public void Produto2500Teste()
{
LMAFunction f = new FuncaoLogNormalAcumulada();
double[] parametrosEstimados = new[] { 0.111989183, 0.735 };
#region Dados Experimentais
double[] xExperimental = new[]
{
0,
0.02,
0.04,
0.06,
0.08,
0.1,
0.12,
0.14,
0.16,
0.18,
0.2,
0.22,
0.24,
0.26,
0.28,
0.3,
0.32,
0.34,
0.36,
0.38,
0.4,
0.42,
0.44,
0.46,
0.48,
0.5,
0.52,
0.54,
0.56,
0.58,
0.6,
0.62,
0.64,
0.66,
0.68,
0.7,
0.72,
0.74,
0.76,
0.78,
0.8,
0.82,
0.84,
0.86,
0.88,
0.9,
0.92,
0.94,
0.96
};
double[] yExperimental = new[]
{
0,
0,
0.03472,
0.06644,
0.09601,
0.12272,
0.14793,
0.17208,
0.19535,
0.21753,
0.23669,
0.25444,
0.27351,
0.29654,
0.32522,
0.35796,
0.39288,
0.42818,
0.46214,
0.4953,
0.52848,
0.56211,
0.59659,
0.63314,
0.67136,
0.70917,
0.74448,
0.77573,
0.80428,
0.83035,
0.85386,
0.87476,
0.89255,
0.90755,
0.92063,
0.93257,
0.94395,
0.95415,
0.96302,
0.97057,
0.97682,
0.98124,
0.98433,
0.98668,
0.98884,
0.9908,
0.9916,
0.99404,
0.99706
};
#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(2, 2), 1d - 30, 100);
algorithm.Fit();
for (int i = 0; i < parametrosEstimados.Length; i++)
{
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
}
public void Produto1724Teste()
{
LMAFunction f = new FuncaoLogNormalAcumulada();
double[] parametrosEstimados = new[] { 0.111989183, 0.735 };
#region Dados Experimentais
double[] xExperimental = new[]
{
0,
0.02,
0.04,
0.06,
0.08,
0.1,
0.12,
0.14,
0.16,
0.18,
0.2,
0.22,
0.24,
0.26,
0.28,
0.3,
0.32,
0.34,
0.36,
0.38,
0.4,
0.42,
0.44,
0.46,
0.48,
0.5,
0.52,
0.54,
0.56,
0.58,
0.6,
0.62,
0.64,
0.66,
0.68,
0.7,
0.72,
0.74,
0.76,
0.78,
0.8,
0.82,
0.84,
0.86,
0.88,
0.9,
0.92,
0.94,
0.96
};
double[] yExperimental = new[]
{
0,
0,
0.01558,
0.02839,
0.04047,
0.05091,
0.05979,
0.06895,
0.07986,
0.09401,
0.11091,
0.12986,
0.15061,
0.17297,
0.19653,
0.22114,
0.2478,
0.27755,
0.31163,
0.35184,
0.396,
0.44082,
0.48298,
0.52204,
0.55988,
0.59661,
0.63235,
0.66734,
0.70202,
0.73575,
0.7677,
0.79702,
0.82366,
0.84807,
0.87005,
0.88941,
0.90594,
0.91933,
0.92999,
0.93881,
0.94672,
0.95361,
0.95951,
0.96473,
0.96941,
0.97359,
0.97704,
0.97868,
0.97878
};
#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(2, 2), 1d - 30, 100);
algorithm.Fit();
for (int i = 0; i < parametrosEstimados.Length; i++)
{
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
}
public void FeedTeste()
{
LMAFunction f = new FuncaoLogNormalAcumulada();
double[] parametrosEstimados = new[] {Math.Log(0.25), Math.Log(0.03) };
#region Dados Experimentais
double[] xExperimental = new[]
{
0.02,
0.04,
0.06,
0.08,
0.1,
0.12,
0.14,
0.16,
0.18,
0.2,
0.22,
0.24,
0.26,
0.28,
0.3,
0.32,
0.34,
0.36,
0.38,
0.4,
0.42,
0.44,
0.46,
0.48,
0.5,
0.52,
0.54,
0.56,
0.58,
0.6,
0.62,
0.64,
0.66,
0.68,
0.7,
0.72,
0.74,
0.76,
0.78,
0.8,
0.82,
0.84,
0.86,
0.88,
0.9,
0.92,
0.94,
0.96
};
double[] yExperimental = new[]
{
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.02532,
0.03191,
0.03557,
0.03855,
0.04199,
0.05473,
0.07439,
0.09963,
0.1324,
0.17218,
0.222,
0.2795,
0.34357,
0.41488,
0.48759,
0.55907,
0.62985,
0.69556,
0.75486,
0.8067,
0.85443,
0.89529,
0.9253,
0.94629,
0.96257,
0.97404,
0.98309
};
#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());
}
}
public void Produto0952Teste()
{
LMAFunction f = new FuncaoLogNormalAcumulada();
double[] parametrosEstimados = new[] { 0.111989183, 0.735 };
#region Dados Experimentais
double[] xExperimental = new[]
{
0,
0.02,
0.04,
0.06,
0.08,
0.1,
0.12,
0.14,
0.16,
0.18,
0.2,
0.22,
0.24,
0.26,
0.28,
0.3,
0.32,
0.34,
0.36,
0.38,
0.4,
0.42,
0.44,
0.46,
0.48,
0.5,
0.52,
0.54,
0.56,
0.58,
0.6,
0.62,
0.64,
0.66,
0.68,
0.7,
0.72,
0.74,
0.76,
0.78,
0.8,
0.82,
0.84,
0.86,
0.88,
0.9,
0.92,
0.94,
0.96
};
double[] yExperimental = new[]
{
0,
0.00608,
0.01391,
0.02146,
0.02825,
0.03357,
0.03764,
0.04171,
0.04676,
0.05379,
0.06234,
0.07212,
0.08319,
0.09557,
0.10882,
0.12271,
0.13817,
0.15615,
0.17769,
0.20325,
0.23188,
0.2624,
0.29354,
0.32523,
0.35833,
0.39221,
0.42622,
0.45978,
0.49319,
0.52665,
0.56012,
0.59358,
0.62763,
0.66244,
0.69672,
0.72918,
0.75868,
0.78573,
0.81087,
0.83445,
0.85676,
0.87774,
0.89717,
0.91506,
0.93141,
0.94569,
0.95773,
0.96857,
0.97905
};
#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(2, 2), 1d - 30, 100);
algorithm.Fit();
for (int i = 0; i < parametrosEstimados.Length; i++)
{
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
}
public void Produto0383Teste()
{
LMAFunction f = new FuncaoLogNormalAcumulada();
double[] parametrosEstimados = new[] { 0.111989183, 0.735 };
#region Dados Experimentais
double[] xExperimental = new[]
{
0,
0.02,
0.04,
0.06,
0.08,
0.1,
0.12,
0.14,
0.16,
0.18,
0.2,
0.22,
0.24,
0.26,
0.28,
0.3,
0.32,
0.34,
0.36,
0.38,
0.4,
0.42,
0.44,
0.46,
0.48,
0.5,
0.52,
0.54,
0.56,
0.58,
0.6,
0.62,
0.64,
0.66,
0.68,
0.7,
0.72,
0.74,
0.76,
0.78,
0.8,
0.82,
0.84,
0.86,
0.88,
0.9,
0.92,
0.94,
0.96
};
double[] yExperimental = new[]
{
0,
0,
0.00874,
0.0121,
0.01537,
0.01847,
0.02116,
0.02346,
0.02575,
0.02838,
0.03166,
0.0356,
0.03978,
0.04442,
0.04979,
0.05615,
0.06377,
0.07248,
0.08224,
0.09298,
0.10462,
0.11696,
0.12955,
0.14288,
0.15755,
0.17419,
0.1933,
0.21393,
0.23625,
0.26089,
0.28852,
0.31983,
0.35482,
0.39311,
0.43451,
0.47878,
0.52576,
0.57747,
0.63319,
0.69075,
0.74798,
0.802,
0.85845,
0.91109,
0.94528,
0.96223,
0.97008,
0.97396,
0.97589
};
#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(2, 2), 1d - 30, 100);
algorithm.Fit();
for (int i = 0; i < parametrosEstimados.Length; i++)
{
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
}