ellisbrown

import sys
from collections import defaultdict
from tqdm import tqdm
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials

CLIENT_ID = "9f64faf742764cf48d556ecc64ea2b1e"
CLIENT_SECRET = "56c58af2d3d845beb7102a466dd4f8a7"
USERNAME = ""

ellisbrown

def name_to_tensor(name, cuda=False):
    """converts a name to a vectorized numerical input for use with a nn
    each character is converted to a one hot (n, 1, 26) tensor
    Args:
        name (string): first name (e.g., "Ellis")
    Return:
        tensor (torch.tensor)
    """
    name = clean_str(name)
    tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

ellisbrown

class RNN(nn.Module):
    """Recurrent Neural Network
    original source: https://goo.gl/12wiKB
    Simple implementation of an RNN with two linear layers and a LogSoftmax
    layer on the output
    Args:
        input_size: (int) size of data
        hidden_size: (int) number of hidden units
        output_size: (int) size of output
    """

ellisbrown

def gender_features(name):
    features = {}
    features["last_letter"] = name[-1].lower()
    features["first_letter"] = name[0].lower()
    # names ending in -yn are mostly female, names ending in -ch are mostly male, so add 3 more features
    features["suffix2"] = name[-2:]
    features["suffix3"] = name[-3:]
    features["suffix4"] = name[-4:]
return features