K-Means Algorithm

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs
X, y_true = make_blobs(n_samples=100, centers =
4,Cluster_std=0.60,random_state=0)
X = X[:, ::-1]
#flip axes for better plotting
from sklearn.mixture import GaussianMixture
gmm = GaussianMixture (n_components = 4).fit(X)
lables = gmm.predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap=‟viridis‟);
probs = gmm.predict_proba(X)
print(probs[:5].round(3))
size = 50 * probs.max(1) ** 2 # square emphasizes differences
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap=‟viridis‟, s=size);
from matplotlib.patches import Ellipse
def draw_ellipse(position, covariance, ax=None, **kwargs);
“””Draw an ellipse with a given position and covariance”””
Ax = ax or plt.gca()
# Convert covariance to principal axes
if covariance.shape ==(2,2):
U, s, Vt = np.linalg.svd(covariance)
Angle = np.degrees(np.arctan2(U[1, 0], U[0,0]))
Width, height = 2 * np.sqrt(s)
else:
angle = 0
width, height = 2 * np.sqrt(covariance)
#Draw the Ellipse
for nsig in range(1,4):
ax.add_patch(Ellipse(position, nsig * width, nsig *height,
angle, **kwargs))
def plot_gmm(gmm, X, label=True, ax=None):
ax = ax or plt.gca()
labels = gmm.fit(X).predict(X)
if label:
ax.scatter(X[:, 0], x[:, 1], c=labels, s=40, cmap=‟viridis‟, zorder=2)
else:
ax.scatter(X[:, 0], x[:, 1], s=40, zorder=2)
ax.axis(„equal‟)
w_factor = 0.2 / gmm.weights_.max()
for pos, covar, w in zip(gmm.means_, gmm.covariances_, gmm.weights_):
draw_ellipse(pos, covar, alpha=w * w_factor)
gmm = GaussianMixture(n_components=4, random_state=42)
plot_gmm(gmm, X)
gmm = GaussianMixture(n_components=4, covariance_type=‟full‟,
random_state=42)
plot_gmm(gmm, X)

// K-means-algorithm

from sklearn.cluster import KMeans
#from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
data=pd.read_csv("kmeansdata.csv")
df1=pd.DataFrame(data)
print(df1)
f1 = df1['Distance_Feature'].values
f2 = df1['Speeding_Feature'].values
X=np.matrix(list(zip(f1,f2)))
plt.plot()
plt.xlim([0, 100])
plt.ylim([0, 50])
plt.title('Dataset')
plt.ylabel('speeding_feature')
plt.xlabel('Distance_Feature')
plt.scatter(f1,f2)
plt.show()
# create new plot and data
plt.plot()
colors = ['b', 'g', 'r']
markers = ['o', 'v', 's']
# KMeans algorithm
#K = 3
kmeans_model = KMeans(n_clusters=3).fit(X)
plt.plot()
for i, l in enumerate(kmeans_model.labels_):
plt.plot(f1[i], f2[i], color=colors[l], marker=markers[l],ls='None')
plt.xlim([0, 100])
plt.ylim([0, 50])
plt.show()