diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..6320cd248dd8aeaab759d5871f8781b5c0505172
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+data
\ No newline at end of file
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..13566b81b018ad684f3a35fee301741b2734c8f4
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/BE1_IA.iml b/.idea/BE1_IA.iml
new file mode 100644
index 0000000000000000000000000000000000000000..2cdb1e3a2d51c24c7ed6b5c19944eee5ccc31e7c
--- /dev/null
+++ b/.idea/BE1_IA.iml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="JAVA_MODULE" version="4">
+ <component name="NewModuleRootManager" inherit-compiler-output="true">
+ <exclude-output />
+ <content url="file://$MODULE_DIR$">
+ <excludeFolder url="file://$MODULE_DIR$/venv" />
+ </content>
+ <orderEntry type="inheritedJdk" />
+ <orderEntry type="sourceFolder" forTests="false" />
+ </component>
+</module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fc2988f82dba12580aeede017df1f508e7475899
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="Black">
+ <option name="sdkName" value="Python 3.9 (BE1_IA)" />
+ </component>
+ <component name="ProjectRootManager" version="2" languageLevel="JDK_21" project-jdk-name="Python 3.9 (BE1_IA)" project-jdk-type="Python SDK">
+ <output url="file://$PROJECT_DIR$/out" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000000000000000000000000000000000000..43fcbf08cc787fcd5999e4464af079164585f616
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="ProjectModuleManager">
+ <modules>
+ <module fileurl="file://$PROJECT_DIR$/.idea/BE1_IA.iml" filepath="$PROJECT_DIR$/.idea/BE1_IA.iml" />
+ </modules>
+ </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000000000000000000000000000000000000..35eb1ddfbbc029bcab630581847471d7f238ec53
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="VcsDirectoryMappings">
+ <mapping directory="" vcs="Git" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/knn.py b/knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff2624e2c15b746d6c1dd3beb9c0b5b0e2cce06b
--- /dev/null
+++ b/knn.py
@@ -0,0 +1,76 @@
+import numpy as np
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+
+def distance_matrix(matrix1, matrix2):
+ # Calculate the squared norms of each row in the input matrices
+ norms1 = np.sum(matrix1**2, axis=1, keepdims=True)
+ norms2 = np.sum(matrix2**2, axis=1, keepdims=True)
+
+ # Compute the dot product between the matrices
+ dot_product = np.dot(matrix1, matrix2.T)
+
+ # Calculate the L2 Euclidean distance using the hint formula
+ dists = np.sqrt(norms1 - 2 * dot_product + norms2.T)
+
+ return dists
+
+def knn_predict(dists, labels_train, k):
+ # Number of test samples
+ num_test_samples = dists.shape[0]
+
+ # Initialize an array to store the predicted labels
+ predicted_labels = np.zeros(num_test_samples, dtype=labels_train.dtype)
+
+ for i in range(num_test_samples):
+ # Get the distances for the current test sample
+ distances = dists[i]
+
+ # Find the indices of the k nearest neighbors
+ k_nearest_indices = np.argsort(distances)[:k]
+
+ # Get the labels of the k nearest neighbors
+ k_nearest_labels = labels_train[k_nearest_indices]
+
+ # Use np.bincount to count the occurrences of each label
+ # and choose the label with the highest count
+ predicted_label = np.argmax(np.bincount(k_nearest_labels))
+
+ # Assign the predicted label to the current test sample
+ predicted_labels[i] = predicted_label
+
+ return predicted_labels
+
+
+
+def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
+ # Use the previously defined knn_predict function to get predictions
+ predicted_labels = knn_predict(distance_matrix(data_test, data_train), labels_train, k)
+
+ # Calculate the accuracy by comparing predicted labels to actual labels
+ accuracy = accuracy_score(labels_test, predicted_labels)
+
+ return accuracy
+
+split_factor = 0.9
+k_values = range(1, 21)
+accuracies = []
+
+for k in k_values:
+ accuracy = evaluate_knn(data_train, labels_train, data_test, labels_test, k)
+ accuracies.append(accuracy)
+
+# Create the plot
+plt.figure(figsize=(8, 6))
+plt.plot(k_values, accuracies, marker='o')
+plt.title('KNN Accuracy vs. k')
+plt.xlabel('k')
+plt.ylabel('Accuracy')
+plt.grid(True)
+
+# Save the plot as "knn.png" in the "results" directory
+plt.savefig('results/knn.png')
+
+# Show the plot (optional)
+plt.show()
+
diff --git a/main.py.txt b/main.py
similarity index 100%
rename from main.py.txt
rename to main.py
diff --git a/read_cifar.py b/read_cifar.py
new file mode 100644
index 0000000000000000000000000000000000000000..73fe59aab9a1b234c893dcf10b312fd2bca19ef1
--- /dev/null
+++ b/read_cifar.py
@@ -0,0 +1,85 @@
+import numpy as np
+import pickle
+import os
+
+def read_cifar_batch(batch_path):
+ with open(batch_path, 'rb') as file:
+ # Load the batch data
+ batch_data = pickle.load(file, encoding='bytes')
+
+ # Extract data and labels from the batch
+ data = batch_data[b'data'] # CIFAR-10 data
+ labels = batch_data[b'labels'] # Class labels
+
+ # Convert data and labels to the desired data types
+ data = np.array(data, dtype=np.float32)
+ labels = np.array(labels, dtype=np.int64)
+
+ return data, labels
+
+
+def read_cifar(directory_path):
+ data_batches = []
+ label_batches = []
+
+ # Iterate through the batch files in the directory
+ for batch_file in ['data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4', 'data_batch_5', 'test_batch']:
+ batch_path = os.path.join(directory_path, batch_file)
+
+ with open(batch_path, 'rb') as file:
+ # Load the batch data
+ batch_data = pickle.load(file, encoding='bytes')
+
+ # Extract data and labels from the batch
+ data = batch_data[b'data'] # CIFAR-10 data
+ labels = batch_data[b'labels'] # Class labels
+
+ data_batches.append(data)
+ label_batches.extend(labels)
+
+ # Combine all batches into a single data matrix and label vector
+ data = np.concatenate(data_batches, axis=0)
+ labels = np.array(label_batches, dtype=np.int64)
+
+ # Convert data to the desired data type
+ data = data.astype(np.float32)
+
+ return data, labels
+
+def split_dataset(data, labels, split):
+ # Check if the split parameter is within the valid range (0 to 1)
+ if split < 0 or split > 1:
+ raise ValueError("Split must be a float between 0 and 1.")
+
+ # Get the number of samples in the dataset
+ num_samples = len(data)
+
+ # Calculate the number of samples for training and testing
+ num_train_samples = int(num_samples * split)
+ num_test_samples = num_samples - num_train_samples
+
+ # Create a random shuffle order for the indices
+ shuffle_indices = np.random.permutation(num_samples)
+
+ # Use the shuffled indices to split the data and labels
+ data_train = data[shuffle_indices[:num_train_samples]]
+ labels_train = labels[shuffle_indices[:num_train_samples]]
+ data_test = data[shuffle_indices[num_train_samples:]]
+ labels_test = labels[shuffle_indices[num_train_samples:]]
+
+ return data_train, labels_train, data_test, labels_test
+
+
+
+
+
+
+
+
+if __name__ == '__main__':
+ batch_path = "data/cifar-10-python\cifar-10-batches-py\data_batch_1" # Update with your path
+ data, labels = read_cifar_batch(batch_path)
+ print("Data shape:", data.shape)
+ print("Labels shape:", labels.shape)
+
+