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kmeans-test.py
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kmeans-test.py
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import numpy as np
import matplotlib.pyplot as plt
import kmeans
def uniformTwoD():
m = 100 # sample size
n = 2 # number of features
K = 3 # number of clusters
X = np.random.uniform(0, 100, (m,n))
clusterings = kmeans.run(X, K)
def uniformThreeD():
m = 200 # sample size
n = 3 # number of features
K = 4 # number of clusters
X = np.random.uniform(0, 100, (m,n))
clusterings = kmeans.run(X, K)
def clusteredTwoD():
m = 100 # sample size
n = 2 # number of features
K = 3 # number of clusters
X = np.zeros((0,n))
centersOfMass = np.random.uniform(0, 100, (K,n))
for i in centersOfMass:
stdDev = 12
samples = np.random.normal(i, stdDev, (int(m / K), n))
X = np.append(X, samples, axis = 0)
clusterings = kmeans.run(X, K)
def clusteredThreeD():
m = 200 # sample size
n = 3 # number of features
K = 4 # number of clusters
X = np.zeros((0,n))
centersOfMass = np.random.uniform(0, 100, (K,n))
for i in centersOfMass:
stdDev = 12
samples = np.random.normal(i, stdDev, (int(m / K), n))
X = np.append(X, samples, axis = 0)
clusterings = kmeans.run(X, K)
clusteredTwoD()