# -*- coding: utf-8 -*-
"""
Created on Tue Dec  3 15:48:32 2019

@author: Eric
"""
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt


sns.set_style("whitegrid")

blue, = sns.color_palette("muted", 1)

def plot_mnist(elts, m, n, name = -99):
    """Plot MNIST images in an m by n table. Note that we crop the images
    so that they appear reasonably close together.  Note that we are
    passed raw MNIST data and it is reshaped.

    Example: plot_mnist(X_train, 10, 10)
    """
    fig = plt.figure()
    images = [elt.reshape(28, 28) for elt in elts]
    img = np.concatenate([np.concatenate([images[m*y+x] for x in range(m)], axis=1)
                          for y in range(n)], axis=0)
    ax = fig.add_subplot(1, 1, 1)
    ax.matshow(img, cmap = matplotlib.cm.binary)
    for spine in plt.gca().spines.values():
        spine.set_visible(False)
    plt.xticks(np.array([]))
    plt.yticks(np.array([]))
    plt.show()
    if (name > -2):
        title = "fig_"
        name = str(name)
        title = title + name + ".png"
        plt.savefig(title)


def PCA(X):
    n,m = X.shape
    #assert np.allclose(X.mean(axis=0),np.zeros(m))

    #Data is centered, Get Covariance
    coVar = np.dot(X.T, X) / (n -1)
    #coVar = np.cov(X)
    #get eigens
    eVal, eVec = np.linalg.eig(coVar)

    xPca = np.dot(X, eVec)

    return xPca, eVal, eVec


def main():
    X_train = np.load('../Data/X_train.npy')
    X_val = np.load('../Data/X_val.npy')

    Y_train = np.load('../Data/Y_train.npy')

    #X_train = X_train.astype(np.float64)
    m = X_train.mean(axis = 0)

    X = X_train - m
    #print(X_train.shape)
    #print(Y_train.shape)
    """
    meanImage = m.reshape(28,28)
    plt.axis('off')
    plt.imshow(meanImage,cmap = matplotlib.cm.binary)
    """
    avgImages = []

    """
    for i in range(10):
        meanDig = X_train[Y_train == i].mean(axis=0)
        print (meanDig.shape)
        meanDig = meanDig.reshape(28,28)
        avgImages.append(meanDig)

    plot_mnist(avgImages,5,2)
    """

    xPca, eVal, eVec = PCA(X)

    for i in range(5):
        eigImg = eVec[i].reshape(28,28)
        avgImages.append(eigImg)

    plot_mnist(avgImages,5,1)
    """
    print(eVal.shape)
    numPC = np.arange(eVal.shape[0])[:25]
    largest = eVal[:25]
    fig, ax = plt.subplots()
    ax.plot(numPC, largest, color = blue, lw = 3)
    ax.set_ylabel("Eigen Values")
    ax.set_xlabel("PCA Number")
    ax.fill_between(numPC,0,largest, alpha = .25)
    ax.set(xlim=(0, len(numPC) - 1), ylim=(0, None))
    """




if __name__ == '__main__':
    main()