Add comprehensive upptst unit testing framework

- Create test directories for all examples (CharGenTest, Regression1DTest, Classify2DTest, etc.)
- Add .cpp and .upp files for each test package
- Implement functionality tests matching each example's core features
- Create build and run scripts for individual and bulk testing
- Add documentation in README_TESTS.md

Author: SeppoPakonen

ConvNetCppTests.upp

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+{
+	"name": "ConvNetCppTests",
+	"include": [
+		"src/ConvNet",
+		"src/ConvNetCtrl",
+		"upptst"
+	],
+	"defs": [],
+	"libs": [],
+	"out": "bin/tests",
+	"src": [
+		"upptst/TestMain.cpp"
+	],
+	"precompile": false,
+	"console": true,
+	"upp": [
+		"Core",
+		"ConvNet"
+	]
+}

README_TESTS.md

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+# ConvNetCpp Unit Tests
+
+This directory contains headless unit tests for ConvNetCpp examples following the U++ upptst pattern.
+
+## Directory Structure
+
+Located in `upptst/` directory, each test follows the U++ convention:
+- `upptst/CharGenTest/` - Tests for the CharGen example functionality
+- `upptst/Regression1DTest/` - Tests for the Regression1D example functionality
+- `upptst/Classify2DTest/` - Tests for the Classify2D example functionality
+- `upptst/SimpleGANTest/` - Tests for the SimpleGAN example functionality
+- `upptst/GANTest/` - Tests for GAN-related functionality
+- `upptst/RegressionPainterTest/` - Tests for regression painter functionality
+- `upptst/ClassifyImagesTest/` - Tests for image classification functionality
+- `upptst/GridWorldTest/` - Tests for grid world reinforcement learning
+- `upptst/HeteroscedasticUncertaintyTest/` - Tests for uncertainty estimation
+- `upptst/MartingaleTest/` - Tests for financial prediction
+- `upptst/NetworkOptimizationTest/` - Tests for network optimization algorithms
+- `upptst/PuckWorldTest/` - Tests for continuous control environments
+- `upptst/ReinforcedLearningTest/` - Tests for reinforcement learning concepts
+- `upptst/TemporalDifferenceTest/` - Tests for temporal difference learning
+- `upptst/TrainerBenchmarkTest/` - Tests for different training algorithms
+- `upptst/WaterWorldTest/` - Tests for multi-agent reinforcement learning
+
+Each test directory contains:
+- `TestName.cpp` - The test implementation
+- `TestName.upp` - The U++ project file
+
+## Running Tests
+
+### Build tests:
+
+1. Build all tests:
+   ```bash
+   ./build-tests.sh
+   ```
+
+2. Build a single test:
+   ```bash
+   ./build-tests.sh Classify2DTest
+   ```
+
+3. Build multiple specific tests:
+   ```bash
+   ./build-tests.sh Classify2DTest Regression1DTest
+   ```
+
+4. Build with clean option (force rebuild):
+   ```bash
+   ./build-tests.sh --clean
+   ```
+   
+   Or for a single test:
+   ```bash
+   ./build-tests.sh --clean Classify2DTest
+   ```
+
+### Run tests:
+
+1. Run all tests:
+   ```bash
+   ./run-tests.sh
+   ```
+
+2. Run a single test:
+   ```bash
+   ./run-test.sh Classify2DTest
+   ```
+
+### Available Test Names:
+`CharGenTest`, `Regression1DTest`, `Classify2DTest`, `SimpleGANTest`, `GANTest`, `RegressionPainterTest`, `ClassifyImagesTest`, `GridWorldTest`, `HeteroscedasticUncertaintyTest`, `MartingaleTest`, `NetworkOptimizationTest`, `PuckWorldTest`, `ReinforcedLearningTest`, `TemporalDifferenceTest`, `TrainerBenchmarkTest`, `WaterWorldTest`
+
+## Framework
+
+Tests use the U++ Core library and ConvNetCpp functionality, with assertions to verify correct behavior. Each test is a standalone console application that follows the `CONSOLE_APP_MAIN` pattern from U++.
+
+All tests are headless (no GUI components) and focus on the core neural network functionality demonstrated in the examples.

build-tests.sh

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+#!/bin/bash
+
+# Build script for ConvNetCpp unit tests following U++ upptst conventions
+
+# Check for --clean argument
+CLEAN=""
+if [ "$1" = "--clean" ]; then
+    CLEAN="a"
+    shift
+fi
+
+# Build single test if argument provided, otherwise build all
+if [ $# -gt 0 ]; then
+    TESTS=("$@")
+    echo "Building specific test(s): ${TESTS[*]} (clean=$CLEAN)"
+else
+    TESTS=("CharGenTest" "Regression1DTest" "Classify2DTest" "SimpleGANTest" "GANTest" "RegressionPainterTest" "ClassifyImagesTest" "GridWorldTest" "HeteroscedasticUncertaintyTest" "MartingaleTest" "NetworkOptimizationTest" "PuckWorldTest" "ReinforcedLearningTest" "TemporalDifferenceTest" "TrainerBenchmarkTest" "WaterWorldTest")
+    echo "Building all ConvNetCpp unit tests... (clean=$CLEAN)"
+fi
+
+for test in "${TESTS[@]}"; do
+    echo "Building $test..."
+    if [ -d "upptst/$test" ]; then
+        if command -v umk >/dev/null 2>&1; then
+            # Use umk to build the test - call from project root with path to .upp file
+            umk upptst,$HOME/upp/uppsrc,$HOME/upp/bazaar,src $test ~/.config/u++/theide/CLANG.bm -bds${CLEAN} +CONSOLE,DEBUG_FULL "bin/${test}"
+            if [ $? -eq 0 ]; then
+                echo "  ✓ $test built successfully"
+            else
+                echo "  ✗ $test build failed"
+            fi
+        else
+            echo "  ✗ U++ build system (umk) not found. Please ensure U++ is installed."
+        fi
+    else
+        echo "  ✗ Directory upptst/$test not found"
+    fi
+done
+
+echo "Build process completed!"

run-test.sh

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+#!/bin/bash
+
+# Script to run specific ConvNetCpp unit tests
+echo "Running specific ConvNetCpp unit tests..."
+
+# Check if a test name was provided
+if [ $# -eq 0 ]; then
+    echo "Usage: $0 <TestName>"
+    echo "Example: $0 Classify2DTest"
+    echo "Available tests: CharGenTest, Regression1DTest, Classify2DTest, SimpleGANTest, GANTest, RegressionPainterTest, ClassifyImagesTest, GridWorldTest, HeteroscedasticUncertaintyTest, MartingaleTest, NetworkOptimizationTest, PuckWorldTest, ReinforcedLearningTest, TemporalDifferenceTest, TrainerBenchmarkTest, WaterWorldTest"
+    exit 1
+fi
+
+TEST_NAME="$1"
+
+# Create bin directory if it doesn't exist
+mkdir -p bin
+
+echo "========================================"
+echo "Running $TEST_NAME..."
+echo "========================================"
+
+if [ -f "bin/$TEST_NAME" ]; then
+    "./bin/$TEST_NAME"
+    result=$?
+    if [ $result -eq 0 ]; then
+        echo "✓ $TEST_NAME PASSED"
+    else
+        echo "✗ $TEST_NAME FAILED with exit code $result"
+    fi
+else
+    echo "✗ $TEST_NAME executable not found in bin/"
+    echo "Did you build the test first? Try: ./build-tests.sh $TEST_NAME"
+fi
+
+echo "Test $TEST_NAME completed!"

run-tests.sh

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+#!/bin/bash
+
+# Script to run all ConvNetCpp unit tests
+echo "Running ALL ConvNetCpp unit tests..."
+
+# Create bin directory if it doesn't exist
+mkdir -p bin
+
+# Run each test executable
+TESTS=("CharGenTest" "Regression1DTest" "Classify2DTest" "SimpleGANTest" "GANTest" "RegressionPainterTest" "ClassifyImagesTest" "GridWorldTest" "HeteroscedasticUncertaintyTest" "MartingaleTest" "NetworkOptimizationTest" "PuckWorldTest" "ReinforcedLearningTest" "TemporalDifferenceTest" "TrainerBenchmarkTest" "WaterWorldTest")
+
+for test in "${TESTS[@]}"; do
+    echo "========================================"
+    echo "Running $test..."
+    echo "========================================"
+    
+    if [ -f "bin/$test" ]; then
+        "./bin/$test"
+        result=$?
+        if [ $result -eq 0 ]; then
+            echo "✓ $test PASSED"
+        else
+            echo "✗ $test FAILED with exit code $result"
+        fi
+    else
+        echo "✗ $test executable not found in bin/"
+        echo "  Try: ./build-tests.sh $test"
+    fi
+    echo ""
+done
+
+echo "All tests completed!"

upptst/CharGenTest/CharGenTest.cpp

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+#include <Core/Core.h>
+#include <ConvNet/ConvNet.h>
+
+using namespace Upp;
+using namespace ConvNet;
+
+CONSOLE_APP_MAIN
+{
+    SeedRandom();
+    
+    // Test CharGen vocabulary setup functionality
+    Vector<WString> sents;
+    sents.Add(WString("hello"));
+    sents.Add(WString("world"));
+    
+    VectorMap<int, int> letterToIndex;
+    VectorMap<int, int> indexToLetter;
+    Vector<WString> vocab;
+    int input_size, output_size, epoch_size;
+    
+    // Simulate the InitVocab logic from CharGen
+    WString txt = Join(sents, ""); // concat all
+    VectorMap<int, int> d;
+    for (int i = 0; i < txt.GetCount(); i++) {
+        int txti = txt[i];
+        d.GetAdd(txti, 0)++;
+    }
+    
+    // filter by count threshold and create pointers
+    letterToIndex.Clear();
+    indexToLetter.Clear();
+    vocab.Clear();
+    
+    int q = 1; // Start at 1 to reserve 0 for special tokens
+    for (int i = 0; i < d.GetCount(); i++) {
+        int ch = d.GetKey(i);
+        if (d[i] >= 1) { // count threshold = 1
+            letterToIndex.Add(ch, q);
+            indexToLetter.Add(q, ch);
+            vocab.Add().Cat(ch);
+            q++;
+        }
+    }
+    
+    input_size = vocab.GetCount() + 1;
+    output_size = vocab.GetCount() + 1;
+    epoch_size = sents.GetCount();
+    
+    // Verify results
+    LOG("CharGen vocabulary test:");
+    LOG("  Vocabulary size: " << vocab.GetCount());
+    LOG("  Input size: " << input_size);
+    LOG("  Output size: " << output_size);
+    LOG("  Epoch size: " << epoch_size);
+    
+    ASSERT(vocab.GetCount() == 6);  // h,e,l,o,w,r,d (duplicates removed)
+    ASSERT(input_size == 7);        // vocab size + 1 (for START token)
+    ASSERT(output_size == 7);       // vocab size + 1 (for END token)
+    ASSERT(epoch_size == 2);        // 2 sentences
+    
+    // Test RecurrentSession with LSTM configuration
+    RecurrentSession ses;
+    
+    String model_str = "{\n"
+        "\t\"generator\":\"lstm\",\n"
+        "\t\"hidden_sizes\":[10,10],\n"
+        "\t\"letter_size\":5,\n"
+        "\t\"regc\":0.000001,\n"
+        "\t\"learning_rate\":0.01,\n"
+        "\t\"clipval\":5.0\n"
+        "}";
+    
+    ValueMap js = ParseJSON(model_str);
+    ses.Load(js);
+    ses.SetInputSize(20);  // arbitrary size
+    ses.SetOutputSize(20); // arbitrary size
+    ses.Init();
+    
+    LOG("CharGen RecurrentSession test:");
+    LOG("  Learning rate: " << ses.GetLearningRate());
+    ASSERT(ses.GetLearningRate() == 0.01);
+    
+    LOG("CharGen tests completed successfully!");
+}

upptst/CharGenTest/CharGenTest.upp

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+uses
+	Core,
+	ConvNet;
+
+file
+	CharGenTest.cpp;
+
+mainconfig
+	"" = "";

upptst/Classify2DTest/Classify2DTest.cpp

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+#include <Core/Core.h>
+#include <ConvNet/ConvNet.h>
+
+using namespace Upp;
+using namespace ConvNet;
+
+CONSOLE_APP_MAIN
+{
+    SeedRandom();
+    
+    // Test the network structure from Classify2D example
+    Session session;
+    
+    String t = "[\n"
+        "\t{\"type\":\"input\", \"input_width\":1, \"input_height\":1, \"input_depth\":2},\n"
+        "\t{\"type\":\"fc\", \"neuron_count\":6, \"activation\": \"tanh\"},\n"
+        "\t{\"type\":\"fc\", \"neuron_count\":2, \"activation\": \"tanh\"},\n"
+        "\t{\"type\":\"softmax\", \"class_count\":2},\n"
+        "\t{\"type\":\"sgd\", \"learning_rate\":0.01, \"momentum\":0.1, \"batch_size\":10, \"l2_decay\":0.001}\n"
+        "]\n";
+
+    bool success = session.MakeLayers(t);
+    
+    LOG("Classify2D network setup test:");
+    LOG("  Network creation success: " << (success ? "true" : "false"));
+    ASSERT(success);
+    ASSERT(session.GetLayerCount() == 5);  // Should have 5 layers
+    
+    // Test with simple binary classification data
+    SessionData& d = session.Data();
+    d.BeginData(2, 6, 2);  // 2 input features, 6 samples, 2 possible labels
+    
+    // Class 0: points with x1 < 0
+    d.SetData(0, 0, -1.0).SetData(0, 1, 0.5).SetLabel(0, 0);
+    d.SetData(1, 0, -0.5).SetData(1, 1, -1.0).SetLabel(1, 0);
+    d.SetData(2, 0, -2.0).SetData(2, 1, 1.5).SetLabel(2, 0);
+    
+    // Class 1: points with x1 > 0
+    d.SetData(3, 0, 1.0).SetData(3, 1, 0.5).SetLabel(3, 1);
+    d.SetData(4, 0, 0.5).SetData(4, 1, -1.0).SetLabel(4, 1);
+    d.SetData(5, 0, 2.0).SetData(5, 1, 1.5).SetLabel(5, 1);
+    
+    d.EndData();
+    
+    session.StartTraining();
+    
+    // Train for a few iterations
+    for (int i = 0; i < 50; i++) {
+        session.Tick();
+    }
+    
+    session.StopTraining();
+    
+    // Test that the network can process inputs and produce outputs
+    Vector<double> input1 = {-1.0, 0.0};  // Should be class 0
+    Vector<double> output1 = session.Predict(input1);
+    
+    Vector<double> input2 = {1.0, 0.0};   // Should be class 1
+    Vector<double> output2 = session.Predict(input2);
+    
+    LOG("Classify2D prediction test:");
+    LOG("  Input 1 [" << input1[0] << ", " << input1[1] << "] -> Output ["
+         << output1[0] << ", " << output1[1] << "]");
+    LOG("  Input 2 [" << input2[0] << ", " << input2[1] << "] -> Output ["
+         << output2[0] << ", " << output2[1] << "]");
+    
+    // Verify that the network produces valid probability outputs (sums to ~1)
+    double sum1 = output1[0] + output1[1];
+    double sum2 = output2[0] + output2[1];
+    
+    ASSERT(abs(sum1 - 1.0) < 0.01);  // Softmax outputs should sum to ~1
+    ASSERT(abs(sum2 - 1.0) < 0.01);  // Softmax outputs should sum to ~1
+    
+    LOG("Classify2D tests completed successfully!");
+}

upptst/Classify2DTest/Classify2DTest.upp

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+uses
+	Core,
+	ConvNet;
+
+file
+	Classify2DTest.cpp;
+
+mainconfig
+	"" = "";