def doLinear(trainSize=0.7): clf = linear_model.LinearRegression(normalize=True, n_jobs=-1) dataSet = load.getSet(trainSize) dataParams_train = dataSet[0] dataParams_test = dataSet[2] dataPrice_train = dataSet[1] dataPrice_test = dataSet[3] clf.fit(dataParams_train, dataPrice_train) print("****LINEAR****") if trainSize!=1: getInfo(dataParams_test, dataPrice_test, clf) return clf
def doSvm(trainSize=0.7): clfSvm = svm.LinearSVR(C=1.0, epsilon=0.2) dataSet = load.getSet(trainSize) dataParams_train = dataSet[0] dataParams_test = dataSet[2] dataPrice_train = dataSet[1] dataPrice_test = dataSet[3] clfSvm.fit(dataParams_train, dataPrice_train) print("****SVR****") if trainSize!=1: getInfo(dataParams_test, dataPrice_test, clfSvm) return clfSvm
def doRidge(trainSize=0.7): clf2 = linear_model.Ridge(alpha=.1, normalize=True) dataSet = load.getSet(trainSize) dataParams_train = dataSet[0] dataParams_test = dataSet[2] dataPrice_train = dataSet[1] dataPrice_test = dataSet[3] clf2.fit(dataParams_train, dataPrice_train) print("****RIDGE****") if trainSize!=1: getInfo(dataParams_test, dataPrice_test, clf2) return clf2