singhrahuldps

// Print names using enhanced for loop
List<String> names = List.of("Rahul", "Virat", "Sourav");
for(String name:names) {
	System.out.println(name);
}

singhrahuldps

// Print a pyramid pattern using for loop
for(int i = 1; i <= 5; i++) {
	for(int j = 1; j <= i; j++) {
		System.out.printf("%d ", j);
	}
	System.out.print("\n");
}

singhrahuldps

// Program to print first 10 odd numbers using while loop
int i = 1, count = 0;
while(count != 10) {
	if(i%2==1) {
		count++;
		System.out.printf("%d ", i);
  }
  i++;
}

singhrahuldps

// Program to perform summation on two numbers using do-while loop
int a, b, c;
Scanner scanner = new Scanner(System.in);
do {
	System.out.println("Enter first number: ");
	a = scanner.nextInt();
	System.out.println("Enter second number: ");
	b = scanner.nextInt();
	System.out.printf("Sum is %d \n", a + b);
	System.out.println("Press 1 to add more numbers, 0 to exit: ");

singhrahuldps

# Making life easier with fast.ai
# required library -> fastai (https://github.com/fastai/fastai/)
from fastai.collab import *
from fastai.tabular import *

# loading the dataframe
ratings = pd.read_csv('ratings.csv')

# creating data object
data = CollabDataBunch.from_df(ratings, seed=42, valid_pct=0.1)

singhrahuldps

def recommend_item_for_user(model, user_id):
    m = model.eval().cpu()
    user_ids = torch.LongTensor([user2idx[u] for u in [user_id]*len(items)])
    item_ids = torch.LongTensor([item2idx[b] for b in items])
    remove = set(ratings[ratings[user_col] == user_id][item_col].values)
    preds = m(user_ids,item_ids).detach().numpy()
    pred_item = [(p,b) for p,b in sorted(zip(preds,items), reverse = True) if b not in remove]
    return pred_item

def recommend_user_for_item(model, item_id):

singhrahuldps

train(epochs=20, bs)

singhrahuldps

def train(epochs = 10, bs = 64):
    for epoch in range(epochs):
        
        # training the model
        i=0
        total_loss = 0.0
        ct = 0
        while i < len(data[0]):
            x1,x2,y = get_batch(data[0],i,i+bs)
            i+=bs

singhrahuldps

# create a model object
# y_range has been extended(0-11) than required(1-10) to make the
# values lie in the linear region of the sigmoid function
model = EmbeddingModel(10, len(users), len(items), [0,11], initialise = 0.01).cuda()

# split the data, returns a list [train, valid]
data = get_data(ratings, 0.1)

# loss = mean((target_rating - predicted_rating)**2)
loss_function = nn.MSELoss()

singhrahuldps

# neural net based on Embedding matrices
# model reference -> https://github.com/fastai/fastai/
class EmbeddingModel(nn.Module):
    def __init__(self, n_factors, n_users, n_items, y_range, initialise = 0.01):
        super().__init__()
        self.y_range = y_range
        self.u_weight = nn.Embedding(n_users, n_factors)
        self.i_weight = nn.Embedding(n_items, n_factors)
        self.u_bias = nn.Embedding(n_users, 1)
        self.i_bias = nn.Embedding(n_items, 1)