def test_output():
post_URL = request.args.get("postURL")
eg_subreddit = fu.subreddit(post_URL)
postAtt = fu.rcommentLink(post_URL)
eg_postTitle = postAtt.title
eg_comment = [comment.body for comment in postAtt.comments][0]
commentCreated = int([comment.created_utc for comment in postAtt.comments][0])
postCreated = postAtt.created_utc
timeDiffC = fu.timePresent(round(commentCreated - postCreated))
timeDiffCur = fu.timePresent(round(time.time() - commentCreated))
comLength = fu.cLength(fu.comLength(eg_comment))
sentiment = ""
sentiment = fu.sentiment(eg_comment)
features = fu.features(eg_subreddit, post_URL, eg_comment, commentCreated)
eg_prob = ""
eg_prob = fu.probability(features)
eg_score = [comment.score for comment in postAtt.comments][0]
eg_commentForm = fu.commentSent(eg_comment)
return render_template(
"testOutput.html",
eg_postTitle=eg_postTitle,
eg_commentForm=eg_commentForm,
eg_score=eg_score,
eg_prob=eg_prob,
sentiment=sentiment,
comLength=comLength,
timeDiffCur=timeDiffCur,
timeDiffC=timeDiffC,
eg_comment=eg_comment,
post_URL=post_URL,
eg_subreddit=eg_subreddit,
)
def output():
post = request.args.get("post")
comment = request.args.get("comment")
postAtt = fu.rcommentLink(post)
postTitle = postAtt.title
the_subreddit = ""
the_subreddit = fu.subreddit(post)
the_post = ""
the_post = post
your_comment = ""
your_comment = comment
features = fu.features(the_subreddit, post, comment, time.time())
timePost = ""
timePost = fu.timePresent(features[4])
comLength = ""
comLength = fu.cLength(features[5])
sentiment = ""
sentiment = fu.sentiment(your_comment)
prob = ""
prob = fu.probability(features)
commentForm = fu.commentSent(your_comment)
return render_template(
"output.html",
postTitle=postTitle,
commentForm=commentForm,
prob=prob,
timePost=timePost,
comLength=comLength,
sentiment=sentiment,
the_subreddit=the_subreddit,
the_post=the_post,
your_comment=your_comment,
)
# Set of actions U
actions = [(1, 0), (-1, 0), (0, -1), (0, 1)]
# Initial state
init = (3, 0)
# Discount factor
gamma = 0.99
# Number of steps
N = 7
# Deterministic: stocha = False; Stochastic: stocha = True
stocha = True
test = f.probability((0, 2), (0, 4), (0, 1), stocha, domain)
# mu* the optimal policy
opt_policy = f.compute_policy(gamma, N, stocha, domain)
dyn_matrix = np.full((916, domain.shape[0], domain.shape[1]), np.inf)
# J_{mu*}^N for every state
J_values = np.zeros((5, 5))
for i in range(5):
for j in range(5):
state = (i, j)
J_values[i, j] = f.expected_return(state, domain, gamma, 916,
stocha, dyn_matrix, opt_policy)
def proba(T, L, cycles):
state=True; proba = True; accept=False
stead = int(1e4)
cycles = cycles
store = monteCarlo(T,L, cycles, False, state, accept, proba)
probability(store, L, T, stead)
