MIL_ID ResultClassId, | //in |
MIL_INT64 LabelOrIndex, | //in |
MIL_INT64 ResultType, | //in |
void *ResultArrayPtr | //out |
This function retrieves results of the specified type from a classification result buffer. Result buffers are allocated using MclassAllocResult(). You can either retrieve training results, which are available after calling MclassTrain(), or you can retrieve prediction results, which are available after calling MclassPredict().
Specifies the identifier of the classification result buffer from which to retrieve results. You can get results from a CNN (M_TRAIN_CNN_RESULT) or a tree ensemble (M_TRAIN_TREE_ENSEMBLE_RESULT) training result buffer, or a CNN (M_PREDICT_CNN_RESULT) or a tree ensemble (M_PREDICT_TREE_ENSEMBLE_RESULT) prediction result buffer.
Specifies what to retrieve. Set this parameter to one of the following values.
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Description | ||||||||||||||||||||||||||||||||||||||
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Same as M_GENERAL. |
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Specifies the index of the class for which to get results. You can get the number of included classes in the result buffer using M_NUMBER_OF_CLASSES. (summarize)Specifies the index of the class for which to get results. (more details...) |
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Parameters | |||||||||||||||||||||||||||||||||||||||
Specifies the index of a class. |
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Specifies to retrieve global results from the result buffer. |
Specifies the type of result to retrieve.
See the Parameter associations section for possible values that can be specified.
Specifies the address of the array in which to write results.
See the Parameter associations section for possible values that can be returned.
The tables below list possible values for the ResultType parameter and possible values returned to the ResultArrayPtr parameter.
To retrieve general results from a CNN training result buffer (ResultClassId), the ResultType can be set to one of the following values. In this case, set the LabelOrIndex parameter to M_GENERAL.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Retrieves an array containing the development dataset accuracy for each epoch. Note that the required array size, which you can retrieve with M_NB_ELEMENTS, is equivalent to the M_MAX_EPOCH (MclassControl()) of the training context used. (summarize)Retrieves an array containing the development dataset accuracy for each epoch. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Specifies the development dataset accuracy for each epoch, as a percentage. These values are stored in an array of MIL_DOUBLE. (summarize)Specifies the development dataset accuracy for each epoch, as a percentage. (more details...) |
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Retrieves an array containing the development dataset error rate for each epoch. Note that the required array size, which you can retrieve with M_NB_ELEMENTS, is equivalent to the M_MAX_EPOCH (MclassControl()) of the training context used. (summarize)Retrieves an array containing the development dataset error rate for each epoch. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Specifies the development dataset error rate for each epoch, as a percentage. These values are stored in an array of MIL_DOUBLE. (summarize)Specifies the development dataset error rate for each epoch, as a percentage. (more details...) |
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Retrieves an array containing the MIL_UUID of each image in the development dataset that could not be loaded, at least once, during training. (summarize)Retrieves an array containing the MIL_UUID of each image in the development dataset that could not be loaded, at least once, during training. (more details...) |
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Data type info: Data
type: array of type MIL_UUID [optionally, in C++: a
reference to a std::vector<MIL_UUID>
] MORE
Data type info: Data
type: array of type MIL_UUID [optionally, in C++: a
reference to a std::vector<MIL_UUID>
] MORE
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Retrieves the index of the epoch where classifier parameters were updated the last time. (summarize)Retrieves the index of the epoch where classifier parameters were updated the last time. (more details...) |
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Data type info: Data
type: address of a MIL_DOUBLE
MORE
Data type info: Data
type: address of a MIL_DOUBLE
MORE
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Specifies that the parameters were not updated. |
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Specifies the index of the epoch. |
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Retrieves the number of mini-batches per epoch. (summarize)Retrieves the number of mini-batches per epoch. (more details...) |
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Data type info: Data
type: address of a MIL_DOUBLE
MORE
Data type info: Data
type: address of a MIL_DOUBLE
MORE
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Specifies the number of mini-batches per epoch. |
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Retrieves an array containing the training dataset accuracy for each epoch. Note that the required array size, which you can retrieve with M_NB_ELEMENTS, is equivalent to the M_MAX_EPOCH (MclassControl()) of the training context used. (summarize)Retrieves an array containing the training dataset accuracy for each epoch. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
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Specifies the training dataset accuracy for each epoch, as a percentage. These values are stored in an array of MIL_DOUBLE. (summarize)Specifies the training dataset accuracy for each epoch, as a percentage. (more details...) |
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Retrieves an array containing the training dataset error rate for each epoch. Note that the required array size, which you can retrieve with M_NB_ELEMENTS, is equivalent to the M_MAX_EPOCH (MclassControl()) of the training context used. (summarize)Retrieves an array containing the training dataset error rate for each epoch. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Specifies the training dataset error rate for each epoch, as a percentage. These values are stored in an array of MIL_DOUBLE. (summarize)Specifies the training dataset error rate for each epoch, as a percentage. (more details...) |
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Retrieves an array containing the MIL_UUID of each image in the training dataset that could not be loaded, at least once, during training. (summarize)Retrieves an array containing the MIL_UUID of each image in the training dataset that could not be loaded, at least once, during training. (more details...) |
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Data type info: Data
type: array of type MIL_UUID [optionally, in C++: a
reference to a std::vector<MIL_UUID>
] MORE
Data type info: Data
type: array of type MIL_UUID [optionally, in C++: a
reference to a std::vector<MIL_UUID>
] MORE
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Retrieves an array containing the loss error values for each mini-batch during training. You can use the loss to evaluate the lack of confidence (doubt) associated with the classification during training. Note that the required array size, which you can retrieve with M_NB_ELEMENTS, is equivalent to M_MAX_EPOCH (MclassControl()) * M_MINI_BATCH_PER_EPOCH (MclassGetResult()) of the training context used. (summarize)Retrieves an array containing the loss error values for each mini-batch during training. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Specifies the training dataset loss for each mini-batch. These values are stored in an array of MIL_DOUBLE. (summarize)Specifies the training dataset loss for each mini-batch. (more details...) |
To retrieve general results from a tree ensemble training result buffer (ResultClassId), the ResultType can be set to one of the following values. In this case, set the LabelOrIndex parameter to M_GENERAL.
Note, bagging information is typically unreliable if your training dataset has augmented entries.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Retrieves an array containing the accuracies on the development dataset after adding each tree to the classifier. (summarize)Retrieves an array containing the accuracies on the development dataset after adding each tree to the classifier. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves an array containing the error rates after on the development dataset after adding each tree to the classifier. (summarize)Retrieves an array containing the error rates after on the development dataset after adding each tree to the classifier. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves an array containing the importance of each feature when training the classifier. This result can be disabled if M_FEATURE_IMPORTANCE_MODE is set to M_DISABLE. (summarize)Retrieves an array containing the importance of each feature when training the classifier. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves the number of out-of-bag dataset entries. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the number of out-of-bag dataset entries. (more details...) |
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Data type info: Data
type: address of a MIL_DOUBLE
MORE
Data type info: Data
type: address of a MIL_DOUBLE
MORE
|
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Specifies the number of out-of-bag dataset entries. |
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Retrieves the number of features. (summarize)Retrieves the number of features. (more details...) |
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Data type info: Data
type: address of a MIL_DOUBLE
MORE
Data type info: Data
type: address of a MIL_DOUBLE
MORE
|
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Specifies the number of features. |
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Retrieves the number of trained trees. This result is equivalent to the number of trees in the classifier. (summarize)Retrieves the number of trained trees. (more details...) |
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Data type info: Data
type: address of a MIL_DOUBLE
MORE
Data type info: Data
type: address of a MIL_DOUBLE
MORE
|
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Specifies the number of trained trees. |
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Retrieves the out-of-bag error accuracy. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the out-of-bag error accuracy. (more details...) |
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Data type info: Data
type: address of a MIL_DOUBLE
MORE
Data type info: Data
type: address of a MIL_DOUBLE
MORE
|
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Specifies the out-of-bag accuracy, as a percentage. |
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Retrieves the array containing the accuracies on the out-of-bag set after adding each tree to the classifier. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the array containing the accuracies on the out-of-bag set after adding each tree to the classifier. (more details...) |
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Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info: Data
type: array of type MIL_DOUBLE [optionally, in C++:
a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves the confusion matrix obtained using the out-of-bag set. This is a square matrix. MIL returns the matrix values, row-by-row, as an array. The array size is equivalent to the square of the total amount of classes, which is the same M_OUT_OF_BAG_CONFUSION_MATRIX_SIZE_X x M_OUT_OF_BAG_CONFUSION_MATRIX_SIZE_Y, or M_NB_ELEMENTS. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the confusion matrix obtained using the out-of-bag set. (more details...) |
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
|
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Retrieves the X-dimension of the confusion matrix. This value is calculated using the out-of-bag set. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the X-dimension of the confusion matrix. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
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Specifies the X-dimension of the confusion matrix. |
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Retrieves the Y-dimension of the confusion matrix. This value is calculated using the out-of-bag set. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the Y-dimension of the confusion matrix. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
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Specifies the Y-dimension of the confusion matrix. |
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Retrieves the out-of-bag error rate. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the out-of-bag error rate. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
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Specifies the out-of-bag error rate. |
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Retrieves the array containing the accuracies on the out-of-bag set after adding each tree to the classifier. This is only available if M_COMPUTE_OUT_OF_BAG_RESULTS is set to M_ENABLE and M_BOOTSTRAP is not set to M_DISABLE. (summarize)Retrieves the array containing the accuracies on the out-of-bag set after adding each tree to the classifier. (more details...) |
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Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves the proximity matrix. This is a square matrix. MIL returns the matrix values, row-by-row, as an array. The array size is equivalent M_PROXIMITY_MATRIX_SIZE_X x M_PROXIMITY_MATRIX_SIZE_Y or M_NB_ELEMENTS. In the proximity matrix, the entry in cell (j, k) is some measure of similarity (or distance) between the entries to which row j and column k correspond. The proximity matrix result can be enabled or disabled using M_COMPUTE_PROXIMITY_MATRIX in the train context. (summarize)Retrieves the proximity matrix. (more details...) |
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
|
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Retrieves the X-dimension of the proximity matrix. (summarize)Retrieves the X-dimension of the proximity matrix. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
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Specifies the X-dimension of the proximity matrix. |
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Retrieves the Y-dimension of the proximity matrix. (summarize)Retrieves the Y-dimension of the proximity matrix. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
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Specifies the Y-dimension of the proximity matrix. |
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Retrieves the seed used for the training dataset. (summarize)Retrieves the seed used for the training dataset. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
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Specifies the seed used for the training dataset. |
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Retrieves the array containing the accuracies on the training dataset after adding each tree to the classifier. (summarize)Retrieves the array containing the accuracies on the training dataset after adding each tree to the classifier. (more details...) |
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Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves the array containing the error rates on the training dataset after adding each tree to the classifier. (summarize)Retrieves the array containing the error rates on the training dataset after adding each tree to the classifier. (more details...) |
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Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
|
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Retrieves the array containing the depth achieved by each tree in the classifier. (summarize)Retrieves the array containing the depth achieved by each tree in the classifier. (more details...) |
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
|
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Retrieves the array containing the number of leaf nodes for each tree in the classifier. (summarize)Retrieves the array containing the number of leaf nodes for each tree in the classifier. (more details...) |
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
|
To retrieve general results from a CNN or tree ensemble training result buffer (ResultClassId), the ResultType parameter can be set to one of the following values. In this case, set the LabelOrIndex parameter to M_GENERAL.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Retrieves the accuracy on the development dataset using the trained context. (summarize)Retrieves the accuracy on the development dataset using the trained context. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the accuracy on the development dataset, as a percentage. |
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Retrieves the confusion matrix obtained using the development dataset. This is a square matrix. MIL returns the matrix values, row-by-row, as an array. The array size is equivalent to the square of the total amount of classes, which is the same M_DEV_DATASET_CONFUSION_MATRIX_SIZE_X x M_DEV_DATASET_CONFUSION_MATRIX_SIZE_Y, or M_NB_ELEMENTS. (summarize)Retrieves the confusion matrix obtained using the development dataset. (more details...) |
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
|
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Retrieves the X-dimension of the confusion matrix. This value is calculated using the development dataset. (summarize)Retrieves the X-dimension of the confusion matrix. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the X-dimension of the confusion matrix. |
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Retrieves the Y-dimension of the confusion matrix. This value is calculated using the development dataset. (summarize)Retrieves the Y-dimension of the confusion matrix. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the Y-dimension of the confusion matrix. |
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Retrieves the error rate on the development dataset. This can be expressed as 100.0 - M_DEV_DATASET_ACCURACY. (summarize)Retrieves the error rate on the development dataset. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the accuracy error rate on the development dataset, as a percentage. |
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Retrieves an array containing the MIL_UUID of each image in the development dataset used during the training phase. (summarize)Retrieves an array containing the MIL_UUID of each image in the development dataset used during the training phase. (more details...) |
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Data type info:
Data type: array of type
MIL_UUID [optionally, in C++: a reference to a std::vector<MIL_UUID>
] MORE
Data type info:
Data type: array of type
MIL_UUID [optionally, in C++: a reference to a std::vector<MIL_UUID>
] MORE
|
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Retrieves the accuracy on the training dataset using the trained context. (summarize)Retrieves the accuracy on the training dataset using the trained context. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the accuracy on the training dataset, as a percentage. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the confusion matrix obtained using the training dataset. This is a square matrix. MIL returns the matrix values, row-by-row, as an array. The array size is equivalent to the square of the total amount of classes, which is the same M_TRAIN_DATASET_CONFUSION_MATRIX_SIZE_X x M_TRAIN_DATASET_CONFUSION_MATRIX_SIZE_Y, or M_NB_ELEMENTS. (summarize)Retrieves the confusion matrix obtained using the training dataset. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] MORE
|
||||||||||||||||||||||||||||||||||||||
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Retrieves the X-dimension of the confusion matrix. This value is calculated using the training dataset. (summarize)Retrieves the X-dimension of the confusion matrix. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the X-dimension of the confusion matrix. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the Y-dimension of the confusion matrix. This value is calculated using the training dataset. (summarize)Retrieves the Y-dimension of the confusion matrix. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the Y-dimension of the confusion matrix. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the error rate on the training dataset. This can be expressed as 100.0 - M_TRAIN_DATASET_ACCURACY. (summarize)Retrieves the error rate on the training dataset. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the accuracy error rate on the training dataset, as a percentage. |
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Retrieves an array containing the MIL_UUID of each image in the training dataset used during the training phase. (summarize)Retrieves an array containing the MIL_UUID of each image in the training dataset used during the training phase. (more details...) |
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Data type info:
Data type: array of type
MIL_UUID [optionally, in C++: a reference to a std::vector<MIL_UUID>
] MORE
Data type info:
Data type: array of type
MIL_UUID [optionally, in C++: a reference to a std::vector<MIL_UUID>
] MORE
|
To retrieve general results from a CNN prediction result buffer (ResultClassId), the ResultType can be set to one of the following values. In this case, set the LabelOrIndex parameter to M_GENERAL.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Retrieves the image type to provide when calling MclassDraw() with M_DRAW_BEST_INDEX_IMAGE. (summarize)Retrieves the image type to provide when calling MclassDraw() with M_DRAW_BEST_INDEX_IMAGE. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies that the image type is 8-bit unsigned. |
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Specifies that the image type is 16-bit unsigned. |
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Retrieves the classification map offset along the X-axis. Apply the scale and offset to M_CLASSIFICATION_MAP_SIZE_X to compute the center of the receptive field of each M_CLASS_SCORES into the target image such as: x * M_CLASSIFICATION_MAP_SCALE_X + M_CLASSIFICATION_MAP_OFFSET_X. (summarize)Retrieves the classification map offset along the X-axis. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the classification map offset along the X-axis. |
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Retrieves the classification map offset along the Y-axis. Apply the scale and offset to M_CLASSIFICATION_MAP_SIZE_Y to compute the center of the receptive field of each M_CLASS_SCORES into the target image such as: y * M_CLASSIFICATION_MAP_SCALE_Y + M_CLASSIFICATION_MAP_OFFSET_Y. (summarize)Retrieves the classification map offset along the Y-axis. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the classification map offset along the Y-axis. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the classification map scale along the X-axis. Apply the scale and offset to M_CLASSIFICATION_MAP_SIZE_X to compute the center of the receptive field of each CLASS_SCORES into the target image such as: x * M_CLASSIFICATION_MAP_SCALE_X + M_CLASSIFICATION_MAP_OFFSET_X. (summarize)Retrieves the classification map scale along the X-axis. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the classification map scale along the X-axis. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the classification map scale along the Y-axis. Apply the scale and offset to M_CLASSIFICATION_MAP_SIZE_Y to compute the center of the receptive field of each CLASS_SCORES into the target image such as: y * M_CLASSIFICATION_MAP_SCALE_Y + M_CLASSIFICATION_MAP_OFFSET_Y. (summarize)Retrieves the classification map scale along the Y-axis. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the classification map scale along the Y-axis. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the number of predictions the deep network has made according to the target image size (see M_TARGET_IMAGE_SIZE_X). Typically, for a target image the same size as the network's source layer the map size will be 1x1, meaning a single prediction for the whole target image. For a larger target image, the map size can be larger, meaning the CNN has convolved the larger target image and made many predictions doing so. (summarize)Retrieves the number of predictions the deep network has made according to the target image size (see M_TARGET_IMAGE_SIZE_X). (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Retrieves the number of entries in the dataset. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the number of predictions the deep network has made according to the target image size (see M_TARGET_IMAGE_SIZE_Y). Typically, for a target image the same size as the network's source layer the map size will be 1x1, meaning a single prediction for the whole target image. For a larger target image, the map size can be larger, meaning the CNN has convolved the larger target image and made many predictions doing so. (summarize)Retrieves the number of predictions the deep network has made according to the target image size (see M_TARGET_IMAGE_SIZE_Y). (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Retrieves the number of entries in the dataset. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the size of the receptive field along the X-axis. (summarize)Retrieves the size of the receptive field along the X-axis. (more details...) |
||||||||||||||||||||||||||||||||||||||
![]() ![]() |
Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the size of the receptive field along the X-axis. |
||||||||||||||||||||||||||||||||||||||
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Retrieves the size of the receptive field along the Y-axis. (summarize)Retrieves the size of the receptive field along the Y-axis. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the size of the receptive field along the Y-axis. |
To retrieve general results from a CNN or tree ensemble prediction result buffer (ResultClassId), the ResultType can be set to one of the following values. In this case, set the LabelOrIndex parameter to M_GENERAL.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Retrieves the highest class index. Note, for coarse segmentation (pixel level classification) of an image with a CNN, this retrieves an array (map) of values that can be arranged in a 2D image, where the size is equal to M_CLASSIFICATION_MAP_SIZE_X multiplied by M_CLASSIFICATION_MAP_SIZE_Y. Each value is the index of the class having the best score. The index of the class begins at 0. (summarize)Retrieves the highest class index. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE | array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info:
Data type: address of a
MIL_DOUBLE | array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the highest class index. |
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Retrieves the highest class score. (summarize)Retrieves the highest class score. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE | array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info:
Data type: address of a
MIL_DOUBLE | array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies the best class score. |
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Retrieves the number of classes. For a CNN, this refers to the number of outputs in the CNN's last layer. For tree ensemble, this refers to the number of tree ensemble in the classifier. This can also be used in a tree ensemble prediction with LabelOrIndex set to M_GENERAL. (summarize)Retrieves the number of classes. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Retrieves the number of entries in the dataset. |
To retrieve general results from a CNN or tree ensemble prediction result buffer, set the LabelOrIndex parameter to M_GENERAL; to retrieve class-specific results from a CNN prediction result buffer, set the LabelOrIndex parameter the index of a specific class (using M_CLASS_INDEX()). In these cases, the ResultType can be set to one of the following values. Note, you cannot receive class-specific results from a tree ensemble result buffer.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
|||||||||||||||||||||||||||||||||||||||
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Retrieves an array of all the class scores. The number of returned values is M_NUMBER_OF_CLASS_SCORES. For general results obtained from a CNN prediction result buffer, the returned values are organized planar-wise in a 3d volume of size M_CLASSIFICATION_MAP_SIZE_X * M_CLASSIFICATION_MAP_SIZE_Y * M_NUMBER_OF_CLASSES. Note, each band contains the score of one given class. The index in this returned array of the score of class C at pixel X, Y is: index = (C * MapSizeX * MapSizeY) + (Y * MapSizeX) + X. For general results obtained from a tree ensemble prediction result buffer, the number of class scores is equivalent to M_NUMBER_OF_CLASS_SCORES. For class-specific results (LabelOrIndex set to M_CLASS_INDEX()) obtained from a CNN prediction result buffer, the returned values are organized in a vector that corresponds to a 2D volume of size M_CLASSIFICATION_MAP_SIZE_X * M_CLASSIFICATION_MAP_SIZE_Y. The index of the score at location X, Y is: index = (Y * M_CLASSIFICATION_MAP_SIZE_X ) + X. (summarize)Retrieves an array of all the class scores. (more details...) |
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Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies all of the class scores. |
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Retrieves the total number of class scores. For general results obtained from a CNN predict result buffer, the total corresponds to M_CLASSIFICATION_MAP_SIZE_X * M_CLASSIFICATION_MAP_SIZE_Y * M_NUMBER_OF_CLASSES scores. The first batch of M_CLASSIFICATION_MAP_SIZE_X * M_CLASSIFICATION_MAP_SIZE_Y scores are for the first class and the subsequent batches are for the remaining classes. In other words, the data is organized planar-wise. For general results obtained from a tree ensemble predict result buffer, the total corresponds to M_NUMBER_OF_CLASSES. For class-specific results (LabelOrIndex set to M_CLASS_INDEX()) obtained from a CNN predict result buffer, the total corresponds to M_CLASSIFICATION_MAP_SIZE_X * M_CLASSIFICATION_MAP_SIZE_Y. (summarize)Retrieves the total number of class scores. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Retrieves the number of entries in the dataset. |
To retrieve the status of the training or prediction operation from a result buffer (ResultClassId), the ResultType can be set to the following value. In this case, set the LabelOrIndex parameter to M_GENERAL. Unless otherwise specified, the following values apply to a CNN or tree ensemble training result buffer, or a CNN prediction result buffer.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
|||||||||||||||||||||||||||||||||||||||
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Retrieves information regarding the state of the operation. (summarize)Retrieves information regarding the state of the operation. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
||||||||||||||||||||||||||||||||||||||
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Specifies that the operation completed successfully. |
||||||||||||||||||||||||||||||||||||||
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Specifies that the prediction operation is currently ongoing. You can only get this status if you're retrieving it from another thread. This only applies if you are retrieving results from a CNN or tree ensemble prediction result buffer (ResultClassId). (summarize)Specifies that the prediction operation is currently ongoing. (more details...) |
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Specifies that the training operation is currently ongoing. You can only get this status if you're retrieving it from another thread. This only applies if you are retrieving results from a CNN or tree ensemble training result buffer (ResultClassId). (summarize)Specifies that the training operation is currently ongoing. (more details...) |
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Specifies that an unexpected error occurred during the operation (contact customer support). |
||||||||||||||||||||||||||||||||||||||
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Specifies that the training terminated because a non-finite value was detected when the network's parameters were last updated. These values include NaN (Not a Number) and INF (infinity) cases. Such invalid values are often caused by numerically unstable computations that occur during training due to a learning rate that is too high. This only applies if you are retrieving results from a CNN training result buffer (ResultClassId). (summarize)Specifies that the training terminated because a non-finite value was detected when the network's parameters were last updated. (more details...) |
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Specifies that a memory allocation error occurred while training on the GPU. This only applies if you are retrieving results from a CNN training result buffer (ResultClassId). (summarize)Specifies that a memory allocation error occurred while training on the GPU. (more details...) |
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Specifies that a memory allocation error occurred during the operation. |
||||||||||||||||||||||||||||||||||||||
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Specifies that the prediction operation was not performed. This is the initial status. This only applies if you are retrieving results from a CNN or tree ensemble prediction result buffer (ResultClassId). (summarize)Specifies that the prediction operation was not performed. (more details...) |
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Specifies that the current execution of the operation was explicitly stopped (MclassControl() with M_STOP_PREDICT). |
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Specifies that the operation ended because the timeout limit was reached (MclassControl() with M_TIMEOUT). |
||||||||||||||||||||||||||||||||||||||
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Specifies that the training operation was not performed. This is the initial status. This only applies if you are retrieving results from a CNN or tree ensemble training result buffer (ResultClassId). (summarize)Specifies that the training operation was not performed. (more details...) |
You can add the following value to the above-mentioned values to determine the required number of elements in the array (array size).
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
|||||||||||||||||||||||||||||||||||||||
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Retrieves the required array size (number of elements) to store the returned values. (summarize)Retrieves the required array size (number of elements) to store the returned values. (more details...) |
||||||||||||||||||||||||||||||||||||||
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Data type info:
Data type: address of a
MIL_DOUBLE MORE
Data type info:
Data type: address of a
MIL_DOUBLE MORE
|
You can add the following value to the above-mentioned values to determine whether a result is available.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Retrieves whether a result is available to be returned. (summarize)Retrieves whether a result is available to be returned. (more details...) |
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Data type info:
Data type: address of a
MIL_DOUBLE
Data type info:
Data type: address of a
MIL_DOUBLE
|
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Specifies that the result is not available to be retrieved. |
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Specifies that the result is available to be retrieved. |
You can add one of the following values to the above-mentioned values to cast the requested results to the required data type.
Note: In C++, when using a standard vector (std::vector) instead of an array, the values listed in this combination table must not be used. If you need to use a specific data type, instantiate the vector with the necessary data type.
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Description | ||||||||||||||||||||||||||||||||||||||
ResultArrayPtr - Possible values returned |
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Casts the requested results to a MIL_DOUBLE. (summarize)Casts the requested results to a MIL_DOUBLE. (more details...) |
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Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] | address of a
MIL_DOUBLE MORE
Data type info:
Data type: array of type
MIL_DOUBLE [optionally, in C++: a reference to a std::vector<MIL_DOUBLE>
] | address of a
MIL_DOUBLE MORE
|
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Casts the requested results to a MIL_ID. (summarize)Casts the requested results to a MIL_ID. (more details...) |
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Data type info:
Data type: array of type
MIL_ID [optionally, in C++: a reference to a std::vector<MIL_ID>
] | address of a
MIL_ID MORE
Data type info:
Data type: array of type
MIL_ID [optionally, in C++: a reference to a std::vector<MIL_ID>
] | address of a
MIL_ID MORE
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Casts the requested results to a MIL_INT. (summarize)Casts the requested results to a MIL_INT. (more details...) |
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Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] | address of a
MIL_INT MORE
Data type info:
Data type: array of type
MIL_INT [optionally, in C++: a reference to a std::vector<MIL_INT>
] | address of a
MIL_INT MORE
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Casts the requested results to a MIL_INT32. (summarize)Casts the requested results to a MIL_INT32. (more details...) |
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Data type info:
Data type: array of type
MIL_INT32 [optionally, in C++: a reference to a std::vector<MIL_INT32>
] | address of a
MIL_INT32 MORE
Data type info:
Data type: array of type
MIL_INT32 [optionally, in C++: a reference to a std::vector<MIL_INT32>
] | address of a
MIL_INT32 MORE
|
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Casts the requested results to a MIL_INT64. (summarize)Casts the requested results to a MIL_INT64. (more details...) |
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Data type info:
Data type: array of type
MIL_INT64 [optionally, in C++: a reference to a std::vector<MIL_INT64>
] | address of a
MIL_INT64 MORE
Data type info:
Data type: array of type
MIL_INT64 [optionally, in C++: a reference to a std::vector<MIL_INT64>
] | address of a
MIL_INT64 MORE
|
Header | Include mil.h. |
Library | Use mil.lib; milclass.lib. |
DLL | Requires mil.dll; milclass.dll. |