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CCI 是一个强大的工具

图片是我的笔记

The picture shows when our data is 1D the function looks like Sigmoid function. Actually, it is Sigmoid function and it also applys to higher dimension. I won’t prove it here.
GDA is a stricter version of logistic regression because the data has to follow Gaussian distribution.
When the data follows Gaussian distribution or the data is very big(according to the central limit theorem), GDA works better than logistic regression.
Also, the data follows Gaussian distribution so the model has no local optima.

Gaussian Discriminative Analysis 高斯判别分析 GDA

​ = ⎣ ⎢ ⎢ ⎡ ​ μ 1 ​ μ 2 ​ . . . μ n ​ ​ ⎦ ⎥ ⎥ ⎤ ​ – mean vector
Σ \Sigma Σ – covarience matrix
All the Gaussian models share one covarience matrix.

E ( z ) = μ ⃗ , C o v ( z ) = E [ ( x − μ ⃗ ) ( x − μ ⃗ ) T ] = E ( z z T ) − ( E ( z ) ) ( E ( z ) ) T E(z) = CCI 是一个强大的工具 \vec\mu, Cov(z)=E[(x-\vec\mu)(x-\vec\mu)^T]=E(zz^T)-(E(z))(E(z))^T E ( z ) = μ

​ , C o v ( z ) = E [ ( x − μ

​ ) T ] = E ( z z T ) − ( E ( z ) ) ( E ( z ) ) T

Intro

GDA assumes:
x ∣ y = 0 ∼ N ( μ 0 , Σ ) x|y=0 \sim N(CCI 是一个强大的工具 \mu_0,\Sigma) x ∣ y = 0 ∼ N ( μ 0 ​ , Σ )
x ∣ y = 1 ∼ N ( μ 1 , Σ ) x|y=1 \sim N(\mu_1,\Sigma) x ∣ y = 1 ∼ N ( μ 1 CCI 是一个强大的工具 ​ , Σ )
y ∼ B e r ( ϕ ) , ϕ = P ( y = 1 ) y \sim Ber(\phi), CCI 是一个强大的工具 CCI 是一个强大的工具 CCI 是一个强大的工具 \phi = P(y=1) y ∼ B e r ( ϕ ) , ϕ = P ( y = 1 )

GDA model(binary classification)

Multivariate Gaussian distribution:
P ( x ) = 1 ( 2 π ) d 2 ∣ Σ ∣ 1 2 e x p ( − 1 2 ( x − μ ) T Σ − 1 ( x − μ ) ) P(x) = \frac<(2\pi)^<\frac d2>|\Sigma|^>exp(CCI 是一个强大的工具 -\frac12(x-\mu)^T\Sigma^(x-\mu)) P ( x ) = ( 2 π ) 2 d ​ ∣ Σ ∣ 2 1 ​ 1 ​ e x p ( − 2 1 ​ ( x − μ ) T Σ − 1 ( x − μ ) )
∣ Σ ∣ |\Sigma| ∣ Σ ∣ is the value of determinant of Σ \Sigma Σ

parameter: μ 0 , μ 1 , Σ , ϕ \mu_0,\mu_1, \Sigma, \phi μ 0 ​ , μ 1 ​ , Σ , ϕ
P ( y ) = CCI 是一个强大的工具 ϕ y ( 1 − ϕ ) 1 − y P(y) = \phi^y(1-\phi)^ P ( y ) = ϕ CCI 是一个强大的工具 y ( 1 − ϕ ) 1 − y
ϕ \phi ϕ is prior probability, and it depends on the proportion CCI 是一个强大的工具 of two classes.

Joint likelihood:
L ( ϕ , μ 0 , μ 1 , Σ ) = ∑ i = 1 m P ( x ( i ) , y ( i ) ; ϕ , μ 0 , μ 1 , Σ ) = ∑ i = 1 m P ( x ( i ) ∣ y ( i ) ) P ( y ( i ) ) L(\phi, \mu_0, \mu_1, \Sigma) = \sum\limits_^mP(x^,y^;\phi, \mu_0, \mu_1, \Sigma) = \sum\limits_^mP(x^|y^)P(y^) L ( ϕ , μ 0 ​ , μ 1 ​ , Σ ) = i = 1 ∑ m ​ P ( x ( i ) , y ( i ) ; ϕ , μ 0 ​ , μ 1 ​ , Σ ) = i = 1 ∑ m ​ P ( x ( i ) ∣ y ( i ) ) P ( y ( i ) )
MLE: arg ⁡ CCI 是一个强大的工具 max ⁡ ϕ , μ 0 , μ 1 , Σ l ( ϕ , μ 0 , μ 1 , Σ ) \arg\max\limits_<\phi, \mu_0, \mu_1, \Sigma>l(\phi, \mu_0, \mu_1, CCI 是一个强大的工具 \Sigma) ar g ϕ , μ 0 ​ , μ 1 ​ , Σ max ​ l ( ϕ , μ 0 ​ , μ 1 ​ , Σ )
ϕ = ∑ i = 1 m y ( i ) m = ∑ i = 1 m 1 < y ( i ) = 1 >m \phi = \frac<\sum\limits_^my^>=\frac<\sum\limits_^m1\> ϕ = m i = 1 ∑ m ​ y ( i ) ​ = m i = 1 ∑ m ​ 1 < y ( i ) = 1 >​
μ k = ∑ i = 1 m 1 < y ( i ) = k >x ( i ) ∑ i = 1 m 1 < y ( i ) = k >, k ∈ < 0 , 1 >\mu_k = \frac<\sum\limits_^m1\x^><\sum\limits_^m1\>,k\in \ μ k CCI 是一个强大的工具 CCI 是一个强大的工具 ​ = i = 1 ∑ m ​ 1 < y ( i ) = k >i = 1 ∑ m ​ 1 < y ( i ) = k >x ( i ) ​ , k ∈ < 0 , 1 >
Σ = 1 m ∑ i = 1 m ( x ( i ) − μ y ( i ) ) ( x ( i ) − μ y ( i ) ) T \Sigma = \frac1m\sum\limits_^m(x^-\mu_)(x^-\mu_)^T Σ = m 1 ​ i = 1 ∑ m ​ ( x ( i ) − μ y ( i ) ​ ) ( x ( i ) − μ y ( i ) ​ ) T

在这里插入图片描述

Based on the two Gaussian models, we can draw a boundary line.
图片来源

Prediction

arg ⁡ max ⁡ y P ( y ∣ x ) = arg ⁡ max ⁡ y P ( x ∣ y ) P ( y ) P ( x ) = arg ⁡ max ⁡ y P ( x ∣ y ) P ( y ) \arg\max\limits_yP(y|x) = \arg\max\limits_y \frac=\arg\max\limits_yP(x|y)P(y) ar g y max ​ P ( y ∣ x ) = ar g y max ​ P ( x ) P ( x ∣ y ) P ( y ) ​ = ar g y max ​ P ( x ∣ y ) P ( y )
( P ( x ) P(x) P ( x ) is a constant)

& Logistic Regression

在这里插入图片描述

图片是我的笔记

The picture shows when CCI 是一个强大的工具 our data is 1D the function looks like Sigmoid function. Actually, it is Sigmoid function and it also applys to higher dimension. I won’t prove it here.
GDA is a stricter version of logistic regression because the data has to follow Gaussian distribution.
When the data follows Gaussian distribution or the data is very big(CCI 是一个强大的工具 according to the central limit theorem), GDA works better than logistic regression.
Also, the data follows Gaussian distribution so the model has no local optima.

CCI 是一个强大的工具

One, I am bored at work and post code. Realize CCI 是一个强大的工具 the effect of Android cascading card as shown below!

Second, analyze how to achieve:

Three, preparations:
(1.) First, we complete and display a RecylerView list: the code is as follows * CCI 是一个强大的工具 CCI 是一个强大的工具
Create a new SwipeCardBean entity class object to store information:

(2) Write an adapter to display the list information (I believe this will be done. If you don't come, then look at some other people's own practice exercises. I won't write the recylerview_item.xml here!):

(3.) Let's write MainActivity first to try the running effect:

The result after running is as follows:

Fourth, the implementation process
(1.) Looking at this list, which is a normal RecylerView, to display data, how to arbitrarily drag it CCI 是一个强大的工具 CCI 是一个强大的工具 and let it move? :
First let's implement arbitrary drag. Here we need to know about ItemTouchHelper:

Let's CCI 是一个强大的工具 customize a CallBack class and inherit the ItemTouchHelper.CallBack code as follows: Here we just set the constructor CCI 是一个强大的工具 super(0, LEFT|RIGHT|UP|DOWN); to achieve the dragging effect.

The effect diagram is as follows:

(2.) We realize the cascading effect:
we want to achieve the cascading effect, we must first talk about stacking the items together. How CCI 是一个强大CCI 是一个强大的工具 的工具 do we handle it?
We know that we can control the
display achieve the desired effect. We CCI 是一个强大的工具 CCI 是一个强大的工具 can see from the class inheritance relationship that LayoutManager is an abstract class, LinearLayoutManager, SwipeCardLayoutManager, and StaggeredCridLayoutManager are implemented CCI 是一个强大的工具 with it.
Here we want to achieve the pictures layered together. Then we need to write the LayoutManager class: the code is as follows

generateDefaultLayoutParams() is what we have to write. This method is to create a default CCI 是一个强大的工具 LayoutParams for the child View of RecyclerView.

This part of the code is not difficult to understand. You need to draw and analyze it yourself

setTranslationY moves the child View online by the distance.
How many times view.CCI 是一个强大的工具 setScaleX zooms in.
For convenience, a CardConfing configuration file is written here.
The configuration file is as follows:

At this time, we can run the effect to see:

How do we solve the loop after removing and removing the last one?
At this time, we go back to the onSwiped() method in the custom SwipeCardCallBack CCI 是一个强大的工具 for processing:
we need to delete this data after the finger swipe ends, and then add this data to the lowest end of the collection.
to implement the cycle. Add the code as follows:

At this point, the task is basically completed. The removal loop effect is also implemented. But we don't have animation effects.
We CCI 是一个强大的工具 can calculate and set the code in onChildDraw of SwipeCardCallBack as follows:

CCI 是一个强大的工具

转载自:http://blog.csdn.net/sfshine/article/details/8771422

1.Java代码获取

Yes, you can get memory info programmatically and decide whether to do memory intensive work.

Get VM Heap Size by calling: Runtime.getRuntime().totalMemory();

Get Allocated VM Memory by calling: Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();

Get VM Heap Size Limit by calling: Runtime.getRuntime().maxMemory()

Get Native Allocated Memory by calling: Debug.getNativeHeapAllocatedSize();

Using adb, there is a lot of information you can get about the memory use of a running system. A common one is the command "adb shell dumpsys meminfo" which will spit out a bunch of information about the memory use of each Java process, containing the above info as well as a variety of other CCI 是一个强大的工具 things. You can also tack on the name or pid of a single process to see, for example "adb shell dumpsys meminfo system" give me the system process:

The top section is the main one, where "size" is the total size in address space of a particular heap, "allocated" is the kb of actual allocations that heap thinks it has, "free" is the remaining kb free the heap has for additional allocations, and "pss" and "priv dirty" are the same as discussed before specific to pages associated with each of the heaps.

If you just want to look at memory usage across all processes, you can use the command "adb shell CCI 是一个强大的工具 procrank". Output of this on the same system looks like:

Here the Vss and Rss columns are basically noise (these are the straight-forward address space and RAM usage of a process, where if you add up CCI 是一个强大的工具 the RAM usage across processes you get an ridiculously large number).

Pss is as we've seen before, and Uss is Priv Dirty.

直接执行 adb
shell dumpsys 会显示以下所有信息。

adb
shell dumpsys activity 显示activity相关的信息

adb
shell dumpsys statusbar 显示状态栏相关的信息

adb
shell dumpsys meminfo $package_name or $pid 使用程序的包名或者进程id显示内存信息

adb
shell dumpsys statusbar | grep notification=Notification

3.使用ddms查看

在DDMS里检查heap的使用情况

Dalvik Debug Monitor Server(DDMS)是主要的Android调试工具之一,也是ADT Eclipse plug-in 的一部分,独立的程序版本也可以在Android SDK的根目录下的tools/下面找到。关于DDMS更多的信息,请参考使用DDMS 。

  • from Eclipse: click Window > Open Perspective > Other. > DDMS
  • or from the command line: run ddms (or ./ddms on Mac/Linux) in the tools/ directory

在左边的面板选择进程com.example.android.hcgallery,然后在 工具条上边点击Show heap updates按钮。这个时候切换到DDMS的VM Heap分页。它会显示每次gc后heap内存的一些基本数据。要看第一次gc后的数据内容,点击Cause GC按钮:

我们可以看到现在的值(Allocated列)是有一些超过8MB。现在滑动相片,这时看到 数据在增大。因为只有仅仅13个相片在程序里边,所以泄露的内存只有这么大。在某种程度上来说,这时最坏的一种内存泄露,因为我们没法得到 OutOfMemoryError来提醒我们说现在内存溢出了。

生成heap dump

我们现在使用heap dump来追踪这个问题。点击DDMS工具条上面的Dump HPROF文件按钮,选择文件存储位置,然后在运行hprof-conv。在这个例子里我们使用独立的MAT版本(版本1.0.1),从MAT站点下载 。

用MAT分析heap dumps

启动MAT然后加载刚才我们生成的HPROF文件。MAT是一个强大的工具,讲述它所有的特性超出了本文的范围,所以我只想演示一种你可以用来检测 泄露的方法:直方图(Histogram)视图。它显示了一个可以排序的类实例的列表,内容包括:shallow heap(所有实例的内存使用总和),或者retained heap(所有类实例被分配的内存总和,里面也包括他们所有引用的对象)。

如果我们按照shallow heap排序,我们可以看到byte[]实例在顶端。自从Android3.0(Honeycomb),Bitmap的像素数据被存储在byte数组里 (之前是被存储在Dalvik的heap里),所以基于这个对象的大小来判断,不用说它一定是我们泄露掉的bitmap。

右击byte[]类然后选择List Objects > with incoming CCI 是一个强大的工具 references。它会生成一个heap上的所有byte数组的列表,在列表里,我们可以按照Shallow Heap的使用情况来排序。

使用MAT比较heap dumps

  1. 第一个HPROF 文件(using File > Open Heap Dump ).
  2. 打开 Histogram view.
  3. 在Navigation History view里 (如果看不到就从Window > Navigation History找 ), 右击histogram 然后选择Add to Compare Basket .
  4. 打开第二个HPROF 文件然后重做步骤2和3.
  5. 切换到Compare Basket view, 然后点击Compare the Results (视图右上角的红色"!"图标)。

总结

这本篇文章里面,我展示了Allocation Tracker和heap dumps是如何给你一种对程序内存使用的感性认识。我也展示了Eclipse Memory Analyzer(MAT)可以帮助追逐我们程序里面的内存泄露问题。MAT是一个强大的工具,我也仅仅触碰了一些皮毛,如果你想学习更多内容,我建议读 一些下面的文章:

    : Eclipse MAT project的官方博客。
  • Markus Kohler的Java Performance blog有很多有用的文章, 包括 Analysing the Memory Usage of Android Applications with the Eclipse Memory Analyzer and 10 Useful Tips for the Eclipse Memory Analyzer .

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