04commit

04-HMM/hmm.py

@@ -0,0 +1,85 @@
+# Author: Kaituo Xu, Fan Yu
+
+def forward_algorithm(O, HMM_model):
+    """HMM Forward Algorithm.
+    Args:
+        O: (o1, o2, ..., oT), observations
+        HMM_model: (pi, A, B), (init state prob, transition prob, emitting prob)
+    Return:
+        prob: the probability of HMM_model generating O.
+    """
+    pi, A, B = HMM_model
+    T = len(O)
+    N = len(pi)
+    prob = 0.0
+    # Begin Assignment
+
+    # Put Your Code Here
+
+    # End Assignment
+    return prob
+
+
+def backward_algorithm(O, HMM_model):
+    """HMM Backward Algorithm.
+    Args:
+        O: (o1, o2, ..., oT), observations
+        HMM_model: (pi, A, B), (init state prob, transition prob, emitting prob)
+    Return:
+        prob: the probability of HMM_model generating O.
+    """
+    pi, A, B = HMM_model
+    T = len(O)
+    N = len(pi)
+    prob = 0.0
+    # Begin Assignment
+
+    # Put Your Code Here
+
+    # End Assignment
+    return prob
+
+
+def Viterbi_algorithm(O, HMM_model):
+    """Viterbi decoding.
+    Args:
+        O: (o1, o2, ..., oT), observations
+        HMM_model: (pi, A, B), (init state prob, transition prob, emitting prob)
+    Returns:
+        best_prob: the probability of the best state sequence
+        best_path: the best state sequence
+    """
+    pi, A, B = HMM_model
+    T = len(O)
+    N = len(pi)
+    best_prob, best_path = 0.0, []
+    # Begin Assignment
+
+    # Put Your Code Here
+
+    # End Assignment
+    return best_prob, best_path
+
+
+if __name__ == "__main__":
+    color2id = { "RED": 0, "WHITE": 1 }
+    # model parameters
+    pi = [0.2, 0.4, 0.4]
+    A = [[0.5, 0.2, 0.3],
+         [0.3, 0.5, 0.2],
+         [0.2, 0.3, 0.5]]
+    B = [[0.5, 0.5],
+         [0.4, 0.6],
+         [0.7, 0.3]]
+    # input
+    observations = (0, 1, 0)
+    HMM_model = (pi, A, B)
+    # process
+    observ_prob_forward = forward_algorithm(observations, HMM_model)
+    print(observ_prob_forward)
+
+    observ_prob_backward = backward_algorithm(observations, HMM_model)
+    print(observ_prob_backward)
+
+    best_prob, best_path = Viterbi_algorithm(observations, HMM_model) 
+    print(best_prob, best_path)