PrologMethods/Classification/logistic_regression.md

@@ -3,13 +3,13 @@
 An implementation of L2-regularized logistic regression for two-class classification.
 
 ```prolog
-:- use_module('path/to/.../src/methods/adaboost/adaboost.pl').
+:- use_module('path/to/.../src/methods/logistic_regression/logistic_regression.pl').
 
 %% usage example
 TrainData = [5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5],
 TestData = [3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5],
-adaboost_initModelWithTraining(TrainData, 3, [0,1,0,1], 2, perceptron, 50, 0.0001),
-adaboost_classify(TestData, 3, PredictionList, ProbabilitiesList, _).
+logistic_regression_initModelWithOptimizer(TrainData, 3, [0,1,0,1], lbfgs, 0.0, 1000, 1.0e-10, 0.01, 64),
+logistic_regression_classifyMatrix(TestData, 3, PredictLabels, ProbsList, _, 0.5).
 ```
 
 # Available Predicates
@@ -44,6 +44,7 @@ foreign(initModelNoOptimizer, c, initModelNoOptimizerI( +pointer(float_array), +
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | data | +matrix | Input training variables. | - |
@@ -58,7 +59,7 @@ Initialize the logistic_regression model and specify the optimizer.
 
 ```prolog
 %% predicate definition 
-logistic_regression_initModelWithOptimizer(DataList, DataRows, ResponsesList, Responses, Lambda, MaxIterations, Tolerance, StepSize, BatchSize) :-
+logistic_regression_initModelWithOptimizer(DataList, DataRows, ResponsesList, Optimizer, Lambda, MaxIterations, Tolerance, StepSize, BatchSize) :-
         Lambda >= 0.0,
         MaxIterations >= 0,
         Tolerance >= 0.0,
@@ -66,7 +67,7 @@ logistic_regression_initModelWithOptimizer(DataList, DataRows, ResponsesList, Re
         BatchSize > 0,
         convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
         convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
-        initModelWithOptimizerI(X, Xsize, Xrownum, Y, Ysize, Responses, Lambda, MaxIterations, Tolerance, StepSize, BatchSize).
+        initModelWithOptimizerI(X, Xsize, Xrownum, Y, Ysize, Optimizer, Lambda, MaxIterations, Tolerance, StepSize, BatchSize).
 
 %% foreign c++ predicate definition 
 foreign(initModelWithOptimizer, c, initModelWithOptimizerI( +pointer(float_array), +integer, +integer, 
@@ -76,12 +77,17 @@ foreign(initModelWithOptimizer, c, initModelWithOptimizerI( +pointer(float_array
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | data | +matrix | Input training variables. | - |
 | responses | +vector | Outputs results from input training variables. | - |
 | optimizer | +string | The optimizer to use: "lbfgs", "psgd" | lbfgs |
 | lambda | +float | L2-regularization parameter. | 0.0 |
+| maxIterations | +integer | Maximum iterations for optimizer (0 indicates no limit). | 10000 |
+| tolerance | +float | Convergence tolerance for optimizer. | 1e-10 |
+| stepSize | +float | Step size for SGD optimizer. | 0.01 |
+| batchSize | +integer | Batch size for SGD. | 64 |
 
 ---
 
@@ -104,6 +110,7 @@ foreign(classifyPoint, c, classifyPointI( +pointer(float_array), +integer,
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | point | +vector | Point to classify. | - |
@@ -134,10 +141,12 @@ foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer, +in
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | data | +matrix | Set of points to classify. | - |
 | predicLabels | -vector | Predicted labels for each point. | - |
+| probabilities | -matrix | Class probabilities for each point. | - |
 | decisionBoundary | +float | Decision boundary (default 0.5). | 0.5 |
 
 ---
@@ -163,6 +172,7 @@ foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer, +
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | data | +matrix | Input predictors. | - |
@@ -190,6 +200,7 @@ foreign(computeError, c, computeErrorI( +pointer(float_array), +integer, +intege
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | data | +matrix | Input predictors. | - |