def start_server(wbconfig, mp): host = wbconfig.get("host", "0.0.0.0") port = wbconfig.getint("port", 2628) backlog = wbconfig.getint("backlog", 512) timeout = wbconfig.getint("timeout", 60) or None address = (host, port) master.init(address, backlog) drop_privs(wbconfig) master.run(timeout, mp)
def main(): app = init(max_size=6) print('Allocation of value 5555 in v:') v = app.allocate(5555) print(v) print('Allocation of list [1,2,3,4] in v2') v2 = app.allocate([i for i in range(1, 5)]) print(v2) print('Allocation of list [1,2,3,4,5,6,7,8,9] in v3') v3 = app.allocate([i for i in range(1, 10)]) print(v3) print('read v:', app.read(v)) print('modify v to 56 :', app.modify(v, 56, 7)) print('read v:', app.read(v)) print('read v2:', app.read(v2)) print('modify v2 to 56 in pos 7 :', app.modify(v2, 56, 7)) print('read v2:', app.read(v2)) print('read v3:', app.read(v3)) print('modify v3 to 56 in pos 7 :', app.modify(v3, 56, 7)) print('read v3:', app.read(v3)) print('free v:', app.free(v)) print('freed v value:', app.read(v)) print('free v2:', app.free(v2)) print('freed v2 value:', app.read(v2)) print('free v3:', app.free(v3)) print('freed v3 value:', app.read(v3)) app.terminate_slaves()
def main(screen): """Main function""" global restaurant, main_scr, command_scr, keys_scr, constant_scr main_scr, command_scr, keys_scr, constant_scr = master.init(screen) if (master.start_menu(main_scr) == '1'): restaurant = master.load_game(main_scr) else: restaurant = master.new_game(main_scr) new_day()
def __init__(self, max_size): self.app = init(max_size)
def gitMaster(path): print(mstr.init(path))
train = pd.concat([train_por, train_mat], axis=0) del train['G1'] del train['G2'] del train['G3'] del train['absences'] del train['studytime'] del train['failures'] print(train.shape) train = train.drop_duplicates(subset=None, keep='first', inplace=False) print("After remove duplicates: " + str(train.shape))''' import master df = master.init() y = df[['G1', 'G2', 'G3']] X = df.drop(['G1', 'G2', 'G3'], axis=1) df['pass_fail'] = df.apply(lambda row: master.label_pass(row), axis=1) replace_binary_attributes_map = { 'school': { 'GP': 0, 'MS': 1 }, # school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira) 'sex': { 'F': 0, 'M': 1
def main(): #DataVisualization.performDataVis() #reading the dataset df = master.init('por') #preprocessing -> feature transformation X, y, feature_names = master.preproc(df, select='novotes') X2, y2, feature_names2 = master.preproc(df, select='G1') X3, y3, feature_names3 = master.preproc(df, select='all') # applying and testing PCA """ master.PCA_study(X,feature_names) #43 components #27 componenti -> 0.85 #30 componenti -> 0.90 #39 componenti -> 0.99 X_pca_30 = master.PCA(X, components=30) t0=time.time() bclassification.kNN(X_pca_30, y, search=False, cv=True, onlycv=True) #0.82 time_30= time.time()-t0 print("Time elapsed: ", time_30) #0.16 t1=time.time() bclassification.kNN(X, y, search=False, cv=True, onlycv=True) #0.83 time_all=time.time()-t1 print("Time elapsed: ", time_all) #0.22 t2=time.time() bclassification.SVM(X_pca_30, y, search=False, cv=True, onlycv=True) #0.84 time_30= time.time()-t2 print("Time elapsed: ", time_30) #0.20 t3=time.time() bclassification.SVM(X, y, search=False, cv=True, onlycv=True) #0.84 time_all=time.time()-t3 print("Time elapsed: ", time_all) #0.28 """ # Binary classification -> hyperparameter tuning and cross validation #bclassification.kNN(X, y, search=True, cv=False, select='novotes') #bclassification.kNN(X,y,search=False,cv=True, select='novotes') #bclassification.kNN(X,y,search=False,cv=True, select='novotes', smote=True) #bclassification.kNN(X,y,search=False,cv=True, select='novotes', onlynum=True) #bclassification.logistic_regression(X,y,search=True, cv=False) #bclassification.logistic_regression(X,y,search=False, cv=True) #bclassification.logistic_regression(X,y,search=False, cv=True,smote=True) #bclassification.LDA(X,y) #bclassification.LDA(X,y,smote=True) #bclassification.SVM(X,y,search=True, cv=False) #bclassification.SVM(X,y) #bclassification.SVM(X,y,smote=True) #bclassification.SVM(X,y,mode_cv='rbf',C_cv=10) #bclassification.SVM(X,y,mode_cv='rbf',C_cv=100, smote=True) #bclassification.SVM_unbalanced(X,y,search=True,cv=False) #bclassification.SVM_unbalanced(X,y) #bclassification.decisionTree(X,y,feature_names) #bclassification.decisionTree(X,y,feature_names,smote=True) #bclassification.randomForest(X,y,feature_names,search=True,cv=False) #bclassification.randomForest(X,y,feature_names) #bclassification.randomForest(X, y, feature_names, smote=True) # Perform binary classification on different configurations of the dataset: # X -> without G1 and G2 # X2 -> with G1 # X3 -> with G1 and G2 """