trainData = HomogenNumericTable(seeded.rand(200, nFeatures))
trainDependentVariables = HomogenNumericTable(
seeded.rand(200, nDependentVariables))
testData = HomogenNumericTable(seeded.rand(50, nFeatures))
testGroundTruth = HomogenNumericTable(seeded.rand(50, nDependentVariables))
#Instantiate Linear Regression object
lr = LinearRegression()
#Training
trainingResult = lr.training(trainData, trainDependentVariables)
#Prediction
pred_array = lr.predict(trainingResult, trainData)
#Serialize
lr.serialize(trainingResult, fileName='trainingResult.npy')
#Deseriailze
de_trainingResult = lr.deserialize(fileName="trainingResult.npy")
#Predict with Metrics
predRes, predResRed, singleBeta, groupBeta = lr.predictWithQualityMetrics(
trainingResult,
trainData,
trainDependentVariables,
reducedBetaIndex=[2, 10])
#Print Metrics results
lr.printAllQualityMetrics(singleBeta, groupBeta)
#print predicted responses and actual response
printNumericTable(predRes,
"Linear Regression prediction results: (first 10 rows):", 10)
printNumericTable(predResRed,
"Linear Regression prediction results: (first 10 rows):", 10)
printNumericTable(trainDependentVariables, "Ground truth (first 10 rows):", 10)
