model = Model(GetCurrentPath() + '/Models/ProbabilityTables.pkl')
model.read()
[faces, nonfaces] = LoadData(GetCurrentPath() + '/Data/CalTech/test/faces/', \
GetCurrentPath() + '/Data/CalTech/test/non-faces/', \
model)
# faces = [[1,1,0,1],[1,0,1,1],[1,1,1,1]]
# nonfaces = [[1,1,1,0],[0,1,0,1],[0,1,1,0]]
# faces = np.array(faces).T
# nonfaces = np.array(nonfaces).T
classifier = SemiNaive(faces.shape[0], model.getNumCutOffs(), model.getSubgroupSize())
classifier.loadModel(model)
likelihoodClass1 = classifier.test(faces)
print '------------------------------------'
likelihoodClass2 = classifier.test(nonfaces)
TP = TP1 = TP2 = 0
FP = 0
Threshold = 0
with open("Output.txt", "w") as output_file:
for i in range(0,likelihoodClass1.shape[0]):
output_file.write("%d %f\n" % (1, likelihoodClass1[i]))
if (likelihoodClass1[i] >= Threshold): TP1 += 1
else: FP += 1
for i in range(0,likelihoodClass2.shape[0]):
return quantizedFaceData, quantizedNonFaceData, cutoffs
############### main function #################
if __name__ == '__main__':
SetupPath()
from ReadDataSet import *
from Quantization import *
from SemiNaive import *
from Model import *
import settings
settings.init()
[faces, nonfaces, cutoffs] = LoadData(GetCurrentPath() + '/Data/CalTech/train/faces/', \
GetCurrentPath() + '/Data/CalTech/train/non-faces/')
model = Model(GetCurrentPath() + '/Models/ProbabilityTables.pkl')
model.setCutOffs(cutoffs)
# faces = [[1,1,0,1],[1,0,1,1]]
# nonfaces = [[1,1,1,0],[0,1,0,1]]
# faces = np.array(faces).T
# nonfaces = np.array(nonfaces).T
classifier = SemiNaive(faces.shape[0], settings.NUM_CUTOFF,
settings.SUBGROUP_SIZE)
classifier.train(faces, nonfaces, model)
del faces
del nonfaces
from SemiNaive import *
from Model import *
model = Model(GetCurrentPath() + '/Models/ProbabilityTables.pkl')
model.read()
[faces, nonfaces] = LoadData(GetCurrentPath() + '/Data/CalTech/test/faces/', \
GetCurrentPath() + '/Data/CalTech/test/non-faces/', \
model)
# faces = [[1,1,0,1],[1,0,1,1],[1,1,1,1]]
# nonfaces = [[1,1,1,0],[0,1,0,1],[0,1,1,0]]
# faces = np.array(faces).T
# nonfaces = np.array(nonfaces).T
classifier = SemiNaive(faces.shape[0], model.getNumCutOffs(),
model.getSubgroupSize())
classifier.loadModel(model)
likelihoodClass1 = classifier.test(faces)
print '------------------------------------'
likelihoodClass2 = classifier.test(nonfaces)
TP = TP1 = TP2 = 0
FP = 0
Threshold = 0
with open("Output.txt", "w") as output_file:
for i in range(0, likelihoodClass1.shape[0]):
output_file.write("%d %f\n" % (1, likelihoodClass1[i]))
if (likelihoodClass1[i] >= Threshold): TP1 += 1
else: FP += 1
for i in range(0, likelihoodClass2.shape[0]):
if __name__ == '__main__':
SetupPath()
from ReadDataSet import *
from Quantization import *
from SemiNaive import *
from Model import *
import settings
settings.init()
[faces, nonfaces, cutoffs] = LoadData(GetCurrentPath() + '/Data/CalTech/train/faces/', \
GetCurrentPath() + '/Data/CalTech/train/non-faces/')
model = Model(GetCurrentPath() + '/Models/ProbabilityTables.pkl')
model.setCutOffs(cutoffs)
#print faces
#print faces.shape[0]
# faces = [[1,1,0,1],[1,0,1,1]]
# nonfaces = [[1,1,1,0],[0,1,0,1]]
# faces = np.array(faces).T
# nonfaces = np.array(nonfaces).T
classifier = SemiNaive(faces.shape[0], settings.NUM_CUTOFF, settings.SUBGROUP_SIZE)
classifier.train(faces, nonfaces, model)
#model.write()
del faces
del nonfaces
