def build_env(self, env_name: str):
self.env_name = env_name
self.env_data = db.load_data(env_name) # 載入數據庫
# 建立環境地圖
self.env_map = np.array(self.env_data['floors']['map'])
# 禁用(清除)指定事件位置
for pos in self.env_data['floors']['disable']:
self.change_map_id(pos, 0)
# 建置玩家初始狀態
self.player.reset(self.env_data['player'])
# 建立初始觀測值
player_id = None
for key, value in self.env_data['maps'].items():
if value['id'] == 'background':
self.background_id = key
if value['id'] == 'player':
if player_id is None: # 只取第一個出現player的ID
player_id = key
player_pos = np.argwhere(self.env_map==player_id)[0]
self.observation = np.append(player_pos, [0,0], axis=0)
# 清除主角位置
self.change_map_id(tuple(player_pos), 0)
# 複製資料對象，用來快速重置
self._original_env_map = self.env_map.copy()
self._original_observation = self.observation.copy()
def build_env(self, env_name: str) -> int:
self.env_name = env_name
self.env_data = db.load_data(env_name) # 載入數據庫
self.env_map = np.array(self.env_data['floors']['map'])
map_ = self.env_data['floors']['map']
disable = self.env_data['floors']['disable']
maps = self.env_data['maps']
enemys = self.env_data['enemys']
items = self.env_data['items']
npcs = self.env_data['npcs']
for z, y, x in self.pos_iter():
pos = (z, y, x)
kw = maps[map_[z][y][x]]
if kw['id'] == 'background':
self.background_id = map_[z][y][x]
if kw['cls'] != 'except': # 非事件id不須建立成節點物件
if kw['cls'] == 'enemys': # 敵人類別
node = Enemy(kw)
node.reset(enemys[kw['id']])
elif kw['cls'] == 'items': # 道具類別
node = Item(kw, items[kw['id']])
elif kw['cls'] == 'terrains': # 地形類別
node = Terrain(kw)
elif kw['cls'] == 'npcs': # 人物類別
if pos not in npcs:
node = NPC(kw, [])
elif npcs[pos] == 'shop':
node = NPC(kw, [])
i = 1
# 4維座標
while pos + (i, ) in npcs:
snode = NPC(kw, npcs[pos + (i, )])
self.n2p[snode] = pos + (i, )
self.p2n[pos + (i, )] = snode
i += 1
else:
node = NPC(kw, npcs[pos])
elif kw['id'] == 'player': # 玩家類別
node = Player(kw)
node.reset(self.env_data['player'])
self.player = node
elif kw['id'] == 'end': # 結束標誌類別
node = End_Flag(kw)
else:
raise TypeError(
f'When creating a node, the {kw["cls"]} class is not defined'
)
# 事件禁用(無法觸發)
if pos in disable:
node.disabled = True
self.n2p[node] = pos
self.p2n[pos] = node
# 設置觀測值
self.observation.append(self.player)
return len(self.n2p)
def __init__(self, directory, load_file=None):
self.img_size = 100
self.n_angles = 6
direc = directory
self.pre = preprocess.Preprocess(self.img_size, self.n_angles)
self.lookup = []
for subdir, dirs, files in os.walk(direc):
for f in files:
filename = os.path.join(subdir, f)
if filename[-3:] == 'jpg':
if not load_file:
raw_img = cv2.imread(filename, 0)
self.pre.process_img(raw_img)
self.lookup.append(filename)
print '%i:\t%s' % (len(self.lookup), filename)
if load_file:
edgel_counts = database.load_data(self.pre.hits, load_file)
self.pre.set_edgel_counts(edgel_counts)
def pca_analysis(goes=True):
_o, xparams, yparam = db.load_data()
components = 6
dates = pd.read_csv("stormlist.csv")
dates.dates = pd.to_datetime(dates.dates)
if goes: _o, xparams, yparam_clf = db.load_data_RB()
else: _o, xparams, yparam_clf = db.load_data()
pca = decomposition.PCA(n_components=components)
df = _o[xparams]
data_scaled = pd.DataFrame(preprocessing.scale(df), columns=df.columns)
X_tr = pca.fit_transform(data_scaled)
comp_desc = pd.DataFrame(pca.components_,
columns=data_scaled.columns,
index=['PC-' + str(i) for i in range(components)])
desc_code = []
for i, row in comp_desc.iterrows():
code = ""
for r, v in zip(row.tolist(), comp_desc.columns):
if r < 0 and len(code) == 0:
code = code[:-2] + str(np.round(r, 2))
elif r < 0 and len(code) > 0:
code = code[:-2] + str(np.round(r, 2))
elif r > 0:
code = code + str(np.round(r, 2))
code = code + " X %s + " % v
pass
code = code[:-2]
desc_code.append("%s=%s" % (i, code))
pass
print desc_code
print pca.explained_variance_ratio_, data_scaled.columns
print np.cumsum(pca.explained_variance_ratio_)
XX = np.append(X_tr, _o[yparam_clf].values, axis=1)
df = pd.DataFrame(
XX, columns=["PC-1", "PC-2", "PC-3", "PC-4", "PC-5", "PC-6", "stormL"])
print df.head()
ns = df[df.stormL == 0.]
s = df[df.stormL == 1.]
fig, axes = plt.subplots(figsize=(9, 6), nrows=2, ncols=3, dpi=120)
fig.subplots_adjust(hspace=0.2, wspace=0.3)
ax = axes[0, 0]
ax.scatter(ns["PC-1"],
ns["PC-2"],
facecolors='none',
edgecolors='b',
s=30,
alpha=1.)
ax.scatter(s["PC-1"],
s["PC-2"],
facecolors='none',
edgecolors='r',
s=30,
alpha=0.5)
ax.tick_params(labelsize=font["size"])
ax.set_xticklabels(ax.get_xticks().astype(np.int), fontdict=font)
ax.set_yticklabels(ax.get_yticks().astype(np.int), fontdict=font)
ax.set_xlabel("PC-1", fontdict=font)
ax.set_ylabel("PC-2", fontdict=font)
ax = axes[0, 1]
ax.scatter(ns["PC-1"],
ns["PC-3"],
facecolors='none',
edgecolors='b',
s=30,
alpha=1.)
ax.scatter(s["PC-1"],
s["PC-3"],
facecolors='none',
edgecolors='r',
s=30,
alpha=0.5)
ax.tick_params(labelsize=font["size"])
ax.set_xticklabels(ax.get_xticks().astype(np.int), fontdict=font)
ax.set_yticklabels(ax.get_yticks().astype(np.int), fontdict=font)
ax.set_xlabel("PC-1", fontdict=font)
ax.set_ylabel("PC-3", fontdict=font)
ax = axes[0, 2]
ax.scatter(ns["PC-1"],
ns["PC-4"],
facecolors='none',
edgecolors='b',
s=30,
alpha=1.)
ax.scatter(s["PC-1"],
s["PC-4"],
facecolors='none',
edgecolors='r',
s=30,
alpha=0.5)
ax.tick_params(labelsize=font["size"])
ax.set_xticklabels(ax.get_xticks().astype(np.int), fontdict=font)
ax.set_yticklabels(ax.get_yticks().astype(np.int), fontdict=font)
ax.set_xlabel("PC-1", fontdict=font)
ax.set_ylabel("PC-4", fontdict=font)
ax = axes[1, 0]
ax.scatter(ns["PC-2"],
ns["PC-3"],
facecolors='none',
edgecolors='b',
s=30,
alpha=1.)
ax.scatter(s["PC-2"],
s["PC-3"],
facecolors='none',
edgecolors='r',
s=30,
alpha=0.5)
ax.tick_params(labelsize=font["size"])
ax.set_xticklabels(ax.get_xticks().astype(np.int), fontdict=font)
ax.set_yticklabels(ax.get_yticks().astype(np.int), fontdict=font)
ax.set_xlabel("PC-2", fontdict=font)
ax.set_ylabel("PC-3", fontdict=font)
ax = axes[1, 1]
ax.scatter(ns["PC-2"],
ns["PC-4"],
facecolors='none',
edgecolors='b',
s=30,
alpha=1.)
ax.scatter(s["PC-2"],
s["PC-4"],
facecolors='none',
edgecolors='r',
s=30,
alpha=0.5)
ax.tick_params(labelsize=font["size"])
ax.set_xticklabels(ax.get_xticks().astype(np.int), fontdict=font)
ax.set_yticklabels(ax.get_yticks().astype(np.int), fontdict=font)
ax.set_xlabel("PC-2", fontdict=font)
ax.set_ylabel("PC-4", fontdict=font)
ax = axes[1, 2]
ax.scatter(ns["PC-3"],
ns["PC-4"],
facecolors='none',
edgecolors='b',
s=30,
alpha=1.)
ax.scatter(s["PC-3"],
s["PC-4"],
facecolors='none',
edgecolors='r',
s=30,
alpha=0.5)
ax.tick_params(labelsize=font["size"])
ax.set_xticklabels(ax.get_xticks().astype(np.int), fontdict=font)
ax.set_yticklabels(ax.get_yticks().astype(np.int), fontdict=font)
ax.set_xlabel("PC-3", fontdict=font)
ax.set_ylabel("PC-4", fontdict=font)
if goes: plt.savefig("figures/pcag.png", bbox_inches="tight")
else: plt.savefig("figures/pca.png", bbox_inches="tight")
return
# Check if all fields are available
if request.json.get('name') and \
request.json.get('location'):
my_persons.append(request.json)
else:
# Data is missing in the request
return jsonify({"error": "Data incomplete"}), 400
save_data('database.json', my_persons)
return jsonify(my_persons), 201
@app.route('/persons/<int:number>', methods=['GET'])
def person_number(number: int):
""" Get the first person """
person: typing.Dict = {}
if len(my_persons) > number:
person = my_persons[number]
return jsonify(person)
if __name__ == '__main__':
# Load dataset before starting the server
my_persons = load_data('database.json')
app.run(host='0.0.0.0', port=8080, debug=True)
from __future__ import division
from flask import Flask, render_template
import database as DB
from model import Model
import json
import numpy as np
import simplejson
app = Flask(__name__)
# Load data from csv file
db = DB.load_data("../BASEVENTAS2010A2015.csv")
model = Model(db)
@app.route("/")
def home():
"""
Simple analytics graphs
"""
return render_template("index.html")
@app.route("/recommend")
def recommend():
"""
Recommend products based on other products
"""
return render_template("recommend.html")
@app.route("/forecast", defaults={'product_id': None})
@app.route("/forecast/<product_id>")
import numpy as np
import pandas as pd
from sklearn import decomposition
from sklearn import preprocessing
import database as db
##########################################################################
## PCA Analysis and find out the contribution made by individual components
##########################################################################
components = 10
dates = pd.read_csv("stormlist.csv")
dates.dates = pd.to_datetime(dates.dates)
_o, xparams, yparam_clf = db.load_data()
pca = decomposition.PCA(n_components=components)
df = _o[xparams]
data_scaled = pd.DataFrame(preprocessing.scale(df), columns=df.columns)
pca.fit_transform(data_scaled)
comp_desc = pd.DataFrame(pca.components_,
columns=data_scaled.columns,
index=['PC-' + str(i) for i in range(components)])
print pca.explained_variance_ratio_, data_scaled.columns
print np.cumsum(pca.explained_variance_ratio_)
desc_code = []
for i, row in comp_desc.iterrows():
code = ""
for r, v in zip(row.tolist(), comp_desc.columns):
if r < 0 and len(code) == 0:
code = code[:-2] + str(np.round(r, 2))
elif r < 0 and len(code) > 0:
code = code[:-2] + str(np.round(r, 2))
from __future__ import division
from flask import Flask, render_template
import database as DB
from model import Model
import json
import numpy as np
import simplejson
app = Flask(__name__)
# Load data from csv file
db = DB.load_data("../BASEVENTAS2010A2015.csv")
model = Model(db)
@app.route("/")
def home():
"""
Simple analytics graphs
"""
return render_template("index.html")
@app.route("/recommend")
def recommend():
"""
Recommend products based on other products
"""
return render_template("recommend.html")
@app.route("/forecast", defaults={'product_id': None})
database.modify_cell(table, **dic)
flash('修改成功')
return redirect(url_for('ad_everything'))
elif op == 'query':
info = database.query(table, **dic)
flash('查询成功')
return render_template('ad_everything.html', info=info)
else:
raise Exception('非法操作')
except Exception as e:
info = str(e) if len(str(e)) < 8 or DEBUG else '未知错误'
flash('操作失败，' + info)
return redirect(url_for('ad_everything'))
if __name__ == "__main__":
acc = input('mysql account:')
pwd = input('mysql password:'******'create table?(y/n)')
while create not in ['y', 'Y', 'n', 'N']:
create = input('create table?(y/n):')
create = True if create in ['y', 'Y'] else False
rebuild = input('rebuild data?(y/n)')
while rebuild not in ['y', 'Y', 'n', 'N']:
rebuild = input('rebuild data?(y/n):')
rebuild = True if rebuild in ['y', 'Y'] else False
print('init database')
database.init_db(ano=acc, apwd=pwd, create=create)
if rebuild:
database.load_data('./sql/load_data.sql')
print('load finished, jiaowu admin account is "a1", and default password is "pwdpwd"')
app.run(host='0.0.0.0',port=80)
@app.route('/')
def home():
return render_template("home.html")
@app.route('/total')
def getTotal():
try:
date_string = request.args.get("day")
return database.read(date_string)
except Exception as e:
return jsonify({"msg": "Invalid date provided", "err":str(e)})
@app.route('/excelreport/')
def get_excel():
try:
return send_excel_file()
except Exception as e:
return str(e)
def send_excel_file():
return send_file( database.get_excel_file(), mimetype="application/vnd.ms-excel", as_attachment=True ,attachment_filename="excelreport.xls" , )
if __name__ == "__main__":
print("Loading database")
database.load_data()
print("Starting server")
app.run('0.0.0.0')