ex1_5_3.py

# exercise 1.5.3
import importlib_resources
from scipy.io import loadmat

# You can load the matlab data (matlab's m-file) to Python environment with
# 'loadmat()' function imported from 'scipy.io' module.
# The matlab workspace is loaded as a dictionary, with keys corresponding to
# matlab variable names, and values to arrays representing matlab matrices.

# Load Matlab data file to python dict structure

filename = importlib_resources.files("dtuimldmtools").joinpath("data/iris.mat")
iris_mat = loadmat(filename, squeeze_me=True)
# The argument squeeze_me ensures that there the variables we get from the
# MATLAB filed are not stored within "unneeded" array dimensions.

# You can check which variables are in the loaded dict by calling
# the function keys() for the dict:
# mat_data.keys()
# this will tell you that X, y, M, N and C are stored in the dictionary,
# as well as some extra information about e.g. the used MATLAB version.

# We'll extract the needed variables by using these keys:
X = iris_mat["X"]
y = iris_mat["y"]
M = iris_mat["M"]
N = iris_mat["N"]
C = iris_mat["C"]
attributeNames = iris_mat["attributeNames"]
classNames = iris_mat["classNames"]

# Loading the Iris data from the .mat-file was quite easy, because all the work
# of putting it into the correct format was already done. This is of course
# likely not the case for your own data, where you'll need to do something
# similar to the two previous exercises. We will, however, sometimes in the
# course use .mat-files in the exercises.