#!/usr/bin/env python
# Train a model from sample data
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework

import matplotlib.pyplot as plt
import sys

from random import randint

from sklearn.neighbors import KNeighborsClassifier
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix  
from sklearn.metrics import accuracy_score

import argparse
import numpy as np

from numpy import genfromtxt
# from numpy import linalg as LA

# subtract the normal face from each row of the face matrix
def normalise_faces(average_face, raw_faces):
    return np.subtract(raw_faces, np.tile(average_face, (raw_faces.shape[1],1)).T)


# usage: train.py [-h] -i DATA -o MODEL [-m M]
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--data", help="Input CSV file", required=True)
parser.add_argument("-m", "--eigen", help="Number of eigenvalues in model", type=int, default = 8 )
parser.add_argument("-n", "--neighbors", help="How many neighbors to use", type=int, default = 6)
parser.add_argument("-f", "--faces", help="Show faces", type=int, default = 0)
parser.add_argument("-c", "--principal", help="Show principal components", action='store_true')
parser.add_argument("-s", "--seed", help="Seed to use", type=int, default=0)
parser.add_argument("-t", "--split", help="Fractoin of data to use for testing", type=float, default=0.25)
parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus principal components", action='store_true')
parser.add_argument("-p", "--pca", help="Use PCA", action='store_true')
parser.add_argument("-l", "--lda", help="Use LDA", action='store_true')
args = parser.parse_args()

if args.pca and args.lda:
    sys.exit("Flags -p and -l are mutually exclusive")

M = args.eigen

raw_faces = genfromtxt(args.data, delimiter=',')
targets = np.repeat(np.arange(10),52)

split = 100

faces_train, faces_test, target_train, target_test = train_test_split(raw_faces, targets, test_size=args.split, random_state=args.seed)


# This remove the mean and scales to unit variance
sc = StandardScaler()
faces_train = sc.fit_transform(faces_train)
faces_test = sc.transform(faces_test)

explained_variances = ()
if args.lda:
    lda = LinearDiscriminantAnalysis(n_components=M)
    faces_train = lda.fit_transform(faces_train, target_train)
    faces_test = lda.transform(faces_test)
    explained_variances = lda.explained_variance_ratio_
else:
    # faces_pca containcts the principial components or the M most variant eigenvectors
    pca = PCA(svd_solver='full', n_components=M)
    faces_train = pca.fit_transform(faces_train)
    faces_test = pca.transform(faces_test)
    explained_variances = pca.explained_variance_ratio_
# Plot the variances (eigenvalues) from the pca object
if args.faces:
    if args.lda:
        sys.exit("Can not plot eigenfaces when using LDA")
    for i in range(args.faces):
        ax = plt.subplot(2, args.faces/2, i + 1)
        ax.imshow(pca.components_[i].reshape([46, 56]))
    plt.show()

if args.principal:
    plt.bar(np.arange(explained_variances.size), explained_variances)
    plt.ylabel('Varaiance ratio');plt.xlabel('Face Number')
    plt.show()

if args.grapheigen:
    # Colors for distinct individuals
    cols = ['#{:06x}'.format(randint(0, 0xffffff)) for i in range(10)]
    pltCol = [cols[int(k)] for k in target_train]
    plt.scatter(faces_train[:, 0], faces_train[:, 1], color=pltCol)
    plt.show()

classifier = KNeighborsClassifier(n_neighbors=args.neighbors)
classifier.fit(faces_train, target_train)
target_pred = classifier.predict(faces_test)

cm = confusion_matrix(target_test, target_pred)  
print(cm)  
print('Accuracy %fl' % accuracy_score(target_test, target_pred))