def get_score(data):
target = 'label'
feature = [x for x in data.columns if x not in [target, 'id']]
score_list = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data[feature],
data[target],
test_size=0.4,
random_state=1)
pred_proba_gdt = 0
pred_proba_xgb = 0
pred_proba_rf = 0
# ff = []
for i in range(5):
pred_proba_gdt += learn(x_train, y_train, x_test, i, 'GDBT')[1]
pred_proba_xgb += learn(x_train, y_train, x_test, i, 'XGB')[1]
pred_proba_rf += learn(x_train, y_train, x_test, i, 'RF')
pred_proba = pred_proba_gdt + pred_proba_rf * 1.5 + pred_proba_xgb * 2.0
pred_max = np.max(pred_proba, axis=1)
zipa = zip(*pred_proba)
zz = pd.DataFrame()
zz['max'] = pred_max
zz['p0'] = (zipa[0] / zz['max']).astype('int') * 0
zz['p1000'] = (zipa[1] / zz['max']).astype('int') * 1000
zz['p1500'] = (zipa[2] / zz['max']).astype('int') * 1500
zz['p2000'] = (zipa[3] / zz['max']).astype('int') * 2000
zz['label'] = zz['p0'] + zz['p1000'] + zz['p1500'] + zz['p2000']
pred = zz['label'].values
score = f1(pred, y_test)
print 'final_score : ' + str(i), score
score_list.append(score)
return score_list
#!/usr/bin/env python
import function
function.f1("F")
import pygame
from function import f1, f2, f3, f4, display_w, display_h
import numpy as np
import sys
intercept = np.load('stieber_scenario_4.npy')
#intercept = intercept * 2
#intercept = None
img_w = 10
img_h = 10
no_of_crumbs = 50
x = np.linspace(0, display_w - 350, no_of_crumbs)
y_s = [f1(x), f2(x), f3(x), f4(x)]
if len(sys.argv) > 1:
y = y_s[int(sys.argv[1])]
else:
y = y_s[0]
black = (0, 0, 0)
white = (255, 255, 255)
gameDisplay = pygame.display.set_mode((display_w, display_h))
pygame.display.set_caption('Interception')
clock = pygame.time.Clock()
gameExit = False
house_img = pygame.image.load('house.jpg')
hansel_gretel_img = pygame.image.load('h_g.jpg')
witch_img = pygame.image.load('game_over.jpg')
from function import extended_rosenbrock_function as f1
from project_messy import extended_rosenbrock_function as f2
from function import extender_powell_singular_function as g1
from project_messy import extender_powell_singular_function as g2
from function import gradient_erf as grad_f1
from function import gradient_epsf as grad_g1
from project_messy import gradient
eps = 1e-6
number = 100
test_points = np.random.normal(scale=1, size=(number, 4))
# test extended_rosenbrock_function
error = 0
for point in test_points:
if np.linalg.norm(f1(point) - f2(point)) > eps:
error += 1
if error == 0:
print "---- function 1 pass test ----"
else:
print error
# test extender_powell_singular_function
error = 0
for point in test_points:
if np.linalg.norm(g1(point) - g2(point)) > eps:
error += 1
if error == 0:
print "---- function 2 pass test ----"
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import function #导入function模块，也就是以function命名的py文件，里面定义了多种函数 直接function.name(arg0...)引用
print(function.my_abs(2))
p = function.move(2, 3, 4, 2)
print(p[0], p[1])
print(function.quadratic(4, 9, 0))
function.f1(1, 2, 8)
l = [8, 88]
function.f2(*l)
#function.f2(1,2,*l)
d = {'city': 'B', 'gender': 'M', 'address': 'C'}
function.f3(1, 2, **d) #传入参数时只是对d的一份拷贝，函数中对dict类型修改不影响d
#function.f3(1,2,city='Beijing')
function.f4(1, 2, city='d', gender='M')
function.f5(1, 2, **d) #当传入**d然后从中取出 city gender对应的值，剩下的在给d
function.f5(*l, **d) #对于任意函数，都可以通过类似func(*args, **kw)的形式调用它
print(function.product(8))
print(function.fact(100))
print(function.moveH(3, 'A', 'B', 'C'))