本文整理汇总了C#中HiddenMarkovModel.Learn方法的典型用法代码示例。如果您正苦于以下问题：C# HiddenMarkovModel.Learn方法的具体用法？C# HiddenMarkovModel.Learn怎么用？C# HiddenMarkovModel.Learn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HiddenMarkovModel的用法示例。
在下文中一共展示了HiddenMarkovModel.Learn方法的2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: horizontalEvalution
/*
* this method passes all the possible directions
* that can create a horizontal line to the HMM library
* as observations. A and B are the state and
* observation probabilities for the model
* pi is the intial state of the model
* the function returns true if the input matches any
* of the observations used to train the model
*/
bool horizontalEvalution(int [] input)
{
if(input.Length != 1){
return false;
}
int[][] sequences = new int[][]
{
new int[]{ EAST },
new int[] { WEST }
};
double [,] A = new double[8,8]
{
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0}
};
double [,] B = new double[8,8]
{
{1, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0, 0},
{0, 0, 0, 1, 0, 0, 0, 0},
{0, 0, 0, 0, 1, 0, 0, 0},
{0, 0, 0, 0, 0, 1, 0, 0},
{0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 0, 1}
};
double [] pi = new double [] {0, 0, 0.5, 0.5, 0, 0, 0, 0};
HiddenMarkovModel model = new HiddenMarkovModel(A, B, pi);
model.Learn(sequences, 0.0001);
if(model.Evaluate(input) >= 0.5){
return true;
}else{
return false;
}
}示例2: squareEvalution
/*
* this method passes all the possible directions
* that can create a square to the HMM library
* as observations. A and B are the state and
* observation probabilities for the model
* pi is the intial state of the model
* the function returns true if the input matches any
* of the observations used to train the model
*/
bool squareEvalution(int [] input)
{
if(input.Length != 4){
return false;
}
int[][] sequences = new int[][]
{
new int[]{ NORTH, EAST, SOUTH, WEST},
new int[] { EAST, SOUTH, WEST, NORTH},
new int[] { SOUTH, WEST, NORTH, EAST},
new int[] { WEST, NORTH, EAST, SOUTH}
};
double [,] A = new double[8,8]
{
{0, 0, 1, 0, 0, 0, 0, 0},
{0, 0, 0, 1, 0, 0, 0, 0},
{0, 1, 0, 0, 0, 0, 0, 0},
{1, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0}
};
double [,] B = new double[8,8]
{
{1, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0, 0},
{0, 0, 0, 1, 0, 0, 0, 0},
{0, 0, 0, 0, 1, 0, 0, 0},
{0, 0, 0, 0, 0, 1, 0, 0},
{0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 0, 1}
};
double [] pi = new double [] {0.25,0.25,0.25,0.25, 0, 0, 0, 0};
HiddenMarkovModel model = new HiddenMarkovModel(A, B, pi);
model.Learn(sequences, 0.0001);
if(model.Evaluate(input) >= 0.25){
return true;
}else{
return false;
}
}