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
