} //+------------------------------------------------------------------+ //| DESCRIPTION: | //| This function creates MCPD (Markov Chains for Population Data) | //| solver. | //| This solver can be used to find transition matrix P for | //| N-dimensional prediction problem where transition from X[i] to | //| X[i+1] is modelled as X[i+1] = P*X[i] | //| where X[i] and X[i+1] are N-dimensional population vectors | //| (components of each X are non-negative), and P is a N*N | //| transition matrix (elements of are non-negative, each column | //| sums to 1.0). | //| Such models arise when when: | //| * there is some population of individuals | //| * individuals can have different states | //| * individuals can transit from one state to another | //| * population size is constant, i.e. there is no new individuals | //| and no one leaves population | //| * you want to model transitions of individuals from one state | //| into another | //| USAGE: | //| Here we give very brief outline of the MCPD. We strongly | //| recommend you to read examples in the ALGLIB Reference Manual | //| and to read ALGLIB User Guide on data analysis which is | //| available at http://www.alglib.net/dataanalysis/ | //| 1. User initializes algorithm state with MCPDCreate() call | //| 2. User adds one or more tracks - sequences of states which | //| describe evolution of a system being modelled from different | //| starting conditions | //| 3. User may add optional boundary, equality and/or linear | //| constraints on the coefficients of P by calling one of the | //| following functions: | //| * MCPDSetEC() to set equality constraints | //| * MCPDSetBC() to set bound constraints | //| * MCPDSetLC() to set linear constraints | //| 4. Optionally, user may set custom weights for prediction errors | //| (by default, algorithm assigns non-equal, automatically chosen| //| weights for errors in the prediction of different components | //| of X). It can be done with a call of | //| MCPDSetPredictionWeights() function. | //| 5. User calls MCPDSolve() function which takes algorithm state | //| and pointer (delegate, etc.) to callback function which | //| calculates F/G. | //| 6. User calls MCPDResults() to get solution | //| INPUT PARAMETERS: | //| N - problem dimension, N>=1 | //| OUTPUT PARAMETERS: | //| State - structure stores algorithm state | //+------------------------------------------------------------------+
我还没有看，今天休息）我有时间会告诉你的，我是说在本周晚些时候。
暂时看一下这些包裹。我认为https://hmmlearn.readthedocs.io/en/latest/tutorial.html
应
然后你采取维特比算法，得出（数据测试） 的结果
你得到两个状态，它应该看起来像这样。
这是一个真正的圣杯）。
也许我糊涂了，但这些照片不是不久前展示的某个人的Inocenti吗？后来发现是个偷窥狂，fiaspalm
我不确定它对非平稳性会有什么影响......如果它很慢，那么就很难再进行蒙特卡洛。
我认为主要的问题是缩放/转换数据，循环提取，因为如果模式重复，即使是非线性回归或SVM也会有好的结果（在人工VR上）。
即选择模式的问题是牵强的是的，我只是想让这个话题变得明亮起来））。
也许我糊涂了，但这些照片不是不久前由一个叫Inokentiy的人展示的吗，后来发现是个偷窥狂，真他妈丢人，打脸。
Imho, 你可以用普通的MAs得到类似的图片。仔细看一下。
看起来真的没有比马什基更好的，实际上对于一个特定的区域可以优化马什基和冷却器，而且，是的，圣杯 又溜走了......。
只是很明显，图表的着色和伊诺肯蒂的一样，这就是为什么我得到了它......我认为，如果你正确地分配模式，你可以得到信息丰富的AR功能。
我还在为如何把它改写成mql而绞尽脑汁，但这是将你的垃圾变成有效模型的缺失因素。
https://www.quantstart.com/articles/market-regime-detection-using-hidden-markov-models-in-qstrader
为什么要重写？见alglib。
CMarkovCPD::~CMarkovCPD(void)
{
}
//+------------------------------------------------------------------+
//| DESCRIPTION: |
//| This function creates MCPD (Markov Chains for Population Data) |
//| solver. |
//| This solver can be used to find transition matrix P for |
//| N-dimensional prediction problem where transition from X[i] to |
//| X[i+1] is modelled as X[i+1] = P*X[i] |
//| where X[i] and X[i+1] are N-dimensional population vectors |
//| (components of each X are non-negative), and P is a N*N |
//| transition matrix (elements of are non-negative, each column |
//| sums to 1.0). |
//| Such models arise when when: |
//| * there is some population of individuals |
//| * individuals can have different states |
//| * individuals can transit from one state to another |
//| * population size is constant, i.e. there is no new individuals |
//| and no one leaves population |
//| * you want to model transitions of individuals from one state |
//| into another |
//| USAGE: |
//| Here we give very brief outline of the MCPD. We strongly |
//| recommend you to read examples in the ALGLIB Reference Manual |
//| and to read ALGLIB User Guide on data analysis which is |
//| available at http://www.alglib.net/dataanalysis/ |
//| 1. User initializes algorithm state with MCPDCreate() call |
//| 2. User adds one or more tracks - sequences of states which |
//| describe evolution of a system being modelled from different |
//| starting conditions |
//| 3. User may add optional boundary, equality and/or linear |
//| constraints on the coefficients of P by calling one of the |
//| following functions: |
//| * MCPDSetEC() to set equality constraints |
//| * MCPDSetBC() to set bound constraints |
//| * MCPDSetLC() to set linear constraints |
//| 4. Optionally, user may set custom weights for prediction errors |
//| (by default, algorithm assigns non-equal, automatically chosen|
//| weights for errors in the prediction of different components |
//| of X). It can be done with a call of |
//| MCPDSetPredictionWeights() function. |
//| 5. User calls MCPDSolve() function which takes algorithm state |
//| and pointer (delegate, etc.) to callback function which |
//| calculates F/G. |
//| 6. User calls MCPDResults() to get solution |
//| INPUT PARAMETERS: |
//| N - problem dimension, N>=1 |
//| OUTPUT PARAMETERS: |
//| State - structure stores algorithm state |
//+------------------------------------------------------------------+
为什么要重写？见alglib。
哇，是在datanalisis还是solvers中？ 在帮助中没有看到。
哇，这是在datanalisis还是solvers中？ 在帮助中没有看到
datanalisis - 搜索文件本身
标题上写着见网站上的例子，但网站上没有例子
在标题中，它说看网站上的例子，但网站上没有例子。
如果你知道如何在其他语言中工作，我想你可以通过类比使用这个版本。I/O参数应该是类似的。