def batch_iter(y, tx, batch_size, num_batches=1, shuffle=True): """ Generate a minibatch iterator for a dataset. Takes as input two iterables (here the output desired values 'y' and the input data 'tx') Outputs an iterator which gives mini-batches of `batch_size` matching elements from `y` and `tx`. Data can be randomly shuffled to avoid ordering in the original data messing with the randomness of the minibatches. Example of use : for minibatch_y, minibatch_tx in batch_iter(y, tx, 32): """ data_size =len(y) if shuffle: shuffle_indices = np.random.permutation(np.arange(data_size)) shuffled_y = y[shuffle_indices] shuffled_tx = tx[shuffle_indices] else: shuffled_y = y shuffled_tx = tx for batch_num inrange(num_batches): start_index = batch_num * batch_size end_index =min((batch_num + 1) * batch_size, data_size) if start_index != end_index: yield shuffled_y[start_index:end_index], shuffled_tx[start_index:end_index]
我实现了以下两个功能：
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def compute_stoch_gradient(y, tx, w): """Compute a stochastic gradient for batch data.""" e = y - tx.dot(w) return(-1/y.shape[0])*tx.transpose().dot(e) def stochastic_gradient_descent(y, tx, initial_w, batch_size, max_epochs, gamma): """Stochastic gradient descent algorithm.""" ws =[initial_w] losses =[] w = initial_w for n_iter inrange(max_epochs): for minibatch_y,minibatch_x in batch_iter(y,tx,batch_size): w = ws[n_iter] - gamma * compute_stoch_gradient(minibatch_y,minibatch_x,ws[n_iter]) ws.append(np.copy(w)) loss = y - tx.dot(w) losses.append(loss) return losses, ws
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