# -*- coding: utf-8 -*-
"""
Created on Fri Jan 18 18:14:40 2019

@author: Dcm
"""
"""Planar data classification with one hidden layer"""
import numpy as np
import matplotlib.pyplot as plt
from testCases_v2 import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets

np.random.seed(1) # set a seed so that the results are consistent

def load_planar_dataset():
    np.random.seed(1)
    m = 400 # 样本数量
    N = int(m/2) # 每个类别的样本量
    D = 2 # 特征维度数
    X = np.zeros((m,D)) # 初始化X
    Y = np.zeros((m,1), dtype='uint8') # 初始化Y
    a = 4 # 花儿的最大长度
    for j in range(2):
        ix = range(N*j,N*(j+1))
        t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta
        r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
        Y[ix] = j
    X = X.T
    Y = Y.T
    return X, Y

X, Y = load_planar_dataset()
# Visualize the data:
#plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);

# =============================================================================
# shape_X = X.shape
# shape_Y = Y.shape
# m = X.shape[1] 
# print ('The shape of X is: ' + str(shape_X))
# print ('The shape of Y is: ' + str(shape_Y))
# print ('I have m = %d training examples!' % (m))
# =============================================================================

def plot_decision_boundary(model, X, y):
    # Set min and max values and give it some padding
    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole grid
    Z = model(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    # Plot the contour and training examples
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    plt.ylabel('x2')
    plt.xlabel('x1')
    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)

# =============================================================================
# clf = sklearn.linear_model.LogisticRegressionCV()
# clf.fit(X.T, Y.T)
# plot_decision_boundary(lambda x: clf.predict(x), X, Y)
# plt.title("Logistic Regression")
# LR_predictions = clf.predict(X.T)
# print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +
#        '% ' + "(percentage of correctly labelled datapoints)")
# =============================================================================

"""4.1 - Defining the neural network structure"""
def layer_sizes(X, Y):
    n_x = X.shape[0]  # the size of the input layer
    n_h = 4  # the size of the hidden layer
    n_y = Y.shape[0]  # the size of the output layer
    return(n_x, n_h, n_y)
    
"""4.2 - Initialize the model’s parameters"""
def initialize_parameters(n_x, n_h, n_y):
    np.random.seed(2)
    W1 = np.random.randn(n_h, n_x)
    b1 = np.zeros((n_h, 1))
    W2 = np.random.randn(n_y, n_h)
    b2 = np.zeros((n_y, 1))
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    return parameters

"""4.3 - The Loop"""
def forward_propagation(X, parameters):
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]
    Z1 = np.dot(W1, X) + b1
    A1 = np.tanh(Z1)
    Z2 = np.dot(W2, A1) + b2
    A2 = sigmoid(Z2)
    assert(A2.shape == (1, X.shape[1]))
    cache = {"Z1": Z1,
             "A1": A1,
             "Z2": Z2,
             "A2": A2}
    return A2, cache

def compute_cost(A2, Y, parameters):
    m = Y.shape[1]  # 样本数量
    logprobs = np.multiply(np.log(A2), Y) + np.multiply(np.log(1-A2), (1-Y))
    cost = -(1.0/m)*np.sum(logprobs)
    cost = np.squeeze(cost)
    assert(isinstance(cost, float))
    return cost
# =============================================================================
# A2, Y_assess, parameters = compute_cost_test_case()
# print("cost = " + str(compute_cost(A2, Y_assess, parameters)))
# =============================================================================

def backward_propagation(parameters, cache, X, Y):
    m = X.shape[1]
    W1 = parameters["W1"]
    W2 = parameters["W2"]
    A1 = cache["A1"]
    A2 = cache["A2"]
    # 计算梯度
    dZ2 = A2 - Y
    dW2 = 1/m * np.dot(dZ2, A1.T)
    db2 = 1/m * np.sum(dZ2, axis=1, keepdims=True)
    dZ1 = np.dot(W2.T, dZ2) * (1 - np.power(A1, 2))
    dW1 = 1/m * np.dot(dZ1, X.T)
    db1 = 1/m * np.sum(dZ1, axis=1, keepdims=True)
    grads = {"dW1": dW1,
             "db1": db1,
             "dW2": dW2,
             "db2": db2}
    return grads
    
def update_parameters(parameters, grads, learning_rate = 1.2):
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]
    dW1 = grads["dW1"]
    db1 = grads["db1"]
    dW2 = grads["dW2"]
    db2 = grads["db2"]
    W1 = W1 - learning_rate * dW1
    b1 = b1 - learning_rate * db1
    W2 = W2 - learning_rate * dW2
    b2 = b2 - learning_rate * db2
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    return parameters

def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):
    np.random.seed(3)
    n_x = layer_sizes(X, Y)[0]
    n_y = layer_sizes(X, Y)[2]
    # 初始化参数
    parameters = initialize_parameters(n_x, n_h, n_y)
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]
    for i in range(num_iterations):
        A2, cache = forward_propagation(X, parameters)
        cost = compute_cost(A2, Y, parameters)
        grads = backward_propagation(parameters, cache, X, Y)
        parameters = update_parameters(parameters, grads, learning_rate = 1.2)
        if print_cost and i % 1000 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
    return parameters

# =============================================================================
# X_assess, Y_assess = nn_model_test_case()
# parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=True)
# print("W1 = " + str(parameters["W1"]))
# print("b1 = " + str(parameters["b1"]))
# print("W2 = " + str(parameters["W2"]))
# print("b2 = " + str(parameters["b2"]))
# =============================================================================
    
"""4.5 Predictions"""
def predict(parameters, X):
    A2, cache = forward_propagation(X, parameters)
    predictions = (A2 > 0.5)
    return predictions

# =============================================================================
# # Build a model with a n_h-dimensional hidden layer
# parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)
# # Plot the decision boundary
# plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
# plt.title("Decision Boundary for hidden layer size " + str(4))
# predictions = predict(parameters, X)
# print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')\
# =============================================================================

"""4.6 - Tuning hidden layer size (optional/ungraded exercise)"""
# =============================================================================
# plt.figure(figsize=(16, 32))
# hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]
# for i, n_h in enumerate(hidden_layer_sizes):
#     plt.subplot(5, 2, i+1)
#     plt.title('Hidden Layer of size %d' % n_h)
#     parameters = nn_model(X, Y, n_h, num_iterations = 5000)
#     plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
#     predictions = predict(parameters, X)
#     accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)
#     print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy))
# =============================================================================
    
"""Performance on other datasets"""
def load_extra_datasets():  
    N = 200
    noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
    noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
    blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
    gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
    no_structure = np.random.rand(N, 2), np.random.rand(N, 2)

    return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

# Datasets
noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()

datasets = {"noisy_circles": noisy_circles,
            "noisy_moons": noisy_moons,
            "blobs": blobs,
            "gaussian_quantiles": gaussian_quantiles}

### START CODE HERE ### (choose your dataset)
dataset = "noisy_moons"
### END CODE HERE ###

X, Y = datasets[dataset]
X, Y = X.T, Y.reshape(1, Y.shape[0])

# make blobs binary
if dataset == "blobs":
    Y = Y%2

# Visualize the data
plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);

