def on_all_m_click(self, do=0): # 0 = get current memory or 1 = plus memory or 2 = minus memory or 3 = clear memory if do == 3: self.memory = 0.0 elif not self.exp.is_error(): self.update_expression() if do == 0: self.history.out() r = str(self.memory) if r.endswith('.0'): r = r.replace('.0', '') self.exp.put_data_on_exp(r) self.update_monitor() elif do in (1, 2): res = get_result(self.exp.exp, self.ui.radio_bt_1.isChecked()) if error(res): self.history.out() self.last_invalid_exp = self.exp.exp self.exp.set_exp(res) self.update_monitor() else: try: exp_float = float(res) self.memory += exp_float if do == 1 else -exp_float except: self.history.out() self.last_invalid_exp = self.exp.exp self.exp.set_exp(ERRORS[2]) self.update_monitor()
def on_equal_click(self): self.update_expression() if not self.exp.is_error(): self.history.out() res = get_result(self.exp.exp, self.ui.radio_bt_1.isChecked()) if self.make_power_enabled: self.on_power_click() if error(res): self.last_invalid_exp = self.exp.exp else: self.history.history.append(self.exp.exp) self.exp.set_exp(res) self.update_monitor()
def func(name): eps = 0.0001 para = parameter.copy() para["r"] += random.uniform(-eps, eps) para["lambda"] += random.uniform(-eps, eps) while para["r"] > 2 or para["r"] < 0: para["r"] += random.uniform(-eps, eps) while para["lambda"] > 2 or para["lambda"] < 0: para["lambda"] += random.uniform(-eps, eps) ans = tools.get_result(para, 6) print('process', os.getpid(), ans, para.__str__()) global min_val filename = os.path.split(__file__)[-1].split(".")[0] + '.txt' with open(filename, "a") as f: f.write(str(os.getpid()) + ',' + name + ',' + str(ans) + ',' + str(para["r"]) + ',' + str(para["lambda"]) + ',' + str( para["epoch"]) + ',' + str(para["n"]) + ',' + '\n')
def func(name): eps = 0.001 para = parameter.copy() para["r"] += random.uniform(-eps, eps) para["lambda"] += random.uniform(-eps, eps) while para["r"] > 2 or para["r"] < 0: para["r"] += random.uniform(-eps, eps) while para["lambda"] > 2 or para["lambda"] < 0: para["lambda"] += random.uniform(-eps, eps) ans = tools.get_result(para, 12) print('process', os.getpid(), ans, para.__str__()) global min_val with open("./SA_F12_3.txt", "a") as f: f.write( str(os.getpid()) + ',' + str(ans) + ',' + str(para["r"]) + ',' + str(para["lambda"]) + ',' + str(para["epoch"]) + ',' + str(para["n"]) + ',' + '\n')
@author: yangydeng """ import pandas as pd from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import ExtraTreesRegressor from sklearn.ensemble import GradientBoostingRegressor import sys sys.path.append('../tools') from tools import get_result,draw_feature_importance day_time = '_03_01_3' train_x = pd.read_csv('../train_0/train_x'+day_time+'.csv') train_y = pd.read_csv('../train_0/train_y'+day_time+'.csv') test_x = pd.read_csv('../test_0/test_x'+day_time+'.csv') #RF = RandomForestRegressor(n_estimators=1200,random_state=1,n_jobs=-1,min_samples_split=2,min_samples_leaf=2,max_depth=25) #RF.fit(train_x,train_y) #pre = (RF.predict(test_x)).round() ET = ExtraTreesRegressor(n_estimators=1200,random_state=1,n_jobs=-1,min_samples_split=2,min_samples_leaf=2,max_depth=25,max_features='sqrt',bootstrap=0) ET.fit(train_x,train_y) pre = (ET.predict(test_x)).round() result = get_result(pre) result.to_csv('../results/result'+day_time+'.csv',index=False,header=False) #draw_feature_importance(train_x,ET)
# -*- coding: utf-8 -*- """ Created on Thu Feb 09 10:21:49 2017 @author: Administrator """ import pandas as pd from sklearn.ensemble import RandomForestRegressor import sys sys.path.append('../tools') from tools import get_result day_time = '_02_11_2' train_x = pd.read_csv('../train_2/train_x'+day_time+'.csv') train_y = pd.read_csv('../train_2/train_y'+day_time+'.csv') test_x = pd.read_csv('../test_2/test_x'+day_time+'.csv') RF = RandomForestRegressor(n_estimators=500,random_state=1,n_jobs=-1,min_samples_split=2,min_samples_leaf=2,max_depth=25) RF.fit(train_x,train_y) pre = (RF.predict(test_x)).round() result = get_result(pre) result.to_csv('../results/result'+day_time+'.csv',index=False,header=False)
param = {'subsample':[1,1,1,1,1,1,1],'min_samples_leaf':[1,1,1,1,1,1,1],'n_estimators':[200,100,200,200,200,200,100],'min_samples_split':[4,8,2,8,2,4,4],\ 'learning_rate':[0.05,0.1,0.05,0.05,0.05,0.05,0.1],'max_features':[270,'auto',280,'auto',270,280,270],'random_state':[1,1,1,1,1,1,1]\ ,'max_depth':[4,6,4,4,4,4,4]} result = DataFrame() for i in range(0, 7): GB = GradientBoostingRegressor(n_estimators=param['n_estimators'][i],learning_rate=0.05,random_state=1,\ min_samples_split=param['min_samples_split'][i],min_samples_leaf=1,max_depth=param['max_depth'][i],max_features=param['max_features'][i],subsample=0.85) GB.fit(train_x, train_y.icol(i)) pre = (GB.predict(test_x)).round() result['col' + str(i)] = pre result = get_result(result.values) result.to_csv('../results/result' + day_time + '.csv', index=False, header=False) #draw_feature_importance(train_x,ET) #0: {'subsample': 1, 'learning_rate': 0.05, 'min_samples_leaf': 1, \ #'n_estimators': 200, 'min_samples_split': 4, 'random_state': 1, 'max_features': 270, 'max_depth': 4} #1: {'subsample': 1, 'learning_rate': 0.1, 'min_samples_leaf': 3,\ # 'n_estimators': 100, 'min_samples_split': 8, 'random_state': 1, 'max_features': auto, 'max_depth': 6} #2: {'subsample': 1, 'learning_rate': 0.05, 'min_samples_leaf': 1,\ # 'n_estimators': 200, 'min_samples_split': 2, 'random_state': 1, 'max_features': 280, 'max_depth': 4}