ios程序怎么与learncloud实现数据对接

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import numpy as np11

import pandas as pd11

names=("Balance,Duration,History,Purpose,Credit amount,Savings,Employment,instPercent,sexMarried,Guarantors,Residence duration,Assets,Age,concCredit,Apartment,Credits,Occupation,Dependents,hasPhone,Foreign,lable")split(',')11

data=pdread_csv("Desktop/sunshengyun/data/german/germandata",sep='\s+',names=names)11

datahead()11

Balance

Duration

History

Purpose

Credit amount

Savings

Employment

instPercent

sexMarried

Guarantors

…

Assets

Age

concCredit

Apartment

Credits

Occupation

Dependents

hasPhone

Foreign

lable

0

A11 6 A34 A43 1169 A65 A75 4 A93 A101 … A121 67 A143 A152 2 A173 1 A192 A201 1

1

A12 48 A32 A43 5951 A61 A73 2 A92 A101 … A121 22 A143 A152 1 A173 1 A191 A201 2

2

A14 12 A34 A46 2096 A61 A74 2 A93 A101 … A121 49 A143 A152 1 A172 2 A191 A201 1

3

A11 42 A32 A42 7882 A61 A74 2 A93 A103 … A122 45 A143 A153 1 A173 2 A191 A201 1

4

A11 24 A33 A40 4870 A61 A73 3 A93 A101 … A124 53 A143 A153 2 A173 2 A191 A201 2

5 rows × 21 columns

dataBalanceunique()11

array([‘A11’, ‘A12’, ‘A14’, ‘A13’], dtype=object)datacount()11

Balance 1000 Duration 1000 History 1000 Purpose 1000 Credit amount 1000 Savings 1000 Employment 1000 instPercent 1000 sexMarried 1000 Guarantors 1000 Residence duration 1000 Assets 1000 Age 1000 concCredit 1000 Apartment 1000 Credits 1000 Occupation 1000 Dependents 1000 hasPhone 1000 Foreign 1000 lable 1000 dtype: int64#部分变量描述性统计分析

datadescribe()1212

Duration

Credit amount

instPercent

Residence duration

Age

Credits

Dependents

lable

count

1000000000 1000000000 1000000000 1000000000 1000000000 1000000000 1000000000 1000000000

mean

20903000 3271258000 2973000 2845000 35546000 1407000 1155000 1300000

std

12058814 2822736876 1118715 1103718 11375469 0577654 0362086 0458487

min

4000000 250000000 1000000 1000000 19000000 1000000 1000000 1000000

25%

12000000 1365500000 2000000 2000000 27000000 1000000 1000000 1000000

50%

18000000 2319500000 3000000 3000000 33000000 1000000 1000000 1000000

75%

24000000 3972250000 4000000 4000000 42000000 2000000 1000000 2000000

max

72000000 18424000000 4000000 4000000 75000000 4000000 2000000 2000000

dataDurationunique()11

array([ 6, 48, 12, 42, 24, 36, 30, 15, 9, 10, 7, 60, 18, 45, 11, 27, 8, 54, 20, 14, 33, 21, 16, 4, 47, 13, 22, 39, 28, 5, 26, 72, 40], dtype=int64)dataHistoryunique()11

array([‘A34’, ‘A32’, ‘A33’, ‘A30’, ‘A31’], dtype=object)datagroupby('Balance')size()order(ascending=False)11

c:\python27\lib\site-packages\ipykernel\__main__py:1: FutureWarning: order is deprecated, use sort_values(…) if __name__ == ‘__main__’: Balance A14 394 A11 274 A12 269 A13 63 dtype: int64datagroupby('Purpose')size()order(ascending=False)11

c:\python27\lib\site-packages\ipykernel\__main__py:1: FutureWarning: order is deprecated, use sort_values(…) if __name__ == ‘__main__’: Purpose A43 280 A40 234 A42 181 A41 103 A49 97 A46 50 A45 22 A44 12 A410 12 A48 9 dtype: int64datagroupby('Apartment')size()order(ascending=False)11

c:\python27\lib\site-packages\ipykernel\__main__py:1: FutureWarning: order is deprecated, use sort_values(…) if __name__ == ‘__main__’: Apartment A152 713 A151 179 A153 108 dtype: int64import matplotlibpyplot as plt

%matplotlib inline

dataplot(x='lable', y='Age', kind='scatter',

alpha=002, s=50);12341234

datahist('Age', bins=15);11

target=datalable11

features_data=datadrop('lable',axis=1)11

numeric_features = [c for c in features_data if features_data[c]dtypekind in ('i', 'f')] # 提取数值类型为整数或浮点数的变量11

numeric_features11

[‘Duration’, ‘Credit amount’, ‘instPercent’, ‘Residence duration’, ‘Age’, ‘Credits’, ‘Dependents’]numeric_data = features_data[numeric_features]11

numeric_datahead()11

Duration

Credit amount

instPercent

Residence duration

Age

Credits

Dependents

0

6 1169 4 4 67 2 1

1

48 5951 2 2 22 1 1

2

12 2096 2 3 49 1 2

3

42 7882 2 4 45 1 2

4

24 4870 3 4 53 2 2

categorical_data = features_datadrop(numeric_features, axis=1)11

categorical_datahead()11

Balance

History

Purpose

Savings

Employment

sexMarried

Guarantors

Assets

concCredit

Apartment

Occupation

hasPhone

Foreign

0

A11 A34 A43 A65 A75 A93 A101 A121 A143 A152 A173 A192 A201

1

A12 A32 A43 A61 A73 A92 A101 A121 A143 A152 A173 A191 A201

2

A14 A34 A46 A61 A74 A93 A101 A121 A143 A152 A172 A191 A201

3

A11 A32 A42 A61 A74 A93 A103 A122 A143 A153 A173 A191 A201

4

A11 A33 A40 A61 A73 A93 A101 A124 A143 A153 A173 A191 A201

categorical_data_encoded = categorical_dataapply(lambda x: pdfactorize(x)[0]) # pdfactorize即可将分类变量转换为数值表示

# apply运算将转换函数应用到每一个变量维度

categorical_data_encodedhead(5)123123

Balance

History

Purpose

Savings

Employment

sexMarried

Guarantors

Assets

concCredit

Apartment

Occupation

hasPhone

Foreign

0

0 0 0 0 0 0 0 0 0 0 0 0 0

1

1 1 0 1 1 1 0 0 0 0 0 1 0

2

2 0 1 1 2 0 0 0 0 0 1 1 0

3

0 1 2 1 2 0 1 1 0 1 0 1 0

4

0 2 3 1 1 0 0 2 0 1 0 1 0

features = pdconcat([numeric_data, categorical_data_encoded], axis=1)#进行数据的合并

featureshead()

# 此处也可以选用one-hot编码来表示分类变量，相应的程序如下：

# features = pdget_dummies(features_data)

# featureshead()1234512345

Duration

Credit amount

instPercent

Residence duration

Age

Credits

Dependents

Balance

History

Purpose

Savings

Employment

sexMarried

Guarantors

Assets

concCredit

Apartment

Occupation

hasPhone

Foreign

0

6 1169 4 4 67 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1

48 5951 2 2 22 1 1 1 1 0 1 1 1 0 0 0 0 0 1 0

2

12 2096 2 3 49 1 2 2 0 1 1 2 0 0 0 0 0 1 1 0

3

42 7882 2 4 45 1 2 0 1 2 1 2 0 1 1 0 1 0 1 0

4

24 4870 3 4 53 2 2 0 2 3 1 1 0 0 2 0 1 0 1 0

X = featuresvaluesastype(npfloat32) # 转换数据类型

y = (targetvalues == 1)astype(npint32) # 1:good,2:bad1212

from sklearncross_validation import train_test_split # sklearn库中train_test_split函数可实现该划分

X_train, X_test, y_train, y_test = train_test_split(

X, y, test_size=02, random_state=0) # 参数test_size设置训练集占比

1234512345

from sklearntree import DecisionTreeClassifier

from sklearncross_validation import cross_val_score

clf = DecisionTreeClassifier(max_depth=8) # 参数max_depth设置树最大深度

# 交叉验证，评价分类器性能，此处选择的评分标准是ROC曲线下的AUC值，对应AUC更大的分类器效果更好

scores = cross_val_score(clf, X_train, y_train, cv=3, scoring='roc_auc')

print("ROC AUC Decision Tree: {:4f} +/-{:4f}"format(

npmean(scores), npstd(scores)))123456789123456789

ROC AUC Decision Tree: 06866 +/-00105

#利用learning curve，以样本数为横坐标，训练和交叉验证集上的评分为纵坐标，对不同深度的决策树进行对比（判断是否存在过拟合或欠拟合）

from sklearnlearning_curve import learning_curve

def plot_learning_curve(estimator, X, y, ylim=(0, 11), cv=3,

n_jobs=1, train_sizes=nplinspace(1, 10, 5),

scoring=None):

plttitle("Learning curves for %s" % type(estimator)__name__)

pltylim(ylim); pltgrid()

pltxlabel("Training examples")

pltylabel("Score")

train_sizes, train_scores, validation_scores = learning_curve(

estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,

scoring=scoring)

train_scores_mean = npmean(train_scores, axis=1)

validation_scores_mean = npmean(validation_scores, axis=1)

pltplot(train_sizes, train_scores_mean, 'o-', color="r",

label="Training score")

pltplot(train_sizes, validation_scores_mean, 'o-', color="g",

label="Cross-validation score")

pltlegend(loc="best")

print("Best validation score: {:4f}"format(validation_scores_mean[-1]))12345678910111213141516171819202122231234567891011121314151617181920212223

clf = DecisionTreeClassifier(max_depth=None)

plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')

# 可以注意到训练数据和交叉验证数据的得分有很大的差距，意味着可能过度拟合训练数据了123123

Best validation score: 06310

clf = DecisionTreeClassifier(max_depth=10)

plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')1212

Best validation score: 06565

clf = DecisionTreeClassifier(max_depth=8)

plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')1212

Best validation score: 06762

clf = DecisionTreeClassifier(max_depth=5)

plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')1212

Best validation score: 07219

clf = DecisionTreeClassifier(max_depth=4)

plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')1212

Best validation score: 07226

welearn视听说再打开有记录。从网页上下载到本地的考试客户端是有，后台会检测有没有退出界面，退出三次会警告，检测字典程序。WELearn随行课堂一键自动刷课助手工具是一款电脑上的刷时长的课程学习的平台，在WELearn随行课堂刷时长工具中你可以体验到一系列非常丰富的课程学习工具哦，一分钟自动完成100%。

welearn系统崩溃扫一扫打开异常修复welearm就可以完成登入。

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2、首先断开目前这台电脑的网络连接，直接断开网线或者是拔掉网线，之后在无网络的情况下输入正确用户名和密码尝试登录，这时候系统会提示没有网络连接，没有关系。之后连接网络尝试登录，就可以拜托闪退的问题。这通常是因为wegame的自动保存账号密码功能，出现了相应的问题。

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4、把杀毒软件影响wegame正常使用。比如qq电脑管家、360安全卫士等等。关闭这类软件，之后重启wegame即可。

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