MLProj2.py

# exercise 2.1.1
import numpy as np
import xlrd

# Load xls sheet with data
doc = xlrd.open_workbook('/home/morten/Documents/DTU/BscEl/4Sem/Machine Learning/Labs/MLProj1/wage2.xls').sheet_by_index(0)

# Extract attribute names (1st row, column 4 to 12)
attributeNames = doc.row_values(0, 1, 17)

# Extract class names to python list,
# then encode with integers (dict)
classLabels = doc.col_values(0, 2, 936)
classNames = sorted(set(classLabels))
classDict = dict(zip(classNames, range(5)))

# Extract vector y, convert to NumPy matrix and transpose
y = np.mat([classDict[value] for value in classLabels]).T

# Preallocate memory, then extract excel data to matrix X
X = np.mat(np.empty((90, 8)))
for i, col_id in enumerate(range(3, 11)):
    X[:, i] = np.mat(doc.col_values(col_id, 2, 92)).T

# Compute values of N, M and C.
N = len(y)
M = len(attributeNames)
C = len(classNames)

print('Ran Exercise 2.1.1')