public void FeedTeste()
{
LMAFunction f = new FuncaoGama();
double[] parametrosEstimados = new[] { 1.0, 10, 0.3 };
#region Dados Experimentais
double[] xExperimental = new[]
{
0,
0.05,
0.1,
0.15,
0.2,
0.25,
0.3,
0.35,
0.4,
0.43,
0.45,
0.47,
0.49,
0.51,
0.53,
0.55,
0.57,
0.59,
0.61,
0.63,
0.65,
0.67,
0.69,
0.71,
0.73,
0.75,
0.77,
0.79,
0.81,
0.83,
0.85,
0.87,
0.89,
0.91,
0.93,
0.95,
0.97,
1
};
double[] yExperimental = new[]
{
0,
0.000477354,
0.000954709,
0.001432063,
0.001909417,
0.002386771,
0.002864126,
0.00334148,
0.003818834,
0.004105247,
0.00777,
0.00485,
0.0031,
0.00267,
0.00787,
0.01618,
0.02244,
0.02912,
0.03585,
0.04481,
0.05397,
0.0607,
0.06801,
0.07284,
0.0717,
0.07164,
0.0687,
0.0627,
0.05525,
0.04971,
0.04483,
0.03567,
0.02498,
0.01816,
0.01397,
0.00995,
0.00872,
0.01255
};
#endregion
double[][] dataPoints = new double[2][];
dataPoints[0] = xExperimental;
dataPoints[1] = yExperimental;
Net.Kniaz.LMA.LMA algorithm = new Net.Kniaz.LMA.LMA(f, parametrosEstimados,
dataPoints, null, new GeneralMatrix(3, 3), 1d - 30, 100);
algorithm.Fit();
for (int i = 0; i < parametrosEstimados.Length; i++)
{
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
}
public void FeedAdam2012Teste()
{
LMAFunction f = new FuncaoGama();
double[] parametrosEstimados = new[] {110 ,6.0, 2.0, 10.0 };
#region Dados Experimentais
double[] xExperimental = new[]
{
6,
6.5,
7,
7.5,
8,
8.5,
9,
9.5,
10,
10.5,
11,
11.5,
12,
12.5,
13,
13.5,
14,
14.5,
15,
15.5,
16,
16.5,
17,
17.5,
18,
18.5,
19,
19.5,
20,
20.5,
21,
21.5,
22,
22.5,
23,
23.5,
24,
24.5,
25,
25.5,
26,
26.5,
27,
27.5,
28,
28.5,
29,
29.5,
30,
30.5,
31,
31.5,
32,
32.5,
33,
33.5,
34,
34.5,
35,
35.5,
36,
36.5,
37,
37.5,
38,
38.5,
39,
39.5,
40,
40.5,
41,
41.5,
42,
42.5,
43,
43.5,
44,
44.5,
45,
45.5,
46,
46.5,
47,
47.5,
48,
48.5,
49,
49.5,
50,
50.5,
51,
51.5,
52,
52.5,
53,
53.5,
54,
54.5,
55,
55.5,
56,
56.5,
57,
57.5,
58,
58.5,
59,
59.5,
60,
60.5,
61,
61.5,
62,
62.5,
63,
63.5,
64,
64.5,
65,
65.5,
66,
66.5,
67,
67.5,
68,
68.5,
69,
69.5
};
double[] yExperimental = new[]
{
0.05038,
0.26068,
0.63623,
0.98765,
1.34988,
1.70814,
2.07049,
2.46031,
2.8452,
3.13967,
3.36847,
3.54668,
3.69239,
3.81627,
3.90255,
3.94637,
3.96881,
3.99722,
4.01619,
4.00553,
3.98141,
3.95254,
3.91177,
3.89293,
3.90277,
3.85827,
3.76256,
3.70522,
3.65271,
3.58838,
3.51878,
3.45647,
3.40999,
3.37829,
3.35271,
3.34001,
3.34441,
3.30962,
3.26256,
3.22453,
3.16822,
2.77531,
2.21383,
2.44192,
2.65645,
2.5645,
2.39322,
2.26306,
2.13443,
2.00384,
1.87456,
1.76221,
1.66783,
1.59939,
1.54273,
1.47008,
1.41341,
1.4354,
1.43696,
1.39685,
1.34782,
1.30362,
1.26077,
1.22248,
1.18638,
1.14808,
1.11094,
1.11799,
1.11113,
1.03607,
0.96056,
0.935,
0.95218,
0.95174,
0.92821,
0.90443,
0.87775,
0.80971,
0.72396,
0.72258,
0.74405,
0.669,
0.57887,
0.54087,
0.51708,
0.49078,
0.46682,
0.44668,
0.43057,
0.41281,
0.39397,
0.37357,
0.35218,
0.33123,
0.31164,
0.29456,
0.27949,
0.26638,
0.25576,
0.24709,
0.23974,
0.23362,
0.22827,
0.22338,
0.21878,
0.21436,
0.21001,
0.20563,
0.20107,
0.19675,
0.19276,
0.18892,
0.18502,
0.18077,
0.17552,
0.16389,
0.14573,
0.19372,
0.2433,
0.22274,
0.18939,
0.15281,
0.11896,
0.09803,
0.08244,
0.06956,
0.05915,
0.05214
};
#endregion
double[][] dataPoints = new double[2][];
dataPoints[0] = xExperimental;
dataPoints[1] = yExperimental;
Net.Kniaz.LMA.LMA algorithm = new Net.Kniaz.LMA.LMA(f, parametrosEstimados,
dataPoints, null, new GeneralMatrix(parametrosEstimados.Length, parametrosEstimados.Length), 1d - 30, 100);
algorithm.Fit();
for (int i = 0; i < parametrosEstimados.Length; i++)
{
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
}
