import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


# Data Preparation

x1_label0 = np.random.normal(1, 1, (100, 1))
x2_label0 = np.random.normal(1, 1, (100, 1))
x1_label1 = np.random.normal(5, 1, (100, 1))
x2_label1 = np.random.normal(4, 1, (100, 1))
x1_label2 = np.random.normal(8, 1, (100, 1))
x2_label2 = np.random.normal(0, 1, (100, 1))

xs_label0 = np.hstack((x1_label0, x2_label0))
xs_label1 = np.hstack((x1_label1, x2_label1))
xs_label2 = np.hstack((x1_label2, x2_label2))
xs = np.vstack((xs_label0, xs_label1, xs_label2))

labels = np.matrix([[1., 0., 0.]] * len(x1_label0)
                   + [[0., 1., 0.]] * len(x1_label1)
                   + [[0., 0., 1.]] * len(x1_label2))

arr = np.arange(xs.shape[0])
np.random.shuffle(arr)
xs = xs[arr, :]
labels = labels[arr, :]

test_x1_label0 = np.random.normal(1, 1, (10, 1))
test_x2_label0 = np.random.normal(1, 1, (10, 1))
test_x1_label1 = np.random.normal(5, 1, (10, 1))
test_x2_label1 = np.random.normal(4, 1, (10, 1))
test_x1_label2 = np.random.normal(8, 1, (10, 1))
test_x2_label2 = np.random.normal(0, 1, (10, 1))

test_xs_label0 = np.hstack((test_x1_label0, test_x2_label0))
test_xs_label1 = np.hstack((test_x1_label1, test_x2_label1))
test_xs_label2 = np.hstack((test_x1_label2, test_x2_label2))

test_xs = np.vstack((test_xs_label0, test_xs_label1, test_xs_label2))

test_labels = np.matrix([[1., 0., 0.]] * len(test_x1_label0)
                   + [[0., 1., 0.]] * len(test_x1_label1)
                   + [[0., 0., 1.]] * len(test_x1_label2))

train_size, num_features = xs.shape

# Training

learning_rate = 0.01
training_epochs = 1000
num_labels = 3
batch_size = 100

X = tf.placeholder("float", shape=[None, num_features])
Y = tf.placeholder("float", shape=[None, num_labels])

W = tf.Variable(tf.zeros([num_features, num_labels]))
b = tf.Variable(tf.zeros([num_labels]))
y_model = tf.nn.softmax(tf.matmul(X, W) + b)

cost = -tf.reduce_sum(Y * tf.log(y_model))
train_op = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for step in range(training_epochs * train_size // batch_size):
        offset = (step * batch_size) % train_size
        batch_xs = xs[offset: (offset + batch_size), :]
        batch_labels = labels[offset: (offset + batch_size)]
        err, _ = sess.run([cost, train_op], feed_dict={
            X: batch_xs,
            Y: batch_labels
        })
        print(step, err)
    W_val = sess.run(W)
    print("weights", W_val)
    b_val = sess.run(b)
    print("b", b_val)
    print("accuracy", sess.run(accuracy, feed_dict={
        X: test_xs,
        Y: test_labels
    }))

