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)
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.
