import scipy.io

from sklearn.svm import SVC, LinearSVC

from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier

import numpy

from sklearn.externals import joblib

from sklearn.metrics import accuracy_score

from sklearn.grid_search import GridSearchCV

from sklearn.neighbors import DistanceMetric

attribute_mapping = [

]

AttributeClassifier = joblib.load('Dumps/AttributeClassifierKnowledgeTransfer.pkl')

features = scipy.io.loadmat("./UIUC1/UIUC1_win_feature.mat")

labels = scipy.io.loadmat("./UIUC1/UIUC1_labels.mat")

action_actor = open("./UIUC1/action_actor.txt")

dist = DistanceMetric.get_metric('euclidean')

mapping = [{}]

for line in action_actor:

line = line.split()

actionvector = numpy.zeros(14, dtype=numpy.int)

actionvector[int(line[0])]=1

mapping.append({'action':int(line[0]),'actionvector':actionvector, 'actor':int(line[1])})

total = len(labels['vlabels'][0])

ConfusionMatrix=numpy.array([[0,0],[0,0]])

NovelClassList=[[0,1],[2,3],[4,5],[6,7],[8,9],[10,11],[12,13]]

for NovelClass in NovelClassList:

ConfusionMatrix2=numpy.array([[0,0],[0,0]])

TRAIN_LABELS = []

TRAIN_FEATURES = []

TRAIN_ATTRIBUTE = []

TEST_LABELS = []

TEST_FEATURES = []

TEST_ATTRIBUTE = []

for feature,label in zip(features['win_feature'],labels['vlabels'][0]):

if mapping[int(label)]['action'] not in NovelClass:

# if label<463:

TRAIN_LABELS.append(mapping[int(label)]['action'])

TRAIN_FEATURES.append(feature)

TRAIN_ATTRIBUTE.append(attribute_mapping[int(mapping[int(label)]['action'])])

# else:

# TEST_LABELS.append(mapping[int(label)]['action'])

# TEST_FEATURES.append(feature)

# TEST_ATTRIBUTE.append(attribute_mapping[int(mapping[int(label)]['action'])])

# if lineno==4000:

# break

TRAIN_FEATURES = numpy.array(TRAIN_FEATURES)

# TEST_FEATURES = numpy.array(TEST_FEATURES)

TEST_FEATURES = TRAIN_FEATURES

TRAIN_LABELS = numpy.array(TRAIN_LABELS)

# TEST_LABELS = numpy.array(TEST_LABELS)

TEST_LABELS = TRAIN_LABELS

TRAIN_ATTRIBUTE = numpy.array(TRAIN_ATTRIBUTE)

# TEST_ATTRIBUTE = numpy.array(TEST_ATTRIBUTE)

TEST_ATTRIBUTE = TRAIN_ATTRIBUTE

pc=0

nc=0

classifier = OneVsRestClassifier(LinearSVC(C=2.0,random_state=0))

classifier.fit(TRAIN_FEATURES,TRAIN_ATTRIBUTE)

decision = classifier.decision_function(TEST_FEATURES)

prediction = classifier.predict(TEST_FEATURES)

for i in range(0,len(TEST_ATTRIBUTE)):

for j in range(22):

if prediction[i][j]==TEST_ATTRIBUTE[i][j]:

pc+=1

else:

nc+=1

# print prediction[i],TEST_ATTRIBUTE[i],TEST_LABELS[i], decision[i]

print pc,nc

print classifier.score(TEST_FEATURES,TEST_ATTRIBUTE)

TRAIN_LABELS = []

TRAIN_FEATURES = []

TRAIN_ATTRIBUTE = []

TEST_LABELS = []

TEST_FEATURES = []

TEST_ATTRIBUTE = []

for feature,label in zip(features['win_feature'],labels['vlabels'][0]):

if mapping[int(label)]['action'] in NovelClass:

attributes = classifier.predict(feature.reshape(1, -1))

TEST_LABELS.append(mapping[int(label)]['action'])

TEST_FEATURES.append(numpy.array(attributes[0]))

A = dist.pairwise([attributes[0],numpy.array(attribute_mapping[NovelClass[0]])])[0][1]

B = dist.pairwise([attributes[0],numpy.array(attribute_mapping[NovelClass[1]])])[0][1]

if A>B:

ConfusionMatrix[1][mapping[int(label)]['action']-NovelClass[0]]+=1

ConfusionMatrix2[1][mapping[int(label)]['action']-NovelClass[0]]+=1

else:

ConfusionMatrix[0][mapping[int(label)]['action']-NovelClass[0]]+=1

ConfusionMatrix2[0][mapping[int(label)]['action']-NovelClass[0]]+=1

print ConfusionMatrix2

print 'Accuracy:'(1.0*numpy.trace(ConfusionMatrix2))/numpy.sum(ConfusionMatrix2)

print ConfusionMatrix

print 'Accuracy:'(1.0*numpy.trace(ConfusionMatrix))/numpy.sum(ConfusionMatrix)