Add cnn model

DIRECTORY.md

@@ -688,6 +688,7 @@
   * [Apriori Algorithm](machine_learning/apriori_algorithm.py)
   * [Astar](machine_learning/astar.py)
   * [Automatic Differentiation](machine_learning/automatic_differentiation.py)
+  * [Cnn](machine_learning/cnn.py)
   * [Data Transformations](machine_learning/data_transformations.py)
   * [Decision Tree](machine_learning/decision_tree.py)
   * [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
@@ -712,14 +713,17 @@
   * [Loss Functions](machine_learning/loss_functions.py)
   * Lstm
     * [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
+  * [Mean Shift](machine_learning/mean_shift.py)
   * [Mfcc](machine_learning/mfcc.py)
   * [Mini Batch Gradient Descent](machine_learning/mini_batch_gradient_descent.py)
   * [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
+  * [Naive Bayes Text Classification](machine_learning/naive_bayes_text_classification.py)
   * [Polynomial Regression](machine_learning/polynomial_regression.py)
   * [Principle Component Analysis](machine_learning/principle_component_analysis.py)
   * [Q Learning](machine_learning/q_learning.py)
   * [Random Forest Classifier](machine_learning/random_forest_classifier.py)
   * [Random Forest Regressor](machine_learning/random_forest_regressor.py)
+  * [Rmsprop](machine_learning/rmsprop.py)
   * [Scoring Functions](machine_learning/scoring_functions.py)
   * [Self Organizing Map](machine_learning/self_organizing_map.py)
   * [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
@@ -736,6 +740,7 @@
   * [Arc Length](maths/arc_length.py)
   * [Area](maths/area.py)
   * [Area Under Curve](maths/area_under_curve.py)
+  * [Autocorrelation](maths/autocorrelation.py)
   * [Average Absolute Deviation](maths/average_absolute_deviation.py)
   * [Average Mean](maths/average_mean.py)
   * [Average Median](maths/average_median.py)
@@ -781,6 +786,7 @@
   * [Fibonacci](maths/fibonacci.py)
   * [Find Max](maths/find_max.py)
   * [Find Min](maths/find_min.py)
+  * [First Fundamental Form](maths/first_fundamental_form.py)
   * [Floor](maths/floor.py)
   * [Gamma](maths/gamma.py)
   * [Gaussian](maths/gaussian.py)
@@ -843,6 +849,7 @@
     * [Square Root](maths/numerical_analysis/square_root.py)
     * [Weierstrass Method](maths/numerical_analysis/weierstrass_method.py)
   * [Odd Sieve](maths/odd_sieve.py)
+  * [Padovan Sequence](maths/padovan_sequence.py)
   * [Pell Number](maths/pell_number.py)
   * [Perfect Cube](maths/perfect_cube.py)
   * [Perfect Number](maths/perfect_number.py)
@@ -872,6 +879,8 @@
   * [Reverse Factorial Recursive](maths/reverse_factorial_recursive.py)
   * [Segmented Sieve](maths/segmented_sieve.py)
   * Series
+    * [Alternate Harmonic Series](maths/series/alternate_harmonic_series.py)
+    * [Alternating Harmonic Series](maths/series/alternating_harmonic_series.py)
     * [Arithmetic](maths/series/arithmetic.py)
     * [Geometric](maths/series/geometric.py)
     * [Geometric Series](maths/series/geometric_series.py)
@@ -911,6 +920,7 @@
     * [Polygonal Numbers](maths/special_numbers/polygonal_numbers.py)
     * [Pronic Number](maths/special_numbers/pronic_number.py)
     * [Proth Number](maths/special_numbers/proth_number.py)
+    * [Spy Number](maths/special_numbers/spy_number.py)
     * [Triangular Numbers](maths/special_numbers/triangular_numbers.py)
     * [Trimorphic Number](maths/special_numbers/trimorphic_number.py)
     * [Ugly Numbers](maths/special_numbers/ugly_numbers.py)

machine_learning/cnn.py

@@ -0,0 +1,67 @@
+"""
+Convolutional Neural Network (CNN) implementation for image classification.
+
+Reference: https://en.wikipedia.org/wiki/Convolutional_neural_network
+
+>>> import numpy as np
+>>> model = SimpleCNN(input_shape=(1, 28, 28), num_classes=10)
+>>> dummy_input = np.random.rand(1, 28, 28)
+>>> output = model.forward(dummy_input)
+>>> output.shape
+(10,)
+"""
+
+import numpy as np
+
+
+class SimpleCNN:
+    def __init__(self, input_shape: tuple[int, int, int], num_classes: int) -> None:
+        """
+        Initialize a simple CNN model.
+
+        Args:
+            input_shape: Tuple of (channels, height, width)
+            num_classes: Number of output classes
+        """
+        self.input_shape = input_shape
+        self.num_classes = num_classes
+        rng = np.random.default_rng()
+        self.filters = rng.normal(0, 0.1, size=(8, input_shape[0], 3, 3))  # 8 filters
+        self.fc_weights = rng.normal(0, 0.1, size=(8 * 26 * 26, num_classes))
+
+    def relu(self, feature_map: np.ndarray) -> np.ndarray:
+        """Apply ReLU activation to the feature map."""
+        return np.maximum(0, feature_map)
+
+    def convolve(self, input_tensor: np.ndarray, filters: np.ndarray) -> np.ndarray:
+        """Apply convolution operation to the input tensor."""
+        _, height, width = input_tensor.shape
+        num_filters, _, fh, fw = filters.shape
+        output = np.zeros((num_filters, height - fh + 1, width - fw + 1))
+
+        for f in range(num_filters):
+            for i in range(height - fh + 1):
+                for j in range(width - fw + 1):
+                    region = input_tensor[:, i : i + fh, j : j + fw]
+                    output[f, i, j] = np.sum(region * filters[f])
+        return output
+
+    def flatten(self, feature_map: np.ndarray) -> np.ndarray:
+        """Flatten the feature map into a 1D array."""
+        return feature_map.reshape(-1)
+
+    def forward(self, input_tensor: np.ndarray) -> np.ndarray:
+        """
+        Forward pass through the CNN.
+
+        Args:
+            input_tensor: Input image of shape (channels, height, width)
+
+        Returns:
+            Output logits of shape (num_classes,)
+        """
+        conv_out = self.convolve(input_tensor, self.filters)
+        activated = self.relu(conv_out)
+        flattened = self.flatten(activated)
+        logits = flattened @ self.fc_weights
+        return logits