feat: comment before class

lab5/code/main.py

@@ -1,14 +1,28 @@
+# numpy for loading dataset
 import numpy as np
+# pytorch for deep learning models
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-# load the dataset, split into input (X) and output (y) variables
+# Pima indians describes pateitn medical data and whether they had diabetes for last 5 years
+# It is binary classification (they could either have diabetes 1 or not 0)
+# load the file as a matrix of numbers,
 dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',')
+# split into input (X) -> in this case everything beside info whether patient had diabetes or not is input
+# We are spliting data into two subsets by using NumPy slie operator : and choose first 8 columns using 0:8 slice
 X = dataset[:,0:8]
+# and output (y) variables -> in this case we are only interested whether patient had diabetes or not as an output
+# you can simplify that y = f(X)
+# We are spliting the data by using slice operator : and choosing last column
 y = dataset[:,8]
 
+# we need to convert this data to pytorch tensors 
+# Pyutoarch usually operates on 32-bit floating point and NumPy by default uses 64 bit floating point
 X = torch.tensor(X, dtype=torch.float32)
+# We can also correct the shape to fit what PyTorch would expect (here we are converting n vectors to n x 1 matrix)
+# This simplifies handling matrix multiplication operations (which are the basis of deep learning models)
+# reshape is converting the output variable y from a 1-dimensional NumPy array to a 2-dimensional PyTorch tensor with a shape of (n, 1), where n is the number of samples in the dataset.
 y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)
 
 # define the model