import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell


class SeriesPredictor:

    def __init__(self, input_dim, seq_size, hidden_dim=10):
        # Hyperparameters
        self.input_dim = input_dim
        self.seq_size = seq_size
        self.hidden_dim = hidden_dim

        # Weight variables and input placeholders
        self.W_out = tf.Variable(tf.random_normal([hidden_dim, 1]), name='W_out')
        self.b_out = tf.Variable(tf.random_normal([1]), name='b_out')
        self.x = tf.placeholder(tf.float32, [None, seq_size, input_dim])
        self.y = tf.placeholder(tf.float32, [None, seq_size])

        # Cost optimizer
        self.cost = tf.reduce_mean(tf.square(self.model() - self.y))
        self.train_op = tf.train.AdamOptimizer().minimize(self.cost)

        # Auxiliary ops
        self.saver = tf.train.Saver()

    def model(self):
        """
        :param x: inputs of size [T, batch_size, input_size]
        :param W: matrix of fully-connected output layer weights
        :param b: vector of fully-connected output layer biases
        """
        cell = rnn_cell.BasicLSTMCell(self.hidden_dim, reuse=tf.get_variable_scope().reuse)
        outputs, states = rnn.dynamic_rnn(cell, self.x, dtype=tf.float32)
        num_examples = tf.shape(self.x)[0]
        W_repeated = tf.tile(tf.expand_dims(self.W_out, 0), [num_examples, 1, 1])
        out = tf.batch_matmul(outputs, W_repeated) + self.b_out
        out = tf.squeeze(out)
        return out

    def train(self, train_x, train_y):
        with tf.Session() as sess:
            tf.get_variable_scope().reuse_variables()
            sess.run(tf.initialize_all_variables())
            for i in range(1000):
                _, mse = sess.run([self.train_op, self.cost], feed_dict={self.x: train_x, self.y: train_y})
                if i % 100 == 0:
                    print(i, mse)
            save_path = self.saver.save(sess, 'Model.ckpt')
            print('Model saved to {}'.format(save_path))

    def test(self, test_x):
        with tf.Session() as sess:
            tf.get_variable_scope().reuse_variables()
            self.saver.restore(sess, 'Model.ckpt')
            output = sess.run(self.model(), feed_dict={self.x: test_x})
            print(output)


if __name__ == '__main__':
    predictor = SeriesPredictor(input_dim=1, seq_size=4, hidden_dim=10)
    train_x = [[[1], [2], [5], [6]],
               [[5], [7], [7], [8]],
               [[3], [4], [5], [7]]]
    train_y = [[1, 3, 7, 11],
               [5, 12, 14, 15],
               [3, 7, 9, 12]]
    predictor.train(train_x, train_y)

    test_x = [[[1], [2], [3], [4]],  # 1, 3, 5, 7
              [[4], [5], [6], [7]]]  # 4, 9, 11, 13
    predictor.test(test_x)
