def read(self):
o = {}
# reasonable defaults
o['layout'] = 'fixed'
o['jQueryUiTheme'] = 'smoothness'
i = SessionHelper().peek('user.account_id')
if not i is None:
with grab_connection('main') as conn:
data = get_row(conn,
'''
select ui_layout, ui_theme from user_account_ui_settings
where ref_user_account_id = %(i)s
''',
{ 'i': i })
if not data is None:
o['layout'] = data['ui_layout']
o['jQueryUiTheme'] = data['ui_theme']
return o
def read(self):
o = {}
userAccountId = SessionHelper().peek('user.account_id')
if not userAccountId is None:
with grab_connection('main') as conn:
data = get_row(
conn,
"""
select user_name, first_name, last_name, email
from user_accounts where user_account_id = %(i)s
""",
{ 'i': userAccountId })
if data:
o['userAccountId'] = userAccountId
o['userName'] = data['user_name']
o['firstName'] = data['first_name']
o['lastName'] = data['last_name']
o['email'] = data['email']
return o
def graphic(table_clicks, add_clicks, clients_number, rel_min1, rel_max1,
rel_min2, rel_max2, norm_min1, norm_max1,
norm_d_min1, norm_d_max1, norm_min2, norm_max2,
norm_d_min2, norm_d_max2, x1_new, x2_new, x3_new, x4_new, x5_new, x6_new):
global df, n_add, experim
for i in range(experim):
array_x[i] = array_x[i][1:]
if (add_clicks > n_add):
n_add = add_clicks
x1_new = float(x1_new)
x2_new = float(x2_new)
x3_new = float(x3_new)
x4_new = float(x4_new)
x5_new = float(x4_new)
x6_new = float(x4_new)
experim += 1
array_x.append(get_row([x1_new, x2_new, x3_new, x4_new, x5_new, x6_new], 6))
f1_b = float(rel_min1)
f1_e = float(rel_max1)
f2_b = float(rel_min2)
f2_e = float(rel_max2)
f3_b = float(norm_min1)
f3_e = float(norm_max1)
f4_b = float(norm_d_min1)
f4_e = float(norm_d_max1)
f5_b = float(norm_min2)
f5_e = float(norm_max2)
f6_b = float(norm_d_min2)
f6_e = float(norm_d_max2)
r = True if 2 * (f1_b + f1_e + f2_b + f2_e) / (f3_b + f3_e + f5_b + f5_e) > 0.8 else False
i = 0
for row in array_x:
rel1 = convert_factor_to_value(float(rel_min1), float(rel_max1), row[1])
rel2 = convert_factor_to_value(float(rel_min2), float(rel_max2), row[2])
norm1 = convert_factor_to_value(float(norm_min1), float(norm_max1), row[3])
norm_d1 = convert_factor_to_value(float(norm_d_min1), float(norm_d_max1), row[4])
norm2 = convert_factor_to_value(float(norm_min2), float(norm_max2), row[5])
norm_d2 = convert_factor_to_value(float(norm_d_min2), float(norm_d_max2), row[6])
c_rel1 = calculate_reley_param(rel1)
c_rel2 = calculate_reley_param(rel2)
c_norm1, c_norm_d1 = calculate_gauss_params(norm1, norm_d1)
c_norm2, c_norm_d2 = calculate_gauss_params(norm2, norm_d2)
model = modeller.Model([c_rel1, c_rel2], [c_norm1, c_norm2], [c_norm_d1, c_norm_d2], 2, 1, 0)
avg_queue_size, avg_queue_time, processed_requests = model.time_based_modellingg(tmax, 0.001)
print(i)
array_x[i][70] = round(avg_queue_time, 5)
i += 1
b0 = calculate_b_ockp(0, 1,
[array_x[i][0] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1 = calculate_b_ockp(f1_b, f1_e,
[array_x[i][1] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b2 = calculate_b_ockp(f2_b, f2_e,
[array_x[i][2] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b3 = calculate_b_ockp(f3_b, f3_e,
[array_x[i][3] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b4 = calculate_b_ockp(f4_b, f4_e,
[array_x[i][4] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b5 = calculate_b_ockp(f5_b, f5_e,
[array_x[i][5] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b6 = calculate_b_ockp(f6_b, f6_e,
[array_x[i][6] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b12 = calculate_b_ockp(f1_b * f2_b, f1_e * f2_e,
[array_x[i][7] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b13 = calculate_b_ockp(f1_b * f3_b, f1_e * f3_e,
[array_x[i][8] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b14 = calculate_b_ockp(f1_b * f4_b, f1_e * f4_e,
[array_x[i][9] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b15 = calculate_b_ockp(f1_b * f5_b, f1_e * f5_e,
[array_x[i][10] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b16 = calculate_b_ockp(f1_b * f6_b, f1_e * f6_e,
[array_x[i][11] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b23 = calculate_b(f2_b * f3_b, f2_e * f3_e,
[array_x[i][12] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b24 = calculate_b_ockp(f2_b * f4_b, f2_e * f4_e,
[array_x[i][13] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b25 = calculate_b_ockp(f2_b * f5_b, f2_e * f5_e,
[array_x[i][14] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b26 = calculate_b_ockp(f2_b * f6_b, f2_e * f6_e,
[array_x[i][15] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b34 = calculate_b_ockp(f3_b * f4_b, f3_e * f4_e,
[array_x[i][16] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b35 = calculate_b_ockp(f3_b * f5_b, f3_e * f5_e,
[array_x[i][17] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b36 = calculate_b_ockp(f3_b * f6_b, f3_e * f6_e,
[array_x[i][18] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b45 = calculate_b_ockp(f4_b * f5_b, f4_e * f5_e,
[array_x[i][19] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b46 = calculate_b_ockp(f4_b * f6_b, f4_e * f6_e,
[array_x[i][20] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b56 = calculate_b_ockp(f5_b * f6_b, f5_e * f6_e,
[array_x[i][21] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b123 = calculate_b_ockp(f1_b * f2_b * f3_b, f1_e * f2_e * f3_e,
[array_x[i][22] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b124 = calculate_b_ockp(f1_b * f2_b * f4_b, f1_e * f2_e * f4_e,
[array_x[i][23] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b125 = calculate_b_ockp(f1_b * f2_b * f5_b, f1_e * f2_e * f5_e,
[array_x[i][24] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b126 = calculate_b_ockp(f1_b * f2_b * f6_b, f1_e * f2_e * f6_e,
[array_x[i][25] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b134 = calculate_b_ockp(f1_b * f3_b * f4_b, f1_e * f3_e * f4_e,
[array_x[i][26] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b135 = calculate_b_ockp(f1_b * f3_b * f5_b, f1_e * f3_e * f5_e,
[array_x[i][27] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b136 = calculate_b_ockp(f1_b * f3_b * f6_b, f1_e * f3_e * f6_e,
[array_x[i][28] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b145 = calculate_b_ockp(f1_b * f4_b * f5_b, f1_e * f4_e * f5_e,
[array_x[i][29] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b146 = calculate_b_ockp(f1_b * f4_b * f6_b, f1_e * f4_e * f6_e,
[array_x[i][30] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b156 = calculate_b_ockp(f1_b * f5_b * f6_b, f1_e * f5_e * f6_e,
[array_x[i][31] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b234 = calculate_b_ockp(f2_b * f3_b * f4_b, f2_e * f3_e * f4_e,
[array_x[i][32] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b235 = calculate_b_ockp(f2_b * f3_b * f5_b, f2_e * f3_e * f5_e,
[array_x[i][33] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b236 = calculate_b_ockp(f2_b * f3_b * f6_b, f2_e * f3_e * f6_e,
[array_x[i][34] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b245 = calculate_b_ockp(f2_b * f4_b * f5_b, f2_e * f4_e * f5_e,
[array_x[i][35] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b246 = calculate_b_ockp(f2_b * f4_b * f6_b, f2_e * f4_e * f6_e,
[array_x[i][36] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b256 = calculate_b_ockp(f2_b * f5_b * f6_b, f2_e * f5_e * f6_e,
[array_x[i][37] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b345 = calculate_b_ockp(f3_b * f4_b * f5_b, f3_e * f4_e * f5_e,
[array_x[i][38] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b346 = calculate_b_ockp(f3_b * f4_b * f6_b, f3_e * f4_e * f6_e,
[array_x[i][39] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b356 = calculate_b_ockp(f3_b * f5_b * f6_b, f3_e * f5_e * f6_e,
[array_x[i][40] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b456 = calculate_b_ockp(f4_b * f5_b * f6_b, f4_e * f5_e * f6_e,
[array_x[i][41] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1234 = calculate_b_ockp(f1_b * f2_b * f3_b * f4_b, f1_e * f2_e * f3_e * f4_e,
[array_x[i][42] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1235 = calculate_b_ockp(f1_b * f2_b * f3_b * f5_b, f1_e * f2_e * f3_e * f5_e,
[array_x[i][43] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1236 = calculate_b_ockp(f1_b * f2_b * f3_b * f6_b, f1_e * f2_e * f3_e * f6_e,
[array_x[i][44] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1245 = calculate_b_ockp(f1_b * f2_b * f4_b * f5_b, f1_e * f2_e * f4_e * f5_e,
[array_x[i][45] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1246 = calculate_b_ockp(f1_b * f2_b * f4_b * f6_b, f1_e * f2_e * f4_e * f6_e,
[array_x[i][46] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1256 = calculate_b_ockp(f1_b * f2_b * f5_b * f6_b, f1_e * f2_e * f5_e * f6_e,
[array_x[i][47] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1345 = calculate_b_ockp(f1_b * f3_b * f4_b * f5_b, f1_e * f3_e * f4_e * f5_e,
[array_x[i][48] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1346 = calculate_b_ockp(f1_b * f3_b * f4_b * f6_b, f1_e * f3_e * f4_e * f6_e,
[array_x[i][49] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1356 = calculate_b_ockp(f1_b * f3_b * f5_b * f6_b, f1_e * f3_e * f5_e * f6_e,
[array_x[i][50] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b1456 = calculate_b_ockp(f1_b * f4_b * f5_b * f6_b, f1_e * f4_e * f5_e * f6_e,
[array_x[i][51] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b2345 = calculate_b_ockp(f2_b * f3_b * f4_b * f5_b, f2_e * f3_e * f4_e * f5_e,
[array_x[i][52] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b2346 = calculate_b_ockp(f2_b * f3_b * f4_b * f6_b, f2_e * f3_e * f4_e * f6_e,
[array_x[i][53] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b2356 = calculate_b_ockp(f2_b * f3_b * f5_b * f6_b, f2_e * f3_e * f5_e * f6_e,
[array_x[i][54] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b2456 = calculate_b_ockp(f2_b * f4_b * f5_b * f6_b, f2_e * f4_e * f5_e * f6_e,
[array_x[i][55] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b3456 = calculate_b_ockp(f3_b * f4_b * f5_b * f6_b, f3_e * f4_e * f5_e * f6_e,
[array_x[i][56] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b12345 = calculate_b_ockp(f1_b * f2_b * f3_b * f4_b * f5_b, f1_e * f2_e * f3_e * f4_e * f5_e,
[array_x[i][57] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b12346 = calculate_b_ockp(f1_b * f2_b * f3_b * f4_b * f6_b, f1_e * f2_e * f3_e * f4_e * f6_e,
[array_x[i][58] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b12356 = calculate_b_ockp(f1_b * f2_b * f3_b * f5_b * f6_b, f1_e * f2_e * f3_e * f5_e * f6_e,
[array_x[i][59] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b12456 = calculate_b_ockp(f1_b * f2_b * f4_b * f5_b * f6_b, f1_e * f2_e * f4_e * f5_e * f6_e,
[array_x[i][60] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b13456 = calculate_b_ockp(f1_b * f3_b * f4_b * f5_b * f6_b, f1_e * f3_e * f4_e * f5_e * f6_e,
[array_x[i][61] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b23456 = calculate_b_ockp(f2_b * f3_b * f4_b * f5_b * f6_b, f2_e * f3_e * f4_e * f5_e * f6_e,
[array_x[i][62] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b123456 = calculate_b_ockp(f1_b * f2_b * f3_b * f4_b * f5_b * f6_b, f1_e * f2_e * f3_e * f4_e * f5_e * f6_e,
[array_x[i][63] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b11 = calculate_b_ockp(0, 1,
[array_x[i][64] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b22 = calculate_b_ockp(0, 1,
[array_x[i][65] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b33 = calculate_b_ockp(0, 1,
[array_x[i][66] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b44 = calculate_b_ockp(0, 1,
[array_x[i][67] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b55 = calculate_b_ockp(0, 1,
[array_x[i][68] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b66 = calculate_b_ockp(0, 1,
[array_x[i][69] for i in range(count)],
[array_x[i][70] for i in range(count)],
count)
b0 = b0 - (b11+b22+b33+b44+b55+b66) * s
no_line = f"y={round(b0, 5)}+({round(b1, 5)})*x1+({round(b2, 5)})*x2+({round(b3, 5)})*x3+({round(b4, 5)})*x4+({round(b5, 5)})*x5+({round(b6, 5)})*x6+\
({round(b12, 5)})*x1*x2+({round(b13, 5)})*x1*x3+({round(b14, 5)})*x1*x4+({round(b15, 5)})*x1*x5+({round(b16, 5)})*x1*x6+\
({round(b23, 5)})*x2*x3+({round(b24, 5)})*x2*x4+({round(b25, 5)})*x2*x5+({round(b26, 5)})*x2*x6+\
({round(b34, 5)})*x3*x4+({round(b35, 5)})*x3*x5+({round(b36, 5)})*x3*x6+\
({round(b45, 5)})*x4*x5+({round(b46, 5)})*x4*x6+\
({round(b56, 5)})*x5*x6+\
({round(b123, 5)})*x1*x2*x3+({round(b124, 5)})*x1*x2*x4+({round(b125, 5)})*x1*x2*x5+({round(b126, 5)})*x1*x2*x6+\
({round(b134, 5)})*x1*x3*x4+({round(b135, 5)})*x1*x3*x5+({round(b136, 5)})*x1*x3*x6+\
({round(b145, 5)})*x1*x4*x5+({round(b146, 5)})*x1*x4*x6+\
({round(b156, 5)})*x1*x5*x6+\
({round(b234, 5)})*x2*x3*x4+({round(b235, 5)})*x2*x3*x5+({round(b236, 5)})*x2*x3*x6+\
({round(b245, 5)})*x2*x4*x5+({round(b246, 5)})*x2*x4*x6+\
({round(b256, 5)})*x2*x5*x6+\
({round(b345, 5)})*x3*x4*x5+({round(b346, 5)})*x3*x4*x6+\
({round(b356, 5)})*x3*x5*x6+\
({round(b456, 5)})*x4*x5*x6+\
({round(b1234, 5)})*x1*x2*x3*x4+({round(b1235, 5)})*x1*x2*x3*x5+({round(b1236, 5)})*x1*x2*x3*x6+\
({round(b1245, 5)})*x1*x2*x4*x5+({round(b1246, 5)})*x1*x2*x4*x6+\
({round(b1256, 5)})*x1*x2*x5*x6+\
({round(b1345, 5)})*x1*x3*x4*x5+({round(b1346, 5)})*x1*x3*x4*x6+\
({round(b1356, 5)})*x1*x3*x5*x6+\
({round(b1456, 5)})*x1*x4*x5*x6+\
({round(b2345, 5)})*x2*x3*x4*x5+({round(b2346, 5)})*x2*x3*x4*x6+\
({round(b2356, 5)})*x2*x3*x5*x6+\
({round(b2456, 5)})*x2*x4*x5*x6+\
({round(b3456, 5)})*x3*x4*x5*x6+\
({round(b12345, 5)})*x1*x2*x3*x4*x5+({round(b12346, 5)})*x1*x2*x3*x4*x6+\
({round(b12356, 5)})*x1*x2*x3*x5*x6+\
({round(b12456, 5)})*x1*x2*x4*x5*x6+\
({round(b13456, 5)})*x1*x3*x4*x5*x6+\
({round(b23456, 5)})*x2*x3*x4*x5*x6+\
({round(b123456, 5)})*x1*x2*x3*x4*x5*x6+\
({round(b11 if b11 > 0 else -b11, 5)})*(x1^2-s)+\
({round(b22 if b22 > 0 else -b22, 5)})*(x2^2-s)+\
({round(b33 if not r else (-b33 if b33 > 0 else b33), 5)})*(x3^2-s)+\
({round(b44, 5)})*(x4^2-s)+\
({round(b55 if not r else (-b55 if b55 > 0 else b55), 5)})*(x5^2-s)+\
({round(b66, 5)})*(x6^2-s)"
i = 0
for row in array_x:
x1 = array_x[i][1]
x2 = array_x[i][2]
x3 = array_x[i][3]
x4 = array_x[i][4]
x5 = array_x[i][5]
x6 = array_x[i][6]
array_x[i][71] = round(fabs(b0 + b1 * x1 + b2 * x2 + b3 * x3 + b4 * x4 + b5 * x5 + b6 * x6 + \
b12 * x1 * x2 + b13 * x1 * x3 + b14 * x1 * x4 + b15 * x1 * x5 + b16 * x1 * x6 + \
b23 * x2 * x3 + b24 * x2 * x4 + b25 * x2 * x5 + b26 * x2 * x6+\
b34 * x3 * x4 + b35 * x3 * x5 + b36 * x3 * x6 + \
b45 * x4 * x5 + b46 * x4 * x6 + \
b56 * x5 * x6 + \
b123 * x1 * x2 * x3 + b124 * x1 * x2 * x4 + b125 * x1 * x2 * x5+ b126 * x1 * x2 * x6 + \
b134 * x1 * x3 * x4 + b135 * x1 * x3 * x5 + b136 * x1 * x3 * x6 + \
b145 * x1 * x4 * x5 + b146 * x1 * x4 * x6 + \
b156 * x1 * x5 * x6 + \
b234 * x2 * x3 * x4 + b235 * x2 * x3 * x5 + b236 * x2 * x3 * x6 + \
b245 * x2 * x4 * x5 + b246 * x2 * x4 * x6 + \
b256 * x2 * x5 * x6 + \
b345 * x3 * x4 * x5 + b346 * x3 * x4 * x6 + \
b356 * x3 * x5 * x6 + \
b456 * x4 * x5 * x6 + \
b1234 * x1 * x2 * x3 * x4 + b1235 * x1 * x2 * x3 * x5 + b1236 * x1 * x2 * x3 * x6+\
b1245 * x1 * x2 * x4 * x5 + b1246 * x1 * x2 * x4 * x6 + \
b1256 * x1 * x2 * x5 * x6 + \
b1345 * x1 * x3 * x4 * x5 + b1346 * x1 * x3 * x4 * x6 + \
b1356 * x1 * x3 * x5 * x6 + \
b1456 * x1 * x4 * x5 * x6 + \
b2345 * x2 * x3 * x4 * x5 + b2346 * x2 * x3 * x4 * x6 + \
b2356 * x2 * x3 * x5 * x6 + \
b2456 * x2 * x4 * x5 * x6 + \
b3456 * x3 * x4 * x5 * x6 + \
b12345 * x1 * x2 * x3 * x4 * x5 + b12346 * x1 * x2 * x3 * x4 * x6 + \
b12356 * x1 * x2 * x3 * x5 * x6 + \
b12456 * x1 * x2 * x4 * x5 * x6 + \
b13456 * x1 * x3 * x4 * x5 * x6 + \
b23456 * x2 * x3 * x4 * x5 * x6 + \
b123456 * x1 * x2 * x3 * x4 * x5 * x6 + \
b11 * (x1*x1 - s) + \
b22 * (x2*x2 - s) + \
b33 * (x3*x3 - s) + \
b44 * (x4*x4 - s) + \
b55 * (x5*x5 - s) + \
b66 * (x6*x6 - s)), 5)
array_x[i][72] = round(fabs(array_x[i][70] - array_x[i][71]), 5)
i += 1
for i in range(experim):
array_x[i] = [i+1] + array_x[i]
df = pd.DataFrame(array_x, columns=columns_table)
return [[{"name": i, "id": i} for i in df.columns], df.to_dict('records'), no_line]
def graphic(table_clicks, add_clicks, clients_number, rel_min, rel_max,
norm_min, norm_max, norm_d_min, norm_d_max, x1_new, x2_new,
x3_new):
global df, n_add
if (add_clicks > n_add):
n_add = add_clicks
x1_new = float(x1_new)
x2_new = float(x2_new)
x3_new = float(x3_new)
array_x.append(get_row([x1_new, x2_new, x3_new], 3))
rel_min = float(rel_min)
rel_max = float(rel_max)
norm_min = float(norm_min)
norm_max = float(norm_max)
norm_d_min = float(norm_d_min)
norm_d_max = float(norm_d_max)
i = 0
for row in array_x:
rel = convert_factor_to_value(float(rel_min), float(rel_max), row[1])
norm = convert_factor_to_value(float(norm_min), float(norm_max),
row[2])
norm_d = convert_factor_to_value(float(norm_d_min), float(norm_d_max),
row[3])
c_rel = calculate_reley_param(rel)
c_norm, c_norm_d = calculate_gauss_params(norm, norm_d)
generator = GenerateRequest(ReleyGenerator(c_rel), clients_number)
processor = ProcessRequest(GaussGenerator(c_norm, c_norm_d))
model = Model([generator], [processor])
result = model.event_mode()
array_x[i][8] = round(result, 5)
i += 1
b0 = calculate_b(0, 1, [array_x[i][0] for i in range(count_experiments)],
[array_x[i][8] for i in range(count_experiments)],
count_experiments)
b1 = calculate_b(rel_min, rel_max,
[array_x[i][1] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
b2 = calculate_b(norm_min, norm_max,
[array_x[i][2] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
b3 = calculate_b(norm_d_min, norm_d_max,
[array_x[i][3] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
b12 = calculate_b(rel_min * norm_min, rel_max * norm_max,
[array_x[i][4] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
b13 = calculate_b(rel_min * norm_d_min, rel_max * norm_d_max,
[array_x[i][5] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
b23 = calculate_b(norm_d_min * norm_min, norm_d_max * norm_max,
[array_x[i][6] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
b123 = calculate_b(rel_min * norm_d_min * norm_min,
rel_max * norm_d_max * norm_max,
[array_x[i][7] for i in range(count_experiments)],
[array_x[i][8]
for i in range(count_experiments)], count_experiments)
line = f"y={round(b0, 5)}+({round(b1, 5)})*x1+({round(b2, 5)})*x2+({round(b3, 5)})*x3"
no_line = f"y={round(b0, 5)}+({round(b1, 5)})*x1+({round(b2, 5)})*x2+({round(b3, 5)})*x3+ \
({round(b12, 5)})*x1*x2+({round(b13, 5)})*x1*x3+({round(b23, 5)})*x2*x3+({round(b123, 5)})*x1*x2*x3"
i = 0
for row in array_x:
x1 = array_x[i][1]
x2 = array_x[i][2]
x3 = array_x[i][3]
array_x[i][9] = round(fabs(b0 + b1 * x1 + b2 * x2 + b3 * x3), 5)
array_x[i][11] = round(fabs(array_x[i][9] - array_x[i][8]), 5)
array_x[i][10] = round(
fabs(b0 + b1 * x1 + b2 * x2 + b3 * x3 + b12 * x1 * x2 +
b23 * x2 * x3 + b13 * x1 * x3 + b123 * x1 * x2 * x3), 5)
array_x[i][12] = round(fabs(array_x[i][10] - array_x[i][8]), 5)
i += 1
df = pd.DataFrame(array_x, columns=columns_table)
return [[{
"name": i,
"id": i
} for i in df.columns],
df.to_dict('records'), line, no_line]
import pandas as pd
import dash_table
from math import fabs
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
columns_table = [
'x0', 'x1', 'x2', 'x3', 'x1*x2', 'x1*x3', 'x2*x3', 'x1*x2*x3', 'Y', 'Yл',
'Yчн', '|Y-Yл|', '|Y-Yчн|'
]
array_x = []
for i in [1, -1]:
for j in [1, -1]:
for k in [1, -1]:
array_x.append(get_row([i, j, k], 3))
df = pd.DataFrame(array_x, columns=columns_table)
df.reset_index()
count_experiments = 8
n_add = 0
app.layout = html.Div(
id='main',
children=[
dbc.Row([
dbc.Col(children=html.H6("Интенсивность поступления"),
width={
"size": 2,
"offset": 1
import os
from model import setup_db, DB_ROWS, get_row
import linear_regression
import graphing
CSV_FILE = os.environ.get('CSV_FILE', './data/housing.csv')
TARGET = os.environ.get('TARGET', 'median_home_value')
LEARNING_RATE = float(os.environ.get('LEARNING_RATE', '0.001'))
ITERATIONS = int(os.environ.get('ITERATIONS', '10000'))
if __name__ == '__main__':
setup_db(CSV_FILE)
feature = os.getenv('FEATURE')
if feature:
graphing.draw_scatter(feature,
get_row(TARGET),
get_row(feature),
None,
None,
'start',
showLine=False)
linear_regression.run(get_row(feature), feature)
else:
for name in DB_ROWS:
graphing.draw_scatter(name,
get_row(TARGET),
get_row(name),
None,
None,
'start',
showLine=False)