import torch
import torch.nn as nn
import numpy as np


# if you changed the MLP architecture during training, change it also here:
class MLP(torch.nn.Module):
    def __init__(self, input_size, xcol="emb", ycol="avg_rating"):
        super().__init__()
        self.input_size = input_size
        self.xcol = xcol
        self.ycol = ycol
        self.layers = nn.Sequential(
            nn.Linear(self.input_size, 1024),
            # nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(1024, 128),
            # nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(128, 64),
            # nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(64, 16),
            # nn.ReLU(),
            nn.Linear(16, 1),
        )

    def forward(self, x):
        return self.layers(x)


def normalized(a, axis=-1, order=2):
    l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
    l2[l2 == 0] = 1
    return a / np.expand_dims(l2, axis)