Initial Commit

kmeans-from-scratch-and-sklearn.py

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+from copy import deepcopy
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+plt.rcParams['figure.figsize'] = (16, 9)
+plt.style.use('ggplot')
+
+# Importing the dataset
+data = pd.read_csv('xclara.csv')
+print("Input Data and Shape")
+print(data.shape)
+data.head()
+
+# Getting the values and plotting it
+f1 = data['V1'].values
+f2 = data['V2'].values
+X = np.array(list(zip(f1, f2)))
+plt.scatter(f1, f2, c='black', s=7)
+
+# Euclidean Distance Caculator
+def dist(a, b, ax=1):
+    return np.linalg.norm(a - b, axis=ax)
+
+# Number of clusters
+k = 3
+# X coordinates of random centroids
+C_x = np.random.randint(0, np.max(X)-20, size=k)
+# Y coordinates of random centroids
+C_y = np.random.randint(0, np.max(X)-20, size=k)
+C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
+print("Initial Centroids")
+print(C)
+
+# Plotting along with the Centroids
+plt.scatter(f1, f2, c='#050505', s=7)
+plt.scatter(C_x, C_y, marker='*', s=200, c='g')
+
+# To store the value of centroids when it updates
+C_old = np.zeros(C.shape)
+# Cluster Lables(0, 1, 2)
+clusters = np.zeros(len(X))
+# Error func. - Distance between new centroids and old centroids
+error = dist(C, C_old, None)
+# Loop will run till the error becomes zero
+while error != 0:
+    # Assigning each value to its closest cluster
+    for i in range(len(X)):
+        distances = dist(X[i], C)
+        cluster = np.argmin(distances)
+        clusters[i] = cluster
+    # Storing the old centroid values
+    C_old = deepcopy(C)
+    # Finding the new centroids by taking the average value
+    for i in range(k):
+        points = [X[j] for j in range(len(X)) if clusters[j] == i]
+        C[i] = np.mean(points, axis=0)
+    error = dist(C, C_old, None)
+
+colors = ['r', 'g', 'b', 'y', 'c', 'm']
+fig, ax = plt.subplots()
+for i in range(k):
+        points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])
+        ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])
+ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')
+
+
+
+'''
+==========================================================
+scikit-learn
+==========================================================
+'''
+
+from sklearn.cluster import KMeans
+
+# Number of clusters
+kmeans = KMeans(n_clusters=3)
+# Fitting the input data
+kmeans = kmeans.fit(X)
+# Getting the cluster labels
+labels = kmeans.predict(X)
+# Centroid values
+centroids = kmeans.cluster_centers_
+
+# Comparing with scikit-learn centroids
+print("Centroid values")
+print("Scratch")
+print(C) # From Scratch
+print("sklearn")
+print(centroids) # From sci-kit learn

kmeans-sklearn.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.cluster import KMeans
+from sklearn.datasets import make_blobs
+
+plt.rcParams['figure.figsize'] = (16, 9)
+
+# Creating a sample dataset with 4 clusters
+X, y = make_blobs(n_samples=800, n_features=3, centers=4)
+fig = plt.figure()
+ax = Axes3D(fig)
+ax.scatter(X[:, 0], X[:, 1], X[:, 2])
+
+# Initializing KMeans
+kmeans = KMeans(n_clusters=4)
+# Fitting with inputs
+kmeans = kmeans.fit(X)
+# Predicting the clusters
+labels = kmeans.predict(X)
+# Getting the cluster centers
+C = kmeans.cluster_centers_
+
+fig = plt.figure()
+ax = Axes3D(fig)
+ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y)
+ax.scatter(C[:, 0], C[:, 1], C[:, 2], marker='*', c='#050505', s=1000)