#!/usr/bin/python3.6
#python 3 syntax
import array as ar
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
#from lr_utils import load_dataset

#TEST_CASE = True
TEST_CASE = False

def load_dataset():
    train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r") #train_dataset is multi dim array. list or numpy array?
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features. 4D array shape=(209, 64, 64, 3)
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels. 1D array shape =(209,)
    
    test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features. 4D array shape=(50, 64, 64, 3)
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels. 1D array shape =(50,)

    classes = np.array(test_dataset["list_classes"][:]) # the list of classes. 1D array shape=(2,) = [b'non-cat' b'cat']
    print("train = ",train_dataset, "test = ",test_dataset, test_set_y_orig, "classes = ",classes,classes.shape,classes.dtype)

    print("OLD", train_set_x_orig.shape, train_set_y_orig.shape, test_set_x_orig.shape, test_set_y_orig.shape)
    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
    print("NEW", train_set_x_orig.shape, train_set_y_orig.shape, test_set_x_orig.shape, test_set_y_orig.shape)
    #print("Y = ", train_set_y_orig, test_set_y_orig)
    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

def sigmoid(z):
    ### START CODE HERE ### (≈ 1 line of code)
    s = 1/(1+np.exp(-z))
    ### END CODE HERE ###    
    return s

if (TEST_CASE):
    print ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2]))))

def initialize_with_zeros(dim):
    """
    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.
    
    Argument:
    dim -- size of the w vector we want (or number of parameters in this case)
    
    Returns:
    w -- initialized vector of shape (dim, 1)
    b -- initialized scalar (corresponds to the bias)
    """
    
    ### START CODE HERE ### (≈ 1 line of code)
    w = np.zeros((dim,1))
    b = 0
    ### END CODE HERE ###

    assert(w.shape == (dim, 1)), "shape of w is not dim,1"
    assert(isinstance(b, float) or isinstance(b, int))
    
    return w, b

if (TEST_CASE):
    dim = 2
    w, b = initialize_with_zeros(dim)
    print ("w = " + str(w))
    print ("b = " + str(b))

def propagate(w, b, X, Y):
    """
    Implement the cost function and its gradient for the propagation explained above

    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of size (num_px * num_px * 3, number of examples)
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)

    Return:
    cost -- negative log-likelihood cost for logistic regression
    dw -- gradient of the loss with respect to w, thus same shape as w
    db -- gradient of the loss with respect to b, thus same shape as b
    
    Tips:
    - Write your code step by step for the propagation. np.log(), np.dot()
    """
    
    m = X.shape[1]
    
    # FORWARD PROPAGATION (FROM X TO COST)
    ### START CODE HERE ### (≈ 2 lines of code)
    A = sigmoid(np.dot(w.T,X)+b)                                              # compute activation
    cost = -1/m*(np.dot(Y,np.log(A.T)) + np.dot((1-Y),np.log((1-A).T)))       # compute cost
    ### END CODE HERE ###
    
    # BACKWARD PROPAGATION (TO FIND GRAD)
    ### START CODE HERE ### (≈ 2 lines of code)
    dw = 1/m*(np.dot(X,(A-Y).T))
    db = 1/m*(np.sum(A-Y))
    ### END CODE HERE ###

    assert(dw.shape == w.shape),"shape of dw and w do not match"
    assert(db.dtype == float)
    cost = np.squeeze(cost)
    assert(cost.shape == ()),"cost is not a scalar but a vector"
    
    grads = {"dw": dw,
             "db": db}
    
    return grads, cost

if (TEST_CASE):
     w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])
     grads, cost = propagate(w, b, X, Y)
     print ("dw = " + str(grads["dw"]))
     print ("db = " + str(grads["db"]))
     print ("cost = " + str(cost))

# GRADED FUNCTION: optimize
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):
    """
    This function optimizes w and b by running a gradient descent algorithm
    
    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of shape (num_px * num_px * 3, number of examples)
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)
    num_iterations -- number of iterations of the optimization loop
    learning_rate -- learning rate of the gradient descent update rule
    print_cost -- True to print the loss every 100 steps
    
    Returns:
    params -- dictionary containing the weights w and bias b
    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function
    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.
    
    Tips:
    You basically need to write down two steps and iterate through them:
        1) Calculate the cost and the gradient for the current parameters. Use propagate().
        2) Update the parameters using gradient descent rule for w and b.
    """
    
    costs = []
    
    for i in range(num_iterations):
        
        
        # Cost and gradient calculation (≈ 1-4 lines of code)
        ### START CODE HERE ### 
        grads, cost = propagate(w, b, X, Y)
        ### END CODE HERE ###
        
        # Retrieve derivatives from grads
        dw = grads["dw"]
        db = grads["db"]
        
        # update rule (≈ 2 lines of code)
        ### START CODE HERE ###
        w = w - learning_rate*dw
        b = b - learning_rate*db
        ### END CODE HERE ###
        
        # Record the costs
        if i % 100 == 0:
            costs.append(cost)
        
        # Print the cost every 100 training iterations
        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
    
    params = {"w": w,
              "b": b}
    
    grads = {"dw": dw,
             "db": db}
    
    return params, grads, costs

if (TEST_CASE):
    params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = True)
    print ("w = " + str(params["w"]))
    print ("b = " + str(params["b"]))
    print ("dw = " + str(grads["dw"]))
    print ("db = " + str(grads["db"]))

# GRADED FUNCTION: predict

def predict(w, b, X):
    '''
    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)
    
    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of size (num_px * num_px * 3, number of examples)
    
    Returns:
    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X
    '''
    print("shape of X = ",X.shape,"shape of w=",w.shape,"shape of X[0]=",X.shape[0],"shape of X[1]=",X.shape[1])
    m = X.shape[1]
    Y_prediction = np.zeros((1,m))
    w = w.reshape(X.shape[0], 1)
    
    # Compute vector "A" predicting the probabilities of a cat being present in the picture
    ### START CODE HERE ### (≈ 1 line of code)
    A = sigmoid(np.dot(w.T,X)+b)    
    ### END CODE HERE ###
    
    for i in range(A.shape[1]):
        
        # Convert probabilities A[0,i] to actual predictions p[0,i]
        ### START CODE HERE ### (≈ 4 lines of code)
        if (A[0,i] > 0.5):
         Y_prediction[0,i] = 1
        else:
         Y_prediction[0,i] = 0
        #if (m==1):
        # print("i=",i,"A[0,i]=",A[0,i],"Y_pred[0,i]=",Y_prediction[0,i])
        ### END CODE HERE ###
    
    assert(Y_prediction.shape == (1, m)),"shape of prediction is not 1,m"
    
    return Y_prediction, A

if (TEST_CASE):
    w = np.array([[0.1124579],[0.23106775]])
    b = -0.3
    X = np.array([[1.,-1.1,-3.2],[1.2,2.,0.1]])
    print ("predictions = " + str(predict(w, b, X)))

# GRADED FUNCTION: model

def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    """
    Builds the logistic regression model by calling the function you've implemented previously
    
    Arguments:
    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
    print_cost -- Set to true to print the cost every 100 iterations
    
    Returns:
    d -- dictionary containing information about the model.
    """
    
    ### START CODE HERE ###
    
    # initialize parameters with zeros (≈ 1 line of code)
    w, b = initialize_with_zeros(X_train.shape[0])

    # Gradient descent (≈ 1 line of code)
    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    
    # Retrieve parameters w and b from dictionary "parameters"
    w = parameters["w"]
    b = parameters["b"]
    
    # Predict test/train set examples (≈ 2 lines of code)
    Y_prediction_test, A_prediction_test = predict(w, b, X_test)
    Y_prediction_train, A_prediction_train = predict(w, b, X_train)
    ### END CODE HERE ###

    #print
    #print("train pred A ",A_prediction_train)
    #print("train pred Y ",Y_prediction_train)
    #print("train actl Y ",Y_train)
    #print("test pred A ",A_prediction_test)
    #print("test pred Y ",Y_prediction_test)
    #print("test actl Y ",Y_test)

    # Print train/test Errors
    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))

    
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test, 
         "Y_prediction_train" : Y_prediction_train, 
         "w" : w, 
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}
    
    return d

#####################
#start of main program
#######################


# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()

# Example of a picture
choice = "Y"
while (choice == "Y"):
 index = int(input("Enter index of picture "))
 plt.imshow(train_set_x_orig[index])
 plt.imshow(test_set_x_orig[index])
 plt.show()
 print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") +  "' picture.")

 #flatten array of image
 train_set_x_flatten = np.transpose(train_set_x_orig.reshape(train_set_x_orig.shape[0],train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3]))
 test_set_x_flatten = np.transpose(test_set_x_orig.reshape(test_set_x_orig.shape[0],test_set_x_orig.shape[1]*test_set_x_orig.shape[2]*test_set_x_orig.shape[3]))
 #train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0],-1).T
 #test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T

 print ("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))
 print ("train_set_y shape: " + str(train_set_y.shape))
 print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape))
 print ("test_set_y shape: " + str(test_set_y.shape))
 print ("sanity check after reshaping: " + str(train_set_x_flatten[0:5,0]))

 #normalize array
 train_set_x = train_set_x_flatten/255
 test_set_x = test_set_x_flatten/255

 #model = run the model on training set to get optimal w,b
 d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1000, learning_rate = 0.005, print_cost = True)

 # Example of a picture that was wrongly classified.
 #index = 1
 num_px=64
 #plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))
 #plt.imshow(test_set_x_orig[1])
 #print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[d["Y_prediction_test"][0,index]].decode("utf-8") +  "\" picture.")
 #plt.show()
 
 # Plot learning curve (with costs)
 costs = np.squeeze(d['costs'])
 plt.plot(costs)
 plt.ylabel('cost')
 plt.xlabel('iterations (per hundreds)')
 plt.title("Learning rate =" + str(d["learning_rate"]))
 plt.show()

 learn_choice = input("Try diff learn rates (Y/N)? ")
 if(learn_choice == "Y"):
     learning_rates = [0.01, 0.001, 0.0001]
     models = {}
     for i in learning_rates:
       print ("learning rate is: " + str(i))
       models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 10000, learning_rate = i, print_cost = False)
       print ('\n' + "-------------------------------------------------------" + '\n')

     for i in learning_rates:
        plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))

     plt.ylabel('cost')
     plt.xlabel('iterations (hundreds)')

     legend = plt.legend(loc='upper center', shadow=True)
     frame = legend.get_frame()
     frame.set_facecolor('0.90')
     plt.show()


 #loop to read your own cat/non-cat images
 image_choice = input("Try diff images (Y/N)? ")
 if(image_choice == "Y"):
    num_pic=10
    for i in range(1,num_pic+1):
        ## START CODE HERE ## (PUT YOUR IMAGE NAME) 
        #my_image = "my_image.jpg"   # change this to the name of your image file 
        my_image = "cat_ex" + str(i) + ".jpg"
        ## END CODE HERE ##
        
        # We preprocess the image to fit your algorithm.
        fname = "/home/user/coding/coursera/AI_projects/" + my_image
        #image = np.array(ndimage.imread(fname, flatten=False))
        #image = plt.imread('my_image.jpg')
        #im = Image.open('my_image.jpg')
        im = Image.open(fname)
        image = np.array(im.resize((num_px,num_px))) #returns in H X W X C = 64x64x3
        #img = Image.fromarray(image)
        #print("img size=",img.size)
        #img.show()
        #print("image type",type(image),type(image.dtype),image.shape,image)
        image = image/255
        if (i==1):
            image_arr = image.reshape(1,image.shape[0],image.shape[1],image.shape[2])
            print("Orig Image:", image.shape,"New image",image_arr.shape)
        else:
            image = image.reshape(1,image.shape[0],image.shape[1],image.shape[2])
            image_arr = np.append(image_arr,image,axis=0)
            print("Orig Image:", image.shape,"New image",image_arr.shape)

    #my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T
    #my_image = image.reshape(1,train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3]).T
    my_image = image_arr.reshape(num_pic,train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3]).T

    my_predicted_image, A_predicted_image = predict(d["w"], d["b"], my_image)

    #try to recover image from array
    tmp_image = ((my_image.T)[index]).reshape(64,64,3)
    plt.imshow(tmp_image)

    #print predictions for test images
    #print("y = " + str(np.squeeze(my_predicted_image)) + ", your algorithm predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture. A=",A_predicted_image)
    print("prediction A",A_predicted_image)
    print("prediction Y",my_predicted_image)
    plt.show()

 choice=input("continue Y/N? ")
#end of pgm
print ("Bye")