Merge pull request #108 from czgdp1807/lnn_02

Ported ``integration_tests/test_pkg_lnn_02.py`` from LPython

integration_tests/lnn/regression/regression_main.hpp

@@ -104,7 +104,7 @@ void print_perceptron(struct Perceptron& p) {
     for( i = 0; i < p.no_of_inputs; i++ ) {
         std::cout << p.weights[i] << ", ";
     }
-    std::cout << p.weights[p.no_of_inputs] << "\n";
+    std::cout << p.weights[p.no_of_inputs] << "(bias)]\n";
     std::cout << "learn_rate = ";
     std::cout << p.learn_rate << "\n";
     std::cout << "error = ";

integration_tests/test_pkg_lnn_02.cpp

@@ -0,0 +1,123 @@
+#include "lnn/regression/regression_main.hpp"
+#include "lnn/utils/utils_main.hpp"
+#include "lpdraw/draw.hpp"
+
+#define assert_(cond) if( !(cond) ) { \
+    exit(2); \
+} \
+
+double compute_decision_boundary(struct Perceptron& p, double x) {
+    double bias = p.weights[1];
+    double slope = p.weights[0];
+    double intercept = bias;
+    return slope * x + intercept;
+}
+
+void plot_graph(struct Perceptron& p, std::vector<std::vector<double>>& input_vectors, std::vector<double>& outputs) {
+    const int32_t Width = 500; // x-axis limits [0, 499]
+    const int32_t Height = 500; // # y-axis limits [0, 499]
+    xt::xtensor<int32_t, 2> Screen = xt::empty<int32_t>({Height, Width});
+    Clear(Height, Width, Screen);
+
+    double x1 = 1.0;
+    double y1 = compute_decision_boundary(p, x1);
+    double x2 = -1.0;
+    double y2 = compute_decision_boundary(p, x2);
+
+    // center the graph using the following offset
+    double scale_offset = Width / 4;
+    double shift_offset = Width / 2;
+    x1 *= scale_offset;
+    y1 *= scale_offset;
+    x2 *= scale_offset;
+    y2 *= scale_offset;
+
+    // print (x1, y1, x2, y2)
+    Line(Height, Width, Screen, int32_t(x1 + shift_offset), int32_t(y1 + shift_offset), int32_t(x2 + shift_offset), int32_t(y2 + shift_offset));
+
+    int32_t i;
+    int32_t point_size = 5;
+    for( i = 0; i < input_vectors.size(); i++ ) {
+        input_vectors[i][0] *= scale_offset;
+        input_vectors[i][0] += shift_offset;
+        outputs[i] *= scale_offset;
+        outputs[i] += shift_offset;
+
+        Circle(Height, Width, Screen, int32_t(input_vectors[i][0]), int32_t(outputs[i]), double(point_size));
+    }
+
+    Display(Height, Width, Screen);
+}
+
+void main0() {
+    struct Perceptron p;
+    p.no_of_inputs = 0;
+    p.weights = {0.0};
+    p.learn_rate = 0.0;
+    p.iterations_limit = 0.0;
+    p.err_limit = 0.0;
+    p.err = 0.0;
+    p.epochs_cnt = 0;
+
+    init_perceptron(p, 1, 0.0005, 10000, 1e-16);
+
+    std::vector<std::vector<double>> input_vectors = {{1.1}, {1.3}, {1.5}, {2.0}, {2.2}, {2.9}, {3.0}, {3.2},
+                                                      {3.2}, {3.7}, {3.9}, {4.0}, {4.0}, {4.1}, {4.5}, {4.9},
+                                                      {5.1}, {5.3}, {5.9}, {6.0}, {6.8}, {7.1}, {7.9}, {8.2},
+                                                      {8.7}, {9.0}, {9.5}, {9.6}, {10.3}, {10.5}, {11.2}, {11.5},
+                                                      {12.3}, {12.9}, {13.5}};
+    std::vector<double> outputs = {39343.0, 46205.0, 37731.0, 43525.0, 39891.0, 56642.0, 60150.0, 54445.0, 64445.0,
+                                   57189.0, 63218.0, 55794.0, 56957.0, 57081.0, 61111.0, 67938.0, 66029.0, 83088.0,
+                                   81363.0, 93940.0, 91738.0, 98273.0, 101302.0, 113812.0, 109431.0, 105582.0, 116969.0,
+                                   112635.0, 122391.0, 121872.0, 127345.0, 126756.0, 128765.0, 135675.0, 139465.0};
+
+    normalize_input_vectors(input_vectors);
+    normalize_output_vector(outputs);
+
+    train_dataset(p, input_vectors, outputs);
+    print_perceptron(p);
+
+    assert_(abs(p.weights[0] - (1.0640975812232145)) <= 1e-12);
+    assert_(abs(p.weights[1] - (0.0786977829749839)) <= 1e-12);
+    assert_(abs(p.err - (0.4735308448814293)) <= 1e-12);
+    assert_(p.epochs_cnt == 4515);
+
+    plot_graph(p, input_vectors, outputs);
+}
+
+void main1() {
+    struct Perceptron p;
+    p.no_of_inputs = 0;
+    p.weights = {0.0};
+    p.learn_rate = 0.0;
+    p.iterations_limit = 0.0;
+    p.err_limit = 0.0;
+    p.err = 0.0;
+    p.epochs_cnt = 0;
+    init_perceptron(p, 1, 0.0005, 10000, 1e-16);
+
+    std::vector<std::vector<double>> input_vectors = {{1.0}, {3.0}, {7.0}};
+    std::vector<double> outputs = {8.0, 4.0, -2.0};
+
+    normalize_input_vectors(input_vectors);
+    normalize_output_vector(outputs);
+
+    train_dataset(p, input_vectors, outputs);
+    print_perceptron(p);
+
+    assert_(abs(p.weights[0] - (-0.9856542200697508)) <= 1e-12);
+    assert_(abs(p.weights[1] - (-0.0428446744717655)) <= 1e-12);
+    assert_(abs(p.err - 0.011428579012311327) <= 1e-12);
+    assert_(p.epochs_cnt == 10000);
+
+    plot_graph(p, input_vectors, outputs);
+}
+
+int main() {
+
+main0();
+main1();
+
+return 0;
+
+}