dodox/CaliGraph
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

smashing 'deees bugs

Browse Source
This commit is contained in:
Dominik Moritz Roth 2022-03-07 17:56:59 +01:00
parent 9a02bdc2a8
commit cbb884b377
2 changed files with 7 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.gitignore vendored
View File

@ -2,5 +2,5 @@ __pycache__
*.html
.venv
neuralWeights.json
neuralWeights.json.bak
neuralWeights.json.*
.imgLinkCache.json

11
caliGraph.py
View File

@ -435,8 +435,9 @@ def scoreUnread(G, globMu, globStd):
score = 0
nb = dict(neuralBins)
act = {}
jig = {}
for b in nb:
act[b] = sum(nb[b])/len(nb[b])
act[b], jig[b] = norm.fit(nb[b])
score += act[b] * getWeightForType(b)
score /= sum([abs(getWeightForType(b)) for b in nb])
node['score'] = math.tanh(score/10)*10
@ -1200,7 +1201,7 @@ def findNewBooks(G, books, mu, num=-1, minRecSco=5):
# while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1)
# but might be necessary to enable later for a larger libary for better training performance...
# maybe try again for 128 books?
def evaluateFitness(books, batchSize=16, debugPrint=False, runPagerank=True):
def evaluateFitness(books, batchSize=16, debugPrint=False, calcPagerank=True):
global weights
G = buildBookGraph(books)
graphAddAuthors(G, books)
@ -1208,7 +1209,7 @@ def evaluateFitness(books, batchSize=16, debugPrint=False, runPagerank=True):
graphAddTopLists(G, books)
graphAddSeries(G, books)
graphAddTags(G, books)
if runPagerank:
if calcPagerank:
runPagerank(G)
ratedBooks = [n for n in list(G.nodes) if 'rating' in G.nodes[n] and G.nodes[n]['rating'] != None]
@ -1259,7 +1260,7 @@ def train(initGamma, full=True, noPagerank=False):
gamma = initGamma
books = loadBooksFromDB()
bestWeights = copy.copy(weights)
mse, gradient = evaluateFitness(books, runPagerank=not noPagerank)
mse, gradient = evaluateFitness(books, calcPagerank=not noPagerank)
delta = math.sqrt(sum(gradient[g]**2 for g in gradient)/len(gradient))
best_mse = mse
stagLen = 0
@ -1277,7 +1278,7 @@ def train(initGamma, full=True, noPagerank=False):
weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
#else:
# del weights[wt]
mse, gradient = evaluateFitness(books, runPagerank=not noPagerank)
mse, gradient = evaluateFitness(books, calcPagerank=not noPagerank)
if mse < last_mse:
gamma = gamma*1.25
else: