X[np.isnan(X)] = 0 scaler = preprocessing.StandardScaler().fit(X) Xn = scaler.fit_transform(X) ### cluster model = KMeans(init='k-means++', n_clusters=6, n_init=10, max_iter=1000) model = AffinityPropagation(preference=-150, verbose=True) #model = Birch(branching_factor=10, n_clusters=4, threshold=0.3, compute_labels=True) model = MeanShift(bandwidth=estimate_bandwidth(X, quantile=0.1, n_samples=100), bin_seeding=True) label = SSRS.Cluster(X, model) ### classification model = tree.DecisionTreeClassifier() model = GaussianNB() model = svm.SVC() model = SGDClassifier() Tp = SSRS.Classification_cross(XXn, T=label, nfold=10, model=model) SSRS.plotErrorMap(label, Tp) ### regression regModel = linear_model.LinearRegression() #regModel=svm.SVC() regModel = KNeighborsRegressor(n_neighbors=10) regModel = tree.DecisionTreeRegressor() regModel = GaussianNB() rmse_band, Yp, Ytest = SSRS.RegressionLearn(X, XXn, 0.2, regModel)
Y2=np.argmax(Y[:,15:30],axis=1) ## Regression regModel=linear_model.LinearRegression() #regModel=svm.SVC() regModel=KNeighborsRegressor(n_neighbors=20) regModel=tree.DecisionTreeRegressor() regModel=GaussianNB() regModel=sklearn.linear_model.SGDRegressor() regModel=RandomForestRegressor() X_train,X_test,Y_train,Y_test = cross_validation.train_test_split(\ Xn,np.column_stack((Y1,Y2)),test_size=0.2,random_state=0) Yp,rmse,rmse_train,rmse_band,rmse_band_train=SSRS.Regression\ (X_train,X_test,Y_train,Y_test,multiband=1,regModel=regModel,doplot=0) print(rmse) print(rmse_train) ## Classification model = tree.DecisionTreeClassifier() model = GaussianNB() model = svm.SVC() model = SGDClassifier() model=sklearn.ensemble.RandomForestClassifier() Yin=Y1 Tp = SSRS.Classification_cross(Xn, T=Yin, nfold=10, model=model) SSRS.plotErrorMap(Yin, Tp) np.sqrt(((Yin - Tp) ** 2).mean()) np.count_nonzero(np.abs(Yin-Tp)<2)/4627.