seanjtaylor

jinglescode

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer


class TransformerModel(nn.Module):

    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):

kaustubhn

# Implementation of a chinese restaurant process function for a given list of word vectors.
def crp(vecs):
    clusterVec = [[0.0] * 25]         # tracks sum of vectors in a cluster
    clusterIdx = [[]]        # array of index arrays. e.g. [[1, 3, 5], [2, 4, 6]]
    ncluster = 0
    # probablity to create a new table if new customer
    # is not strongly "similar" to any existing table
    pnew = 1.0/ (1 + ncluster)  
    N = len(vecs)
    rands = [random.random() for x in range(N)]         # N rand variables sampled from U(0, 1)