A tabular training data model: each SupportVector row contains the feature values and the class labels of multiple topics.
ProblemTable
01 Syntax
02 Methods
| Name | Overloads | Summary |
|---|---|---|
| GetTopics | 1 | Get all of the topic names which are defined by the SupportVector.labels of the sample rows. |
| Clone | 1 | Create a deep copy of this problem table. |
| GetTopicLabels | 1 | 获取所指定的topic下的所有标签数据,不去重 |
| GetProblem | 1 | create a problem model under the given topic |
| Append | 1 | row append |
03 Properties
| Name | Overloads | Summary |
|---|---|---|
| vectors | 1 | The sample rows of this problem table. |
| dimensionNames | 1 | the key collection of the support vector: DynamicPropertyBase.Properties inputs. |
04 Members
Get all of the topic names which are defined by the SupportVector.labels of the sample rows.
An array of the distinct topic names.
Create a deep copy of this problem table.
A new ProblemTable object with the same data.
String)获取所指定的topic下的所有标签数据,不去重
| Name | Type | Description |
|---|---|---|
topic | String | The name of the target topic. |
The class label of each sample row under the topic; the duplicated label values are kept.
String)create a problem model under the given topic
| Name | Type | Description |
|---|---|---|
topic | String | The name of the target topic, which provides the class label values. |
A Problem object which is ready for the SVM training.
row append
| Name | Type | Description |
|---|---|---|
a | ProblemTable | The first problem table. |
b | ProblemTable | The second problem table which will be appended to the a. |
A new ProblemTable object which contains all of the sample rows of both a and b, the dimension names are the union of the columns of the two tables.
The sample rows of this problem table.
An array of the SupportVector objects.
the key collection of the support vector: DynamicPropertyBase.Properties inputs.
An array of the feature dimension names.