other relevant details. # Define your maze layout, e.g., as a 2D array maze = [ [0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 0, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0] ] # Define transition probabilities, emission probabilities, and other HMM parameters # These will depend on your specific game mechanics Create an HMM Model Create an HMM model using the 'hmmlearn' library. You'll need to define the number of states, initialize the model, and fit it to your data. # Create an HMM model num_states = 4 # cell free, cell has a trap, cell has a block, or cell has a player num_iterations = 30 model = hmm.MultinomialHMM(n_components=num_states, n_iter=num_iterations) model.fit(training_data)
I'm having trouble with creating the input into hmmlearn function fit for my model as defined by the Python snippet
Define your maze, including the layout, traps, exit points, and any other relevant details.
# Define your maze layout, e.g., as a 2D array
maze = [
[0, 0, 0, 0, 0],
[0, 1, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]
]
# Define transition probabilities, emission probabilities, and other HMM parameters
# These will depend on your specific game
Create an HMM Model
Create an HMM model using the 'hmmlearn' library. You'll need to define the number of states, initialize the model, and fit it to your data.
# Create an HMM model
num_states = 4 # cell free, cell has a trap, cell has a block, or cell has a player
num_iterations = 30
model = hmm.MultinomialHMM(n_components=num_states, n_iter=num_iterations)
model.fit(training_data)
Step by step
Solved in 3 steps
