Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>

numpy/knn/README.md

@@ -1,4 +1,4 @@
-# ABC
+# K Nearest Neighbour
 {:.no_toc}
 
 <nav markdown="1" class="toc-class">
@@ -10,13 +10,74 @@
 
 Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
 
-{: .topic-optional}
-This is an optional topic!
-
+## Test and train data
 
 ```python
+import numpy as np
+
+rng = np.random.default_rng(1)
+
+a_x: np.ndarray = rng.normal(1.5, 1.0, size=(1000))[:, np.newaxis]
+a_y: np.ndarray = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis]
+data_train_0: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
+class_train_0: np.ndarray = np.full((data_train_0.shape[0],), -1)
+
+a_x = rng.normal(1.5, 1.0, size=(1000))[:, np.newaxis]
+a_y = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis]
+data_test_0: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
+class_test_0: np.ndarray = np.full((data_test_0.shape[0],), -1)
+del a_x
+del a_y
+
+a_x = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
+a_y = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
+data_train_1: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
+class_train_1: np.ndarray = np.full((data_train_0.shape[0],), +1)
+
+a_x = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
+a_y = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
+data_test_1: np.ndarray = np.concatenate((a_x, a_y), axis=-1)
+class_test_1: np.ndarray = np.full((data_test_0.shape[0],), +1)
+del a_x
+del a_y
+
+data_train: np.ndarray = np.concatenate((data_train_0, data_train_1), axis=0)
+data_test: np.ndarray = np.concatenate((data_test_0, data_test_1), axis=0)
+label_train: np.ndarray = np.concatenate((class_train_0, class_train_1), axis=0)
+label_test: np.ndarray = np.concatenate((class_test_0, class_test_1), axis=0)
+
+np.save("data_train.npy", data_train)
+np.save("data_test.npy", data_test)
+np.save("label_train.npy", label_train)
+np.save("label_test.npy", label_test)
+```
+
+## Train and test
+
+```python
+import numpy as np
+
+k: int = 3
+
+data_train = np.load("data_train.npy")
+data_test = np.load("data_test.npy")
+label_train = np.load("label_train.npy")
+label_test = np.load("label_test.npy")
+
+prediction = np.zeros((data_test.shape[0]), dtype=np.int8)
+
+for id in range(0, label_test.shape[0]):
+    distance = ((data_train - data_test[id, :][np.newaxis, :]) ** 2).sum(axis=-1)
+
+    recall = label_train[np.argsort(distance)[:k]]
+    if (recall == -1).sum() > (recall == 1).sum():
+        prediction[id] = -1
+    else:
+        prediction[id] = +1
+
+performance = 100.0 * (prediction == label_test).sum() / prediction.shape[0]
+
+print(f"Performance correct: {performance}%") # -> Performance correct: 95.1%
 ```
 
 
-```shell
-```