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#!/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 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", required=True, type=int)
parser.add_argument("-g", "--graph", help="Should we show graphs", 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)
faces_train, faces_test, target_train, target_test = train_test_split(raw_faces, targets, test_size=0.2, random_state=0)
# 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.pca:
# faces_pca containcts the principial components or the M most variant eigenvectors
pca = PCA(n_components=M)
faces_train = pca.fit_transform(faces_train)
faces_test = pca.transform(faces_test)
explained_variances = pca.explained_variance_ratio_
else:
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_
# Plot the variances (eigenvalues) from the pca object
if args.graph:
plt.bar(np.arange(explained_variances.size), explained_variances)
plt.ylabel('Varaiance ratio');plt.xlabel('Face Number')
plt.show()
classifier = KNeighborsClassifier(n_neighbors=3)
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))
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