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