Percentual proximity scoring algorithm added

Author: markmelnic

scalg.py

@@ -0,0 +1,74 @@
+
+def score(source_data : list, weights : list, *args) -> list:
+
+    '''
+    int list - weights
+    possible values - 0 / 1
+    0 if lower values have higher weight in the data set
+    1 if higher values have higher weight in the data set
+    ==========
+    Optional arguments:
+    str - "score_lists"
+    get a list with all the scores for each piece of data
+
+    str - "scores"
+    get only the final scores for each data set
+    '''
+
+    # getting data
+    data_lists = []
+    for item in source_data:
+        for i in range(len(item)):
+            try:
+                data_lists[i].append(float(item[i]))
+            except IndexError:
+                data_lists.append([])
+                data_lists[i].append(float(item[i]))
+
+    score_lists = []
+    # calculating price score
+    for dlist, weight in zip(data_lists, weights):
+        mind = min(dlist)
+        maxd = max(dlist)
+
+        score = []
+        if weight == 0:
+            for item in dlist:
+                try:
+                    score.append(1 - ((item - mind) / (maxd - mind)))
+                except ZeroDivisionError:
+                    score.append(1)
+
+        elif weight == 1:
+            for item in dlist:
+                try:
+                    score.append((item - mind) / (maxd - mind))
+                except ZeroDivisionError:
+                    score.append(0)
+
+        else:
+            raise ValueError("Invalid weight of %f provided" % (weight))
+
+        score_lists.append(score)
+
+    # return score lists
+    if "score_lists" in args:
+        return score_lists
+
+    # initialize final scores
+    final_scores = [0 for i in range(len(score_lists[0]))]
+
+    # generate final scores
+    for i, slist in enumerate(score_lists):
+        for j, ele in enumerate(slist):
+            final_scores[j] = final_scores[j] + ele
+
+    # return only scores
+    if "scores" in args:
+        return final_scores
+
+    # append scores to source data
+    for i, ele in enumerate(final_scores):
+        source_data[i].append(ele)
+
+    return source_data