def discrete_network_embedding(A: np.ndarray, T: List[int]):
S = (A + (A @ A)) / 2
W = np.random.uniform(-1, 1, (M, C))
B = np.random.uniform(-1, 1, (M, N))
# W = np.random.rand(M, C) # これは正の値しか出ない
# B = np.random.rand(M, N)
before_W = W
before_B = B
for _ in count(1, 20):
for _ in count(1, 9):
B = equation_11(B, S, W, T)
print("updating B" + str(loss(B, S, W, T)))
W = equation_13(B, T)
print("updating W" + str(loss(B, S, W, T)))
# x = []
# y = []
# colors = []
# fig, ax = plt.subplots()
# for i in range(0, N):
# b_i = B[:, i]
# x.append(b_i[0])
# y.append(b_i[1])
# ax.annotate(str(i), (b_i[0], b_i[1]))
# if np.argmax(W.T @ b_i) == 0:
# colors.append("r")
# else:
# colors.append("b")
# for i in range(len(x)):
# ax.scatter(x[i], y[i], c=colors[i])
# plt.show()
return before_B, before_W, B, W
def WO(W: np.ndarray, T: List[int]):
assert W.shape == (M, C)
assert len(T) == L
w_mean = W.mean(axis=1)
sum_mc = W.sum(axis=1)
wo = []
for _i in range(0, L):
ci = T[_i]
woi = sum_mc - (W[:, ci] * C)
wo.append(woi)
for _ in count(L + 1, N):
woi = sum_mc - sum_mc # 0
wo.append(woi)
return np.column_stack(wo)
# Q3: How many part-time employees are there?
import helper
data = helper.read_salaries()
employee_type = helper.get_column(data,
4) # get employee type (full or part-time)
print(helper.count(employee_type, 'P')) # print full-time count
# Q5: How many detectives? import helper data = helper.read_salaries() jobs = helper.get_column(data, 2) print(helper.count(jobs, 'DETECTIVE'))
# Q2: How many full time employees are there? import helper data = helper.read_salaries() job_term = helper.get_column(data, 4) full_time_employees = helper.count(job_term, "F") print(full_time_employees) #31,090 full_time_employees
# Q3: How many part-time employees are there? import helper data = helper.read_salaries() timing = helper.get_column(data, 4) print(helper.count(timing, 'P'))
# Q4: How many employees in the police department? import helper data = helper.read_salaries() departments = helper.get_column(data, 3) print(helper.count(departments, 'POLICE'))
# Q5: How many detectives?
import helper
data = helper.read_salaries()
detective_only = helper.get_column(data, 2)
detective_count = helper.count(detective_only,
"POLICE OFFICER (ASSIGNED AS DETECTIVE)")
print(detective_count)
#989 detectives
# Q3: How many part-time employees are there? import helper #import module data = helper.read_salaries() part_time = helper.get_column(data, 4) #assign variable name to look in position type count_parttime = helper.count(part_time, 'P') #count the specific type of part-time employees print(count_parttime)
# Q2: How many full time employees are there? import helper data = helper.read_salaries() full_time = helper.get_column(data, 4) #assign variable name to look in 'position type' column count_fulltime = helper.count(full_time, 'F') #count the specific number of full time employees print(count_fulltime)
# Q5: How many detectives?
import helper
data = helper.read_salaries()
positions = helper.get_column(data, 2) # get positions
print(helper.count(
positions,
'POLICE OFFICER (ASSIGNED AS DETECTIVE)')) # print police detective count
# Q3: How many part-time employees are there? import helper data = helper.read_salaries() job_term = helper.get_column(data,4) part_time_employees = helper.count(job_term, "P") print(part_time_employees) #2,093 part_time_employees
# Q5: How many detectives? import helper data = helper.read_salaries() detectives_list = helper.get_column(data, 2) # #assign variable name to look in Job Title column detectives_count = helper.count(detectives_list, "POLICE OFFICER (ASSIGNED AS DETECTIVE)") #count the number of detectives print(detectives_count)
# Q4: How many employees in the police department? import helper data = helper.read_salaries() police_only = helper.get_column(data, 3) police_count = helper.count(police_only, "POLICE") print(police_count) #13,414 employees in the police department
