Version 2.0.22.
(supports only Firefox)
Supported programs
Thunderbird
Yandex Browser
Epic Privacy Browser
Viber for Windows
Slimjet Browser
Mentioned programs start working much more slowly with the lapse of time. The reason is fragmentation of profile databases. SpeedyFox is specially designed to resolve this problem. The method used in SpeedyFox is 100% safe for your profile (e.g. bookmarks, passwords, etc), it's well documented and has been tested on millions of computers.
How Speedyfox works
With the passage of time SQLITE databases slow down considerably. It takes time to start apps that use such databases and the overall speed is affected. This is a very common problem and it occurs largely because of fragmentation of databases.
SpeedyFox is able to fix this problem with a single click! It seems unbelievable but after you optimize your Firefox with this tool, you will get a fresh newly-installed feel because the speed indeed gets considerably faster. You will get up to 3 times faster startup speeds, browsing history will become faster, and performing operations with cookies will be quicker than before.
SpeedyFox compacts those databases without losing any data. Databases are optimized to operate faster and are decreased in size.
How to use Speedyfox
Once installed, SpeedyFox automatically detects profiles of the supported apps. If you have more than one profile, you can select the one you want to optimize from the list. If you have a portable version of any of the supported app, choose your profile path manually by selecting 'Add custom profile...' profile from the context menu in the list. All you have to do is hit the 'Optimize' button.
The optimization process can take from 5 seconds to a minute depending on how large your databases are. The whole optimization process is safe as it does not effect your history, bookmarks, passwords, etc.
Depending on your browsing activity we recommend optimizing your profile once every 1-2 weeks.
Command line usage
/&program_name&:&profile_name& - optimize a certain profile (you can find its name in the SpeedyFox profiles tree)
/&program_name&:&profile_path& - optimize custom profile of program_name located at profile_path (e.g. for portable versions of supported programs)
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sklearn-xgboost AI-NN-PR 人工智能/神经网络 267万源代码下载- www.pudn.com
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&&开发工具: Python
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&详细说明：sklearn-xgboost
sklearn-xgboost的使用以及创建，这个是学习机器学习时的作业，希望大家指正-sklearn-xgboost sklearn-xgboost use and create, this is the time to learn the job of machine learning, I hope you correct
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&&作业三：sklearn和xgboost的使用.ipynb
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Embrace open and shared national standards. Download a copy and spread the word.Sklearn库例子1：Sklearn库中AdaBoost和Decision Tree运行结果的比较 - 波比12 - 博客园
随笔 - 91, 文章 - 0, 评论 - 8, 引用 - 0
DisCrete Versus Real AdaBoost
关于Discrete 和Real AdaBoost 可以参考博客：http://www.cnblogs.com/jcchen1987/p/4581651.html
本例是Sklearn网站上的关于决策树桩、决策树、和分别使用AdaBoost&SAMME和AdaBoost&SAMME.R的AdaBoost算法在分类上的错误率。这个例子基于Sklearn.datasets里面的make_Hastie_10_2数据库。取了12000个数据，其他前2000个作为训练集，后面10000个作为了测试集。
原网站链接：
代码如下：
#- *- encoding:utf-8 -*-
Sklearn adaBoost @Dylan
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import zero_one_loss
from sklearn.ensemble import
AdaBoostClassifier
import time
a=time.time()
n_estimators=400
learning_rate=1
X,y=datasets.make_hastie_10_2(n_samples=12000,random_state=1)
X_test,y_test=X[2000:],y[2000:]
X_train,y_train=X[:2000],y[:2000]
dt_stump=DecisionTreeClassifier(max_depth=1,min_samples_leaf=1)
dt_stump.fit(X_train,y_train)
dt_stump_err=1.0-dt_stump.score(X_test,y_test)
dt=DecisionTreeClassifier(max_depth=9,min_samples_leaf=1)
dt.fit(X_train,y_train)
dt_err=1.0-dt.score(X_test,y_test)
ada_discrete=AdaBoostClassifier(base_estimator=dt_stump,learning_rate=learning_rate,n_estimators=n_estimators,algorithm='SAMME')
ada_discrete.fit(X_train,y_train)
ada_real=AdaBoostClassifier(base_estimator=dt_stump,learning_rate=learning_rate,n_estimators=n_estimators,algorithm='SAMME.R')
ada_real.fit(X_train,y_train)
fig=plt.figure()
ax=fig.add_subplot(111)
ax.plot([1,n_estimators],[dt_stump_err]*2,'k-',label='Decision Stump Error')
ax.plot([1,n_estimators],[dt_err]*2,'k--',label='Decision Tree Error')
ada_discrete_err=np.zeros((n_estimators,))
for i,y_pred in enumerate(ada_discrete.staged_predict(X_test)):
ada_discrete_err[i]=zero_one_loss(y_pred,y_test)
######zero_one_loss
ada_discrete_err_train=np.zeros((n_estimators,))
for i,y_pred in enumerate(ada_discrete.staged_predict(X_train)):
ada_discrete_err_train[i]=zero_one_loss(y_pred,y_train)
ada_real_err=np.zeros((n_estimators,))
for i,y_pred in enumerate(ada_real.staged_predict(X_test)):
ada_real_err[i]=zero_one_loss(y_pred,y_test)
ada_real_err_train=np.zeros((n_estimators,))
for i,y_pred in enumerate(ada_real.staged_predict(X_train)):
ada_discrete_err_train[i]=zero_one_loss(y_pred,y_train)
ax.plot(np.arange(n_estimators)+1,ada_discrete_err,label='Discrete AdaBoost Test Error',color='red')
ax.plot(np.arange(n_estimators)+1,ada_discrete_err_train,label='Discrete AdaBoost Train Error',color='blue')
ax.plot(np.arange(n_estimators)+1,ada_real_err,label='Real AdaBoost Test Error',color='orange')
ax.plot(np.arange(n_estimators)+1,ada_real_err_train,label='Real AdaBoost Train Error',color='green')
ax.set_ylim((0.0,0.5))
ax.set_xlabel('n_estimators')
ax.set_ylabel('error rate')
leg=ax.legend(loc='upper right',fancybox=True)
leg.get_frame().set_alpha(0.7)
b=time.time()
print('total running time of this example is :',b-a)
plt.show()
1.运行时间：
total running time of this example is : 6.8545
2.对比图：
从图中可以看出：弱分类器（Decision Tree Stump）单独分类的效果很差，错误率将近50%，强分类器（Decision Tree）的效果要明显好于他。但是AdaBoost的效果要明显好于这两者。同时在AdaBoost中，Real AdaBoost的分类效果更佳好一点。}