Metrics cleanup

docs/methods/linear_model/lasso_regularization.md

@@ -0,0 +1,50 @@
+# Lasso Regularization
+
+It stands for Least Absolute Shrinkage and Selection Operator. It adds L1 the penalty.
+
+L1 is the sum of the absolute value of the beta coefficients
+
+## Parameters
+
+| Name          | Definition                                                                                  | Defaults | Type     |
+| ------------- | ------------------------------------------------------------------------------------------- | -------- | ---------|
+| lambda        | Constant that multiplies the L1 term, controlling regularization strength                   | 0.01     | `double` |
+
+
+## Attributes
+
+| Name         | Definition                                                | Shape      |
+| ------------ | --------------------------------------------------------- | ---------- |
+| Coefficients | Estimated coefficients for the lasso regularization       | n_features |
+
+## Methods
+
+| Name                            | Definition                                                               | Return value      |
+| ------------------------------- | -----------------------------------------------------                    | ----------------- |
+| `lossFunction(vector<vector<T>> x, vector<T> y)` |regularize loss function                                 | `double`          |
+| `gradient(vector<vector<T>> x,vector<T> y)` | Find gradient                                                | `vector<double>`  |
+| `gradientDescent(vector<vector<T>> x, vector<T> y, double alpha)` | gradient optimization algorithm        | `void`            |
+| `fit(vector<vector<T>> x, vector<T> y,int epochs,double alpha)` | Fit linear model                          | `vector<double>`  |
+| `predict(vector<vector<T>> x)`          | Predict using the linear model                                    | `vector<T>`       |
+| `printCoefficients()`           | Print coefficient of determination of the prediction                     | `void`            |
+
+## Example
+
+```cpp
+double alpha = 0.01;
+std::vector<std::vector<double>> x = {{1, 2, 3}, {2, 3, 4}, {3, 4, 5}, {4, 5, 6}};
+std::vector<double> y = {2, 3, 4, 5};
+double lambda = 0.1;
+int epochs = 1000;
+RidgeRegularization<double> model(alpha);
+model.fit(x, y, epochs, alpha);
+model.printCoefficients();
+std::vector<double> yPred;
+for (int i = 0; i < x.size(); i++)
+{
+  yPred.push_back(predict(x[i]));
+}
+for (int i = 0; i < y.size(); i++)
+  std::cout << "Actual value: " << y[i] << ", Predicted value: " << yPred[i]<< std::endl;
+
+```

docs/methods/linear_model/ridge_regularization.md

@@ -0,0 +1,50 @@
+# Ridge Regularization
+
+It adds L2 as the penalty.
+
+L2 is the sum of the square of the magnitude of beta coefficients.
+
+## Parameters
+
+| Name          | Definition                                                                                  | Defaults | Type     |
+| ------------- | ------------------------------------------------------------------------------------------- | -------- | ---------|
+| lambda        | Constant that multiplies the L1 term, controlling regularization strength                   | 0.01     | `double` |
+
+
+## Attributes
+
+| Name         | Definition                                                | Shape      |
+| ------------ | --------------------------------------------------------- | ---------- |
+| Coefficients | Estimated coefficients for the ridge regularization       | n_features |
+
+## Methods
+
+| Name                            | Definition                                                               | Return value      |
+| ------------------------------- | -----------------------------------------------------                    | ----------------- |
+| `lossFunction(vector<T> x, double y)` |regularize loss function                                 | `double`          |
+| `gradient(vector<T> x,double y)` | Find gradient                                                | `vector<double>`  |
+| `gradientDescent(vector<vector<T>> x, vector<T> y, double alpha,int epochs)` | gradient optimization algorithm        | `void`            |
+| `fit(vector<vector<T>> x, vector<T> y,int epochs,double alpha)` | Fit linear model                                            | `vector<double>`  |
+| `predict(vector<T> x)`          | Predict using the linear model                                           | `double`          |
+| `printCoefficients()`           | Print coefficient of determination of the prediction                     | `void`            |
+
+## Example
+
+```cpp
+double alpha = 0.01;
+std::vector<std::vector<double>> x = {{1, 2, 3}, {2, 3, 4}, {3, 4, 5}, {4, 5, 6}};
+std::vector<double> y = {2, 3, 4, 5};
+double lambda = 0.1;
+int epochs = 1000;
+RidgeRegularization<double> model(alpha);
+model.fit(x, y, epochs, alpha);
+model.printCoefficients();
+std::vector<double> yPred;
+for (int i = 0; i < x.size(); i++)
+{
+   yPred.push_back(predict(x[i]));
+}
+for (int i = 0; i < y.size(); i++)
+  std::cout << "Actual value: " << y[i] << ", Predicted value: " << yPred[i] << std::endl;
+
+```

examples/methods/cluster/kMeans.cpp → examples/cluster/kMeans.cpp

@@ -1,4 +1,5 @@
-#include "../../../src/slowmokit/methods/cluster/kMeans.hpp"
+#include "../../src/slowmokit/methods/cluster/kMeans.hpp"
+#include <bits/stdc++.h>
 
 int main()
 {

examples/linear_model/lasso_regression.cpp

@@ -0,0 +1,22 @@
+// #include "../src/slowmokit/methods/linear_model/lasso_regression.hpp"
+
+// int main()
+// {
+//   std::vector<std::vector<double>> x = {
+//       {1.0, 2.0, 3.0}, {2.0, 3.0, 4.0}, {3.0, 4.0, 5.0}};
+//   std::vector<double> y = {1.0, 2.0, 3.0};
+//   double alpha = 0.01;
+//   double lambda = 0.1;
+//   int epochs = 100;
+
+//   LassoRegularization<double> model(alpha);
+//   model.fit(x, y, epochs, alpha);
+//   model.printCoefficients();
+
+//   std::vector<double> yPred = model.predict(x);
+//   for (int i = 0; i < y.size(); i++)
+//     std::cout << "Actual value: " << y[i] << ", Predicted value: " << yPred[i]
+//               << std::endl;
+
+//   return 0;
+// }

examples/linear_model/linear_regression.cpp

@@ -0,0 +1,18 @@
+#include "../../src/slowmokit/methods/linear_model/linear_regression.hpp"
+#include "../../src/slowmokit/core.hpp"
+
+int main()
+{
+    LinearRegression<double> model;
+    std::vector<vector<double>> x = {{1, 2}, {2, 3}, {3, 4}, {4, 5}, {5, 6}};
+    std::vector<double> y = {2, 3, 4, 5, 6};
+
+    model.fit(x, y);
+    model.printCoefficients();
+
+    std::vector<double> yPred = model.predict(x);
+    for (int i = 0; i < y.size(); i++)
+        cout << "Actual value: " << y[i] << ", Predicted value: " << yPred[i] << endl;
+
+    return 0;
+}

examples/methods/linear_model/logistic_regression.cpp → examples/linear_model/logistic_regression.cpp

@@ -1,4 +1,5 @@
-// #include "../../../src/slowmokit/methods/linear_model/logistic_regression.hpp"
+// #include "../../src/slowmokit/methods/linear_model/logistic_regression.hpp"
+// #include "../../src/slowmokit/core.hpp"
 
 // int main()
 // {

examples/linear_model/ridge_regression.cpp

@@ -0,0 +1,25 @@
+// #include "../src/slowmokit/methods/linear_model/ridge_regression.hpp"
+
+// int main()
+// {
+//   double alpha = 0.01;
+//   std::vector<std::vector<double>> x = {
+//       {1, 2, 3}, {2, 3, 4}, {3, 4, 5}, {4, 5, 6}};
+//   std::vector<double> y = {2, 3, 4, 5};
+
+//   double lambda = 0.1;
+//   int epochs = 1000;
+//   RidgeRegularization<double> model(alpha);
+//   model.fit(x, y, epochs, alpha);
+//   model.printCoefficients();
+//   std::vector<double> yPred;
+//   for (int i = 0; i < x.size(); i++)
+//   {
+//     yPred.push_back(predict(x[i]));
+//   }
+//   for (int i = 0; i < y.size(); i++)
+//     std::cout << "Actual value: " << y[i] << ", Predicted value: " << yPred[i]
+//               << std::endl;
+
+//   return 0;
+// }

examples/matrix.cpp

@@ -3,9 +3,7 @@
 int main()
 {
     int n = 3, m = 3;
-    Matrix<int> mat(2, 2);
 
-    // std::cout << mat << " 2";
 
     return 0;
-}
+}

examples/methods/metrics/accuracy_eg.cpp → examples/metrics/accuracy_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/metrics/accuracy.hpp"
+// #include "../src/slowmokit/methods/metrics/accuracy.hpp"
 
 // int main() {
 //   std::vector<int> pred = {1, 0, 1, 1, 0, 1};

examples/methods/metrics/classification_report_eg.cpp → examples/metrics/classification_report_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/metrics/classification_report.hpp"
+// #include "../src/slowmokit/methods/metrics/classification_report.hpp"
 
 // int main()
 // {

examples/methods/metrics/mean_squared_error_eg.cpp → examples/metrics/mean_squared_error_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/metrics/mean_squared_error.hpp"
+// #include "../src/slowmokit/methods/metrics/mean_squared_error.hpp"
 // int main() {
 //     std::vector<double> actual = {1.0, 2.0, 3.0};
 //     std::vector<double> pred = {0.5, 1.5, 2.5};

examples/methods/metrics/precision_eg.cpp → examples/metrics/precision_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/metrics/precision.hpp"
+// #include "../src/slowmokit/methods/metrics/precision.hpp"
 // int main()
 // {
 //     std::vector<int> pred = {0, 1, 2, 1, 0, 2, 1, 0, 1, 2};

examples/methods/metrics/recall_eg.cpp → examples/metrics/recall_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/metrics/recall.hpp"
+// #include "../src/slowmokit/methods/metrics/recall.hpp"
 
 // int main()
 // {

examples/methods/neighbors/bernoulli_nb.cpp → examples/neighbors/bernoulli_nb.cpp

@@ -1,4 +1,5 @@
-// #include "../../../src/slowmokit/methods/neighbors/bernoulli_nb.hpp"
+// #include "../../src/slowmokit/methods/neighbors/bernoulli_nb.hpp"
+// #include "../../src/slowmokit/core.hpp"
 
 // signed main(){
 //     std::vector<std::vector<int>> xTrain{

examples/methods/neighbors/gaussian_nb.cpp → examples/neighbors/gaussian_nb.cpp

@@ -1,4 +1,5 @@
-// #include "../../../src/slowmokit/methods/neighbors/gaussian_nb.hpp"
+// #include "../../src/slowmokit/methods/neighbors/gaussian_nb.hpp"
+// #include "../../src/slowmokit/core.hpp"
 
 // signed main(){
 //     std::vector<std::vector<double>> x_train{

examples/methods/neighbors/knn.cpp → examples/neighbors/knn.cpp

@@ -1,4 +1,5 @@
-// #include "../../../src/slowmokit/methods/neighbors/knn.hpp"
+// #include "../../src/slowmokit/methods/neighbors/knn.hpp"
+// #include "../../src/slowmokit/core.hpp"
 
 // signed main(){
     // std::vector<std::vector<double>> x{

examples/methods/preprocessing/label_encoder.cpp → examples/preprocessing/label_encoder.cpp

@@ -1,4 +1,4 @@
-//#include "../../src/slowmokit/methods/preprocessing/label_encoder.hpp"
+//#include "src/slowmokit/methods/preprocessing/label_encoder.hpp"
 
 //int main() {
 // std::vector<std::string> data = {"luffy","zoro","sanji","luffy","law","zoro"};

examples/methods/preprocessing/normalization_eg.cpp → examples/preprocessing/normalization_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/preprocessing/normalization.hpp"
+// #include "../src/slowmokit/methods/preprocessing/normalization.hpp"
 // int main(){
 //     std::vector<double> values={1,2,3,4,5};
 //     normalize(values);

examples/methods/preprocessing/one_hot_encoder_eg.cpp → examples/preprocessing/one_hot_encoder_eg.cpp

@@ -1,4 +1,4 @@
-//#include "../../src/slowmokit/methods/preprocessing/one_hot_encoder.hpp"
+//#include "src/slowmokit/methods/preprocessing/one_hot_encoder.hpp"
 
 //int main() {
 // std::vector<std::string> data = {"apples", "banana", "mango", "pear", "mango","apples","pear"};

examples/methods/preprocessing/standardization_eg.cpp → examples/preprocessing/standardization_eg.cpp

@@ -1,4 +1,4 @@
-// #include "../../src/slowmokit/methods/preprocessing/standardization.hpp"
+// #include "../src/slowmokit/methods/preprocessing/standardization.hpp"
 
 // int main(){
 //     std::vector<double> values={1,2,3,4,5};

src/slowmokit/core.hpp

@@ -10,4 +10,7 @@
 // pre-requisites of the library
 #include "prereqs.hpp"
 
+// standard model class
+#include "models/model.hpp"
+
 #endif // SLOWMOKIT_CORE_HPP

src/slowmokit/ducks/matrix/matrix_main.cpp → src/slowmokit/ducks/matrix/matrix.cpp

@@ -1,7 +1,7 @@
 /**
- * @file ducks/matrix/matrix_main.cpp
+ * @file ducks/matrix/matrix.cpp
  *
- * Implementation of the main methods of Matrix
+ * Implementation of the matrix main program
  */
 
 #include "matrix.hpp"