Integration of the OpenCV Machine Learning library for Logistic Regression based classification of gridded features.
Optimization algorithms like Batch Gradient Descent and Mini-Batch Gradient Descent are supported in Logistic Regression. It is important that we mention the number of iterations these optimization algorithms have to run. The number of iterations can be thought as number of steps taken and learning rate specifies if it is a long step or a short step. This and previous parameter define how fast we arrive at a possible solution.
In order to compensate for overfitting regularization can be performed. (L1 or L2 norm).
Logistic regression implementation provides a choice of two training methods with Batch Gradient Descent or the Mini-Batch Gradient Descent.
| Name | Type | Identifier | Description | Constraints |
Input | Features | grid list, input | FEATURES | - | - |
Training Samples | table, input | TRAIN_SAMPLES | Provide a class identifier in the first field followed by sample data corresponding to the input feature grids. | - |
Training Areas | shapes, input | TRAIN_AREAS | - | - |
Output | Classification | grid, output | CLASSES | - | - |
Look-up Table | table, output, optional | CLASSES_LUT | A reference list of the grid values that have been assigned to the training classes. | - |
Options | Normalize | boolean | NORMALIZE | - | Default: 0 |
Colors from Features | boolean, GUI | RGB_COLORS | Use the first three features in list to obtain blue, green, red components for class colour in look-up table. | Default: 0 |
Grid System | grid system | GRID_SYSTEM | - | - |
Training | choice | MODEL_TRAIN | - | Available Choices:
[0] training areas
[1] training samples
[2] load from file
Default: 0 |
Class Identifier | table field | TRAIN_CLASS | - | - |
Buffer Size | floating point number | TRAIN_BUFFER | For non-polygon type training areas, creates a buffer with a diameter of specified size. | Minimum: 0.000000
Default: 1.000000 |
Load Model | file path | MODEL_LOAD | Use a model previously stored to file. | - |
Save Model | file path | MODEL_SAVE | Stores model to file to be used for subsequent classifications instead of training areas. | - |
Learning Rate | floating point number | LOGR_LEARNING_RATE | The learning rate determines how fast we approach the solution. | Minimum: 0.000000
Default: 1.000000 |
Number of Iterations | integer number | LOGR_ITERATIONS | - | Minimum: 1
Default: 300 |
Regularization | choice | LOGR_REGULARIZATION | - | Available Choices:
[0] disabled
[1] L1 norm
[2] L2 norm
Default: 0 |
Training Method | choice | LOGR_TRAIN_METHOD | - | Available Choices:
[0] Batch Gradient Descent
[1] Mini-Batch Gradient Descent
Default: 0 |
Mini-Batch Size | integer number | LOGR_MINIBATCH_SIZE | - | Minimum: 1
Default: 1 |