From be0afb59c718a917eeae9c252ee120fe0ff9f3d3 Mon Sep 17 00:00:00 2001
From: Dominik Roth <dominik.roth.dev@gmail.com>
Date: Sun, 4 Jul 2021 20:08:25 +0200
Subject: [PATCH] Fined tuned wheight of TopLists and Tags

---
 caliGraph.py | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/caliGraph.py b/caliGraph.py
index 5ca158f..6111a8f 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -86,7 +86,7 @@ def getTopListWheight(book, topList):
             minScope = min(minScope, scope)
     if minScope == 100000:
         raise Exception("You stupid?")
-    return 100/minScope
+    return minScope / 10
 
 
 def removeRead(G):
@@ -287,6 +287,7 @@ def scoreUnread(G, globMu, globStd, errorFac=0.6):
     for n in list(G.nodes):
         feedbacks = []
         deepFeedbacks = []
+        deepLen = 0
         tagFeedbacks = []
         node = G.nodes[n]
         if node['t'] == 'book':
@@ -295,20 +296,27 @@ def scoreUnread(G, globMu, globStd, errorFac=0.6):
                 for adj in adjacens:
                     adjNode = G.nodes[adj]
                     if 'score' in adjNode and adjNode['score'] != None:
-                        if adjNode['t'] != 'tag':
-                            feedbacks.append(adjNode['score'])
-                            for fb in adjNode['feedbacks']:
-                                deepFeedbacks.append(fb)
+                        if adjNode['t'] == 'tag':
+                            w = int(10/(len(G.adj[adj])))
+                        elif adjNode['t'] == 'topList':
+                            w = int(G[n][adj]['wheight']*5)
                         else:
-                            tagFeedbacks.append(adjNode['score'])
+                            w = 10
+                        feedbacks.append(adjNode['score'])
+                        for fb in adjNode['feedbacks']:
+                            for i in range(w):
+                                deepFeedbacks.append(fb)
+                            deepLen += w
                 if len(feedbacks):
                     node['mean'], node['std'] = norm.fit(deepFeedbacks)
                     node['mean2'], node['std2'] = norm.fit(feedbacks)
-                    f_mean, f_std = norm.fit(feedbacks)
-                    node['se'] = globStd / math.sqrt(len(deepFeedbacks))
+                    if deepLen:
+                        node['se'] = globStd / math.sqrt(deepLen)
                     # - errorFac*node['se']
-                    node['score'] = (
-                        (node['mean'] - errorFac*node['se'])*4 + node['mean2']*2 + (f_mean)*1)/7 + node['std2'] * 0.25
+                        node['score'] = (
+                            (node['mean'] - errorFac*node['se'])*3 + node['mean2']*2)/5
+                    else:
+                        node['score'] = globMu - errorFac*globStd
                     if 'series' in node:
                         if node['series_index'] == 1.0:
                             node['score'] += 0.000000001