utils.py

'''
MIT License

Copyright (c) 2017 Mat Leonard

'''

import numpy as np

def get_lookup_tables(text):
    chars = tuple(set(text))
    int2char = dict(enumerate(chars))
    char2int = {ch: ii for ii, ch in int2char.items()}
    
    return int2char, char2int

def get_batches(arr, n_seqs, n_steps):
    '''Create a generator that returns batches of size
       n_seqs x n_steps from arr.
    '''
    
    batch_size = n_seqs * n_steps
    n_batches = len(arr)//batch_size
    
    # Keep only enough characters to make full batches
    arr = arr[:n_batches * batch_size]
    # Reshape into n_seqs rows
    arr = arr.reshape((n_seqs, -1))
    
    for n in range(0, arr.shape[1], n_steps):
        # The features
        x = arr[:, n:n+n_steps]
        # The targets, shifted by one
        y = np.zeros_like(x)
        try:
            y[:, :-1], y[:, -1] = x[:, 1:], arr[:, n+n_steps]
        except IndexError:
            y[:, :-1], y[:, -1] = x[:, 1:], arr[:, 0]
        yield x, y

def one_hot_encode(arr, n_labels):
    
    # Initialize the the encoded array
    one_hot = np.zeros((np.multiply(*arr.shape), n_labels), dtype=np.float32)
    
    # Fill the appropriate elements with ones
    one_hot[np.arange(one_hot.shape[0]), arr.flatten()] = 1.
    
    # Finally reshape it to get back to the original array
    one_hot = one_hot.reshape((*arr.shape, n_labels))
    
    return one_hot