first model

include/neural_network/activation/activation_ReLU.hpp

@@ -0,0 +1,94 @@
+/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ *                                              PANIC
+ *                              Portable Algorithms and Numerics In C++
+ *
+ *                       Scientific computing from scratch, with feeling.
+ *
+ * Copyright (c) 2026 Michelle Bausager
+ *
+ * This file is part of PANIC.
+ *
+ * PANIC is free software licensed under the GNU General Public License v3.0 or later.
+ * You may redistribute and/or modify it under the terms of the GPL.
+ *
+ * PANIC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
+ * without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+ * See the LICENSE file for the full license text.
+ *
+ * SPDX-License-Identifier: GPL-3.0-or-later
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ * Project Name:      PANIC
+ * Module Name:       neural_network
+ * File Name:         activation_ReLU.hpp
+ * Revision:          0.1.0
+ * Date:              29-08-2026
+ * Author:            Michelle Bausager
+ *
+ * Description:
+ * Defines the activation layer for ReLU
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
+#pragma once
+
+//---------------------------------------------------------------------------------------------------------------------------
+// INCLUDE DESCRIPTION
+//---------------------------------------------------------------------------------------------------------------------------
+#include <config/types.hpp> // panic::uint_t, panic::int_t, and panic::real_t
+#include <neural_network/layer/layer.hpp> // for base layer struct
+
+#include <tensor/matrix.hpp>
+
+
+namespace panic{
+	namespace neural_network{
+
+/**
+ * @brief struct for ReLU activation layer used in neural networks
+ * 
+ * Computes:
+ * @code
+ * panic::neural_network::activation_ReLU myactivation();
+ * myactivation.forward(inputMatrix);
+ * @endcode
+ *
+ * The struct is used in PANIC nural_network library.
+ */
+struct activation_ReLU : public layer{
+
+    /**
+     * @brief Empthy constructor
+     *
+     */
+	activation_ReLU();
+
+    /**
+     * @brief Default de-constructor
+     *
+     */
+	~activation_ReLU() = default;
+
+    /**
+     * @brief Forward function for layer
+     *
+     * @param inputs Data input for forward pass.
+     * 
+     * @Note Calculates -> outputs = inputs * weights + biases
+     */
+	bool forward(const panic::tensor::real_matrix& inputs);
+
+    /**
+     * @brief Backward function for layer
+     *
+     * @param inputs Data input for bacward pass.
+     * 
+     * @Note Calculates derivative of forward function.
+     */
+	bool backward(const panic::tensor::real_matrix& dinpus);
+};
+
+	} // namespace tensor
+} // namespace panic
+

include/neural_network/datasets/sine_data.hpp

@@ -0,0 +1,96 @@
+/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ *                                              PANIC
+ *                              Portable Algorithms and Numerics In C++
+ *
+ *                       Scientific computing from scratch, with feeling.
+ *
+ * Copyright (c) 2026 Michelle Bausager
+ *
+ * This file is part of PANIC.
+ *
+ * PANIC is free software licensed under the GNU General Public License v3.0 or later.
+ * You may redistribute and/or modify it under the terms of the GPL.
+ *
+ * PANIC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
+ * without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+ * See the LICENSE file for the full license text.
+ *
+ * SPDX-License-Identifier: GPL-3.0-or-later
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ * Project Name:      PANIC
+ * Module Name:       neural_network
+ * File Name:         sine_data.hpp
+ * Revision:          0.1.0
+ * Date:              29-07-2026
+ * Author:            Michelle Bausager
+ *
+ * Description:
+ * Functions to generate dataset for neural networks;
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
+
+#include <config/types.hpp>
+#include <tensor/vector.hpp>
+#include <tensor/matrix.hpp>
+//---------------------------------------------------------------------------------------------------------------------------
+// INPLEMENTATION
+//---------------------------------------------------------------------------------------------------------------------------
+
+namespace panic {
+	namespace neural_network {
+
+
+/**
+ * @brief Generates a dataset of sines values
+ *
+ * Computes:
+ * @code
+ * csine(10, 1, X, y)
+ * @endcode
+ * 
+ * @tparam T Numeric element type.
+ * @param samples Number of samples dataset.
+ * @param lenght Interval of the sinus cuve form zero.
+ * @param X Output X matrix
+ * @param y Output y matrix
+ *
+ * @return true if @p X and @p y was resized and filled successfully.
+ * @return false if resizing @p X or @p y failed.
+ *
+ * @note This funtion writes the result into an existing vector to avoid
+ *       unnecessary temporary allocations.
+ */
+template <typename T>
+bool sine_data(const panic::types::uint_t samples, const T lenght, panic::tensor::matrix<T>& X, panic::tensor::vector<T>& y);
+
+/**
+ * @brief Generates a dataset of sines and cosine values
+ *
+ * Computes:
+ * @code
+ * csine(10, 1, X, y)
+ * @endcode
+ * 
+ * @tparam T Numeric element type.
+ * @param samples Number of samples dataset.
+ * @param lenght Interval of the sinus cuve form zero.
+ * @param X Output X matrix
+ * @param y Output y matrix
+ *
+ * @return true if @p X and @p y was resized and filled successfully.
+ * @return false if resizing @p X or @p y failed.
+ *
+ * @note This funtion writes the result into an existing vector to avoid
+ *       unnecessary temporary allocations.
+ */
+template <typename T>
+bool sine_cosine_data(const panic::types::uint_t samples, const T lenght, panic::tensor::matrix<T>& X, panic::tensor::matrix<T>& y);
+
+
+
+
+	} // namespace neural_network
+} // namespace panic

include/neural_network/datasets/spiral_data.hpp

@@ -0,0 +1,74 @@
+/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ *                                              PANIC
+ *                              Portable Algorithms and Numerics In C++
+ *
+ *                       Scientific computing from scratch, with feeling.
+ *
+ * Copyright (c) 2026 Michelle Bausager
+ *
+ * This file is part of PANIC.
+ *
+ * PANIC is free software licensed under the GNU General Public License v3.0 or later.
+ * You may redistribute and/or modify it under the terms of the GPL.
+ *
+ * PANIC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
+ * without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+ * See the LICENSE file for the full license text.
+ *
+ * SPDX-License-Identifier: GPL-3.0-or-later
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ * Project Name:      PANIC
+ * Module Name:       neural_network
+ * File Name:         spiral_data.hpp
+ * Revision:          0.1.0
+ * Date:              29-07-2026
+ * Author:            Michelle Bausager
+ *
+ * Description:
+ * Functions to generate dataset for neural networks;
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
+
+#include <config/types.hpp>
+#include <tensor/vector.hpp>
+#include <tensor/matrix.hpp>
+//---------------------------------------------------------------------------------------------------------------------------
+// INPLEMENTATION
+//---------------------------------------------------------------------------------------------------------------------------
+
+namespace panic {
+	namespace neural_network {
+
+
+/**
+ * @brief Generates a dataset of spiral values
+ *
+ * Computes:
+ * @code
+ * spiral_data(10, 1, X, y)
+ * @endcode
+ * 
+ * @tparam T Numeric element type.
+ * @param samples Number of samples dataset.
+ * @param classes Number of classes in the spiral data.
+ * @param X Output X matrix
+ * @param y Output y matrix
+ *
+ * @return true if @p X and @p y was resized and filled successfully.
+ * @return false if resizing @p X or @p y failed.
+ *
+ * @note This funtion writes the result into an existing vector to avoid
+ *       unnecessary temporary allocations.
+ */
+template <typename T>
+bool spiral_data(const panic::types::uint_t samples, const panic::types::uint_t classes, panic::tensor::matrix<T>& X, panic::tensor::uint_vector& y);
+
+
+
+
+
+	} // namespace neural_network
+} // namespace panic

include/neural_network/datasets/vertical_data.hpp

@@ -0,0 +1,74 @@
+/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ *                                              PANIC
+ *                              Portable Algorithms and Numerics In C++
+ *
+ *                       Scientific computing from scratch, with feeling.
+ *
+ * Copyright (c) 2026 Michelle Bausager
+ *
+ * This file is part of PANIC.
+ *
+ * PANIC is free software licensed under the GNU General Public License v3.0 or later.
+ * You may redistribute and/or modify it under the terms of the GPL.
+ *
+ * PANIC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
+ * without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+ * See the LICENSE file for the full license text.
+ *
+ * SPDX-License-Identifier: GPL-3.0-or-later
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ *
+ * Project Name:      PANIC
+ * Module Name:       neural_network
+ * File Name:         vertical_data.hpp
+ * Revision:          0.1.0
+ * Date:              29-07-2026
+ * Author:            Michelle Bausager
+ *
+ * Description:
+ * Functions to generate dataset for neural networks;
+ *
+ *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
+
+#include <config/types.hpp>
+#include <tensor/vector.hpp>
+#include <tensor/matrix.hpp>
+//---------------------------------------------------------------------------------------------------------------------------
+// INPLEMENTATION
+//---------------------------------------------------------------------------------------------------------------------------
+
+namespace panic {
+	namespace neural_network {
+
+
+/**
+ * @brief Generates a dataset of vertical values
+ *
+ * Computes:
+ * @code
+ * vertical_data(10, 1, X, y)
+ * @endcode
+ * 
+ * @tparam T Numeric element type.
+ * @param samples Number of samples dataset.
+ * @param classes Number of classes in the vertical data.
+ * @param X Output X matrix
+ * @param y Output y matrix
+ *
+ * @return true if @p X and @p y was resized and filled successfully.
+ * @return false if resizing @p X or @p y failed.
+ *
+ * @note This funtion writes the result into an existing vector to avoid
+ *       unnecessary temporary allocations.
+ */
+template <typename T>
+bool vertical_data(const panic::types::uint_t samples, const panic::types::uint_t classes, panic::tensor::matrix<T>& X, panic::tensor::uint_vector& y);
+
+
+
+
+
+	} // namespace neural_network
+} // namespace panic

include/neural_network/model/model.hpp

@@ -118,8 +118,6 @@ struct model{
      */
     bool add(layer* new_layer);
 
-    // Create and add a dense layer.
-
     /**
      * @brief Adds a dense layer to the model.
      *
@@ -140,6 +138,21 @@ struct model{
         panic::types::uint_t neuron_count
     );
 
+    /**
+     * @brief Adds a activation ReLU layer to the model.
+     *
+     * Computes:
+     * @code
+     * model.add_activation_ReLU(3,4);
+     * @endcode
+     *
+     *
+     * @return true if layer is added
+     *
+     * @note This function is convenient, but it allocates a new layer.
+     */
+    bool add_activation_ReLU();
+
     /**
      * @brief Loops over all layers forward function
      *