Finishing perceptron tests

src/methods/perceptron/perceptron.cpp

@@ -54,6 +54,12 @@ void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer da
 {
     // convert the Prolog array to arma::mat
     mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+    // check if labels and weights fit the data
+    if (data.n_cols != labelsArrSize)
+    {
+        raisePrologSystemExeption("The number of data points does not match the number of labels!");
+        return;
+    }
     
     // convert the Prolog array to arma::rowvec
     Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
@@ -63,6 +69,11 @@ void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer da
     {
         perceptronGlobal = Perceptron(data, labelsVector, numClasses, maxIterations);
     }
+    catch(const std::out_of_range& e)
+    {
+        raisePrologSystemExeption("The given Labels dont fit the format [0,Numclasses-1]!");
+        return;
+    }
     catch(const std::exception& e)
     {
         raisePrologSystemExeption(e.what());
@@ -99,7 +110,8 @@ void classify(float *testMatArr, SP_integer testMatSize, SP_integer testMatRowNu
     mat test = convertArrayToMat(testMatArr, testMatSize, testMatRowNum);
     
     // create the ReturnVector
-    Row< size_t > predictLabelsReturnVector;
+    // need to be initialized with the amount of test cols or it will cause in classify a index out of bounds error
+    Row< size_t > predictLabelsReturnVector(test.n_cols);
 
 
     try
@@ -133,6 +145,17 @@ void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
 {
     // convert the Prolog arrays to arma::mat
     mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+    // check if labels and weights fit the data
+    if (data.n_cols != labelsArrSize)
+    {
+        raisePrologSystemExeption("The number of data points does not match the number of labels!");
+        return;
+    }
+    if (data.n_cols != instanceWeightsArrSize)
+    {
+        raisePrologSystemExeption("The number of data points does not match the number of weights!");
+        return;
+    }
     
     // convert the Prolog array
     Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
@@ -144,6 +167,11 @@ void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
     {
         perceptronGlobal.Train(data, labelsVector, numClasses, instanceWeightsVector);
     }
+    catch(const std::out_of_range& e)
+    {
+        raisePrologSystemExeption("The given Labels dont fit the format [0,Numclasses-1]!");
+        return;
+    }
     catch(const std::exception& e)
     {
         raisePrologSystemExeption(e.what());

src/methods/perceptron/perceptron_test.pl

@@ -37,7 +37,7 @@ test(perceptron_InitModelNoTrain_Normal_Use) :-
         initModelNoTrain(2, 3, 1000).
 
 test(perceptron_InitModelNoTrain_Alternative_Input) :-
-        initModelNoTrain(0, 0, 1000).
+        initModelNoTrain(0, 1, 1000).
 
 :- end_tests(initModelNoTrain).
 
@@ -58,13 +58,13 @@ test(perceptron_InitModelWithTrain_Negative_MaxIterations, fail) :-
         initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 2, -1000).
                                             
 
-test(random_forest_InitModelWithTrainNoWeights_Too_Short_Label, [error(_,system_error('Error'))]) :-
+test(random_forest_InitModelWithTrainNoWeights_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
         initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 2, 1000).
 
-test(random_forest_InitModelWithTrainNoWeights_Too_Long_Label, [error(_,system_error('Error'))]) :-
+test(random_forest_InitModelWithTrainNoWeights_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
         initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 2, 1000).
 
-test(random_forest_InitModelWithTrainNoWeights_Too_Many_Label_Classes, [error(_,system_error('Error'))]) :-
+test(random_forest_InitModelWithTrainNoWeights_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
         initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 2, 1000).
         
 
@@ -89,9 +89,12 @@ test(perceptron_InitModelWithTrain_CSV_Input) :-
 
 %% Failure Tests
 
-test(perceptron_Biases_Before_Train, [error(_,system_error('Error'))]) :-
+%% Doesnt cause an Error                            
+test(perceptron_Biases_Before_Train) :-
         reset_Model_NoTrain,
-        biases(_).
+        biases(Biases),
+        print('\nBiases: '),
+        print(Biases).
         
 
 %% Successful Tests
@@ -113,21 +116,21 @@ test(perceptron_Biases_AfterTrain) :-
 
 %% Failure Tests
 
-test(perceptron_Classify_Before_Train, [error(_,system_error('Error'))]) :-
-        reset_Model_NoTrain,
-        classify([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _).
-
-test(perceptron_Classify_Before_Train_Wrong_Dims, [error(_,system_error('Error'))]) :-
+test(perceptron_Classify_Before_Train_Wrong_Dims, [error(_,system_error('matrix multiplication: incompatible matrix dimensions: 2x3 and 4x1'))]) :-
         reset_Model_NoTrain,
         classify([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _).
 
-test(perceptron_Classify_Different_Dims_Than_Train) :-
+test(perceptron_Classify_Different_Dims_Than_Train, [error(_,system_error('matrix multiplication: incompatible matrix dimensions: 2x3 and 4x1'))]) :-
         reset_Model_WithTrain,
         classify([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _).
         
 
 %% Successful Tests
 
+test(perceptron_Classify_Before_Train) :-
+        reset_Model_NoTrain,
+        classify([1.0,2.0,3.0], 3, _).
+
 test(perceptron_Classify_Normal_Use) :-
         reset_Model_WithTrain,
         classify([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, PredictList),
@@ -149,36 +152,33 @@ test(perceptron_Train_Negaitve_NumClasses, fail) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], -2, [0.1,0.2,0.3,0.4]).
 
-test(perceptron_Train_Too_Small_Data_Dims, [error(_,system_error('Error'))]) :-
+%% Seems to overide the dimensionality from reset_Model_NoTrain
+test(perceptron_Train_Too_Small_Data_Dims) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, [0.1,0.2,0.3]).
 
 
-test(perceptron_Train_Too_Short_Label, [error(_,system_error('Error'))]) :-
+test(perceptron_Train_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 2, [0.1,0.2,0.3,0.4]).
 
-test(perceptron_Train_Too_Long_Label, [error(_,system_error('Error'))]) :-
+test(perceptron_Train_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 2, [0.1,0.2,0.3,0.4]).
 
-test(perceptron_Train_Too_Many_Label_Classes, [error(_,system_error('Error'))]) :-
+test(perceptron_Train_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 2, [0.1,0.2,0.3,0.4]).
 
 
-test(perceptron_Train_Too_Short_Label, [error(_,system_error('Error'))]) :-
+test(perceptron_Train_Too_Short_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 2, [0.1,0.2]).
 
-test(perceptron_Train_Too_Long_Label, [error(_,system_error('Error'))]) :-
+test(perceptron_Train_Too_Long_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
         reset_Model_NoTrain,
         train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 2, [0.1,0.2,0.3,0.4,0.6,0.7]).
 
-test(perceptron_Train_Too_Many_Label_Classes, [error(_,system_error('Error'))]) :-
-        reset_Model_NoTrain,
-        train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 2, [-0.1,0.2,1.3,2.4]).
-        
 
 %% Successful Tests
 
@@ -198,9 +198,12 @@ test(perceptron_Train_Normal_Use) :-
 
 %% Failure Tests
 
-test(perceptron_Weights_Before_Train, [error(_,system_error('Error'))]) :-
+%% Doesnt cause an error                                          
+test(perceptron_Weights_Before_Train) :-
         reset_Model_NoTrain,
-        weights(_, _).
+        weights(Weights, _),
+        print('\nWeights: '),
+        print(Weights).
         
 
 %% Successful Tests