'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/1.10/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.10/howto/static-files/
STATIC_URL = '/static/'
MEDIA_URL = '/media/'
STATICFILES_DIRS = [
os.path.join(BASE_DIR, "HTMLTemplates/assets"),
#'/var/www/static/',
]
print "Base dir:", BASE_DIR
STATIC_ROOT = os.path.join(os.path.join(dirname(BASE_DIR)), "static_cdn")
MEDIA_ROOT = os.path.join(os.path.join(dirname(BASE_DIR)), "media")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from macpath import dirname, join
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
path = join(dirname(__file__), "california_housing_train.csv")
data = pd.read_csv(path)
data = data.values
X = np.c_[data[:, 1], data[:, 2], data[:, 3], data[:, 4]]
Y = np.c_[data[:, 0]]
plt.scatter(X[:, 0], Y, s=10, color='g', marker='x')
plt.title(" housing_meidan_age and median_house_value", fontsize=14)
plt.xlabel('housing_median_age', fontsize=14)
plt.ylabel('median_house_value', fontsize=10)
plt.grid(True)
plt.show()
plt.scatter(X[:, 1], Y, s=10, color='g', marker='x')
plt.title(" total_rooms and median_house_value", fontsize=14)
plt.xlabel('total_rooms', fontsize=14)
plt.ylabel('median_house_value', fontsize=10)
plt.grid(True)
plt.show()
def get_tens(iter):
global cargo
while 1:
print "\nTENS State: ",
while jump_to(cargo) == 'TENS':
print "#%2.1f " % cargo,
cargo = iter.next()
yield (jump_to(cargo), cargo)
def exit(iter):
jump = raw_input('\n\n[co-routine for jump?] ').upper()
print "...Jumping into middle of", jump
yield (jump, iter.next())
print "\nExiting from exit()..."
sys.exit()
def toJSON(input):
reader = Reader(input)
parser = Parser(reader, asJson=True)
return json.dumps(parser.runtime)
# num_stream = math_gen(1)
# cargo = num_stream.next()
# gendct = {'ONES': get_ones(num_stream),'TENS': get_tens(num_stream), 'OUT_OF_RANGE': exit(num_stream)}
# scheduler(gendct, jump_to(cargo))
if __name__ == "__main__":
DIR = dirname(abspath(__file__))
TOMLFiles = glob(join(DIR, '*.toml'))
if __name__ == '__main__':
for filename in TOMLFiles:
with open(filename) as file:
print toJSON(file)
'''
macpath 模块
macpath 模块( 参见 Example 13-2 )提供了 Macintosh 平台下的 os.path 功能.
你也可以使用它在其他平台处理 Macintosh 路径.
'''
import macpath
file = 'my:little:pony'
print("isabs", "=>", macpath.isabs(file))
print("dirname", "=>", macpath.dirname(file))
print("basename", "=>", macpath.basename(file))
print("normpath", "=>", macpath.normpath(file))
print("split", "=>", macpath.split(file))
print("join", "=>", macpath.join(file, "zorba"))
'''
isabs => True
dirname => my:little
basename => pony
normpath => my:little:pony
split => ('my:little', 'pony')
join => my:little:pony:zorba
'''
'''
macpath 模块
macpath 模块( 参见 Example 13-2 )提供了 Macintosh 平台下的 os.path 功能.
你也可以使用它在其他平台处理 Macintosh 路径.
'''
import macpath
file = 'my:little:pony'
print("isabs", "=>", macpath.isabs(file))
print("dirname", "=>", macpath.dirname(file))
print("basename", "=>", macpath.basename(file))
print("normpath", "=>", macpath.normpath(file))
print("split", "=>", macpath.split(file))
print("join", "=>", macpath.join(file, "zorba"))
'''
isabs => True
dirname => my:little
basename => pony
normpath => my:little:pony
split => ('my:little', 'pony')
join => my:little:pony:zorba
'''