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enum | TrainingMode { BEST_ITERATIVE_SPILT =0,
BEST_RANDOM_SPLIT,
NUM_TRAINING_MODES
} |
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Definition at line 38 of file Tree.h.
GRT::Tree::Tree |
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const UINT |
numSplittingSteps = 100 , |
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const UINT |
minNumSamplesPerNode = 5 , |
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const UINT |
maxDepth = 10 , |
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const bool |
removeFeaturesAtEachSpilt = false , |
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const UINT |
trainingMode = BEST_ITERATIVE_SPILT |
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) |
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Default Constructor
- Parameters
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UINT | numSplittingSteps: sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100 |
UINT | minNumSamplesPerNode: sets the minimum number of samples that are allowed per node, if the number of samples is below that, the node will become a leafNode. Default value = 5 |
UINT | maxDepth: sets the maximum depth of the tree. Default value = 10 |
bool | removeFeaturesAtEachSpilt: sets if a feature is removed at each spilt so it can not be used again. Default value = false |
UINT | trainingMode: sets the training mode, this should be one of the TrainingMode enums. Default value = BEST_ITERATIVE_SPILT |
Definition at line 25 of file Tree.cpp.
GRT::Tree::~Tree |
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void |
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virtual |
Default Destructor
Definition at line 40 of file Tree.cpp.
bool GRT::Tree::clear |
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virtual |
This overrides the clear function in the Regressifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.
- Returns
- returns true if the module was cleared succesfully, false otherwise
Reimplemented in GRT::DecisionTree, GRT::ClusterTree, and GRT::RegressionTree.
Definition at line 45 of file Tree.cpp.
Node * GRT::Tree::deepCopyTree |
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const |
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virtual |
Deep copies the tree, returning a pointer to the new tree. The user is in charge of cleaning up the memory so must delete the pointer when they no longer need it. NULL will be returned if the tree could not be copied.
- Returns
- returns a pointer to a deep copy of the tree
Reimplemented in GRT::DecisionTree, GRT::ClusterTree, and GRT::RegressionTree.
Definition at line 78 of file Tree.cpp.
UINT GRT::Tree::getMaxDepth |
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const |
Gets the maximum depth of the tree.
- Returns
- returns the maximum depth of the tree
Definition at line 103 of file Tree.cpp.
UINT GRT::Tree::getMinNumSamplesPerNode |
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const |
Gets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node.
- Returns
- returns the minimum number of samples that are allowed per node
Definition at line 99 of file Tree.cpp.
bool GRT::Tree::getModel |
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ostream & |
stream | ) |
const |
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virtual |
This function adds the current model to the formatted stream. This function should be overwritten by the derived class.
- Parameters
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ostream | &file: a reference to the stream the model will be added to |
- Returns
- returns true if the model was added successfully, false otherwise
Reimplemented in GRT::DecisionTree.
Definition at line 67 of file Tree.cpp.
UINT GRT::Tree::getNumSplittingSteps |
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const |
Gets the number of steps that will be used to search for the best spliting value for each node.
If the trainingMode is set to BEST_ITERATIVE_SPILT, then the numSplittingSteps controls how many iterative steps there will be per feature. If the trainingMode is set to BEST_RANDOM_SPLIT, then the numSplittingSteps controls how many random searches there will be per feature.
- Returns
- returns the number of steps that will be used to search for the best spliting value for each node
Definition at line 95 of file Tree.cpp.
UINT GRT::Tree::getPredictedNodeID |
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const |
This function returns the predictedNodeID, this is ID of the leaf node that was reached during the last prediction call
- Returns
- returns the predictedNodeID, this will be zero if the tree does not exist or predict has not been called
Definition at line 107 of file Tree.cpp.
bool GRT::Tree::getRemoveFeaturesAtEachSpilt |
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const |
Gets if a feature is removed at each spilt so it can not be used again.
- Returns
- returns true if a feature is removed at each spilt so it can not be used again, false otherwise
Definition at line 116 of file Tree.cpp.
UINT GRT::Tree::getTrainingMode |
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const |
Gets the current training mode. This will be one of the TrainingModes enums.
- Returns
- returns the training mode
Definition at line 91 of file Tree.cpp.
const Node * GRT::Tree::getTree |
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const |
Gets a pointer to the root node of the tree. NULL will be returned if the tree model has not be trained.
- Returns
- returns a const pointer to the tree
Definition at line 87 of file Tree.cpp.
bool GRT::Tree::print |
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const |
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virtual |
bool GRT::Tree::setMaxDepth |
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const UINT |
maxDepth | ) |
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Sets the maximum depth of the tree, any node that reaches this depth will automatically become a leaf node. Value must be larger than zero.
- Parameters
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UINT | maxDepth: the maximum depth of the tree |
- Returns
- returns true if the parameter was set, false otherwise
Definition at line 147 of file Tree.cpp.
bool GRT::Tree::setMinNumSamplesPerNode |
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const UINT |
minNumSamplesPerNode | ) |
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Sets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node. Value must be larger than zero.
- Parameters
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UINT | minNumSamplesPerNode: the minimum number of samples that are allowed per node |
- Returns
- returns true if the parameter was set, false otherwise
Definition at line 138 of file Tree.cpp.
bool GRT::Tree::setNumSplittingSteps |
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const UINT |
numSplittingSteps | ) |
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Sets the number of steps that will be used to search for the best spliting value for each node.
If the trainingMode is set to BEST_ITERATIVE_SPILT, then the numSplittingSteps controls how many iterative steps there will be per feature. If the trainingMode is set to BEST_RANDOM_SPLIT, then the numSplittingSteps controls how many random searches there will be per feature.
A higher value will increase the chances of building a better model, but will take longer to train the model. Value must be larger than zero.
- Parameters
-
UINT | numSplittingSteps: sets the number of steps that will be used to search for the best spliting value for each node. |
- Returns
- returns true if the parameter was set, false otherwise
Definition at line 129 of file Tree.cpp.
bool GRT::Tree::setRemoveFeaturesAtEachSpilt |
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const bool |
removeFeaturesAtEachSpilt | ) |
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Sets if a feature is removed at each spilt so it can not be used again. If true then the best feature selected at each node will be removed so it can not be used in any children of that node. If false, then the feature that provides the best spilt at each node will be used, regardless of how many times it has been used again.
- Parameters
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bool | removeFeaturesAtEachSpilt: if true, then each feature is removed at each spilt so it can not be used again |
- Returns
- returns true if the parameter was set, false otherwise
Definition at line 156 of file Tree.cpp.
bool GRT::Tree::setTrainingMode |
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const UINT |
trainingMode | ) |
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Sets the training mode, this should be one of the TrainingModes enums.
- Parameters
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const | UINT trainingMode: the new trainingMode, this should be one of the TrainingModes enums |
- Returns
- returns true if the trainingMode was set successfully, false otherwise
Definition at line 120 of file Tree.cpp.
The documentation for this class was generated from the following files:
- /Users/ngillian/Documents/dev/libs/grt/GRT/CoreAlgorithms/Tree/Tree.h
- /Users/ngillian/Documents/dev/libs/grt/GRT/CoreAlgorithms/Tree/Tree.cpp