shawlu95

select 
  communityID
  ,min(date) as join_date
  ,max(date) as leave_date
  ,count(*) as duration
from history
where accountID = 'shawlu'
group by communityID;

shawlu95

select date, count(accountID) as size 
from history 
where communityID = 10 
group by date 
order by date;

shawlu95

p_hat = heads / n
se = np.sqrt(p_hat * (1 - p_hat) / n)
f_up = p_hat + 1.96 * se
f_lo = p_hat - 1.96 * se

print("95%% confidence interval: [%.3f, %.3f]"%(f_lo, f_up))
# 95% confidence interval: [0.416, 0.984]

shawlu95

prior_a = 1
prior_b = 1

a = prior_a + heads
b = prior_b + tails
rv = beta(a, b)

x = np.linspace(0, 1, 100)

b_up = btdtri(a, b, 0.95)

shawlu95

import numpy as np
from scipy.stats import beta, t, norm
from scipy.special import btdtri
import matplotlib.pyplot as plt

p = 0.5
n = 10

success = np.random.binomial(p=p, n=n)
failure = n - success

shawlu95

// objective C
if (@available(iOS 13.0, *)) {
  cell.backgroundColor = [UIColor systemBackgroundColor];
} else {
  cell.backgroundColor = [UIColor whiteColor];
}

shawlu95

def find_path(idiom, length, limit):
    cql = "match ()-[e:idiom {idiom: '%s'}]->()"%idiom
    path = "".join(["-[e%i:idiom]->()"%(i + 1) for i in range(length)])
    ret = ", ".join(["e%i.idiom as i%i"%(i + 1, i + 1) for i in range(length)])
    query = cql + path + " return e.idiom as i0, " + ret + " limit %i"%limit
    return graph.run(query).to_data_frame()

shawlu95

from py2neo import Graph
graph = Graph("bolt://localhost:7687", password="12345678")

# Parse the source
with open('dataset/THUOCL_chengyu.txt', 'r', encoding='utf-8') as source:
    idioms = [i for i in map(lambda x: x.split()[0], source.readlines()) if len(i) <= 4]

nodes = set([i[0] for i in idioms] + [i[-1] for i in idioms])
edges = [{"src": i[0], "dst": i[-1], "idiom": i} for i in idioms]

shawlu95

pip install pandas py2neo

shawlu95

  
X_trn, Y_trn = load_dataset("trn_set", limit=5)

print(X_trn.head().T)
#                         0       1       2      3      4
# category                1       1       1      1      1
# event1                  0       0       0      0      0
# order_count             1       2       2      1      1
# revenue_sum         30.49  191.33  191.33  76.96  66.77
# revenue_max         30.49   98.38   98.38  76.96  66.77