%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import random
from IPython import display
from sklearn import datasets, preprocessing

(X, y) = datasets.make_circles(n_samples=1024, shuffle=True, noise=0.2, factor=0.4)
ind = np.logical_or(y==1, X[:,1] > X[:,0] - 0.5)
X = X[ind,:]
X = preprocessing.scale(X)
y = y[ind]
y = 2*y - 1
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.show()

h = 0.01
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
def visualize(X, y, w, loss, n_iter):
    plt.clf()
    Z = classify(np.c_[xx.ravel(), yy.ravel()], w)
    Z = Z.reshape(xx.shape)
    plt.subplot(1,2,1)
    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.subplot(1,2,2)
    plt.plot(loss)
    plt.grid()
    ymin, ymax = plt.ylim()
    plt.ylim(0, ymax)
    display.clear_output(wait=True)
    display.display(plt.gcf())

"""
Explanation: Homework 2 (Linear models, Optimization)
In this homework you will implement a simple linear classifier using numpy and your brain.
Two-dimensional classification
End of explanation
"""


def expand(X):
    X_ = np.zeros((X.shape[0], 6))
    X_[:,0:2] = X
    X_[:,2:4] = X**2
    X_[:,4] = X[:,0] * X[:,1]
    X_[:,5] = 1
    return X_

def classify(X, w):
    """
    Given feature matrix X [n_samples,2] and weight vector w [6],
    return an array of +1 or -1 predictions
    """
    #<your code here>


"""
Explanation: Your task starts here
First, let's write a function that predicts class for given X.
Since the problem above isn't linearly separable, we add quadratic features to the classifier. This transformation is implemented in the expand function.
Don't forget to expand X inside classify and other functions
Sample classification should not be much harder than computation of sign of dot product.
End of explanation
"""


def compute_loss(X, y, w):
    """
    Given feature matrix X [n_samples,2], target vector [n_samples] of +1/-1,
    and weight vector w [6], compute scalar loss function using formula above.
    """
    #<your code here>

    
def compute_grad(X, y, w):
    """
    Given feature matrix X [n_samples,2], target vector [n_samples] of +1/-1,
    and weight vector w [6], compute vector [6] of derivatives of L over each weights.
    """
    #<your code here>


"""
Explanation: The loss you should try to minimize is the Hinge Loss:
$$ L =  {1 \over N} \sum_{i=1}^N max(0,1-y_i \cdot  w^T x_i) $$
End of explanation
"""


w = np.array([1,0,0,0,0,0])

alpha = 0.0 # learning rate

n_iter = 50
batch_size = 4
loss = np.zeros(n_iter)
plt.figure(figsize=(12,5))
for i in range(n_iter):
    ind = random.sample(range(X.shape[0]), batch_size)
    loss[i] = compute_loss(X, y, w)
    visualize(X[ind,:], y[ind], w, loss, n_iter)
    
    w = w - alpha * compute_grad(X[ind,:], y[ind], w)

visualize(X, y, w, loss, n_iter)
plt.clf()

"""
Explanation: Training
Find an optimal learning rate for gradient descent for given batch size. 
You can see the example of correct output below this cell before you run it.
Don't change the batch size!
End of explanation
"""


w = np.array([1,0,0,0,0,0])

alpha = 0.0 # learning rate
mu    = 0.0 # momentum

n_iter = 50
batch_size = 4
loss = np.zeros(n_iter)
plt.figure(figsize=(12,5))
for i in range(n_iter):
    ind = random.sample(range(X.shape[0]), batch_size)
    loss[i] = compute_loss(X, y, w)
    visualize(X[ind,:], y[ind], w, loss, n_iter)
    
    #<update w and anything else here>

visualize(X, y, w, loss, n_iter)
plt.clf()

"""
Explanation: Implement gradient descent with momentum and test it's performance for different learning rate and momentum values.
End of explanation
"""


w = np.array([1,0,0,0,0,0])

alpha = 0.0 # learning rate
mu    = 0.0 # momentum

n_iter = 50
batch_size = 4
loss = np.zeros(n_iter)
plt.figure(figsize=(12,5))
for i in range(n_iter):
    ind = random.sample(range(X.shape[0]), batch_size)
    loss[i] = compute_loss(X, y, w)
    visualize(X[ind,:], y[ind], w, loss, n_iter)
    
    #<update w and anything else here>

visualize(X, y, w, loss, n_iter)
plt.clf()

"""
Explanation: Same task but for Nesterov's accelerated gradient:
End of explanation
"""


w = np.array([1,0,0,0,0,0])

alpha = 0.0 # learning rate
beta = 0.0  # (beta1 coefficient in original paper) exponential decay rate for the 1st moment estimates
mu   = 0.0  # (beta2 coefficient in original paper) exponential decay rate for the 2nd moment estimates
eps = 1e-8  # A small constant for numerical stability

n_iter = 50
batch_size = 4
loss = np.zeros(n_iter)
plt.figure(figsize=(12,5))
for i in range(n_iter):
    ind = random.sample(range(X.shape[0]), batch_size)
    loss[i] = compute_loss(X, y, w)
    visualize(X[ind,:], y[ind], w, loss, n_iter)
    
    #<update w and anything else here>

visualize(X, y, w, loss, n_iter)
plt.clf()

"""
Explanation: Finally, try Adam algorithm. You can start with beta = 0.9 and mu = 0.999
End of explanation
"""


