Weight.

bit-lsi · Sep 8, 2017 · a645dac · a645dac
1 parent feccac6
commit a645dac
Showing 1 changed file with 43 additions and 42 deletions.
diff --git a/weights.py b/weights.py
@@ -157,45 +157,46 @@ def get_weights_above_hypothesis_score_for_a_three_by_two_table(
                 if m_pp + m_mm - (m_pm + m_mp) >= hypothesisScore:
                     weights[1] = weights[1] + weight_contrib
 
-        # The loop below does exactly the same thing as the one above; it covers the cases that are not done in the loop above. A lower bound for possible
-        # values of n++ is 0 To avoid n_pp become negative or double counting when n_pp = 0, we add the following if statement
-        if (n_pp > 0):
-            for (m_pp in range(n_pp - 1, 0)):
-
-        # Iterate over all values of m_mp
-        for (m_mp in 0: min(r_m, n_p)) {
-
-            # n+-
-            m_pm = r_p - m_pp
-        # n--
-        m_mm = r_m - m_mp
-        # n0+
-        m_zp = n_p - (m_pp + m_mp)
-        # n0-
-        m_zm = n_m - (m_pm + m_mm)
-        contingencyTableValues = c(m_pp, m_pm, m_mp, m_mm, m_zp, m_zm)
-
-        m_pz = prediction_list_stats[1] - r_p
-        m_mz = prediction_list_stats[2] - r_m
-        m_zz = prediction_list_stats[3] - r_z
-
-        # None of these values can be less than zero
-        if min(contingencyTableValues) >= 0:
-
-
-            threeByThreeContingencyTable = [m_pp, m_pm, m_pz, m_mp, m_mm, m_mz, m_zp, m_zm, m_zz]
-            logDValue = math.log(math.factorial(log_of_factorial_of_prediction_list_stats)) - math.log(math.factorial(threeByThreeContingencyTable))
-
-        # Only need to compute the score if the D-value is non-neglible
-        if logDValue > logepsDMax:
-
-            # Rather than just calculate the DValue by taking the exponential of logDvalue, we first subtract a constant.  This means when we take the
-            # exponential we get the DValue divided by a different constant, but this cancels out when we take ratios later when computing the p-value
-            weight_contrib = math.exp(logDValue - logDMax)
-            weights[2] = weights[2] + weight_contrib
-
-        if (m_pp + m_mm - (m_pm + m_mp) >= hypothesisScore):
-
-            weights[1] =hts weights[1] + weight_contrib
-
-        return weights
+            # The loop below does exactly the same thing as the one above; it covers the cases that are not done in the loop above. A lower bound for possible
+            # values of n++ is 0 To avoid n_pp become negative or double counting when n_pp = 0, we add the following if statement
+            if (n_pp > 0):
+                for m_pp in range(n_pp - 1, 0):
+
+                    # Iterate over all values of m_mp
+                    for m_mp in range(0, min(r_m, n_p)):
+
+                        # n+-
+                        m_pm = r_p - m_pp
+                        # n--
+                        m_mm = r_m - m_mp
+                        # n0+
+                        m_zp = n_p - (m_pp + m_mp)
+                        # n0-
+                        m_zm = n_m - (m_pm + m_mm)
+                        contingencyTableValues = [m_pp, m_pm, m_mp, m_mm, m_zp, m_zm]
+
+                        m_pz = prediction_list_stats[1] - r_p
+                        m_mz = prediction_list_stats[2] - r_m
+                        m_zz = prediction_list_stats[3] - r_z
+
+                        # None of these values can be less than zero
+                        if min(contingencyTableValues) < 0:
+                            continue
+
+                        threeByThreeContingencyTable = [m_pp, m_pm, m_pz, m_mp, m_mm, m_mz, m_zp, m_zm, m_zz]
+                        logDValue = math.log(math.factorial(log_of_factorial_of_prediction_list_stats)) - math.log(
+                            math.factorial(threeByThreeContingencyTable))
+
+                        # Only need to compute the score if the D-value is non-neglible
+                        if logDValue <= logepsDMax:
+                            continue
+
+                        # Rather than just calculate the DValue by taking the exponential of logDvalue, we first subtract a constant.  This means when we take the
+                        # exponential we get the DValue divided by a different constant, but this cancels out when we take ratios later when computing the p-value
+                        weight_contrib = math.exp(logDValue - logDMax)
+                        weights[2] = weights[2] + weight_contrib
+
+                        if (m_pp + m_mm - (m_pm + m_mp) >= hypothesisScore):
+                            weights[1] = weights[1] + weight_contrib
+
+    return weights