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Симченко Сергей Александрович N1

2019-11-12T10:25:45Z

Симченко Сергей:

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:

матожидание от:

x1 = 0.76

x2 = 1.46

y = 27.9595

дисперсия от:

x1 = 2819.3028571428576

x2 = 217.845306122449

y = 823.5539839285711

коэффициент корреляции:

r1 = -0.007186278697424209

r2 = 0.00922060063083846

b0 = 27.936277002146113

b1 = -0.00388400004646967

b2 = 0.01792797115698568

матрица коэффициентов b для уравнения y=b1*x1+b2*x2 -0.41449559 3.25507464

матрица коэффициентов b для уравнения y=b0+b1*x1+b2*x2 24.88403238 0.19342754 -0.4548107

значения y при параллельных опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963

дисперсия аппроксимации y=b0+b1*x1+b2*x2 0.008928958199990443

дисперсия воспроизводимости 0.006150000000000082

Адекватность модели y=b1*x1+b2*x2 2009.15116234013

Адекватность модели y=b0+b1*x1+b2*x2 1.4518631219496463

Симченко Сергей Александрович N2

2019-11-11T09:28:34Z

Симченко Сергей:

import numpy as np
import math
f = open("order.txt", "r")
X = []
Y = []
for line in f:
q, x1, x2, x3, x4, x5, x6, y = map(float, line.split())
x = []
x.append(x1)
x.append(x2)
x.append(x3)
x.append(x4)
x.append(x5)
x.append(x6)
x.append(x1 * x1)
x.append(x1 * x2)
x.append(x1 * x3)
x.append(x1 * x4)
x.append(x1 * x5)
x.append(x1 * x6)
x.append(x2 * x2)
x.append(x2 * x3)
x.append(x2 * x4)
x.append(x2 * x5)
x.append(x2 * x6)
x.append(x3 * x3)
x.append(x3 * x4)
x.append(x3 * x5)
x.append(x3 * x6)
x.append(x4 * x4)
x.append(x4 * x5)
x.append(x4 * x6)
x.append(x5 * x5)
x.append(x5 * x6)
x.append(x6 * x6)
X.append(x)
Y.append(y)
f.close()
Mx = []
for j in range(27):
Mx1 = np.sum(X[j]) / len(X)
Mx.append(Mx1)
My = np.sum(Y)/len(Y)
print(Mx)
print("Mx =", Mx)
Dx = []
Dx11 = 0
Dy = 0
for i in range(len(X)):
Dy += (Y[i] - My) ** 2
for i in range(27):
Dxs = 0.0
for j in range(len(X)):
Dxs += (X[j][i] - Mx[i]) ** 2
Dx.append(Dxs/len(X))
print("Dx =", Dx)
kx = []
for i in range(27):
kxs = 0.0
for j in range(len(X)):
kxs += ((X[j][i] - Mx[i]) * (Y[j] - My))
kx.append(kxs)
print("kx=", kx)
r = []
rABS = []
for i in range(len(kx)):
r1 = (kx[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dy))
r.append(r1)
rABS.append(math.fabs(r1))
print("r= ", r, len(r))
print(np.max(rABS))
print("Первый иднекс = ", rABS.index(np.max(rABS)) + 1)
phi = rABS.index(np.max(rABS))
kxxp = []
rp1 = []
for i in range(27):
if i != phi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][phi] - Mx[phi]))
kxxp.append(kxsp)

print("KxP1 = ", kxxp, len(kxxp))
for i in range(len(kxxp)):
r11 = (kxxp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[phi]))
rp1.append(r11)
print("rP1 = ", rp1, len(rp1))
poo = []
pABS = []

for i in range(len(kxxp)):
po = (r[i]-r[phi] * rp1[i])/(math.sqrt((1 - (r[phi] ** 2)) * (1 - (rp1[i] ** 2))))
poo.append(po)
pABS.append(math.fabs(po))
print(poo, len(poo))
print("Ro1 = ", pABS)
print(np.max(pABS))
print("Второй иднекс = ", pABS.index(np.max(pABS)))
hi = pABS.index(np.max(pABS))
print(hi)
kxxpp = []
rp2 = []
for i in range(26):
if i != hi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][hi] - Mx[hi]))
kxxpp.append(kxsp)

print("KxP2 = ", kxxpp, len(kxxpp))

for i in range(len(kxxpp)):
r12 = (kxxpp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[hi]))
rp2.append(r12)
print("rP2 = ", rp2, len(rp2))
poo2 = []
pABS2 = []

for i in range(len(kxxpp)):
po2 = (poo[i]-poo[hi] * rp2[i])/(math.sqrt((1 - (poo[hi] ** 2)) * (1 - (rp2[i] ** 2))))
poo2.append(po2)
pABS2.append(math.fabs(po2))
print("Ro2 = ", poo2)
print(np.max(pABS2))
print("Третий иднекс = ", pABS2.index(np.max(pABS2)))

pci = pABS2.index(np.max(pABS2))
NX =[]

for i in range(len(X)):
nx = []
nx.append(1)
nx.append(X[i][phi])
nx.append(X[i][hi+1])
nx.append(X[i][11])
NX.append(nx)

print(NX)
mx = np.matrix(NX)
B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, Y)
print(B)

[1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]

Mx = [1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]

Dx = [1659258.1155999997, 754443.9304, 379602.43519999995, 3961540.3232000005, 449808.15560000023, 635512.8047999997, 14138395.549199998, 6166171.7336, 5880905.65, 5249955.5592, 5365614.194000002, 5789465.8035999965, 21136843.943200003, 10309052.828, 9864509.986, 8430312.786400001, 8533613.187600002, 10426114.358800001, 10471508.498000002, 9124746.9816, 5074529.4368, 17810661.261200003, 3852981.4899999993, 8255880.387600002, 8374177.3664, 4688800.792399999, 10117382.17]

kx= [-2776631.077800071, -11323.659000057727, -2735901.143200034, 3105383.4707999006, -11160997.33720005, 2315010.113599949, -273350159.7777998, -58983768.219, -301590411.4512, 39447842.285800055, -683243134.0051999, 348329.1580000855, -32212932.626199804, -201659940.13939995, 155491144.33280015, -594381813.9689999, 109825695.22500008, -326175185.54639995, 20186866.695600104, -743100160.9481997, 28495753.510400046, 336702932.01880014, -420651751.0204, 267864778.1958001, -1068006106.8863999, -409266213.3656, 222422589.25959998]

r= [-0.0007446489161933638, -4.503639866226383e-06, -0.0015340031899526577, 0.0005389811210388624, -0.0057488330751216616, 0.0010031851544639405, -0.025113637718238133, -0.008205689190284362, -0.042962127592449856, 0.005947518428501872, -0.10189571280879187, 5.001047047484491e-05, -0.0024204747166797435, -0.021697045918858165, 0.017102447191693397, -0.07071866043526835, 0.01298756820434501, -0.03489635230329191, 0.002155036583942118, -0.08498206828597968, 0.004369911222963678, 0.027561143546511683, -0.07403116717027007, 0.032205071833952055, -0.1274949594679776, -0.06529280992029947, 0.02415656458240398] 27
0.1274949594679776

Первый индекс = 25

KxP1 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28, 59381811.91999998]

rP1 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122, 0.19339952213264006] 26
[-0.06298458549173727, -0.06243375392962137, -0.06251915017475687, -0.06236375455268047, -0.06234598188909586, -0.061991871562410504, 0.019167882791482262, 0.010453917781259191, -0.022900942265358486, 0.016363173573288228, -0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, -0.07233822191417215, -0.04175807928693842]

Ro1 = [0.06298458549173727, 0.06243375392962137, 0.06251915017475687, 0.06236375455268047, 0.06234598188909586, 0.061991871562410504, 0.019167882791482262, 0.010453917781259191, 0.022900942265358486, 0.016363173573288228, 0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, 0.07233822191417215, 0.04175807928693842]
0.07233822191417215

Второй индекс = 24

KxP2 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28]

rP2 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122]

Ro2 = [-0.10521121025550913, -0.1048136931971474, -0.10365296460104942, -0.10515563369714033, -0.09979854050045422, -0.10485685480684008, 0.04640612802018981, 0.021209188522782474, -0.011422963601053113, 0.022297481808092927, 0.05274928229613816, 0.04202736920015787, 0.07140837467741024, 0.05817758930648912, 0.051220630978477226, 0.11520511723259053, 0.0607342368582472, 0.046302663546079516, 0.06047142278180437, 0.14533210230123292, 0.029633989355312956, 0.06223210667100266, 0.05643379763974496, 0.051797689084524874, -0.0408153190905274]

Третий индекс = 19

[[1, 3481.0, 1770.0, 1680.0], [1, 256.0, 1200.0, 1125.0], [1, 36.0, 282.0, 799.0], [1, 4761.0, 6417.0, 7626.0], [1, 400.0, 740.0, 1776.0], [1, 2601.0, 1479.0, 1276.0], [1, 6724.0, 1312.0, 48.0], [1, 1.0, 12.0, 948.0], [1, 5476.0, 7252.0, 3332.0], [1, 4225.0, 520.0, 768.0], [1, 49.0, 602.0, 4558.0], [1, 5476.0, 2146.0, 116.0], [1, 1296.0, 684.0, 570.0], [1, 5329.0, 2628.0, 1044.0], [1, 6241.0, 5372.0, 5712.0], [1, 484.0, 1100.0, 2900.0], [1, 81.0, 144.0, 112.0], [1, 5929.0, 3542.0, 3312.0], [1, 324.0, 540.0, 1050.0], [1, 2809.0, 212.0, 288.0], [1, 3600.0, 3540.0, 5782.0], [1, 6241.0, 2133.0, 648.0], [1, 361.0, 1634.0, 8514.0], [1, 729.0, 2079.0, 462.0], [1, 49.0, 609.0, 6612.0], [1, 2704.0, 4628.0, 178.0], [1, 2209.0, 752.0, 800.0], [1, 529.0, 690.0, 1890.0], [1, 1521.0, 2652.0, 5712.0], [1, 3844.0, 4526.0, 511.0], [1, 1024.0, 64.0, 56.0], [1, 4225.0, 130.0, 194.0], [1, 1444.0, 380.0, 600.0], [1, 121.0, 572.0, 3016.0], [1, 7744.0, 1144.0, 1027.0], [1, 7396.0, 4816.0, 3976.0], [1, 4761.0, 4692.0, 612.0], [1, 5625.0, 2475.0, 1353.0], [1, 1225.0, 1645.0, 3149.0], [1, 4.0, 154.0, 1155.0], [1, 3969.0, 4536.0, 1224.0], [1, 9025.0, 4180.0, 2376.0], [1, 1296.0, 1944.0, 2592.0], [1, 625.0, 1300.0, 260.0], [1, 1.0, 55.0, 1705.0], [1, 5041.0, 0.0, 0.0], [1, 2116.0, 1196.0, 1664.0], [1, 6889.0, 4316.0, 2496.0], [1, 7744.0, 3344.0, 1102.0], [1, 1681.0, 3198.0, 1404.0]]

[-140.81971322 -3.335835 0.49445777 -0.29292457]

Симченко Сергей Александрович N2

2019-11-11T09:27:55Z

Симченко Сергей:

import numpy as np
import math
f = open("order.txt", "r")
X = []
Y = []
for line in f:
q, x1, x2, x3, x4, x5, x6, y = map(float, line.split())
x = []
x.append(x1)
x.append(x2)
x.append(x3)
x.append(x4)
x.append(x5)
x.append(x6)
x.append(x1 * x1)
x.append(x1 * x2)
x.append(x1 * x3)
x.append(x1 * x4)
x.append(x1 * x5)
x.append(x1 * x6)
x.append(x2 * x2)
x.append(x2 * x3)
x.append(x2 * x4)
x.append(x2 * x5)
x.append(x2 * x6)
x.append(x3 * x3)
x.append(x3 * x4)
x.append(x3 * x5)
x.append(x3 * x6)
x.append(x4 * x4)
x.append(x4 * x5)
x.append(x4 * x6)
x.append(x5 * x5)
x.append(x5 * x6)
x.append(x6 * x6)
X.append(x)
Y.append(y)
f.close()
Mx = []
for j in range(27):
Mx1 = np.sum(X[j]) / len(X)
Mx.append(Mx1)
My = np.sum(Y)/len(Y)
print(Mx)
print("Mx =", Mx)
Dx = []
Dx11 = 0
Dy = 0
for i in range(len(X)):
Dy += (Y[i] - My) ** 2
for i in range(27):
Dxs = 0.0
for j in range(len(X)):
Dxs += (X[j][i] - Mx[i]) ** 2
Dx.append(Dxs/len(X))
print("Dx =", Dx)
kx = []
for i in range(27):
kxs = 0.0
for j in range(len(X)):
kxs += ((X[j][i] - Mx[i]) * (Y[j] - My))
kx.append(kxs)
print("kx=", kx)
r = []
rABS = []
for i in range(len(kx)):
r1 = (kx[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dy))
r.append(r1)
rABS.append(math.fabs(r1))
print("r= ", r, len(r))
print(np.max(rABS))
print("Первый иднекс = ", rABS.index(np.max(rABS)) + 1)
phi = rABS.index(np.max(rABS))
kxxp = []
rp1 = []
for i in range(27):
if i != phi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][phi] - Mx[phi]))
kxxp.append(kxsp)

print("KxP1 = ", kxxp, len(kxxp))
for i in range(len(kxxp)):
r11 = (kxxp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[phi]))
rp1.append(r11)
print("rP1 = ", rp1, len(rp1))
poo = []
pABS = []

for i in range(len(kxxp)):
po = (r[i]-r[phi] * rp1[i])/(math.sqrt((1 - (r[phi] ** 2)) * (1 - (rp1[i] ** 2))))
poo.append(po)
pABS.append(math.fabs(po))
print(poo, len(poo))
print("Ro1 = ", pABS)
print(np.max(pABS))
print("Второй иднекс = ", pABS.index(np.max(pABS)))
hi = pABS.index(np.max(pABS))
print(hi)
kxxpp = []
rp2 = []
for i in range(26):
if i != hi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][hi] - Mx[hi]))
kxxpp.append(kxsp)

print("KxP2 = ", kxxpp, len(kxxpp))

for i in range(len(kxxpp)):
r12 = (kxxpp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[hi]))
rp2.append(r12)
print("rP2 = ", rp2, len(rp2))
poo2 = []
pABS2 = []

for i in range(len(kxxpp)):
po2 = (poo[i]-poo[hi] * rp2[i])/(math.sqrt((1 - (poo[hi] ** 2)) * (1 - (rp2[i] ** 2))))
poo2.append(po2)
pABS2.append(math.fabs(po2))
print("Ro2 = ", poo2)
print(np.max(pABS2))
print("Третий иднекс = ", pABS2.index(np.max(pABS2)))

pci = pABS2.index(np.max(pABS2))
NX =[]

for i in range(len(X)):
nx = []
nx.append(1)
nx.append(X[i][phi])
nx.append(X[i][hi+1])
nx.append(X[i][11])
NX.append(nx)

print(NX)
mx = np.matrix(NX)
B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, Y)
print(B)

[1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]

Mx = [1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]

Dx = [1659258.1155999997, 754443.9304, 379602.43519999995, 3961540.3232000005, 449808.15560000023, 635512.8047999997, 14138395.549199998, 6166171.7336, 5880905.65, 5249955.5592, 5365614.194000002, 5789465.8035999965, 21136843.943200003, 10309052.828, 9864509.986, 8430312.786400001, 8533613.187600002, 10426114.358800001, 10471508.498000002, 9124746.9816, 5074529.4368, 17810661.261200003, 3852981.4899999993, 8255880.387600002, 8374177.3664, 4688800.792399999, 10117382.17]

kx= [-2776631.077800071, -11323.659000057727, -2735901.143200034, 3105383.4707999006, -11160997.33720005, 2315010.113599949, -273350159.7777998, -58983768.219, -301590411.4512, 39447842.285800055, -683243134.0051999, 348329.1580000855, -32212932.626199804, -201659940.13939995, 155491144.33280015, -594381813.9689999, 109825695.22500008, -326175185.54639995, 20186866.695600104, -743100160.9481997, 28495753.510400046, 336702932.01880014, -420651751.0204, 267864778.1958001, -1068006106.8863999, -409266213.3656, 222422589.25959998]

r= [-0.0007446489161933638, -4.503639866226383e-06, -0.0015340031899526577, 0.0005389811210388624, -0.0057488330751216616, 0.0010031851544639405, -0.025113637718238133, -0.008205689190284362, -0.042962127592449856, 0.005947518428501872, -0.10189571280879187, 5.001047047484491e-05, -0.0024204747166797435, -0.021697045918858165, 0.017102447191693397, -0.07071866043526835, 0.01298756820434501, -0.03489635230329191, 0.002155036583942118, -0.08498206828597968, 0.004369911222963678, 0.027561143546511683, -0.07403116717027007, 0.032205071833952055, -0.1274949594679776, -0.06529280992029947, 0.02415656458240398] 27
0.1274949594679776

Первый индекс = 25

KxP1 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28, 59381811.91999998]

rP1 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122, 0.19339952213264006] 26
[-0.06298458549173727, -0.06243375392962137, -0.06251915017475687, -0.06236375455268047, -0.06234598188909586, -0.061991871562410504, 0.019167882791482262, 0.010453917781259191, -0.022900942265358486, 0.016363173573288228, -0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, -0.07233822191417215, -0.04175807928693842]

Ro1 = [0.06298458549173727, 0.06243375392962137, 0.06251915017475687, 0.06236375455268047, 0.06234598188909586, 0.061991871562410504, 0.019167882791482262, 0.010453917781259191, 0.022900942265358486, 0.016363173573288228, 0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, 0.07233822191417215, 0.04175807928693842]
0.07233822191417215

Второй иднекс = 24

24
KxP2 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28]

rP2 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122]

Ro2 = [-0.10521121025550913, -0.1048136931971474, -0.10365296460104942, -0.10515563369714033, -0.09979854050045422, -0.10485685480684008, 0.04640612802018981, 0.021209188522782474, -0.011422963601053113, 0.022297481808092927, 0.05274928229613816, 0.04202736920015787, 0.07140837467741024, 0.05817758930648912, 0.051220630978477226, 0.11520511723259053, 0.0607342368582472, 0.046302663546079516, 0.06047142278180437, 0.14533210230123292, 0.029633989355312956, 0.06223210667100266, 0.05643379763974496, 0.051797689084524874, -0.0408153190905274]

Третий иднекс = 19

[[1, 3481.0, 1770.0, 1680.0], [1, 256.0, 1200.0, 1125.0], [1, 36.0, 282.0, 799.0], [1, 4761.0, 6417.0, 7626.0], [1, 400.0, 740.0, 1776.0], [1, 2601.0, 1479.0, 1276.0], [1, 6724.0, 1312.0, 48.0], [1, 1.0, 12.0, 948.0], [1, 5476.0, 7252.0, 3332.0], [1, 4225.0, 520.0, 768.0], [1, 49.0, 602.0, 4558.0], [1, 5476.0, 2146.0, 116.0], [1, 1296.0, 684.0, 570.0], [1, 5329.0, 2628.0, 1044.0], [1, 6241.0, 5372.0, 5712.0], [1, 484.0, 1100.0, 2900.0], [1, 81.0, 144.0, 112.0], [1, 5929.0, 3542.0, 3312.0], [1, 324.0, 540.0, 1050.0], [1, 2809.0, 212.0, 288.0], [1, 3600.0, 3540.0, 5782.0], [1, 6241.0, 2133.0, 648.0], [1, 361.0, 1634.0, 8514.0], [1, 729.0, 2079.0, 462.0], [1, 49.0, 609.0, 6612.0], [1, 2704.0, 4628.0, 178.0], [1, 2209.0, 752.0, 800.0], [1, 529.0, 690.0, 1890.0], [1, 1521.0, 2652.0, 5712.0], [1, 3844.0, 4526.0, 511.0], [1, 1024.0, 64.0, 56.0], [1, 4225.0, 130.0, 194.0], [1, 1444.0, 380.0, 600.0], [1, 121.0, 572.0, 3016.0], [1, 7744.0, 1144.0, 1027.0], [1, 7396.0, 4816.0, 3976.0], [1, 4761.0, 4692.0, 612.0], [1, 5625.0, 2475.0, 1353.0], [1, 1225.0, 1645.0, 3149.0], [1, 4.0, 154.0, 1155.0], [1, 3969.0, 4536.0, 1224.0], [1, 9025.0, 4180.0, 2376.0], [1, 1296.0, 1944.0, 2592.0], [1, 625.0, 1300.0, 260.0], [1, 1.0, 55.0, 1705.0], [1, 5041.0, 0.0, 0.0], [1, 2116.0, 1196.0, 1664.0], [1, 6889.0, 4316.0, 2496.0], [1, 7744.0, 3344.0, 1102.0], [1, 1681.0, 3198.0, 1404.0]]

[-140.81971322 -3.335835 0.49445777 -0.29292457]

Симченко Сергей Александрович N2

2019-11-11T09:25:32Z

Симченко Сергей:

import numpy as np
import math
f = open("order.txt", "r")
X = []
Y = []
for line in f:
q, x1, x2, x3, x4, x5, x6, y = map(float, line.split())
x = []
x.append(x1)
x.append(x2)
x.append(x3)
x.append(x4)
x.append(x5)
x.append(x6)
x.append(x1 * x1)
x.append(x1 * x2)
x.append(x1 * x3)
x.append(x1 * x4)
x.append(x1 * x5)
x.append(x1 * x6)
x.append(x2 * x2)
x.append(x2 * x3)
x.append(x2 * x4)
x.append(x2 * x5)
x.append(x2 * x6)
x.append(x3 * x3)
x.append(x3 * x4)
x.append(x3 * x5)
x.append(x3 * x6)
x.append(x4 * x4)
x.append(x4 * x5)
x.append(x4 * x6)
x.append(x5 * x5)
x.append(x5 * x6)
x.append(x6 * x6)
X.append(x)
Y.append(y)
f.close()
Mx = []
for j in range(27):
Mx1 = np.sum(X[j]) / len(X)
Mx.append(Mx1)
My = np.sum(Y)/len(Y)
print(Mx)
print("Mx =", Mx)
Dx = []
Dx11 = 0
Dy = 0
for i in range(len(X)):
Dy += (Y[i] - My) ** 2
for i in range(27):
Dxs = 0.0
for j in range(len(X)):
Dxs += (X[j][i] - Mx[i]) ** 2
Dx.append(Dxs/len(X))
print("Dx =", Dx)
kx = []
for i in range(27):
kxs = 0.0
for j in range(len(X)):
kxs += ((X[j][i] - Mx[i]) * (Y[j] - My))
kx.append(kxs)
print("kx=", kx)
r = []
rABS = []
for i in range(len(kx)):
r1 = (kx[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dy))
r.append(r1)
rABS.append(math.fabs(r1))
print("r= ", r, len(r))
print(np.max(rABS))
print("Первый иднекс = ", rABS.index(np.max(rABS)) + 1)
phi = rABS.index(np.max(rABS))
kxxp = []
rp1 = []
for i in range(27):
if i != phi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][phi] - Mx[phi]))
kxxp.append(kxsp)

print("KxP1 = ", kxxp, len(kxxp))
for i in range(len(kxxp)):
r11 = (kxxp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[phi]))
rp1.append(r11)
print("rP1 = ", rp1, len(rp1))
poo = []
pABS = []

for i in range(len(kxxp)):
po = (r[i]-r[phi] * rp1[i])/(math.sqrt((1 - (r[phi] ** 2)) * (1 - (rp1[i] ** 2))))
poo.append(po)
pABS.append(math.fabs(po))
print(poo, len(poo))
print("Ro1 = ", pABS)
print(np.max(pABS))
print("Второй иднекс = ", pABS.index(np.max(pABS)))
hi = pABS.index(np.max(pABS))
print(hi)
kxxpp = []
rp2 = []
for i in range(26):
if i != hi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][hi] - Mx[hi]))
kxxpp.append(kxsp)

print("KxP2 = ", kxxpp, len(kxxpp))

for i in range(len(kxxpp)):
r12 = (kxxpp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[hi]))
rp2.append(r12)
print("rP2 = ", rp2, len(rp2))
poo2 = []
pABS2 = []

for i in range(len(kxxpp)):
po2 = (poo[i]-poo[hi] * rp2[i])/(math.sqrt((1 - (poo[hi] ** 2)) * (1 - (rp2[i] ** 2))))
poo2.append(po2)
pABS2.append(math.fabs(po2))
print("Ro2 = ", poo2)
print(np.max(pABS2))
print("Третий иднекс = ", pABS2.index(np.max(pABS2)))

pci = pABS2.index(np.max(pABS2))
NX =[]

for i in range(len(X)):
nx = []
nx.append(1)
nx.append(X[i][phi])
nx.append(X[i][hi+1])
nx.append(X[i][11])
NX.append(nx)

print(NX)
mx = np.matrix(NX)
B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, Y)
print(B)

[1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]
Mx = [1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]
Dx = [1659258.1155999997, 754443.9304, 379602.43519999995, 3961540.3232000005, 449808.15560000023, 635512.8047999997, 14138395.549199998, 6166171.7336, 5880905.65, 5249955.5592, 5365614.194000002, 5789465.8035999965, 21136843.943200003, 10309052.828, 9864509.986, 8430312.786400001, 8533613.187600002, 10426114.358800001, 10471508.498000002, 9124746.9816, 5074529.4368, 17810661.261200003, 3852981.4899999993, 8255880.387600002, 8374177.3664, 4688800.792399999, 10117382.17]
kx= [-2776631.077800071, -11323.659000057727, -2735901.143200034, 3105383.4707999006, -11160997.33720005, 2315010.113599949, -273350159.7777998, -58983768.219, -301590411.4512, 39447842.285800055, -683243134.0051999, 348329.1580000855, -32212932.626199804, -201659940.13939995, 155491144.33280015, -594381813.9689999, 109825695.22500008, -326175185.54639995, 20186866.695600104, -743100160.9481997, 28495753.510400046, 336702932.01880014, -420651751.0204, 267864778.1958001, -1068006106.8863999, -409266213.3656, 222422589.25959998]
r= [-0.0007446489161933638, -4.503639866226383e-06, -0.0015340031899526577, 0.0005389811210388624, -0.0057488330751216616, 0.0010031851544639405, -0.025113637718238133, -0.008205689190284362, -0.042962127592449856, 0.005947518428501872, -0.10189571280879187, 5.001047047484491e-05, -0.0024204747166797435, -0.021697045918858165, 0.017102447191693397, -0.07071866043526835, 0.01298756820434501, -0.03489635230329191, 0.002155036583942118, -0.08498206828597968, 0.004369911222963678, 0.027561143546511683, -0.07403116717027007, 0.032205071833952055, -0.1274949594679776, -0.06529280992029947, 0.02415656458240398] 27
0.1274949594679776

Первый индекс = 25

KxP1 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28, 59381811.91999998] 26
rP1 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122, 0.19339952213264006] 26
[-0.06298458549173727, -0.06243375392962137, -0.06251915017475687, -0.06236375455268047, -0.06234598188909586, -0.061991871562410504, 0.019167882791482262, 0.010453917781259191, -0.022900942265358486, 0.016363173573288228, -0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, -0.07233822191417215, -0.04175807928693842] 26
Ro1 = [0.06298458549173727, 0.06243375392962137, 0.06251915017475687, 0.06236375455268047, 0.06234598188909586, 0.061991871562410504, 0.019167882791482262, 0.010453917781259191, 0.022900942265358486, 0.016363173573288228, 0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, 0.07233822191417215, 0.04175807928693842]
0.07233822191417215

Второй иднекс = 24

24
KxP2 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28] 25
rP2 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122] 25
Ro2 = [-0.10521121025550913, -0.1048136931971474, -0.10365296460104942, -0.10515563369714033, -0.09979854050045422, -0.10485685480684008, 0.04640612802018981, 0.021209188522782474, -0.011422963601053113, 0.022297481808092927, 0.05274928229613816, 0.04202736920015787, 0.07140837467741024, 0.05817758930648912, 0.051220630978477226, 0.11520511723259053, 0.0607342368582472, 0.046302663546079516, 0.06047142278180437, 0.14533210230123292, 0.029633989355312956, 0.06223210667100266, 0.05643379763974496, 0.051797689084524874, -0.0408153190905274]
0.14533210230123292

Третий иднекс = 19

[[1, 3481.0, 1770.0, 1680.0], [1, 256.0, 1200.0, 1125.0], [1, 36.0, 282.0, 799.0], [1, 4761.0, 6417.0, 7626.0], [1, 400.0, 740.0, 1776.0], [1, 2601.0, 1479.0, 1276.0], [1, 6724.0, 1312.0, 48.0], [1, 1.0, 12.0, 948.0], [1, 5476.0, 7252.0, 3332.0], [1, 4225.0, 520.0, 768.0], [1, 49.0, 602.0, 4558.0], [1, 5476.0, 2146.0, 116.0], [1, 1296.0, 684.0, 570.0], [1, 5329.0, 2628.0, 1044.0], [1, 6241.0, 5372.0, 5712.0], [1, 484.0, 1100.0, 2900.0], [1, 81.0, 144.0, 112.0], [1, 5929.0, 3542.0, 3312.0], [1, 324.0, 540.0, 1050.0], [1, 2809.0, 212.0, 288.0], [1, 3600.0, 3540.0, 5782.0], [1, 6241.0, 2133.0, 648.0], [1, 361.0, 1634.0, 8514.0], [1, 729.0, 2079.0, 462.0], [1, 49.0, 609.0, 6612.0], [1, 2704.0, 4628.0, 178.0], [1, 2209.0, 752.0, 800.0], [1, 529.0, 690.0, 1890.0], [1, 1521.0, 2652.0, 5712.0], [1, 3844.0, 4526.0, 511.0], [1, 1024.0, 64.0, 56.0], [1, 4225.0, 130.0, 194.0], [1, 1444.0, 380.0, 600.0], [1, 121.0, 572.0, 3016.0], [1, 7744.0, 1144.0, 1027.0], [1, 7396.0, 4816.0, 3976.0], [1, 4761.0, 4692.0, 612.0], [1, 5625.0, 2475.0, 1353.0], [1, 1225.0, 1645.0, 3149.0], [1, 4.0, 154.0, 1155.0], [1, 3969.0, 4536.0, 1224.0], [1, 9025.0, 4180.0, 2376.0], [1, 1296.0, 1944.0, 2592.0], [1, 625.0, 1300.0, 260.0], [1, 1.0, 55.0, 1705.0], [1, 5041.0, 0.0, 0.0], [1, 2116.0, 1196.0, 1664.0], [1, 6889.0, 4316.0, 2496.0], [1, 7744.0, 3344.0, 1102.0], [1, 1681.0, 3198.0, 1404.0]]
[-140.81971322 -3.335835 0.49445777 -0.29292457]

Симченко Сергей Александрович N2

2019-11-11T09:25:02Z

Симченко Сергей: Новая страница: «import numpy as np import math f = open("order.txt", "r") X = [] Y = [] for line in f: q, x1, x2, x3, x4, x5, x6, y = map(float, line.split()) x = [] …»

import numpy as np
import math
f = open("order.txt", "r")
X = []
Y = []
for line in f:
q, x1, x2, x3, x4, x5, x6, y = map(float, line.split())
x = []
x.append(x1)
x.append(x2)
x.append(x3)
x.append(x4)
x.append(x5)
x.append(x6)
x.append(x1 * x1)
x.append(x1 * x2)
x.append(x1 * x3)
x.append(x1 * x4)
x.append(x1 * x5)
x.append(x1 * x6)
x.append(x2 * x2)
x.append(x2 * x3)
x.append(x2 * x4)
x.append(x2 * x5)
x.append(x2 * x6)
x.append(x3 * x3)
x.append(x3 * x4)
x.append(x3 * x5)
x.append(x3 * x6)
x.append(x4 * x4)
x.append(x4 * x5)
x.append(x4 * x6)
x.append(x5 * x5)
x.append(x5 * x6)
x.append(x6 * x6)
X.append(x)
Y.append(y)
f.close()
Mx = []
for j in range(27):
Mx1 = np.sum(X[j]) / len(X)
Mx.append(Mx1)
My = np.sum(Y)/len(Y)
print(Mx)
print("Mx =", Mx)
Dx = []
Dx11 = 0
Dy = 0
for i in range(len(X)):
Dy += (Y[i] - My) ** 2
for i in range(27):
Dxs = 0.0
for j in range(len(X)):
Dxs += (X[j][i] - Mx[i]) ** 2
Dx.append(Dxs/len(X))
print("Dx =", Dx)
kx = []
for i in range(27):
kxs = 0.0
for j in range(len(X)):
kxs += ((X[j][i] - Mx[i]) * (Y[j] - My))
kx.append(kxs)
print("kx=", kx)
r = []
rABS = []
for i in range(len(kx)):
r1 = (kx[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dy))
r.append(r1)
rABS.append(math.fabs(r1))
print("r= ", r, len(r))
print(np.max(rABS))
print("Первый иднекс = ", rABS.index(np.max(rABS)) + 1)
phi = rABS.index(np.max(rABS))
kxxp = []
rp1 = []
for i in range(27):
if i != phi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][phi] - Mx[phi]))
kxxp.append(kxsp)

print("KxP1 = ", kxxp, len(kxxp))
for i in range(len(kxxp)):
r11 = (kxxp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[phi]))
rp1.append(r11)
print("rP1 = ", rp1, len(rp1))
poo = []
pABS = []

for i in range(len(kxxp)):
po = (r[i]-r[phi] * rp1[i])/(math.sqrt((1 - (r[phi] ** 2)) * (1 - (rp1[i] ** 2))))
poo.append(po)
pABS.append(math.fabs(po))
print(poo, len(poo))
print("Ro1 = ", pABS)
print(np.max(pABS))
print("Второй иднекс = ", pABS.index(np.max(pABS)))
hi = pABS.index(np.max(pABS))
print(hi)
kxxpp = []
rp2 = []
for i in range(26):
if i != hi:
kxsp = 0.0
for j in range(len(X)):
kxsp += ((X[j][i] - Mx[i]) * (X[j][hi] - Mx[hi]))
kxxpp.append(kxsp)

print("KxP2 = ", kxxpp, len(kxxpp))

for i in range(len(kxxpp)):
r12 = (kxxpp[i] / (len(X)-1)) / (math.sqrt(Dx[i]) * math.sqrt(Dx[hi]))
rp2.append(r12)
print("rP2 = ", rp2, len(rp2))
poo2 = []
pABS2 = []

for i in range(len(kxxpp)):
po2 = (poo[i]-poo[hi] * rp2[i])/(math.sqrt((1 - (poo[hi] ** 2)) * (1 - (rp2[i] ** 2))))
poo2.append(po2)
pABS2.append(math.fabs(po2))
print("Ro2 = ", poo2)
print(np.max(pABS2))
print("Третий иднекс = ", pABS2.index(np.max(pABS2)))

pci = pABS2.index(np.max(pABS2))
NX =[]

for i in range(len(X)):
nx = []
nx.append(1)
nx.append(X[i][phi])
nx.append(X[i][hi+1])
nx.append(X[i][11])
NX.append(nx)

print(NX)
mx = np.matrix(NX)
B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, Y)
print(B)

[1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]
Mx = [1334.18, 918.92, 668.16, 2043.48, 717.22, 842.16, 548.82, 1680.96, 2531.5, 1410.28, 1285.9, 753.14, 300.08, 857.4, 1008.1, 622.32, 587.74, 2295.62, 865.58, 797.32, 1728.48, 599.98, 1901.18, 1006.02, 1755.24, 877.06, 1086.5]
Dx = [1659258.1155999997, 754443.9304, 379602.43519999995, 3961540.3232000005, 449808.15560000023, 635512.8047999997, 14138395.549199998, 6166171.7336, 5880905.65, 5249955.5592, 5365614.194000002, 5789465.8035999965, 21136843.943200003, 10309052.828, 9864509.986, 8430312.786400001, 8533613.187600002, 10426114.358800001, 10471508.498000002, 9124746.9816, 5074529.4368, 17810661.261200003, 3852981.4899999993, 8255880.387600002, 8374177.3664, 4688800.792399999, 10117382.17]
kx= [-2776631.077800071, -11323.659000057727, -2735901.143200034, 3105383.4707999006, -11160997.33720005, 2315010.113599949, -273350159.7777998, -58983768.219, -301590411.4512, 39447842.285800055, -683243134.0051999, 348329.1580000855, -32212932.626199804, -201659940.13939995, 155491144.33280015, -594381813.9689999, 109825695.22500008, -326175185.54639995, 20186866.695600104, -743100160.9481997, 28495753.510400046, 336702932.01880014, -420651751.0204, 267864778.1958001, -1068006106.8863999, -409266213.3656, 222422589.25959998]
r= [-0.0007446489161933638, -4.503639866226383e-06, -0.0015340031899526577, 0.0005389811210388624, -0.0057488330751216616, 0.0010031851544639405, -0.025113637718238133, -0.008205689190284362, -0.042962127592449856, 0.005947518428501872, -0.10189571280879187, 5.001047047484491e-05, -0.0024204747166797435, -0.021697045918858165, 0.017102447191693397, -0.07071866043526835, 0.01298756820434501, -0.03489635230329191, 0.002155036583942118, -0.08498206828597968, 0.004369911222963678, 0.027561143546511683, -0.07403116717027007, 0.032205071833952055, -0.1274949594679776, -0.06529280992029947, 0.02415656458240398] 27
0.1274949594679776

Первый индекс = 25

KxP1 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28, 59381811.91999998] 26
rP1 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122, 0.19339952213264006] 26
[-0.06298458549173727, -0.06243375392962137, -0.06251915017475687, -0.06236375455268047, -0.06234598188909586, -0.061991871562410504, 0.019167882791482262, 0.010453917781259191, -0.022900942265358486, 0.016363173573288228, -0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, -0.07233822191417215, -0.04175807928693842] 26
Ro1 = [0.06298458549173727, 0.06243375392962137, 0.06251915017475687, 0.06236375455268047, 0.06234598188909586, 0.061991871562410504, 0.019167882791482262, 0.010453917781259191, 0.022900942265358486, 0.016363173573288228, 0.015178659078068582, 0.026486594817603203, 0.042966436339393094, 0.027338622401860684, 0.03864977324493508, 0.029005404534484927, 0.042888152127402344, 0.01505233196077994, 0.03845029157529726, 0.02885090171432413, 0.020411361114511997, 0.04943044272969443, 0.0022370041739808912, 0.04467667393055186, 0.07233822191417215, 0.04175807928693842]
0.07233822191417215

Второй иднекс = 24

24
KxP2 = [-79506220.36, -53805610.000000015, -37358587.239999995, -124156252.63999999, -37995638.63999999, -49823076.88000001, 179866345.23999998, 50995362.60000001, 55411610.76000001, 26068662.360000007, 235466232.96, 68726020.2, 217763610.96000004, 167514617.12, 72384806.56, 293511694.00000006, 92516579.60000001, 174868426.52000004, 124359416.32000002, 343064884.16, 39385415.28000001, 97668676.96000001, 165511347.12000003, 38069917.96000001, 212948213.28] 25
rP2 = [-0.4352882811706083, -0.4368646633446207, -0.4276207708101426, -0.4399156355268059, -0.3995332502453671, -0.440759045674489, 0.3373515335626213, 0.14482906220819677, 0.16114302474751857, 0.08023683254673793, 0.7168887369838249, 0.20143493434795184, 0.3340396972170328, 0.3679392924712604, 0.16253335576842676, 0.7129127915795442, 0.22334998829844832, 0.3819293226001716, 0.2710234645738416, 0.8009376207227143, 0.1233020744806429, 0.1632105112455183, 0.5946510014165292, 0.09344005539753297, 0.5189622470991122] 25
Ro2 = [-0.10521121025550913, -0.1048136931971474, -0.10365296460104942, -0.10515563369714033, -0.09979854050045422, -0.10485685480684008, 0.04640612802018981, 0.021209188522782474, -0.011422963601053113, 0.022297481808092927, 0.05274928229613816, 0.04202736920015787, 0.07140837467741024, 0.05817758930648912, 0.051220630978477226, 0.11520511723259053, 0.0607342368582472, 0.046302663546079516, 0.06047142278180437, 0.14533210230123292, 0.029633989355312956, 0.06223210667100266, 0.05643379763974496, 0.051797689084524874, -0.0408153190905274]
0.14533210230123292

Третий иднекс = 19

[[1, 3481.0, 1770.0, 1680.0], [1, 256.0, 1200.0, 1125.0], [1, 36.0, 282.0, 799.0], [1, 4761.0, 6417.0, 7626.0], [1, 400.0, 740.0, 1776.0], [1, 2601.0, 1479.0, 1276.0], [1, 6724.0, 1312.0, 48.0], [1, 1.0, 12.0, 948.0], [1, 5476.0, 7252.0, 3332.0], [1, 4225.0, 520.0, 768.0], [1, 49.0, 602.0, 4558.0], [1, 5476.0, 2146.0, 116.0], [1, 1296.0, 684.0, 570.0], [1, 5329.0, 2628.0, 1044.0], [1, 6241.0, 5372.0, 5712.0], [1, 484.0, 1100.0, 2900.0], [1, 81.0, 144.0, 112.0], [1, 5929.0, 3542.0, 3312.0], [1, 324.0, 540.0, 1050.0], [1, 2809.0, 212.0, 288.0], [1, 3600.0, 3540.0, 5782.0], [1, 6241.0, 2133.0, 648.0], [1, 361.0, 1634.0, 8514.0], [1, 729.0, 2079.0, 462.0], [1, 49.0, 609.0, 6612.0], [1, 2704.0, 4628.0, 178.0], [1, 2209.0, 752.0, 800.0], [1, 529.0, 690.0, 1890.0], [1, 1521.0, 2652.0, 5712.0], [1, 3844.0, 4526.0, 511.0], [1, 1024.0, 64.0, 56.0], [1, 4225.0, 130.0, 194.0], [1, 1444.0, 380.0, 600.0], [1, 121.0, 572.0, 3016.0], [1, 7744.0, 1144.0, 1027.0], [1, 7396.0, 4816.0, 3976.0], [1, 4761.0, 4692.0, 612.0], [1, 5625.0, 2475.0, 1353.0], [1, 1225.0, 1645.0, 3149.0], [1, 4.0, 154.0, 1155.0], [1, 3969.0, 4536.0, 1224.0], [1, 9025.0, 4180.0, 2376.0], [1, 1296.0, 1944.0, 2592.0], [1, 625.0, 1300.0, 260.0], [1, 1.0, 55.0, 1705.0], [1, 5041.0, 0.0, 0.0], [1, 2116.0, 1196.0, 1664.0], [1, 6889.0, 4316.0, 2496.0], [1, 7744.0, 3344.0, 1102.0], [1, 1681.0, 3198.0, 1404.0]]
[[-140.81971322 -3.335835 0.49445777 -0.29292457]]

Достижения студентов группы МК-12

2019-11-11T09:22:57Z

Симченко Сергей:

<table>
<tr>
<td>
Васильев Михаил
</td>
<td>
[[Васильев N1]]
</td>
<td>
[[Васильев N2]]
</td>
</tr>
<tr>
<td>
Гамидов Роман Акимович
</td>
<td>
[[Гамидов Роман Акимович N1]]
</td>
<td>
</td>
</tr>

<tr>
<td>
Генералов Кирилл
</td>
<td>
[[Генералов Кирилл N1]]
</td>
<td>
[[Генералов Кирилл N2]]
</td>
</tr>

<tr>
<td>
Глаголев Геннадий Александрович
</td>
<td>
[[Глаголев Геннадий Александрович N1]]
</td>
<td>
</td>
</tr>

<tr>
<td>
Калайчев Глеб
</td>
<td>
[[Калайчев N1]]
</td>
<td>
[[Калайчев N2]]
</td>
</tr>

<tr>
<td>
Лебедев Данила
</td>
<td>
[[Лебедев Данила 1]]
</td>
<td>
[[Лебедев Данила 2]]
</td>
</tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td>
Симченко Сергей Александрович
</td>
<td>
[[Симченко Сергей Александрович N1]]
</td>
<td>
[[Симченко Сергей Александрович N2]]
</td>
</tr>

<tr>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
</table>

Достижения студентов группы МК-12

2019-11-11T09:22:29Z

Симченко Сергей:

<table>
<tr>
<td>
Васильев Михаил
</td>
<td>
[[Васильев N1]]
</td>
<td>
[[Васильев N2]]
</td>
</tr>
<tr>
<td>
Гамидов Роман Акимович
</td>
<td>
[[Гамидов Роман Акимович N1]]
</td>
<td>
</td>
</tr>

<tr>
<td>
Генералов Кирилл
</td>
<td>
[[Генералов Кирилл N1]]
</td>
<td>
[[Генералов Кирилл N2]]
</td>
</tr>

<tr>
<td>
Глаголев Геннадий Александрович
</td>
<td>
[[Глаголев Геннадий Александрович N1]]
</td>
<td>
</td>
</tr>

<tr>
<td>
Калайчев Глеб
</td>
<td>
[[Калайчев N1]]
</td>
<td>
[[Калайчев N2]]
</td>
</tr>

<tr>
<td>
Лебедев Данила
</td>
<td>
[[Лебедев Данила 1]]
</td>
<td>
[[Лебедев Данила 2]]
</td>
</tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td>
Симченко Сергей Александрович
</td>
<td>
[[Симченко Сергей Александрович N1]]
</td>
<td>
</td>
</tr>
[[Симченко Сергей Александрович N2]]

<tr>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
</table>

Симченко Сергей Александрович N1

2019-11-08T20:55:58Z

Симченко Сергей:

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:

матожидание от:

x1 = 0.76

x2 = 1.46

y = 27.9595

дисперсия от:

x1 = 2819.3028571428576

x2 = 217.845306122449

y = 823.5539839285711

коэффициент корреляции:

r1 = -0.007186278697424209

r2 = 0.00922060063083846

b0 = 27.936277002146113

b1 = -0.00388400004646967

b1 = 0.01792797115698568

матрица коэффициентов b для уравнения y=b1*x1+b2*x2 -0.41449559 3.25507464

матрица коэффициентов b для уравнения y=b0+b1*x1+b2*x2 24.88403238 0.19342754 -0.4548107

значения y при параллельных опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963

дисперсия аппроксимации y=b0+b1*x1+b2*x2 0.008928958199990443

дисперсия воспроизводимости 0.006150000000000082

Адекватность модели y=b1*x1+b2*x2 2009.15116234013

Адекватность модели y=b0+b1*x1+b2*x2 1.4518631219496463

Симченко Сергей Александрович N1

2019-11-08T08:38:30Z

Симченко Сергей:

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:

матожидание от:

x1 = 0.76

x2 = 1.46

y = 27.9595

дисперсия от:

x1 = 2819.3028571428576

x2 = 217.845306122449

y = 823.5539839285711

коэффициент корреляции:

r1 = -0.007186278697424209

r2 = 0.00922060063083846

b0 = 27.936277002146113

b1 = -0.00388400004646967

b1 = 0.01792797115698568

матрица коэффициенто b для уравнения y=b1*x1+b2*x2 -0.41449559 3.25507464

матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2 24.88403238 0.19342754 -0.4548107

значения y при паралленых опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963

дисперсия аппроксимации y=b0+b1*x1+b2*x2 812.9902003819129

дисперсия воспроизводимости 0.006150000000000082

Адекватность модели y=b1*x1+b2*x2 2009.15116234013

Адекватность модели y=b0+b1*x1+b2*x2 132193.52851738245

Симченко Сергей Александрович N1

2019-11-08T08:38:05Z

Симченко Сергей:

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:

матожидание от:
x1 = 0.76

x2 = 1.46

y = 27.9595

дисперсия от:

x1 = 2819.3028571428576

x2 = 217.845306122449

y = 823.5539839285711

коэффициент корреляции:

r1 = -0.007186278697424209

r2 = 0.00922060063083846

b0 = 27.936277002146113

b1 = -0.00388400004646967

b1 = 0.01792797115698568

матрица коэффициенто b для уравнения y=b1*x1+b2*x2 -0.41449559 3.25507464

матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2 24.88403238 0.19342754 -0.4548107

значения y при паралленых опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963

дисперсия аппроксимации y=b0+b1*x1+b2*x2 812.9902003819129

дисперсия воспроизводимости 0.006150000000000082

Адекватность модели y=b1*x1+b2*x2 2009.15116234013

Адекватность модели y=b0+b1*x1+b2*x2 132193.52851738245

Симченко Сергей Александрович N1

2019-11-08T08:37:40Z

Симченко Сергей:

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:
матожидание от:
x1 = 0.76

x2 = 1.46

y = 27.9595

дисперсия от:

x1 = 2819.3028571428576

x2 = 217.845306122449

y = 823.5539839285711

коэффициент корреляции:

r1 = -0.007186278697424209

r2 = 0.00922060063083846

b0 = 27.936277002146113

b1 = -0.00388400004646967

b1 = 0.01792797115698568

матрица коэффициенто b для уравнения y=b1*x1+b2*x2 -0.41449559 3.25507464

матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2 24.88403238 0.19342754 -0.4548107

значения y при паралленых опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963

дисперсия аппроксимации y=b0+b1*x1+b2*x2 812.9902003819129

дисперсия воспроизводимости 0.006150000000000082

Адекватность модели y=b1*x1+b2*x2 2009.15116234013

Адекватность модели y=b0+b1*x1+b2*x2 132193.52851738245

Лебедев Данила 1

2019-11-08T08:36:22Z

Симченко Сергей:

<source lang="python">
import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2", B01)
yPs = sum/count
print("значения y при паралленых опытов",yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(" ")
print("дисперсия аппроксимации для уравнения y=b1*x1+b2*x2",sumA/(len(NY)-2))
print("дисперсия аппроксимации y=b0+b1*x1+b2*x2",sumA0/(len(NX)-3))
print("дисперсия воспроизводимости",sumP/(count-1))
print("Адекватность модели y=b1*x1+b2*x2",(sumA/(len(NY)-2))/(sumP/(count-1)))
print("Адекватность модели y=b0+b1*x1+b2*x2",(sumA0/(len(NY)-3))/(sumP/(count-1)))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print(" ")
print("Mx1=",Mx1,"Mx2=", Mx2,"My=", My,)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("Dx1=", Dx1/(len(NX)-1),"Dx2=", Dx2/(len(NX)-1),"Dy=", Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("r1=",r1)
print("r2=",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0=",b0)
print("b1=",b1)
print("b2=",b2)
</source>
вывод:

Mx1= 1.7 Mx2= 0.26 My= 36.545100000000005

Dx1= 3272.7204081632644 Dx2= 238.25836734693874 Dy= 8671.004347438775

r1= -0.006944522138339006

r2= 0.0066401943443432365

b0= 36.553901246661596

b1= -0.011303751887757618

b2= 0.04005819825998865

матрица коэффициентов b для уравнения y=b1*x1+b2*x2 -2.13088259 10.20497587

матрица коэффициентов b для уравнения y=b0+b1*x1+b2*x2 -5.04256469 -2.09981756 10.40195907

значения y при параллельных опытов 46.004999999999995

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 4.436452750556096

дисперсия аппроксимации y=b0+b1*x1+b2*x2 0.009872640020999046

дисперсия воспроизводимости 0.007950000000000259

Адекватность модели y=b1*x1+b2*x2 558.0443711391133

Адекватность модели y=b0+b1*x1+b2*x2 1.2418415120753112

Симченко Сергей Александрович N1

2019-11-08T08:35:48Z

Симченко Сергей:

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:
матожидание от:
x1 = 0.76
x2 = 1.46
y = 27.9595
дисперсия от:
x1 = 2819.3028571428576
x2 = 217.845306122449
y = 823.5539839285711
коэффициент корреляции:
r1 = -0.007186278697424209
r2 = 0.00922060063083846
b0 = 27.936277002146113
b1 = -0.00388400004646967
b1 = 0.01792797115698568

матрица коэффициенто b для уравнения y=b1*x1+b2*x2 -0.41449559 3.25507464
матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2 24.88403238 0.19342754 -0.4548107
значения y при паралленых опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963
дисперсия аппроксимации y=b0+b1*x1+b2*x2 812.9902003819129
дисперсия воспроизводимости 0.006150000000000082
Адекватность модели y=b1*x1+b2*x2 2009.15116234013
Адекватность модели y=b0+b1*x1+b2*x2 132193.52851738245

Симченко Сергей Александрович N1

2019-11-08T08:34:42Z

Симченко Сергей: Новая страница: «import numpy as np import math f = open("order", "r") f1 = open("import", "w") f2 = open("export", "w") X = [] Y = [] for line in f: q, x, y, z = map(float,li…»

import numpy as np
import math
f = open("order", "r")
f1 = open("import", "w")
f2 = open("export", "w")
X = []
Y = []
for line in f:
q, x, y, z = map(float,line.split())
xy =[]
xy.append(x)
xy.append(y)
X.append(xy)
Y.append(z)
f1.write(str(x))
f1.write(" ")
f1.write(str(y))
f1.write("\n")
f2.write(str(z))
f2.write("\n")
f.close()
f1.close()
f2.close()
sum = 0.0
count = 1
for i in range(len(Y)-1):
if X[i+1][0] == X[i][0] and X[i+1][1] == X[i][1]:
sum += Y[i]
count += 1
sum += Y[54]
NY = Y[:-count]
NY.append(sum/count)
NX = X[:-count+1]
f4 = open("import2","r")
X1 = []
for line in f4:
x, y, z = map(float, line.split())
xy = []
xy.append(x)
xy.append(y)
xy.append(z)
X1.append(xy)
f4.close()

mx1 = np.matrix(X1)
mx = np.matrix(NX)
my = np.matrix(NY)

B1 = np.matmul(np.transpose(mx), mx)
B2 = np.matmul(np.linalg.inv(B1), np.transpose(mx))
B = np.matmul(B2, NY)
print("матрица коэффициенто b для уравнения y=b1*x1+b2*x2",B)

B10 = np.matmul(np.transpose(mx1), mx1)
B20 = np.matmul(np.linalg.inv(B10), np.transpose(mx1))
B01 = np.matmul(B20, NY)
print("матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2",B01)
yPs = sum/count
print("значения y при паралленых опытах", yPs)
sumP = 0.0
sumA = 0.0
sumA0 = 0.0
i = 0
print(" ")
print((X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2]))
print(Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))
print((Y[i] - (X[i][0] * B[0, 0] + X[i][1] * B[0, 1]))**2)
print((Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2)
print(" ")

while i < len(Y):
if i >= 49:
sumP += (Y[i] - yPs)**2
else:
sumA += (Y[i]-(X[i][0] * B[0, 0] + X[i][1] * B[0, 1])) ** 2
sumA0 += (Y[i] - (X1[i][0] * B01[0, 0] + X1[i][1] * B01[0, 1] + X1[i][2] * B01[0, 2])) ** 2
i += 1
print(sumP)
print(sumA)
print(sumA0)
print(" ")
print(sumA/(len(NY)-2))
print(sumA0/(len(NX)-3))
print(sumP/(count-1))
print((sumA/(len(NY)-2))/(sumP/(count-1)))
print((sumA0/(len(NY)-3))/(sumP/(count-1)))
print((np.sum(NY)/50)-np.sum(np.sum(NX[0]) * B01[0, 0]))
Mx1 = np.sum(NX[0])/len(NX)
Mx2 = np.sum(NX[1])/len(NX)
My = np.sum(NY) / len(NY)
print("матожидание от x1 =",Mx1, "матожидание от x2 =",Mx2,"матожидание от y =",My)
Dx1 = 0
Dx2 = 0
Dy = 0
for i in range(len(NY)):
Dx1 += (NX[i][0]-Mx1) ** 2
Dx2 += (NX[i][1]-Mx2) ** 2
Dy += (NY[i]-My) ** 2
print("дисперсия от x1 =",Dx1/(len(NX)-1), "дисперсия от x1 =",Dx2/(len(NX)-1), "дисперсия от y =",Dy/(len(NY)-1))
kxl1 = 0
kxl2 = 0
for i in range(len(NY)):
kxl1 += ((NX[i][0] - Mx1) * (NY[i] - My))
kxl2 += ((NX[i][1] - Mx2) * (NY[i] - My))
r1 = (kxl1/(len(NX)-1))/(math.sqrt(Dx1)*math.sqrt(Dy))
r2 = (kxl2/(len(NX)-1))/(math.sqrt(Dx2)*math.sqrt(Dy))
print("коэфкорел r1 =",r1, "коэфкорел r2 =",r2)
b1 = r1*math.sqrt(Dy)/(math.sqrt(Dx1))
b2 = r2*math.sqrt(Dy)/(math.sqrt(Dx2))
b0 = My - b1 * Mx1 - b2 * Mx2
print("b0 =",b0,"b1 =",b1,"b1 =",b2)

Вывод:
матожидание от:
x1 = 0.76
x2 = 1.46
y = 27.9595
дисперсия от:
x1 = 2819.3028571428576
x2 = 217.845306122449
y = 823.5539839285711
коэффициент корреляции:
r1 = -0.007186278697424209
r2 = 0.00922060063083846
b0 = 27.936277002146113
b1 = -0.00388400004646967
b1 = 0.01792797115698568

матрица коэффициенто b для уравнения y=b1*x1+b2*x2 [[-0.41449559 3.25507464]]
матрица коэффициенто b для уравнения y=b0+b1*x1+b2*x2 [[24.88403238 0.19342754 -0.4548107 ]]
значения y при паралленых опытах 28.515

дисперсия аппроксимации для уравнения y=b1*x1+b2*x2 12.356279648391963
дисперсия аппроксимации y=b0+b1*x1+b2*x2 812.9902003819129
дисперсия воспроизводимости 0.006150000000000082
Адекватность модели y=b1*x1+b2*x2 2009.15116234013
Адекватность модели y=b0+b1*x1+b2*x2 132193.52851738245