intuit w2

theX_min, X_max = x[:, 0].min ()-1, x[:, 0].max () + 1 theY_min, Y_max = x[:, 1].min ()-1, x[:, 1].max () + 1 +xx, yy =Np.meshgrid (Np.arange (X_min, X_max, h), - Np.arange (Y_min, Y_max, h)) theZ = Np.dot (Np.c_[xx.ravel (), Yy.ravel ()], W) +bBayiz = Np.argmax (z, Axis=1) theZ =Z.reshape (Xx.shape) theFig =plt.figure () -Plt.contourf (xx, yy, Z, Cmap=plt.cm.spectral, alpha=0.8) -Plt.scatter (x[:, 0], x[:, 1], c=y, s=40, cmap=plt.cm.Spectral) the Plt.xlim (Xx.min (), Xx.max ()) the Plt.ylim (Y

Bit-domain structure to save some memory space, but improper use will produce race conditions, resulting in program exceptions, the following brief analysis of the cause of the error and solution.First, define a simple bit field structure.+structbit_filed {+ Unsigned A:1;+ unsigned B:1;+ unsigned C:1;+ unsigned d:1;+ unsigned e:1;+ unsigned f:1;+ unsigned g:1;+ unsigned h:1;+} val;+++intTest_bitfield1 (void)+{+ VAL.A =1;+return 0;+}++intTest_bitfield2 (void)+{+ val.b =1;+return 0;+}Then disassem

Statistical language models, statistical Language model. 1. Introduction Natural language has context-related characteristics, so the statistical language model is to establish a language model for this correlation feature. It is the foundation of all natural language processing today.To judge whether a sentence is reasonable, it is measured by probability.A sentence is a sequence of words, assuming that with S, s=w1,w2,..., wn s=w_1, W_2, ..., w_n,

had only the Caffe, Torch, Theano and tensorflow these deep learning frameworks to use; but this year, on this basis, we have added a new Caffe2, Pytorch, TensorFlow, Paddlepaddle, CNDK, mxnet and so on a series of new framework, is called "Blossom." The most commonly used frameworks today are pytorch and TensorFlow, while Caffe and Caffe2 are second. The key point of the deep learning Framework is: (1) Easy to build large computer graphics; (2) It is easy to calculate the gradient in computer

high-quality software requires the development of software for each engineer to complete the work of high quality. The Personal Software Process (PSP) and the team Software process (TSP) are specifically designed to optimize and standardize the work of individuals and teams, improve quality by enabling individuals and teams to use predefined standard processes to build measurable goals, track goal completion, and integrate with CMMI To build a high-performance team to optimize the entire organi

type we mentioned earlier. So let's change the data type and see what happens. Experiment Three: var a = 3578;var a= A;alert (A1);//3578a=8735;alert (A1);//3578 Experiment Four: var a = {W1:2,w2:3}var a1= A;alert (A1);//{w1:2,w2:3}A1.w1= "Deng";alert (a);//{W1: "Deng", W2:3} In both sets of experiments, we replaced the data types with the number type and object

, such as machine translation, speech recognition to obtain a number of candidates, you can use the language model to choose the best possible knot Fruit. It can also be used in other tasks in NLP.The formal description of a language model is given a string, which is the probability P (w1,w2,..., wt) of natural language. W1 to WT, in turn, to denote each word in this sentence. There is a very simple corollary to this: P (w1,

. The stable match requirement cannot occur under an existing match: in Match (M1,W1) and (M2,W2) two pair match, M1 compared to W1 prefer W2, and W2 prefer M2 compared to M1, otherwise there will be derailment or elope situation.In the G-S algorithm, the basic idea is as follows, there are three male and female states: free, dating, married. Make a match accordi

=-J * IJ * k = I =-K * jK * I = J =-I * k The hypothetical device has two quartile numbers: Q1= W1+X1I + Y1J+ Z1KQ2= W2+X2I + Y2J+ Z2K The legal meaning of adding four elements is as follows: Q1 + q2 = (W1 + W2) + (X1 + x2) I + (Y1 + y2) J + (Z1 + Z2) k The theory of multiplication of four elements is as follows: Q1 * q2 = (W1 * W2-X1 * x2-Y1 * Y2-z1 * Z2) +(W1

label: backpack 超大背包问题：有n个重量和价值分别为w[i]和v[i]的物品，从这些物品中挑选总重量不超过W的物品，求所有挑选方案中价值总和的最大值。其中，1 ≤ n≤ 40， 1≤ w[i], v[i]≤ 10^15, 1≤ W≤ 10^15. 这个问题给人的第一感觉就是普通的01背包。不过，看完数据范围会发现，这次价值和重量都可以是非常大的数值，相比之下n比较小。使用DP求解背包为题的复杂度是O(nW)，因此不能用来解决这个问题。此时我们应该利用n比较小的特点来寻找其他方法。 挑选物品的方案总共有2^n种，所以不能直接枚举，但是如果将物品分成两半再枚举的话，由于每部分最多只有20个，这是可行的。我们把前半部分中的挑选方法对应的重量和价值总和记为w1、v1，这样在后半部分寻找总重w2≤ W - w1时使v2最大的选取方法即可。 因此，我们要思考从枚举得到的（w2，v2）集合中高效寻找max{

language processing master, successfully solved the natural language processing problem using mathematical methods ). At that time, Janik took an academic vacation (sabbatical leave) at IBM and led a group of outstanding scientists to use computers to handle human language problems. The statistical language model was proposed at that time. For example, in many fields that involve natural language processing, such as machine translation, speech recognition, printed or handwritten recognition, sp

intu=Que.front (); Que.pop (); - for(intI=head[u]; i!=-1; I=Edge[i].next) { to intv=edge[i].v; + if(Edge[i].cap d[v]>d[u]+edge[i].cost) { -d[v]=d[u]+Edge[i].cost; thepre[v]=i; * if(!Vis[v]) { $vis[v]=1;Panax Notoginseng Que.push (v); - } the } + } Avis[u]=0; the } + returnd[vt]!=INF; - } $ inttot; $ intMCMF () { - intres=0, mxflow=0; - while(SPFA ()) { the intflow=inf,cost=0; -

to solve, because we need to try all the combinations, and if there is a large amount of data, ---- Computers are also hard to handle quickly. --- With the greedy algorithm, this algorithm is often near optimal. The core of this algorithm is to "Bite the biggest bite" until the goal is achieved or exceeded. --- -- 1. The first trick is to insert some empty dummy warehouses in the table. If you need to select at most n times, n-1 dummy warehouses. Insert stock Select-1, '200', '1970-1-1 ', 10561

) VALUES (2, ' 2015-01-02 ', 25); insertintoweather (id,date,temperature) VALUES (3, ' 2015-01-03 ', 20); Insertinto Weather (id,date,temperature) VALUES (4, ' 2015-01-04 ', 30); INSERT INTOWeather (id,date,temperature) VALUES (5, ' 1998-10-31 ', 29 );insertintoweather (id,date,temperature) VALUES (6, ' 1998-11-01 ', NBSP;30); 5. Solving SQL Before giving the correct SQL statement, give me a wrong way to write it: SELECT W2. Idfrom Weather W1, Weathe

.name = "A2"; - the +Wife W1 =NewWife (); AW1.setage (18); the w1.getage (); + -Wife W2 =NewWife (); $W2.setage (19); $ w2.getage (); - - the - System.out.println (h1.age);Wuyi System.out.println (W2.getage ()); the - } Wu}Change: Husband

time, this problem also fully embodies one-dimensional optimization of space superiority#include #include#include#include#include#include#include#includestring>#includeusing namespacestd;Const intn=155;Const intm= the;intv[n],w1[n],w2[n],c[n],dp[m][m],n,m1,m2;intMain () {scanf ("%d%d%d",n,m1,m2); for(intI=1; i) scanf ("%d%d%d%d",w1[i],w2[i],c[i],V[i]); for(intI=1; i) { if(!C[i]) for(intj=w1[i];j)

, ----computers are hard to handle quickly. ---So with the "greedy algorithm", this algorithm is often calculated to be almost optimal. The core of this algorithm is to "bite the biggest mouthful" until it reaches or exceeds the target. --- --1. The first trick is to insert some empty dumb warehouses into the table, and if you need to select up to n times, add n-1 to the dummy warehouse Insert Stock Select-1, ' 10561122 ', ' 1900-1-1 ', 0,0 Union Select-2, ' 10561122 ', ' 1900-1-1 ', 0,0

SQL Server Paging Query1.Select top article WHERE ID not in (SELECT top 45000 ID from Article ORDER by year DESC, ID DESC) Order by year D Esc,id DESC The simplest and most common2.SELECT * FROM (select top 45030 * from Article ORDER by year DESC, ID DESC) F ORDER by F.year ASC, F.id desc) s ORDER by S.year desc,s.id DESC3.SELECT * FROM article W1,(SELECT TOP ID from(SELECT TOP 50030 ID, year from article ORDER by year DESC, id desc) W ORDER by w.year ASC, w.id ASC)

goal is achieved or exceeded.----- 1. The first trick is to insert some empty dummy warehouses in the table. If you need to select at most n times, n-1 dummy warehouses.Insert stockSelect-1, '200', '1970-1-1 ', 10561122 unionSelect-2, '20180101', '20180101', 10561122-- Select-3, '2014-1-1-1 ', 1900----GoCreate view pickcombosAsSelect distinct (w1.qty + w2.qty + w3.qty) as totalpick, Case when w1.id Case when w2