#!/usr/bin/env python
import os
import tensorflow as tf

# initialize variables/model parameters
W = tf.Variable(tf.zeros([4, 3]), name="weights")
b = tf.Variable(tf.zeros([3]), name="bias")

def read_csv(batch_size, file_name, record_defaults):
    full_path = os.path.dirname(os.path.realpath(__file__)) + "/" + file_name
    filename_queue = tf.train.string_input_producer([full_path])
    reader = tf.TextLineReader(skip_header_lines=1)
    key, value = reader.read(filename_queue)

    # decode_csv will convert a Tensor from type string (the text line) in
    # a tuple of tensor columns with the specified defaults, which also
    # sets the data type for each column
    decoded = tf.decode_csv(value, record_defaults=record_defaults)

    # batch actually reads the file and loads "batch_size" rows in a single
    # tensor
    return tf.train.shuffle_batch(decoded,
                                  batch_size=batch_size,
                                  capacity=batch_size * 50,
                                  min_after_dequeue=batch_size)

def inference(X):
    # compute inference model over data X and return the result
    return tf.nn.softmax(tf.matmul(X, W) + b)

def loss(X, Y):
    # compute loss over training data X and expected outputs Y
    return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
        tf.matmul(X, W) + b, Y))

def inputs():
    sepal_length, sepal_width, petal_length, petal_width, label =\
        read_csv(100, "iris.data", [[0.0], [0.0], [0.0], [0.0], [""]])

    # convert class names to a 0 based class index.
    label_number = tf.to_int32(tf.argmax(tf.to_int32(tf.pack([
        tf.equal(label, ["Iris-setosa"]),
        tf.equal(label, ["Iris-versicolor"]),
        tf.equal(label, ["Iris-virginica"])
    ])), 0))

    # Pack all the features that we care about in a single matrix;
    # We then transpose to have a matrix with one example per row and one
    # feature per column.
    features = tf.transpose(
        tf.pack([sepal_length, sepal_width, petal_length, petal_width]))

    return features, label_number


def train(total_loss):
    # train / adjust model parameters according to computed total loss
    learning_rate = 0.01
    return tf.train.GradientDescentOptimizer(learning_rate).minimize(
        total_loss)


def evaluate(sess, X, Y):
    # evaluate the resulting trained model
    predicted = tf.cast(tf.arg_max(inference(X), 1), tf.int32)
    return sess.run(tf.reduce_mean(tf.cast(tf.equal(predicted, Y),
                                           tf.float32)))

# Create a saver.
# saver = tf.train.Saver()

# Launch the graph in a session, setup boilerplate
with tf.Session() as sess:
    tf.initialize_all_variables().run()
    X, Y = inputs()
    total_loss = loss(X, Y)
    train_op = train(total_loss)

    #coord = tf.train.Coordinator()
    #threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    # actual training loop
    training_steps = 1000
    for step in range(training_steps):
        sess.run([train_op])
        # for debugging and learning purposes, see how the loss gets decremented
        # through training steps
        if step % 10 == 0:
            print "loss at step ", step, ":", sess.run([total_loss])
        # save training checkpoints in case loosing them
        # if step % 1000 == 0:
        #     saver.save(sess, 'my-model', global_step=step)

    print evaluate(sess, X, Y)
    # coord.request_stop()
    # coord.join(threads)
    # saver.save(sess, 'my-model', global_step=training_steps)