def fetch_stats(request):
if request == "about":
return discord.Embed(
title="About this bot",
description=
"A bot made by NSNull#6107, built on the opensource reddit bot HowStat by @pranavrc. Source code is available at [jamiebishop/discord-cricket-stat-bot](https://github.com/jamiebishop/discord-cricket-stat-bot)",
color=discord.Color.green())
# Create a mapper instance
init = Mapper()
# Create an URL mapping using the input request.
try:
mapped = init.map_string(request)
except:
return build_error_embed("Unable to parse your command.")
# Find the player using the player name in the request.
try:
player_url = PlayerFinder(init.player_name)
except:
return build_error_embed(
"Sorry, the service seems to be unavailable right now.")
# Scrape and parse the statistics for the corresponding player.
try:
zeroed_in = player_url.zero_in()
if not player_url.test_player:
base_url = zeroed_in.replace("class=11;", "")
else:
base_url = zeroed_in.replace("class=1;", "")
except:
return build_error_embed("I couldn't find that, sorry.")
# Create a Prettifier instance if it's a valid stats url.
try:
if base_url[-1] == ";":
base_url += mapped
if init.has_type_override:
base_url = base_url.replace("type=allround;", "")
prettifier = Prettifier(base_url, player_url.test_player)
else:
return build_error_embed("I couldn't find that, sorry.")
except:
return build_error_embed("I couldn't find that, sorry.")
try:
stat_dict = prettifier.parse_into_dict(init.class_allround)
except Exception as e:
print(e)
return build_error_embed(
"An unknown error occured. Contact <@507975140567416843> with the command which caused this error."
)
return build_embed_fields_from_stats(stat_dict, base_url, init)
class MainHandler(object):
"""
"""
def __init__(self, *apps):
self.mapper = Mapper()
self.apps = []
for app in apps:
self.add_app(app)
@dec.wsgify
def __call__(self, req):
_app, _kwargs = self.mapper.match(req)
if not _app:
return exc.HTTPNotFound()
_app, _func_name = _app
req.environ['_func_name'] = _func_name
req.environ['_kwargs'] = _kwargs
return req.get_response(_app)
def add_app(self, app):
"""
:param app:
:return:
"""
if not is_app(app):
logger.error('not a app')
raise Exception()
app(self)
def fetch_stats(request):
# Create a mapper instance
init = Mapper()
# Create an URL mapping using the input request.
try:
mapped = init.map_string(request)
except:
return "Uh, like..I mean, like..really..like, y'know...What?"
# Find the player using the player name in the request.
try:
player_url = PlayerFinder(init.player_name)
except:
return "Sorry, the service seems to be unavailable right now."
# Scrape and parse the statistics for the corresponding player.
try:
zeroed_in = player_url.zero_in()
if not player_url.test_player:
base_url = zeroed_in.replace("class=11;", "")
else:
base_url = zeroed_in.replace("class=1;", "")
except:
return "I couldn't find that, sorry."
# Create a Prettifier instance if it's a valid stats url.
try:
if base_url[-1] == ";":
base_url += mapped
prettifier = Prettifier(base_url, player_url.test_player)
else:
return base_url
except:
return base_url
# Format the content for a reddit comment.
try:
final = prettifier.prettify(init.class_allround)
except:
return request + ":\n\n" + "Ouch, nothing to see here, I think. " + \
"You can check out the [records](%s)." % base_url
# Url for complete stats.
elaborate = "Detailed Stats [here.](%s)" % base_url
return request + ':\n\n' + final + '\n\n' + elaborate
def fetch_stats(request):
# Create a mapper instance
init = Mapper()
# Create an URL mapping using the input request.
try:
mapped = init.map_string(request)
except:
return "Uh, like..I mean, like..really..like, y'know...What?"
# Find the player using the player name in the request.
try:
player_url = PlayerFinder(init.player_name)
except:
return "Sorry, the service seems to be unavailable right now."
# Scrape and parse the statistics for the corresponding player.
try:
zeroed_in = player_url.zero_in()
if not player_url.test_player:
base_url = zeroed_in.replace("class=11;", "")
else:
base_url = zeroed_in.replace("class=1;", "")
except:
return "I couldn't find that, sorry."
# Create a Prettifier instance if it's a valid stats url.
try:
if base_url[-1] == ";":
base_url += mapped
prettifier = Prettifier(base_url, player_url.test_player)
else:
return base_url
except:
return base_url
# Format the content for a reddit comment.
try:
final = prettifier.prettify(init.class_allround)
except:
return request + ":\n\n" + "Ouch, nothing to see here, I think. " + \
"You can check out the [records](%s)." % base_url
# Url for complete stats.
elaborate = "Detailed Stats [here.](%s)" % base_url
return request + ':\n\n' + final + '\n\n' + elaborate
def __init__(self, *apps):
self.mapper = Mapper()
self.apps = []
for app in apps:
self.add_app(app)
import json
import numpy as np
from utils import Mapper, LinearLearning, Saver, PendulumEnv
parameters_file = "experiments/exp_1_linear_learning.json"
with open(parameters_file) as j:
parameters = json.loads(j.read())
mapping = Mapper()
env = PendulumEnv()
saver = Saver()
state_map, state_reverse_map = mapping.get_state_map(
parameters["step_state"], parameters["decimal_state"])
action_map, action_reverse_map = mapping.get_action_map(
parameters["step_action"], parameters["decimal_action"])
steps = []
rewards = []
final_mean_reward = []
for i in range(parameters["n_simulations"]):
lr_learner = LowRankLearning(env=env,
state_set=parameters["state_set"],
state_map=state_map,
action_map=action_map,
state_reverse_map=state_reverse_map,
action_reverse_map=action_reverse_map,
decimal_state=parameters["decimal_state"],
decimal_action=parameters["decimal_action"],
step_state=parameters["step_state"],
import json
import numpy as np
from utils import Mapper, LowRankTD, Saver
parameters_file = "experiments/exp_lr_learning.json"
with open(parameters_file) as j:
parameters = json.loads(j.read())
mapper = Mapper()
env = gym.make('Acrobot-v1')
env._max_episode_steps = np.inf
saver = Saver()
rewards = []
steps = []
for _ in range(parameters["n_simulations"]):
low_rank_learner = LowRankTD(
env=env,
k=parameters["k"],
mapper=mapper,
episodes=parameters["episodes"], # 50000,
max_steps=parameters["max_steps"],
epsilon=parameters["epsilon"],
decay=parameters["decay"],
alpha=parameters["alpha"],
gamma=parameters["gamma"])
low_rank_learner.train()
def main():
input_NPZNB_file_name = "npz-nb.csv"
input_NBNB_file_name = "nb-nb.csv"
input_NBAZS_file_name = "nb-azs.csv"
input_NPZAZS_file_name = "npz-azs.csv"
input_supply_demand_file_name = "sup-dem.csv"
out_csv_file_name = "out.csv"
out_info_file_name = "info.txt"
edgesNPZNB = edges_from_csv(input_NPZNB_file_name)
edgesNBNB = edges_from_csv(input_NBNB_file_name)
edgesNBAZS = edges_from_csv(input_NBAZS_file_name)
edgesNPZAZS = edges_from_csv(input_NPZAZS_file_name)
supply, demand, capacity = sup_dem_from_csv(input_supply_demand_file_name)
print("Solving for: numEdges={}".format(len(edgesNPZNB+edgesNBAZS+edgesNPZAZS+edgesNBNB)))
print("supply ={}, demand ={}".format(supply, demand))
# отображать метки узлов как целые числа от 1 до n
# Реализация MCMF требует, чтобы мы использовали от 0 до n для меток узлов, чтобы
# удобно было использовать матрицы для весов, потоков и т. д.
# При выводе ответа получим инверсию меток узлов.
mapper = Mapper(1)
edgesNPZ_NBmapped = [(mapper.mapQ(e[0]), mapper.mapP(e[1]), e[2])
for e in edgesNPZNB]
edgesNB_NBmapped = [(mapper.mapP(e[0]), mapper.mapP(e[1]), e[2])
for e in edgesNBNB]
edgesNB_AZSmapped = [(mapper.mapP(e[0]), mapper.mapU(e[1]), e[2])
for e in edgesNBAZS]
edgesNPZ_AZSmapped = [(mapper.mapQ(e[0]), mapper.mapU(e[1]), e[2])
for e in edgesNPZAZS]
unique_qs = set(mapper.qs.values())
all_qs = list(unique_qs)
unique_us = set(mapper.us.values())
all_us = list(unique_us)
all_us = sorted(all_us) #confusing undefined bug
unique_bs = set(mapper.bs.values())
all_bs = list(unique_bs)
n = len(all_qs) + len(all_us) +len(all_bs) + 1 + 1
## Назначаются первая и последняя метки узлов, то есть исток и сток.
src_label = 0
snk_label = n - 1
desired_flow = 0.0
possible_flow = 0.0
for i in range(len(demand)):
desired_flow += demand[i]
for i in range(len(supply)):
possible_flow += supply[i]
edgesNPZ_AZSdesired_flow = [(e[0], e[1], desired_flow, e[2])
for e in edgesNPZ_AZSmapped]
edgesNPZ_NBdesired_flow = [(e[0], e[1], desired_flow, e[2])
for e in edgesNPZ_NBmapped]
edgesNB_AZSdesired_flow = [(e[0], e[1], desired_flow, e[2])
for e in edgesNB_AZSmapped]
sizeofList = len(edgesNB_NBmapped)
edgesNB_NBdesired_flow = []
i = 0
while i< sizeofList:
edgesNB_NBdesired_flow.append((edgesNB_NBmapped[i][0],edgesNB_NBmapped[i][1], \
capacity[i], edgesNB_NBmapped[i][2]))
i += 1
edges = edgesNPZ_NBdesired_flow + edgesNB_NBdesired_flow \
+ edgesNB_AZSdesired_flow + edgesNPZ_AZSdesired_flow
num_edges_in_the_middle = len(edges)
i = 0
for q in all_qs:
edges.append((src_label, q, supply[i], 0)) # src -> q (cap: h, cost: 0)
i += 1
i = 0
for u in all_us:
edges.append((u, snk_label, demand[i], 0)) # u -> snk (cap: h, cost: 0)
i += 1
edge_list = [Edge(e[0], e[1], e[2], e[3]) for e in edges]
# Максимальный поток минимальной стоимости
mcf = MCF(n, edge_list)
flow, min_cost = mcf.min_cost_flow(desired_flow, src_label, snk_label)
assigned_u_grouped_by_q = {}
for i in range(num_edges_in_the_middle):
e = mcf.edges[i]
q = e.fr
u = e.to
c = mcf.capacity[q][u]
if 0.0 < c < desired_flow :
if assigned_u_grouped_by_q.get(q, None) is None:
assigned_u_grouped_by_q[q] = []
assigned_u_grouped_by_q[q].append(u)
with open(out_csv_file_name, "w") as f:
for q in assigned_u_grouped_by_q.keys():
assigned_u_grouped_by_q[q] = sorted(assigned_u_grouped_by_q[q])
for u in assigned_u_grouped_by_q[q]:
f.write("{} --> {}".format(mapper.invQBU(q), mapper.invQBU(u)))
f.write(" passed flow= {}\n".format(desired_flow - mcf.capacity[q][u]))
with open(out_info_file_name, "w") as f:
total_benefit = min_cost
f.write("Value of left cut: |Q|.h = {}\n".format(possible_flow))
f.write("Value of right cut: |U|.l = {}\n".format(desired_flow))
f.write("Total flow sent: {}\n".format(flow))
f.write("Min cost: {}\n".format(total_benefit))
if flow > 0:
f.write("Min cost per flow: {}\n".format(total_benefit/flow))
from utils import Mapper, QLearning, LowRankLearning, Saver, PendulumEnv
mapping = Mapper()
env = PendulumEnv()
saver = Saver()
step = .1
decimal = 1
episodes = 30000
max_steps = 100
alpha_q = .1
alpha_lr = .005
gamma = .9
epsilon = .2
k = 5
lambda_l = .1
lambda_r = .1
state_map, state_reverse_map = mapping.get_state_map(step, decimal)
action_map, action_reverse_map = mapping.get_action_map(step, decimal)
n_states = len(state_map)
n_actions = len(action_map)
q_learner = QLearning(env=env,
state_map=state_map,
action_map=action_map,
state_reverse_map=state_reverse_map,
action_reverse_map=action_reverse_map,
n_states=n_states,
n_actions=n_actions,
