mkaz

10 Scala One Liners to Impress Your Friends

Here are 10 one-liners which show the power of scala programming, impress your friends and woo women; ok, maybe not. However, these one liners are a good set of examples using functional programming and scala syntax you may not be familiar with. I feel there is no better way to learn than to see real examples.

Updated: June 17, 2011 - I'm amazed at the popularity of this post, glad everyone enjoyed it and to see it duplicated across so many languages. I've included some of the suggestions to shorten up some of my scala examples. Some I intentionally left longer as a way for explaining / understanding what the functions were doing, not necessarily to produce the shortest possible code; so I'll include both.

1. Multiple Each Item in a List by 2

The map function takes each element in the list and applies it to the corresponding function. In this example, we take each element and multiply it by 2. This will return a list of equivalent size, compare to o

giuseppebonaccorso

import numpy as np
import tensorflow as tf

# Set random seed for reproducibility
np.random.seed(1000)

nb_users = 5000
nb_products = 2000
nb_factors = 500
max_rating = 5

L0SG

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim


# toy feed-forward net
class Net(nn.Module):
    def __init__(self):

thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...