• 2020年高教社杯全国大学生数学建模C题中小微企业信贷决策（Matlab代码）

    文章目录

    第一问

    第二问

    1、A评级客户流失率相对误差

    % 需要论文和代码，请联系：isfuture@outlook.com

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    2、B评级客户流失率相对误差

    % 需要论文和代码，请联系：isfuture@outlook.com
clear;

X = [0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y = [0.447704483	0.094809717	-0.042357953	-0.1093183	-0.029504019	-0.08177512	-0.05374639	-0.067354229	-0.078529943	-0.066123709	-0.004042344	-0.010993686	-0.014989933	0.020250078	0.007373335	0.013539864	0.037840941	0.016710394	-0.000100478	0.043073939	0.024437232	0.011348263	0.015418915	0.009166262	0.002801528	0.009866109	-0.021299217	-0.044380264
];


scatter(X, Y, 20,'filled');
% axis([1 2 1 2]);
ylim([-0.35 0.35]);
xlabel('贷款年利率');
ylabel('客户流失率相对误差');
title('B评级客户流失率相对误差');

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    3、C评级客户流失率相对误差

    
%% 需要论文和代码，请联系：isfuture@outlook.com
clear;

X = [0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y = [0.241022873	0.132760257	0.045307686	-0.107807025	-0.02105494	-0.061770852	-0.047328371	-0.100791547	-0.080190917	-0.037289585	0.004480784	-0.034160824	0.020210034	-0.013643313	0.008513513	-0.006049881	-0.000383586	0.042776403	0.018494763	0.009725945	0.021647558	0.026102498	0.011434163	0.008849048	0.003877692	0.018148552	-0.020697357	-0.04726721
];


scatter(X, Y, 20,'filled');
% axis([1 2 1 2]);
ylim([-0.35 0.35]);
xlabel('贷款年利率');
ylabel('客户流失率相对误差');
title('C评级客户流失率相对误差');

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    4、金额图

    % 需要论文和代码，请联系：isfuture@outlook.com

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    5、利率图

    X = [1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	37	38	39	40	41	42	43	44	46	47	48	49	50	51	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	83	84	85	86	88	89	90	91	92	93	94	95	96	97	98	104	105	106	110
];
Y=[0.04469888	0.107693406	0.096619827	0.144685372	0.06636303	0.116944557	0.069812132	0.05061235	0.078094652	0.083281223	0.099200728	0.140004869	0.091563938	0.069765165	0.06788777	0.064739764	0.108193238	0.075133482	0.126360685	0.052800813	0.083106487	0.128767168	0.080650381	0.094275505	0.049011169	0.138551071	0.083023976	0.071178613	0.136069627	0.121975718	0.127088231	0.051643079	0.096493128	0.096395806	0.116349402	0.119465089	0.119493027	0.140231878	0.059490346	0.127780826	0.096021022	0.041814038	0.064495806	0.093069603	0.068412479	0.055995192	0.094441436	0.067372248	0.055106626	0.055108386	0.093345062	0.079170723	0.137030095	0.042081024	0.107283791	0.104653562	0.127604769	0.086307113	0.042951928	0.105569879	0.12013018	0.139460741	0.090264797	0.093717376	0.07826072	0.115461523	0.06138162	0.097924008	0.059309414	0.122929398	0.072518809	0.148251397	0.047491648	0.075253214	0.103024773	0.053933738	0.108433576	0.128485428	0.140770949	0.04237487	0.050250473	0.060157075	0.0762983	0.067692723	0.068172086	0.103401843	0.09293262	0.064891824	0.082360703	0.061259464	0.079560398	0.082533283	0.110705129	0.08238775	0.044939352	0.071069688	0.086866292
];

plot(X, Y);
% axis([1 2 1 2]);
 ylim([0.03 0.16]);
xlabel('企业代号');
ylabel('贷款利率');
title('各企业贷款利率');

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    6、模拟A等级曲线

    @[TOC]
# 第一问
# 第二问

## 1、A评级客户流失率相对误差

```c
% 需要论文和代码，请联系：isfuture@outlook.com
clear;

X = [0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y = [0.309554608	0.302980047	0.017579194	-0.076669863	-0.056297703	-0.092750467	-0.060296874	-0.065628885	-0.007390326	-0.044560631	-0.037774003	-0.045669423	-0.014690112	-0.006554997	0.017250496	0.023616607	-0.001116445	-0.000927442	0.03507357	0.026073175	0.046149231	0.011734214	0.005247229	0.012198912	0.00041122	0.014946501	-0.018942205	-0.044111182
];


scatter(X, Y, 20,'filled');
% axis([1 2 1 2]);
ylim([-0.35 0.35]);
xlabel('贷款年利率');
ylabel('客户流失率相对误差');
title('A评级客户流失率相对误差');

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    2、B评级客户流失率相对误差

    % 需要论文和代码，请联系：isfuture@outlook.com
clear;

X = [0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y = [0.447704483	0.094809717	-0.042357953	-0.1093183	-0.029504019	-0.08177512	-0.05374639	-0.067354229	-0.078529943	-0.066123709	-0.004042344	-0.010993686	-0.014989933	0.020250078	0.007373335	0.013539864	0.037840941	0.016710394	-0.000100478	0.043073939	0.024437232	0.011348263	0.015418915	0.009166262	0.002801528	0.009866109	-0.021299217	-0.044380264
];


scatter(X, Y, 20,'filled');
% axis([1 2 1 2]);
ylim([-0.35 0.35]);
xlabel('贷款年利率');
ylabel('客户流失率相对误差');
title('B评级客户流失率相对误差');

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    3、C评级客户流失率相对误差

    
%% 需要论文和代码，请联系：isfuture@outlook.com
clear;

X = [0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y = [0.241022873	0.132760257	0.045307686	-0.107807025	-0.02105494	-0.061770852	-0.047328371	-0.100791547	-0.080190917	-0.037289585	0.004480784	-0.034160824	0.020210034	-0.013643313	0.008513513	-0.006049881	-0.000383586	0.042776403	0.018494763	0.009725945	0.021647558	0.026102498	0.011434163	0.008849048	0.003877692	0.018148552	-0.020697357	-0.04726721
];


scatter(X, Y, 20,'filled');
% axis([1 2 1 2]);
ylim([-0.35 0.35]);
xlabel('贷款年利率');
ylabel('客户流失率相对误差');
title('C评级客户流失率相对误差');

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    4、金额图

    % 需要论文和代码，请联系：isfuture@outlook.com
X = [1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	37	38	39	40	41	42	43	44	46	47	48	49	50	51	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	83	84	85	86	88	89	90	91	92	93	94	95	96	97	98	104	105	106	110
];
Y=[96.09256621	62.92096242	87.26917098	85.40369545	49.4773881	45.47527427	85.8468782	48.37472738	82.29843514	62.46964367	40.04966899	94.20618175	52.48648768	25.7092855	93.46826675	57.03208207	77.68674466	93.06778901	42.44205961	31.88137146	61.45602364	92.55863128	93.45820869	35.79336892	48.71468183	48.85516538	96.80882815	25.28178114	48.47810187	88.35881261	72.65022186	82.41694495	93.22000663	98.61064552	66.48678977	91.6189466	40.41252055	96.02059094	37.05275933	63.82392609	84.26330175	99.67044485	71.21717471	80.33484345	62.47731646	76.92284993	54.63021415	58.10043624	41.00234442	95.87078924	43.17394839	50.68348758	37.14170246	74.25088491	44.57958933	83.86201486	75.86626122	86.91810459	54.41480306	24.41652558	87.38655562	83.57750051	40.90327845	85.66731091	96.19076752	42.76286987	56.53812568	93.17888594	82.09517174	36.87571381	66.83639565	68.50537571	29.27377556	44.40851517	78.7151542	39.48243457	30.06297082	41.61679352	47.58691255	79.25325903	84.08786715	94.97133833	91.02132601	91.31838535	51.70416706	19.2331108	51.46526851	43.38370108	72.77267053	92.84116712	51.1658314	46.26955569	94.12684682	91.17750621	48.4143125	49.973333	96.52759109
];

plot(X, Y);
% axis([1 2 1 2]);
 ylim([10 110]);
xlabel('企业代号');
ylabel('贷款金额/万元');
title('各企业贷款金额');

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    5、利率图

    X = [1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	37	38	39	40	41	42	43	44	46	47	48	49	50	51	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	83	84	85	86	88	89	90	91	92	93	94	95	96	97	98	104	105	106	110
];
Y=[0.04469888	0.107693406	0.096619827	0.144685372	0.06636303	0.116944557	0.069812132	0.05061235	0.078094652	0.083281223	0.099200728	0.140004869	0.091563938	0.069765165	0.06788777	0.064739764	0.108193238	0.075133482	0.126360685	0.052800813	0.083106487	0.128767168	0.080650381	0.094275505	0.049011169	0.138551071	0.083023976	0.071178613	0.136069627	0.121975718	0.127088231	0.051643079	0.096493128	0.096395806	0.116349402	0.119465089	0.119493027	0.140231878	0.059490346	0.127780826	0.096021022	0.041814038	0.064495806	0.093069603	0.068412479	0.055995192	0.094441436	0.067372248	0.055106626	0.055108386	0.093345062	0.079170723	0.137030095	0.042081024	0.107283791	0.104653562	0.127604769	0.086307113	0.042951928	0.105569879	0.12013018	0.139460741	0.090264797	0.093717376	0.07826072	0.115461523	0.06138162	0.097924008	0.059309414	0.122929398	0.072518809	0.148251397	0.047491648	0.075253214	0.103024773	0.053933738	0.108433576	0.128485428	0.140770949	0.04237487	0.050250473	0.060157075	0.0762983	0.067692723	0.068172086	0.103401843	0.09293262	0.064891824	0.082360703	0.061259464	0.079560398	0.082533283	0.110705129	0.08238775	0.044939352	0.071069688	0.086866292
];

plot(X, Y);
% axis([1 2 1 2]);
 ylim([0.03 0.16]);
xlabel('企业代号');
ylabel('贷款利率');
title('各企业贷款利率');

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    6、模拟A等级曲线

    X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];

YY = [];
A = -200:0.1:-190;
B = -15:0.1:-11;
C = 14:0.1:16;
D = -1:0.1:1;



E = [];

XA = [];
XC=[];
XB=[];
XD=[];

XX = [];

ii = 0;

e = 0;

for i=1:length(A)
    a=A(i);
    for j=1:length(B)
        b=B(j);
        for p=1:length(D)
            d=D(p);
            for k=1:length(C)
                c=C(k);
                e = 0;
                for s=1:length(X)
                    x=X(s);
                    f = a*x^3+b*x^2+c*x+d;
                    e = e+(f-Y1(s))^2;

                end

                if e < 0.02255
                    ii = ii + 1;
                    XA(ii) =a;
                    XB(ii)=b;
                    XC(ii)=c;
                    XD(ii)=d;
                    XX(ii) = ii;
                    E(ii) = e;
                end
            end
        end
    end
end
          
[~,position]=min(E);
XA(position)
XB(position)
XC(position)
XD(position)

            
plot(XX, E);






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    7、模拟B等级曲线

    X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];

Y2=[0	0.066799583	0.13505206	0.20658008	0.276812293	0.302883401	0.370215852	0.406296668	0.458295295	0.508718692	0.544408837	0.548493958	0.588765696	0.625764576	0.635605146	0.673527424	0.696925431	0.705315993	0.742936326	0.776400729	0.762022595	0.791503697	0.814998933	0.822297861	0.835301602	0.845747745	0.842070844	0.868159536	0.885864919];
Y3=[0	0.068725306	0.122099029	0.181252146	0.263302863	0.290189098	0.34971559	0.390771683	0.45723807	0.492660433	0.513660239	0.530248706	0.587762408	0.590097045	0.642993656	0.658839416	0.696870573	0.719103552	0.711101237	0.750627656	0.776816043	0.784480512	0.795566274	0.820051434	0.832288422	0.844089875	0.836974326	0.872558957	0.895164739];

A = -200:0.1:-190;
B = -10:0.1:-8;
C = 14:0.1:16;
D = -1:0.1:1;



E = [];

XA = [];
XC=[];
XB=[];
XD=[];

XX = [];

ii = 0;

e = 0;

for i=1:length(A)
    a=A(i);
    for j=1:length(B)
        b=B(j);
        for p=1:length(D)
            d=D(p);
            for k=1:length(C)
                c=C(k);
                e = 0;
                for s=1:length(X)
                    x=X(s);
                    f = a*x^3+b*x^2+c*x+d;
                    e = e+(f-Y2(s))^2;

                end

                if e < 0.02255
                    ii = ii + 1;
                    XA(ii) =a;
                    XB(ii)=b;
                    XC(ii)=c;
                    XD(ii)=d;
                    XX(ii) = ii;
                    E(ii) = e;
                end
            end
        end
    end
end
          
[~,position]=min(E);
XA(position)
XB(position)
XC(position)
XD(position)

            
plot(XX, E);






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    8、模拟C等级曲线

    X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];

Y2=[0	0.066799583	0.13505206	0.20658008	0.276812293	0.302883401	0.370215852	0.406296668	0.458295295	0.508718692	0.544408837	0.548493958	0.588765696	0.625764576	0.635605146	0.673527424	0.696925431	0.705315993	0.742936326	0.776400729	0.762022595	0.791503697	0.814998933	0.822297861	0.835301602	0.845747745	0.842070844	0.868159536	0.885864919];
Y3=[0	0.068725306	0.122099029	0.181252146	0.263302863	0.290189098	0.34971559	0.390771683	0.45723807	0.492660433	0.513660239	0.530248706	0.587762408	0.590097045	0.642993656	0.658839416	0.696870573	0.719103552	0.711101237	0.750627656	0.776816043	0.784480512	0.795566274	0.820051434	0.832288422	0.844089875	0.836974326	0.872558957	0.895164739];

A = -165:0.1:-160;
B = -15:0.1:-10;
C = 14:0.1:16;
D = -1:0.1:1;



E = [];

XA = [];
XC=[];
XB=[];
XD=[];

XX = [];

ii = 0;

e = 0;

for i=1:length(A)
    a=A(i);
    for j=1:length(B)
        b=B(j);
        for p=1:length(D)
            d=D(p);
            for k=1:length(C)
                c=C(k);
                e = 0;
                for s=1:length(X)
                    x=X(s);
                    f = a*x^3+b*x^2+c*x+d;
                    e = e+(f-Y3(s))^2;

                end

                if e < 0.02255
                    ii = ii + 1;
                    XA(ii) =a;
                    XB(ii)=b;
                    XC(ii)=c;
                    XD(ii)=d;
                    XX(ii) = ii;
                    E(ii) = e;
                end
            end
        end
    end
end
          
[~,position]=min(E);
XA(position)
XB(position)
XC(position)
XD(position)

            
plot(XX, E);






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    09、信誉评级A客户流失率实际值与预测曲线

    clear;
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];



a=-194.7;

b=-13.4;
c=15.6;
d=-0.5;


for i=1:length(X)
    x=X(i);
    f = a*x^3+b*x^2+c*x+d;     
    F(i) = f;
end
          
plot(X, Y1, '*');
hold on;
plot(X, F, 'r','linewidth', 2);
axis([0.02 0.18 0 1]);

xlabel('贷款年利率');
ylabel('信誉评级A客户流失率');
title('信誉评级A客户流失率实际值与预测曲线');
legend('实际值' ,'预测曲线');






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    10、信誉评级B客户流失率实际值与预测曲线

    clear;
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];


Y2=[0	0.066799583	0.13505206	0.20658008	0.276812293	0.302883401	0.370215852	0.406296668	0.458295295	0.508718692	0.544408837	0.548493958	0.588765696	0.625764576	0.635605146	0.673527424	0.696925431	0.705315993	0.742936326	0.776400729	0.762022595	0.791503697	0.814998933	0.822297861	0.835301602	0.845747745	0.842070844	0.868159536	0.885864919];

a=-194.8;
b=-9.6;
c=14.8;
d=-0.5;

for i=1:length(X)
    x=X(i);
     f = a*x^3+b*x^2+c*x+d;     
     F(i) = f;
end
          
plot(X, Y2, '*');
hold on;
plot(X, F, 'r','linewidth', 2);
axis([0.02 0.18 0 1]);

xlabel('贷款年利率');
ylabel('信誉评级B客户流失率');
title('信誉评级B客户流失率实际值与预测曲线');
legend('实际值' ,'预测曲线');

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    11、信誉评级C客户流失率实际值与预测曲线

    clear;
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y3=[0	0.068725306	0.122099029	0.181252146	0.263302863	0.290189098	0.34971559	0.390771683	0.45723807	0.492660433	0.513660239	0.530248706	0.587762408	0.590097045	0.642993656	0.658839416	0.696870573	0.719103552	0.711101237	0.750627656	0.776816043	0.784480512	0.795566274	0.820051434	0.832288422	0.844089875	0.836974326	0.872558957	0.895164739];


a=-159.7;
b=-14.5;
c=14.8;
d=-0.5;
F = [];

for i=1:length(X)
    x=X(i);
     f = a*x^3+b*x^2+c*x+d;     
     F(i) = f;
end
          
plot(X, Y3, '*');
hold on;
plot(X, F, 'r','linewidth', 2);
axis([0.02 0.18 0 1]);

xlabel('贷款年利率');
ylabel('信誉评级C客户流失率');
title('信誉评级C客户流失率实际值与预测曲线');
legend('实际值' ,'预测曲线');


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    12、求最优解

    clear;
a1=-194.7;
b1=-13.4;
c1=15.6;
d1=-0.5;
a2=-194.8;
b2=-9.6;
c2=14.8;
d2=-0.5;
a3=-159.7;
b3=-14.5;
c3=14.8;
d3=-0.5;

% 代号-得分-等级
E_Z_DJ = [
1	0.1172	9
2	0.1173	9
12	0.5834	7
25	0.1163	7
34	0.1151	7
38	0.1841	7
45	0.1447	7
47	0.1157	7
49	0.1167	7
53	0.1155	7
56	0.1155	7
60	0.1579	7
61	0.2009	7
62	0.8322	7
63	0.1165	7
64	0.1061	5
66	0.113	5
69	0.3324	5
71	0.1166	7
73	0.1161	7
75	0.1152	7
76	0.1263	5
77	0.1157	7
78	0.1161	7
80	0.1138	5
82	0.1178	7
86	0.1148	7
87	0.1132	5
88	0.1146	5
89	0.1188	5
90	0.1156	7
91	0.1153	7
92	0.1142	5
93	0.1124	5
94	0.1058	5
95	0.1098	5
96	0.1111	5
98	0.1129	5
99	0.1138	7
100	0.1122	5
102	0.1164	7
103	0.1382	7
104	0.1662	7
105	0.1147	7
106	0.1156	7
107	0.1149	5
108	0.1128	5
109	0.1149	7
110	0.1129	5
111	0.1132	5
112	0.1134	5
113	0.1126	5
114	0.1111	5
115	0.1134	5
116	0.1125	5
117	0.1068	5
118	0.1134	5
119	0.1062	5
120	0.1134	5
121	0.1118	5
122	0.1126	5
123	0.1709	5
124	0.1128	5
125	0.1123	5
126	0.113	5
127	0.1123	5
128	0.1381	5
129	0.1127	5
130	0.112	5
131	0.1141	5
132	0.1126	5
133	0.1145	5
134	0.1124	5
135	0.113	5
136	0.1119	5
137	0.1127	5
138	0.1128	5
139	0.1134	5
140	0.112	5
141	0.1057	5
142	0.1118	5
143	0.1132	5
144	0.1136	5
145	0.1333	5
146	0.1126	5
147	0.1119	5
148	0.1115	5
149	0.1123	5
150	0.1148	5
151	0.1153	5
152	0.1117	5
153	0.1116	5
154	0.1129	5
155	0.1128	5
156	0.1127	5
157	0.1132	5
158	0.1129	5
159	0.1115	5
160	0.1132	5
161	0.1126	5
162	0.1118	5
163	0.1115	5
164	0.1119	5
165	0.1118	5
166	0.1133	5
167	0.1127	5
168	0.1113	5
169	0.1141	5
170	0.1133	5
171	0.1125	5
172	0.1118	5
173	0.1115	5
174	0.1124	5
175	0.1192	5
176	0.1126	5
177	0.1125	5
178	0.1128	5
179	0.1123	5
180	0.1129	5
181	0.1135	5
182	0.1127	5
183	0.1123	5
184	0.1123	5
185	0.1128	5
186	0.1116	5
187	0.1129	5
188	0.1122	5
189	0.1129	5
190	0.113	5
191	0.1121	5
192	0.1145	5
193	0.1125	5
194	0.1117	5
195	0.1127	5
196	0.1139	5
197	0.1127	5
198	0.1144	5
199	0.1128	5
200	0.1108	5
201	0.1118	5
202	0.1156	5
203	0.1127	5
204	0.1129	5
205	0.1123	5
206	0.5633	5
207	0.1166	5
208	0.1126	5
209	0.1132	5
210	0.1129	5
211	0.113	5
212	0.1123	5
213	0.1165	5
214	0.1155	5
215	0.1131	5
216	0.1133	5
217	0.1143	5
218	0.1134	5
219	0.1118	5
220	0.1545	5
221	0.1126	5
222	0.1124	5
223	0.1132	5
224	0.1152	5
225	0.1206	5
226	0.1151	5
227	0.1123	5
228	0.1125	5
229	0.1126	5
230	0.1117	5
231	0.1139	5
232	0.1574	5
233	0.1633	5
234	0.13	5
235	0.1844	5
236	0.134	5
237	0.2421	5
238	0.1118	5
239	0.1123	5
240	0.118	5
241	0.1132	5
242	0.1124	5
243	0.1138	5
244	0.1213	5
245	0.1107	5
246	0.1129	5
247	0.2526	5
248	0.1121	5
249	0.1134	5
250	0.1133	5
251	0.1351	5
252	0.2107	5
253	0.1157	5
254	0.1305	5
255	0.112	5
256	0.1105	5
257	0.1711	5
258	0.1115	5
259	0.1821	5
260	0.1164	5
261	0.112	5
262	0.1086	5
263	0.1119	5
264	0.1198	5
265	0.1219	5
266	0.1186	5
267	0.1138	5
268	0.1562	5
269	0.114	5
270	0.1426	5
271	0.1075	5
272	0.1107	5
273	0.1123	5
274	0.1166	5
275	0.1139	5
276	0.1128	5
277	0.1208	5
278	0.1101	5
279	0.1121	5
280	0.1127	5
281	0.1109	5
282	0.1103	5
283	0.1122	5
284	0.1109	5
285	0.1121	5
286	0.1154	5
287	0.1149	5
288	0.1122	5
289	0.1139	5
290	0.1139	5
291	0.1124	5
292	0.2104	5
293	0.1129	5
294	0.1135	5
295	0.1126	5
296	0.1133	5
297	0.113	5
298	0.1139	5
299	0.1153	5
300	0.1122	5
301	0.1179	5
302	0.1098	5


];
E = E_Z_DJ(:, 1);
DJ = E_Z_DJ(:, 3);
Z = E_Z_DJ(:, 2);


fun_range = [0,3.08;0,2.1];    %每个维度的求解范围，行代表维度，第一列最小值，第二列最大值
density = 0.01;                         %图像中取点的密度
d = 2;                                  %维度
popsize = 10;                           %种群大小
maxgen = 100;                           %最大迭代次数
limit_v = 10;                           %最大运动速度
maxormin = 0;                           %求最大值还是最小值。最大值为1，最小值为0
dynamic = 1;                            %是否采用动态参数
W = [0.7,1.4];                          %惯性因子
C2 = [0.5,2.5];
C1 = [0.5,2.5];                         %学习因子。


ZZ = pso1_im(funct,fun_range,density,d);
[x,v,fitness,pbest,gbest,gbest_fitness,gbest_index,pbest_fitness] =...
   pso1_in(funct,popsize,d,fun_range,limit_v,maxormin);
[gbest_fitness,gbest_index,record] =...
    pso1_in2(funct,d,popsize,maxgen,dynamic,W,C1,C2,fun_range,limit_v,density...
    ,x,v,fitness,pbest,gbest,gbest_fitness,gbest_index,pbest_fitness,maxormin);





% sigma = 4:1:15;  年利率
% D = 10:1:100;  % 投放金额
T = 1;  %周期为 1 年
M = 10000; % 总贷款额度1亿

Sigma = [];
DD = [];
Beta = [];
W = [];         % 利润，即最大化目标

Z_S = ones(1, length(E));  % 每个利率  1不用管
Z_D = ones(1, length(E));  % 每个投资

N = 10000;    % 迭代次数

nnn = 0;
for i=1:1:N
    Sigma = [];         % 该次遍历的利率
    DD = [];            % 该次遍历的贷款
    w = 0;              % 该次遍历的目标值
    for j=1:length(E)
        Sigma(j) = 0.04 + (0.15-0.04).*rand(1,1);
        x = Sigma(j);
        DD(j) = 10 + (100-10).*rand(1,1);
        d = DD(j);
        if DJ(j) == 1
           Beta(j) =  a1*x^3+b1*x^2+c1*x+d1;
        elseif DJ(j) == 2
            Beta(j) =  a2*x^3+b2*x^2+c2*x+d2;
        else
            Beta(j) =  a3*x^3+b3*x^2+c3*x+d3;
        end
        w = w + d*x*(1-Beta(j))*Z(j);
    end
    if w > 60
        nnn  = nnn+1;
        W(nnn) = w;
        Z_S(nnn,:) = Sigma(:);
        Z_D(nnn,:) = DD(:);
    end
end

XX = 1:1:nnn;
plot(XX, W, '*');
           
for i=1:length(Z_S(:, 1))
    sum(Z_D(i,:))
end
            
        
        


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    13、神经网络

    import numpy as np
import pandas as pd
import time
import tensorflow.compat.v1 as tf  # 本行报错不用处理

tf.disable_v2_behavior()

# 图网络 和 生成对抗网络

IN_DIV = 24  # 输入维度
OUT_DIV = 4  # 输出维度

nIter = 10000  # 迭代次数为 10000 次
speed = 0.08
N = 100         # 每层神经元个数

NATrain = 25    # 每一类的训练样本数
NBTrain = 25
NCTrain = 30
NDTrain = 20

NATest = 2  # 每一类的测试样本数
NBTest = 13
NCTest = 4
NDTest = 4

# 数据集导入
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

train = np.array(train)
test = np.array(test)

def noramlization(data):
    minVals = data.min(0)
    maxVals = data.max(0)
    ranges = maxVals - minVals
    normData = np.zeros(np.shape(data))
    m = data.shape[0]
    normData = data - np.tile(minVals, (m, 1))
    normData = normData/np.tile(ranges, (m, 1))
    return normData, ranges, minVals






A_train = train[0:NATrain, :]
B_train = train[NATrain:NATrain+NBTrain, :]
C_train = train[NATrain+NBTrain:NATrain+NBTrain+NCTrain, :]
D_train = train[NATrain+NBTrain+NCTrain:, :]

A_test = test[0:NATest, :]
B_test = test[NATest:NATest+NBTest, :]
C_test = test[NATest+NBTest:NATest+NBTest+NCTest, :]
D_test = test[NATest+NBTest+NCTest:, :]


# 数据格式化
X_train = np.vstack((A_train, B_train, C_train, D_train))

X_test = np.vstack((A_test, B_test, C_test, D_test))


train_label = np.matrix([[1, 0, 0, 0]] * NATrain + [[0, 1, 0, 0]] * NBTrain + [[0, 0, 1, 0]] * NCTrain + \
                        [[0, 0, 0, 1]] * NDTrain)

test_label  = np.matrix([[1, 0, 0, 0]] * NATest + [[0, 1, 0, 0]] * NBTest + [[0, 0, 1, 0]] * NCTest + \
                        [[0, 0, 0, 1]] * NDTest)

print(test_label)

'''
我们采用三层前向神经网络来优化这个任务, 其中每层的神经元个数取20, 
初始化取 xavier initialization,优化处理器取 GradientDescentOptimizer, 
学习率取 0.001, 激活函数选取 sigmoid 函数, 迭代次数为 1000.
'''

layers = [IN_DIV, N, N, N, OUT_DIV]  # 输入层 2 个维度，中间层为 3 层，每层 20 个神经元，输出层维度为 5，因为要分为 5 类

# define neural network structure

# tf.palceholder
x_tf = tf.placeholder(tf.float32, shape=[None, X_train.shape[1]])
y_tf = tf.placeholder(tf.float32, shape=[None, train_label.shape[1]])


def initialize_NN(layers):
    weights = []  # 权重矩阵
    biases = []  # 截距矩阵          Y = W*X + b
    num_layers = len(layers)
    for l in range(0, num_layers - 1):
        W = xavier_init(size=[layers[l], layers[l + 1]])
        b = tf.Variable(tf.zeros([1, layers[l + 1]], dtype=tf.float32), dtype=tf.float32)
        weights.append(W)
        biases.append(b)
    return weights, biases


def xavier_init(size):
    in_dim = size[0]  # 第 n 层的维度
    out_dim = size[1]  # 第 n+1 层的维度
    xavier_stddev = np.sqrt(2 / (in_dim + out_dim))  # 使用 xavier 对权重进行初始化
    return tf.Variable(tf.truncated_normal([in_dim, out_dim], stddev=xavier_stddev), dtype=tf.float32)


# 定义神经网络
def neural_net(X, weights, biases):
    num_layers = len(weights) + 1  # 神经网络层数
    H = X
    for l in range(0, num_layers - 2):
        W = weights[l]
        b = biases[l]
        H = tf.sigmoid(tf.add(tf.matmul(H, W), b))
    W = weights[-1]
    b = biases[-1]
    Y = tf.add(tf.matmul(H, W), b)
    return Y


in_weights, in_biases = initialize_NN(layers)


def net(X):
    h = neural_net(X, in_weights, in_biases)
    return h


output = net(x_tf)

# loss
y_model = tf.nn.softmax(output)
loss = -tf.reduce_sum(y_tf * tf.log(y_model))
correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_tf, 1))

accuracy = 0

# Optimization

# 10000 100 0.08
optimizer_GradientDescent = tf.train.GradientDescentOptimizer(speed)
train_op_Adam = optimizer_GradientDescent.minimize(loss)

# tf session

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

tf_dict = {x_tf: X_train, y_tf: train_label}
start_time = time.time()
for it in range(nIter):
    sess.run(train_op_Adam, tf_dict)

    # Print
    if it % 1000 == 0:
        elapsed = time.time() - start_time
        loss_value = sess.run(loss, tf_dict)
        print('It: %d, Loss: %.7e, Time: %.2f' % (it, loss_value, elapsed))
        start_time = time.time()


for i in range(23):
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    XX = [[]]
    XX[0] = X_test[i]
    X = XX
    Y = test_label[i]
    a = sess.run(accuracy, feed_dict={x_tf: X, y_tf: Y})
    if a==1:
        print(i+1)

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
accuracy = sess.run(accuracy, feed_dict={x_tf: X_test, y_tf: test_label})
print(accuracy)



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    一些图片

    
## 7、模拟B等级曲线

```c
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];

Y2=[0	0.066799583	0.13505206	0.20658008	0.276812293	0.302883401	0.370215852	0.406296668	0.458295295	0.508718692	0.544408837	0.548493958	0.588765696	0.625764576	0.635605146	0.673527424	0.696925431	0.705315993	0.742936326	0.776400729	0.762022595	0.791503697	0.814998933	0.822297861	0.835301602	0.845747745	0.842070844	0.868159536	0.885864919];
Y3=[0	0.068725306	0.122099029	0.181252146	0.263302863	0.290189098	0.34971559	0.390771683	0.45723807	0.492660433	0.513660239	0.530248706	0.587762408	0.590097045	0.642993656	0.658839416	0.696870573	0.719103552	0.711101237	0.750627656	0.776816043	0.784480512	0.795566274	0.820051434	0.832288422	0.844089875	0.836974326	0.872558957	0.895164739];

A = -200:0.1:-190;
B = -10:0.1:-8;
C = 14:0.1:16;
D = -1:0.1:1;



E = [];

XA = [];
XC=[];
XB=[];
XD=[];

XX = [];

ii = 0;

e = 0;

for i=1:length(A)
    a=A(i);
    for j=1:length(B)
        b=B(j);
        for p=1:length(D)
            d=D(p);
            for k=1:length(C)
                c=C(k);
                e = 0;
                for s=1:length(X)
                    x=X(s);
                    f = a*x^3+b*x^2+c*x+d;
                    e = e+(f-Y2(s))^2;

                end

                if e < 0.02255
                    ii = ii + 1;
                    XA(ii) =a;
                    XB(ii)=b;
                    XC(ii)=c;
                    XD(ii)=d;
                    XX(ii) = ii;
                    E(ii) = e;
                end
            end
        end
    end
end
          
[~,position]=min(E);
XA(position)
XB(position)
XC(position)
XD(position)

            
plot(XX, E);






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    8、模拟C等级曲线

    X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];

Y2=[0	0.066799583	0.13505206	0.20658008	0.276812293	0.302883401	0.370215852	0.406296668	0.458295295	0.508718692	0.544408837	0.548493958	0.588765696	0.625764576	0.635605146	0.673527424	0.696925431	0.705315993	0.742936326	0.776400729	0.762022595	0.791503697	0.814998933	0.822297861	0.835301602	0.845747745	0.842070844	0.868159536	0.885864919];
Y3=[0	0.068725306	0.122099029	0.181252146	0.263302863	0.290189098	0.34971559	0.390771683	0.45723807	0.492660433	0.513660239	0.530248706	0.587762408	0.590097045	0.642993656	0.658839416	0.696870573	0.719103552	0.711101237	0.750627656	0.776816043	0.784480512	0.795566274	0.820051434	0.832288422	0.844089875	0.836974326	0.872558957	0.895164739];

A = -165:0.1:-160;
B = -15:0.1:-10;
C = 14:0.1:16;
D = -1:0.1:1;



E = [];

XA = [];
XC=[];
XB=[];
XD=[];

XX = [];

ii = 0;

e = 0;

for i=1:length(A)
    a=A(i);
    for j=1:length(B)
        b=B(j);
        for p=1:length(D)
            d=D(p);
            for k=1:length(C)
                c=C(k);
                e = 0;
                for s=1:length(X)
                    x=X(s);
                    f = a*x^3+b*x^2+c*x+d;
                    e = e+(f-Y3(s))^2;

                end

                if e < 0.02255
                    ii = ii + 1;
                    XA(ii) =a;
                    XB(ii)=b;
                    XC(ii)=c;
                    XD(ii)=d;
                    XX(ii) = ii;
                    E(ii) = e;
                end
            end
        end
    end
end
          
[~,position]=min(E);
XA(position)
XB(position)
XC(position)
XD(position)

            
plot(XX, E);






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    09、信誉评级A客户流失率实际值与预测曲线

    clear;
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y1=[0	0.094574126	0.135727183	0.224603354	0.302038102	0.347315668	0.41347177	0.447890973	0.497634453	0.511096612	0.573393087	0.609492115	0.652944774	0.667541843	0.694779921	0.708302023	0.731275401	0.775091405	0.798227368	0.790527266	0.815196986	0.814421029	0.854811097	0.870317343	0.871428085	0.885925945	0.874434682	0.902725909	0.922060687];



a=-194.7;

b=-13.4;
c=15.6;
d=-0.5;


for i=1:length(X)
    x=X(i);
    f = a*x^3+b*x^2+c*x+d;     
    F(i) = f;
end
          
plot(X, Y1, '*');
hold on;
plot(X, F, 'r','linewidth', 2);
axis([0.02 0.18 0 1]);

xlabel('贷款年利率');
ylabel('信誉评级A客户流失率');
title('信誉评级A客户流失率实际值与预测曲线');
legend('实际值' ,'预测曲线');






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    10、信誉评级B客户流失率实际值与预测曲线

    clear;
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];


Y2=[0	0.066799583	0.13505206	0.20658008	0.276812293	0.302883401	0.370215852	0.406296668	0.458295295	0.508718692	0.544408837	0.548493958	0.588765696	0.625764576	0.635605146	0.673527424	0.696925431	0.705315993	0.742936326	0.776400729	0.762022595	0.791503697	0.814998933	0.822297861	0.835301602	0.845747745	0.842070844	0.868159536	0.885864919];

a=-194.8;
b=-9.6;
c=14.8;
d=-0.5;

for i=1:length(X)
    x=X(i);
     f = a*x^3+b*x^2+c*x+d;     
     F(i) = f;
end
          
plot(X, Y2, '*');
hold on;
plot(X, F, 'r','linewidth', 2);
axis([0.02 0.18 0 1]);

xlabel('贷款年利率');
ylabel('信誉评级B客户流失率');
title('信誉评级B客户流失率实际值与预测曲线');
legend('实际值' ,'预测曲线');

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    11、信誉评级C客户流失率实际值与预测曲线

    clear;
X = [0.04	0.0425	0.0465	0.0505	0.0545	0.0585	0.0625	0.0665	0.0705	0.0745	0.0785	0.0825	0.0865	0.0905	0.0945	0.0985	0.1025	0.1065	0.1105	0.1145	0.1185	0.1225	0.1265	0.1305	0.1345	0.1385	0.1425	0.1465	0.15
];

Y3=[0	0.068725306	0.122099029	0.181252146	0.263302863	0.290189098	0.34971559	0.390771683	0.45723807	0.492660433	0.513660239	0.530248706	0.587762408	0.590097045	0.642993656	0.658839416	0.696870573	0.719103552	0.711101237	0.750627656	0.776816043	0.784480512	0.795566274	0.820051434	0.832288422	0.844089875	0.836974326	0.872558957	0.895164739];


a=-159.7;
b=-14.5;
c=14.8;
d=-0.5;
F = [];

for i=1:length(X)
    x=X(i);
     f = a*x^3+b*x^2+c*x+d;     
     F(i) = f;
end
          
plot(X, Y3, '*');
hold on;
plot(X, F, 'r','linewidth', 2);
axis([0.02 0.18 0 1]);

xlabel('贷款年利率');
ylabel('信誉评级C客户流失率');
title('信誉评级C客户流失率实际值与预测曲线');
legend('实际值' ,'预测曲线');


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    12、求最优解

    clear;
a1=-194.7;
b1=-13.4;
c1=15.6;
d1=-0.5;
a2=-194.8;
b2=-9.6;
c2=14.8;
d2=-0.5;
a3=-159.7;
b3=-14.5;
c3=14.8;
d3=-0.5;

% 代号-得分-等级
E_Z_DJ = [
1	0.1172	9
2	0.1173	9
12	0.5834	7
25	0.1163	7
34	0.1151	7
38	0.1841	7
45	0.1447	7
47	0.1157	7
49	0.1167	7
53	0.1155	7
56	0.1155	7
60	0.1579	7
61	0.2009	7
62	0.8322	7
63	0.1165	7
64	0.1061	5
66	0.113	5
69	0.3324	5
71	0.1166	7
73	0.1161	7
75	0.1152	7
76	0.1263	5
77	0.1157	7
78	0.1161	7
80	0.1138	5
82	0.1178	7
86	0.1148	7
87	0.1132	5
88	0.1146	5
89	0.1188	5
90	0.1156	7
91	0.1153	7
92	0.1142	5
93	0.1124	5
94	0.1058	5
95	0.1098	5
96	0.1111	5
98	0.1129	5
99	0.1138	7
100	0.1122	5
102	0.1164	7
103	0.1382	7
104	0.1662	7
105	0.1147	7
106	0.1156	7
107	0.1149	5
108	0.1128	5
109	0.1149	7
110	0.1129	5
111	0.1132	5
112	0.1134	5
113	0.1126	5
114	0.1111	5
115	0.1134	5
116	0.1125	5
117	0.1068	5
118	0.1134	5
119	0.1062	5
120	0.1134	5
121	0.1118	5
122	0.1126	5
123	0.1709	5
124	0.1128	5
125	0.1123	5
126	0.113	5
127	0.1123	5
128	0.1381	5
129	0.1127	5
130	0.112	5
131	0.1141	5
132	0.1126	5
133	0.1145	5
134	0.1124	5
135	0.113	5
136	0.1119	5
137	0.1127	5
138	0.1128	5
139	0.1134	5
140	0.112	5
141	0.1057	5
142	0.1118	5
143	0.1132	5
144	0.1136	5
145	0.1333	5
146	0.1126	5
147	0.1119	5
148	0.1115	5
149	0.1123	5
150	0.1148	5
151	0.1153	5
152	0.1117	5
153	0.1116	5
154	0.1129	5
155	0.1128	5
156	0.1127	5
157	0.1132	5
158	0.1129	5
159	0.1115	5
160	0.1132	5
161	0.1126	5
162	0.1118	5
163	0.1115	5
164	0.1119	5
165	0.1118	5
166	0.1133	5
167	0.1127	5
168	0.1113	5
169	0.1141	5
170	0.1133	5
171	0.1125	5
172	0.1118	5
173	0.1115	5
174	0.1124	5
175	0.1192	5
176	0.1126	5
177	0.1125	5
178	0.1128	5
179	0.1123	5
180	0.1129	5
181	0.1135	5
182	0.1127	5
183	0.1123	5
184	0.1123	5
185	0.1128	5
186	0.1116	5
187	0.1129	5
188	0.1122	5
189	0.1129	5
190	0.113	5
191	0.1121	5
192	0.1145	5
193	0.1125	5
194	0.1117	5
195	0.1127	5
196	0.1139	5
197	0.1127	5
198	0.1144	5
199	0.1128	5
200	0.1108	5
201	0.1118	5
202	0.1156	5
203	0.1127	5
204	0.1129	5
205	0.1123	5
206	0.5633	5
207	0.1166	5
208	0.1126	5
209	0.1132	5
210	0.1129	5
211	0.113	5
212	0.1123	5
213	0.1165	5
214	0.1155	5
215	0.1131	5
216	0.1133	5
217	0.1143	5
218	0.1134	5
219	0.1118	5
220	0.1545	5
221	0.1126	5
222	0.1124	5
223	0.1132	5
224	0.1152	5
225	0.1206	5
226	0.1151	5
227	0.1123	5
228	0.1125	5
229	0.1126	5
230	0.1117	5
231	0.1139	5
232	0.1574	5
233	0.1633	5
234	0.13	5
235	0.1844	5
236	0.134	5
237	0.2421	5
238	0.1118	5
239	0.1123	5
240	0.118	5
241	0.1132	5
242	0.1124	5
243	0.1138	5
244	0.1213	5
245	0.1107	5
246	0.1129	5
247	0.2526	5
248	0.1121	5
249	0.1134	5
250	0.1133	5
251	0.1351	5
252	0.2107	5
253	0.1157	5
254	0.1305	5
255	0.112	5
256	0.1105	5
257	0.1711	5
258	0.1115	5
259	0.1821	5
260	0.1164	5
261	0.112	5
262	0.1086	5
263	0.1119	5
264	0.1198	5
265	0.1219	5
266	0.1186	5
267	0.1138	5
268	0.1562	5
269	0.114	5
270	0.1426	5
271	0.1075	5
272	0.1107	5
273	0.1123	5
274	0.1166	5
275	0.1139	5
276	0.1128	5
277	0.1208	5
278	0.1101	5
279	0.1121	5
280	0.1127	5
281	0.1109	5
282	0.1103	5
283	0.1122	5
284	0.1109	5
285	0.1121	5
286	0.1154	5
287	0.1149	5
288	0.1122	5
289	0.1139	5
290	0.1139	5
291	0.1124	5
292	0.2104	5
293	0.1129	5
294	0.1135	5
295	0.1126	5
296	0.1133	5
297	0.113	5
298	0.1139	5
299	0.1153	5
300	0.1122	5
301	0.1179	5
302	0.1098	5


];
E = E_Z_DJ(:, 1);
DJ = E_Z_DJ(:, 3);
Z = E_Z_DJ(:, 2);


fun_range = [0,3.08;0,2.1];    %每个维度的求解范围，行代表维度，第一列最小值，第二列最大值
density = 0.01;                         %图像中取点的密度
d = 2;                                  %维度
popsize = 10;                           %种群大小
maxgen = 100;                           %最大迭代次数
limit_v = 10;                           %最大运动速度
maxormin = 0;                           %求最大值还是最小值。最大值为1，最小值为0
dynamic = 1;                            %是否采用动态参数
W = [0.7,1.4];                          %惯性因子
C2 = [0.5,2.5];
C1 = [0.5,2.5];                         %学习因子。


ZZ = pso1_im(funct,fun_range,density,d);
[x,v,fitness,pbest,gbest,gbest_fitness,gbest_index,pbest_fitness] =...
   pso1_in(funct,popsize,d,fun_range,limit_v,maxormin);
[gbest_fitness,gbest_index,record] =...
    pso1_in2(funct,d,popsize,maxgen,dynamic,W,C1,C2,fun_range,limit_v,density...
    ,x,v,fitness,pbest,gbest,gbest_fitness,gbest_index,pbest_fitness,maxormin);





% sigma = 4:1:15;  年利率
% D = 10:1:100;  % 投放金额
T = 1;  %周期为 1 年
M = 10000; % 总贷款额度1亿

Sigma = [];
DD = [];
Beta = [];
W = [];         % 利润，即最大化目标

Z_S = ones(1, length(E));  % 每个利率  1不用管
Z_D = ones(1, length(E));  % 每个投资

N = 10000;    % 迭代次数

nnn = 0;
for i=1:1:N
    Sigma = [];         % 该次遍历的利率
    DD = [];            % 该次遍历的贷款
    w = 0;              % 该次遍历的目标值
    for j=1:length(E)
        Sigma(j) = 0.04 + (0.15-0.04).*rand(1,1);
        x = Sigma(j);
        DD(j) = 10 + (100-10).*rand(1,1);
        d = DD(j);
        if DJ(j) == 1
           Beta(j) =  a1*x^3+b1*x^2+c1*x+d1;
        elseif DJ(j) == 2
            Beta(j) =  a2*x^3+b2*x^2+c2*x+d2;
        else
            Beta(j) =  a3*x^3+b3*x^2+c3*x+d3;
        end
        w = w + d*x*(1-Beta(j))*Z(j);
    end
    if w > 60
        nnn  = nnn+1;
        W(nnn) = w;
        Z_S(nnn,:) = Sigma(:);
        Z_D(nnn,:) = DD(:);
    end
end

XX = 1:1:nnn;
plot(XX, W, '*');
           
for i=1:length(Z_S(:, 1))
    sum(Z_D(i,:))
end
            
        
        


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    13、神经网络

    import numpy as np
import pandas as pd
import time
import tensorflow.compat.v1 as tf  # 本行报错不用处理

tf.disable_v2_behavior()

# 图网络 和 生成对抗网络

IN_DIV = 24  # 输入维度
OUT_DIV = 4  # 输出维度

nIter = 10000  # 迭代次数为 10000 次
speed = 0.08
N = 100         # 每层神经元个数

NATrain = 25    # 每一类的训练样本数
NBTrain = 25
NCTrain = 30
NDTrain = 20

NATest = 2  # 每一类的测试样本数
NBTest = 13
NCTest = 4
NDTest = 4

# 数据集导入
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

train = np.array(train)
test = np.array(test)

def noramlization(data):
    minVals = data.min(0)
    maxVals = data.max(0)
    ranges = maxVals - minVals
    normData = np.zeros(np.shape(data))
    m = data.shape[0]
    normData = data - np.tile(minVals, (m, 1))
    normData = normData/np.tile(ranges, (m, 1))
    return normData, ranges, minVals






A_train = train[0:NATrain, :]
B_train = train[NATrain:NATrain+NBTrain, :]
C_train = train[NATrain+NBTrain:NATrain+NBTrain+NCTrain, :]
D_train = train[NATrain+NBTrain+NCTrain:, :]

A_test = test[0:NATest, :]
B_test = test[NATest:NATest+NBTest, :]
C_test = test[NATest+NBTest:NATest+NBTest+NCTest, :]
D_test = test[NATest+NBTest+NCTest:, :]


# 数据格式化
X_train = np.vstack((A_train, B_train, C_train, D_train))

X_test = np.vstack((A_test, B_test, C_test, D_test))


train_label = np.matrix([[1, 0, 0, 0]] * NATrain + [[0, 1, 0, 0]] * NBTrain + [[0, 0, 1, 0]] * NCTrain + \
                        [[0, 0, 0, 1]] * NDTrain)

test_label  = np.matrix([[1, 0, 0, 0]] * NATest + [[0, 1, 0, 0]] * NBTest + [[0, 0, 1, 0]] * NCTest + \
                        [[0, 0, 0, 1]] * NDTest)

print(test_label)

'''
我们采用三层前向神经网络来优化这个任务, 其中每层的神经元个数取20, 
初始化取 xavier initialization,优化处理器取 GradientDescentOptimizer, 
学习率取 0.001, 激活函数选取 sigmoid 函数, 迭代次数为 1000.
'''

layers = [IN_DIV, N, N, N, OUT_DIV]  # 输入层 2 个维度，中间层为 3 层，每层 20 个神经元，输出层维度为 5，因为要分为 5 类

# define neural network structure

# tf.palceholder
x_tf = tf.placeholder(tf.float32, shape=[None, X_train.shape[1]])
y_tf = tf.placeholder(tf.float32, shape=[None, train_label.shape[1]])


def initialize_NN(layers):
    weights = []  # 权重矩阵
    biases = []  # 截距矩阵          Y = W*X + b
    num_layers = len(layers)
    for l in range(0, num_layers - 1):
        W = xavier_init(size=[layers[l], layers[l + 1]])
        b = tf.Variable(tf.zeros([1, layers[l + 1]], dtype=tf.float32), dtype=tf.float32)
        weights.append(W)
        biases.append(b)
    return weights, biases


def xavier_init(size):
    in_dim = size[0]  # 第 n 层的维度
    out_dim = size[1]  # 第 n+1 层的维度
    xavier_stddev = np.sqrt(2 / (in_dim + out_dim))  # 使用 xavier 对权重进行初始化
    return tf.Variable(tf.truncated_normal([in_dim, out_dim], stddev=xavier_stddev), dtype=tf.float32)


# 定义神经网络
def neural_net(X, weights, biases):
    num_layers = len(weights) + 1  # 神经网络层数
    H = X
    for l in range(0, num_layers - 2):
        W = weights[l]
        b = biases[l]
        H = tf.sigmoid(tf.add(tf.matmul(H, W), b))
    W = weights[-1]
    b = biases[-1]
    Y = tf.add(tf.matmul(H, W), b)
    return Y


in_weights, in_biases = initialize_NN(layers)


def net(X):
    h = neural_net(X, in_weights, in_biases)
    return h


output = net(x_tf)

# loss
y_model = tf.nn.softmax(output)
loss = -tf.reduce_sum(y_tf * tf.log(y_model))
correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_tf, 1))

accuracy = 0

# Optimization

# 10000 100 0.08
optimizer_GradientDescent = tf.train.GradientDescentOptimizer(speed)
train_op_Adam = optimizer_GradientDescent.minimize(loss)

# tf session

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

tf_dict = {x_tf: X_train, y_tf: train_label}
start_time = time.time()
for it in range(nIter):
    sess.run(train_op_Adam, tf_dict)

    # Print
    if it % 1000 == 0:
        elapsed = time.time() - start_time
        loss_value = sess.run(loss, tf_dict)
        print('It: %d, Loss: %.7e, Time: %.2f' % (it, loss_value, elapsed))
        start_time = time.time()


for i in range(23):
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    XX = [[]]
    XX[0] = X_test[i]
    X = XX
    Y = test_label[i]
    a = sess.run(accuracy, feed_dict={x_tf: X, y_tf: Y})
    if a==1:
        print(i+1)

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
accuracy = sess.run(accuracy, feed_dict={x_tf: X_test, y_tf: test_label})
print(accuracy)



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    一些图片

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  • 原文地址：https://blog.csdn.net/qq_35500719/article/details/127453668