def evaluateMetrics(self,matrix,metric=None,do_print=False):
		
		accuracy = (matrix['TP'] + matrix['TN'])/ float(sum(matrix.values()))
		
		sensitivity = (matrix['TP'])/ float(matrix['TP'] + matrix['FN'])
		
		specificity = (matrix['TN'])/float(matrix['TN'] + matrix['FP'])
		
		efficiency = (sensitivity + specificity) / 2.0
		
		positivePredictiveValue =  matrix['TP'] / float(matrix['TP'] + matrix['FP'])

		NegativePredictiveValue = matrix['TN'] / float(matrix['TN'] + matrix['FN'])
		
		PhiCoefficient = (matrix['TP'] * matrix['TN'] - matrix['FP'] * matrix['FN'])/(
							math.sqrt( (matrix['TP'] + matrix['FP']) *
							           (matrix['TP'] + matrix['FN']) *
									   (matrix['TN'] + matrix['FP']) *
									   (matrix['TN'] + matrix['FN']))) or 1.0
									
		if do_print:
			print 'Sensitivity: ' , sensitivity
			print 'Specificity: ' , specificity
			print 'Efficiency: ' , efficiency
			print 'Accuracy: ' , accuracy
			print 'PositivePredictiveValue: ' , positivePredictiveValue
			print 'NegativePredictiveValue' , NegativePredictiveValue
			print 'PhiCoefficient' , PhiCoefficient
			
		
		return {'SENS': sensitivity, 'SPEC': specificity, 'ACC': accuracy, 'EFF': efficiency,
				'PPV':positivePredictiveValue, 'NPV':NegativePredictiveValue , 'PHI':  PhiCoefficient}
Returns the edges of the network.
dataset/ETHPy150Open pivotalsoftware/pymadlib/pymadlib/pyroc.py/ROCData.evaluateMetrics--crossover with--dataset/ETHPy150Open pgmpy/pgmpy/pgmpy/readwrite/UAI.py/UAIReader.get_edges