diff --git a/A1/README.md b/A1/README.md
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+# A1
+
+
+## a)
+
+
+## b)
+Es wurden die initialen Centeroiden P7, P8, P9 gewählt.
+Zu sehen ist, dass ein anderes Cluster entsteht.
+Die Cluster aus A1a) machen jedoch mehr Sinn, da dort tatsächlich drei Cluster zu sehen sind.
+Bei dem unten stehenden Bild sind die zwei Unterend Punkte-Wolden disjunkt aber dennoch in einem Cluster.
+
+
+## c)
+Ja es kommt auf die Reihenfolge an.
+Spiegelt man die Reihenfolge der Datan P1, ..., P10 zu P10, ..., P1, und lässt man die Center P3, P4 und P8 gleich, so sieht man, dass die
+Cluster-Label anders sind.
+Die Reihenfolge in der die Daten mit den Centroiden verglichen werden, wird durch das Spiegeln der Daten auch gespiegelt.
+
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diff --git a/A1/images/a.jpg b/A1/images/a.jpg
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diff --git a/A1/images/c.jpg b/A1/images/c.jpg
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diff --git a/A1/kmeans.py b/A1/kmeans.py
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+from typing import List
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def euclidean(vector1: List, vector2: List) -> float:
+ """
+ This method calculates the euclidean distance.
+
+ :param vector1: Vector as list
+ :param vector2: Vector as list
+ :return: Euclidean distance between vector1 and vector2.
+ """
+ return np.linalg.norm(np.subtract(vector1, vector2))
+
+
+def pairwise_arg_min(X: List, Y: List) -> np.ndarray:
+ """
+ This method returns a list of all pairwise distances from X to Y.
+
+ :param X: Vector with features
+ :param Y: Centroids
+ :return: List of all pairwise distances from X to Y.
+ """
+ return np.asarray([np.argmin([euclidean(x, y) for y in Y]) for x in X])
+
+
+def find_clusters_with_fix_init_centers(X, n_clusters, centers):
+ """
+ This method finds all clusters.
+
+ :param centers: pre defined centers
+ :param X: Data to be clustered
+ :param n_clusters: amount of clusters
+ :return: All labels for clustering and the centroids.
+ """
+ while True:
+ labels = pairwise_arg_min(X, centers)
+ new_centers = np.array([X[labels == i].mean(0) for i in range(n_clusters)])
+
+ if np.all(centers == new_centers):
+ break
+ centers = new_centers
+
+ return centers, labels
+
+
+if __name__ == '__main__':
+ X = np.array([[1, 1],
+ [1, 4],
+ [2, 2],
+ [10, 3],
+ [11, 2],
+ [11, 4],
+ [4, 12],
+ [6, 11],
+ [7, 10],
+ [8, 10]])
+
+ centers, labels = find_clusters_with_fix_init_centers(X, 3, [X[2], X[3], X[7]])
+ plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
+ plt.scatter(centers[:, 0], centers[:, 1], c='Red', marker='X')
+ plt.savefig('./images/a.jpg', dpi=300, bbox_inches='tight')
+ plt.close()
+
+ centers, labels = find_clusters_with_fix_init_centers(X, 3, [X[6], X[7], X[8]])
+ plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
+ plt.scatter(centers[:, 0], centers[:, 1], c='Red', marker='X')
+ plt.savefig('./images/b.jpg', dpi=300, bbox_inches='tight')
+ plt.close()
+
+ X = X[::-1]
+ centers, labels = find_clusters_with_fix_init_centers(X, 3, [X[2], X[3], X[7]])
+ plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
+ plt.scatter(centers[:, 0], centers[:, 1], c='Red', marker='X')
+ plt.savefig('./images/c.jpg', dpi=300, bbox_inches='tight')
+ plt.close()
diff --git a/A2/README.md b/A2/README.md
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index 8e62dcfa72811b1e3c2a533aa1a4dbfdda42c94a..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,16 +0,0 @@
-# Histograms
-
-| Histogram
-:---------------------------------------------:|
- |
- |
- |
- |
- |
- |
-
-Es lässt sich erkenne, dass beide Histograme ähnlich arbeiten.
-Mit der Partitionierung ist es möglich zu erkennen in welchen Intervall sich Farben häufen.
-Mit dem einfachen Histogram ist es nur möglich das gesamte Sprektrum zu begutachten.
-
-Durch die Partitionierung können so Intervalle festgelegt werden in denen sich besonders viele Farben/Feature häufen.
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diff --git a/A2/test.py b/A2/test.py
deleted file mode 100644
index d39b4cae449f69ae5151e8e72d8eda0a01c92250..0000000000000000000000000000000000000000
--- a/A2/test.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import numpy as np
-import cv2 as cv
-from matplotlib import pyplot as plt
-
-img = cv.imread('./images/martian/martian.jpg', 0)
-# Initiate ORB detector
-orb = cv.ORB_create()
-# find the keypoints with ORB
-kp = orb.detect(img, None)
-# compute the descriptors with ORB
-kp, des = orb.compute(img, kp)
-key_points = [k.pt for k in kp]
-
-# draw only keypoints location,not size and orientation
-img2 = cv.drawKeypoints(img, kp, None, color=(0, 255, 0), flags=0)
-# plt.scatter(*zip(*key_points))
-plt.imshow(img2)
-# plt.show()
-
-from sklearn.metrics import pairwise_distances_argmin
-
-
-def find_clusters(X, n_clusters, rseed=2):
- # 1. Randomly choose clusters
- rng = np.random.RandomState(rseed)
- i = rng.permutation(X.shape[0])[:n_clusters]
- centers = X[i]
-
- while True:
- # 2a. Assign labels based on closest center
- labels = pairwise_distances_argmin(X, centers)
-
- # 2b. Find new centers from means of points
- new_centers = np.array([X[labels == i].mean(0)
- for i in range(n_clusters)])
-
- # 2c. Check for convergence
- if np.all(centers == new_centers):
- break
- centers = new_centers
-
- return centers, labels
-
-
-X = np.array([list(x) for x in key_points])
-
-centers, labels = find_clusters(X, 3)
-plt.scatter(X[:, 0], X[:, 1], marker='o', c=labels,
- s=50, cmap='viridis')
-
-plt.scatter(centers[:, 0], centers[:, 1], marker='+', color='red')
-plt.show()