k-means-seeds

%matplotlib inline
import csv
import numpy as np
import scipy.cluster.vq as vq
import matplotlib.pyplot as plt

bank_csv = csv.reader(open('seeds_dataset.txt','rU'), delimiter="\t")
data = []

# Read data
for row in bank_csv:
    missing = False
    float_arr = []
    for cell in row:
        if not cell:
            missing = True
            break
        else:
            # Convert each cell to float
            float_arr.append(float(cell))
    # Take row if row is not missing data
    if not missing:
        data.append(float_arr)
            

data = np.array(data)
    
# Normalize vectors
whitened = vq.whiten(data)

# Perform k means on all features to classify into 3 groups
centroids, _ = vq.kmeans(whitened, 3)

# Classify data by distance to centroids
cls, _ = vq.vq(whitened, centroids)

# Plot first two features (area vs perimter in this case)
plt.plot(data[cls==0,0], data[cls==0,1],'ob',
        data[cls==1,0], data[cls==1,1],'or',
        data[cls==2,0], data[cls==2,1],'og')
plt.show()