Implement compute_emission_probability

python/tests/beagle_numba.py

@@ -322,6 +322,20 @@ def get_transition_probs(cm, h, ne):
     return trans_probs
 
 
+def compute_emission_probability(mismatch_prob, is_match, *, num_alleles=2):
+    """
+    :param float mismatch_prob: Mismatch probability.
+    :param bool is_match: True if match, otherwise mismatch.
+    :param int num_alleles: Number of distinct alleles (default = 2).
+    :return: Emission probability
+    :rtype: float
+    """
+    em_prob = mismatch_prob
+    if is_match:
+        em_prob = 1.0 - (num_alleles - 1) * mismatch_prob
+    return em_prob
+
+
 @njit
 def compute_forward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
     """
@@ -353,9 +367,7 @@ def compute_forward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
             # Get allele at genotyped position i on reference haplotype j.
             ref_a = ref_h[i, j]
             # Get emission probability.
-            em_prob = mismatch_probs[i]
-            if query_a == ref_a:
-                em_prob = 1.0 - mismatch_probs[i]
+            em_prob = compute_emission_probability(mismatch_probs[i], query_a == ref_a)
             fwd_mat[i, j] = em_prob
             if i > 0:
                 fwd_mat[i, j] *= scale * fwd_mat[i - 1, j] + shift
@@ -390,17 +402,18 @@ def compute_backward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
     bwd_mat = np.zeros((m, h), dtype=np.float64)
     bwd_mat[-1, :] = 1.0 / h  # Initialise the last column.
     for i in range(m - 2, -1, -1):
-        query_a = query_h[i + 1]
+        iP1 = i + 1
+        query_a = query_h[iP1]
         for j in range(h):
-            ref_a = ref_h[i + 1, j]
-            em_prob = mismatch_probs[i + 1]
-            if ref_a == query_a:
-                em_prob = 1.0 - mismatch_probs[i + 1]
-            bwd_mat[i + 1, j] *= em_prob
-        site_sum = np.sum(bwd_mat[i + 1, :])
-        scale = (1 - trans_probs[i + 1]) / site_sum
-        shift = trans_probs[i + 1] / h
-        bwd_mat[i, :] = scale * bwd_mat[i + 1, :] + shift
+            ref_a = ref_h[iP1, j]
+            em_prob = compute_emission_probability(
+                mismatch_probs[iP1], query_a == ref_a
+            )
+            bwd_mat[iP1, j] *= em_prob
+        site_sum = np.sum(bwd_mat[iP1, :])
+        scale = (1 - trans_probs[iP1]) / site_sum
+        shift = trans_probs[iP1] / h
+        bwd_mat[i, :] = scale * bwd_mat[iP1, :] + shift
     return bwd_mat