本文整理汇总了C#中numl.Math.LinearAlgebra.Vector.Copy方法的典型用法代码示例。如果您正苦于以下问题：C# Vector.Copy方法的具体用法？C# Vector.Copy怎么用？C# Vector.Copy使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numl.Math.LinearAlgebra.Vector的用法示例。
在下文中一共展示了Vector.Copy方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Logistic_Regression_Test_CostFunction_2_WithoutRegularization
public void Logistic_Regression_Test_CostFunction_2_WithoutRegularization()
{
Matrix X = new[,] {
{ 8, 1, 6 },
{ 3, 5 ,7 },
{ 4, 9, 2 }};
Vector y = new Vector(new double[] { 1, 1, 0 });
Vector theta = new Vector(new double[] { 0, 1, 0 });
ICostFunction logisticCostFunction = new LogisticCostFunction()
{
X = X,
Y = y,
Lambda = 0,
};
double cost = logisticCostFunction.ComputeCost(theta.Copy());
theta = logisticCostFunction.ComputeGradient(theta.Copy());
Assert.Equal(3.1067d, System.Math.Round(cost, 4));
Assert.Equal(0.6093d, System.Math.Round(theta[0], 4));
Assert.Equal(2.8988d, System.Math.Round(theta[1], 4));
Assert.Equal(0.1131d, System.Math.Round(theta[2], 4));
}示例2: Run
/// <summary>
/// Performs gradient descent to optomise theta parameters.
/// </summary>
/// <param name="theta">Initial Theta (Zeros)</param>
/// <param name="x">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="maxIterations">Maximum number of iterations to run gradient descent</param>
/// <param name="learningRateAlpha">The learning rate (Alpha)</param>
/// <param name="costFunction">Cost function to use for gradient descent</param>
/// <param name="lambda">The regularization constant to apply</param>
/// <param name="regularizer">The regularization function to apply</param>
/// <returns></returns>
public static Tuple<double, Vector> Run(
Vector theta,
Matrix x,
Vector y,
int maxIterations,
double learningRateAlpha,
ICostFunction costFunction,
double lambda,
IRegularizer regularizer)
{
var bestTheta = theta.Copy();
var bestCost = double.PositiveInfinity;
double currentCost = 0;
var currentGradient = theta.Copy();
for (var i = 0; i <= maxIterations; i++)
{
currentCost = costFunction.ComputeCost(bestTheta, x, y, lambda, regularizer);
currentGradient = costFunction.ComputeGradient(bestTheta, x, y, lambda, regularizer);
if (currentCost < bestCost)
{
bestTheta = bestTheta - learningRateAlpha * currentGradient;
bestCost = currentCost;
}
else
{
learningRateAlpha = learningRateAlpha * 0.99;
}
}
return new Tuple<double, Vector>(bestCost, bestTheta);
}示例3: Logistic_Regression_Test_CostFunction_1
public void Logistic_Regression_Test_CostFunction_1()
{
Matrix X = new[,]
{{ 1, 1, 1 },
{ 1, 1, 1 },
{ 1, 1, 1 },
{ 8, 1, 6 },
{ 3, 5 ,7 },
{ 4, 9, 2 }};
Vector y = new Vector(new double[] { 1, 0, 1, 0, 1, 0 });
Vector theta = new Vector(new double[] { 0, 1, 0 });
ICostFunction logisticCostFunction = new LogisticCostFunction()
{
X = X,
Y = y,
Lambda = 3,
Regularizer = new L2Regularizer()
};
double cost = logisticCostFunction.ComputeCost(theta.Copy());
theta = logisticCostFunction.ComputeGradient(theta.Copy());
Assert.Equal(2.2933d, System.Math.Round(cost, 4));
Assert.Equal(1.6702d, System.Math.Round(theta[0], 4));
Assert.Equal(2.1483d, System.Math.Round(theta[1], 4));
Assert.Equal(1.0887d, System.Math.Round(theta[2], 4));
}示例4: Calc
public static Vector Calc(Vector v, Func<int, double, double> f)
{
var result = v.Copy();
for (int i = 0; i < v.Length; i++)
result[i] = f(i, result[i]);
return result;
}示例5: IncreaseDimensions
/// <summary>
/// Adds a specified number of polynomial features to the training / test Vector.
/// </summary>
/// <param name="x">Training / Testing record</param>
/// <param name="polynomialFeatures">Number of polynomial features to add</param>
/// <returns></returns>
public static Vector IncreaseDimensions(Vector x, int polynomialFeatures)
{
Vector xtemp = x.Copy();
int maxCols = xtemp.Length;
for (int j = 0; j < maxCols - 1; j++)
{
for (int k = 0; k <= polynomialFeatures; k++)
{
for (int m = 0; m <= k; m++)
{
double v = (System.Math.Pow(xtemp[j], k - m) * System.Math.Pow(xtemp[j + 1], m));
xtemp = xtemp.Insert(xtemp.Length - 1, v);
}
}
}
return xtemp;
}示例6: IncreaseDimensions
/// <summary>
/// Adds a specified number of polynomial features to the training / test Vector.
/// </summary>
/// <param name="x">Training / Testing record</param>
/// <param name="polynomialFeatures">Number of polynomial features to add</param>
/// <returns></returns>
public static Vector IncreaseDimensions(Vector x, int polynomialFeatures)
{
var xtemp = x.Copy();
var maxCols = xtemp.Length;
for (var j = 0; j < maxCols - 1; j++)
{
for (var k = 0; k <= polynomialFeatures; k++)
{
for (var m = 0; m <= k; m++)
{
var v = Math.Pow(xtemp[j], (double)(k - m)) * Math.Pow(xtemp[j + 1], (double)m);
xtemp = xtemp.Insert(xtemp.Length - 1, v);
}
}
}
return xtemp;
}示例7: Logistic_Regression_Test_CostFunction_1
public void Logistic_Regression_Test_CostFunction_1()
{
Matrix X = new[,]
{{ 1, 1, 1 },
{ 1, 1, 1 },
{ 1, 1, 1 },
{ 8, 1, 6 },
{ 3, 5 ,7 },
{ 4, 9, 2 }};
Vector y = new Vector(new double[] { 1, 0, 1, 0, 1, 0 });
Vector theta = new Vector(new double[] { 0, 1, 0 });
numl.Math.Functions.Cost.ICostFunction logisticCostFunction = new numl.Math.Functions.Cost.LogisticCostFunction();
numl.Math.Functions.Regularization.IRegularizer regularizer = new numl.Math.Functions.Regularization.Regularization();
double cost = logisticCostFunction.ComputeCost(theta.Copy(), X, y, 3, regularizer);
theta = logisticCostFunction.ComputeGradient(theta.Copy(), X, y, 3, regularizer);
Assert.AreEqual(2.2933d, System.Math.Round(cost, 4));
Assert.AreEqual(1.6702d, System.Math.Round(theta[0], 4));
Assert.AreEqual(2.1483d, System.Math.Round(theta[1], 4));
Assert.AreEqual(1.0887d, System.Math.Round(theta[2], 4));
}