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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

; } } Private Static voidCount (ObjectState ) { varresult = State asWorkerasyncresult; Result. R= result. A +result. B Thread.Sleep ( +); result.completedsynchronously=false; Result.iscompleted=true; ((ManualResetEvent) result. AsyncWaitHandle). Set (); if(Result.callback! =NULL) {result.callback (result); } } } Public intNUM1 {Get;Set; } Public intNum2 {Get;Set; } PublicIAsyncResult Beginwork (AsyncCa

the y that makes P (Y | x) the largest, and use a Bayesian to get it:P (Y | x) ∝ P (y) * p (x | Y)In natural language, this is the possibility of word segmentation (word string) multiplied by the possibility that the word string will generate our sentence. We can further easily see that p (x | Y) can be considered as a constant equal to 1, because any hypothetical word splitting method generates sentences accurately (you only need to discard the boundary between Word Segmentation ). So we turn

= (B * P1)-(A * Q1)If S> 0 thenX = p1ElseX = (0-P1) +End ifEuler = xExit FunctionError2:Euler = 0End Function Public Function mult (byval X as long, byval P as long, byval M as long) as longY = 1On Error goto error1Do While P> 0Do While (p/2) = (p/2)X = (x * X) mod mP = p/2LoopY = (x * Y) mod mP = p-1LoopMult = yExit FunctionError1:Y = 0End Function Public Function isprime (lngnumber as long) as BooleanDim lngcount as longDim lngsqr as longDim X as long Lngsqr = sqr (lngnumber) 'Get the int squ

Report: http://starforever.blog.hexun.com/2097115_d.html Because there are only four squares, the order and placement direction (horizontal or vertical) of each square are enumerated. The placement method is only the six basic modes given by the question, calculate the minimum area in different modes and update the optimal solution. 4th and 5 are essentially the same ., The minimum area for different modes is as follows: Set W1, W2, W3,

). Because Workerasyncresult.count executes in a new thread, the state of the new thread cannot be accurately learned outside the thread. To satisfy the need for external threads to synchronize with new threads, add the Endwork method to the Newworker, with the parameter type IAsyncResult. To call the Endwork method, you should pass in the Workerasyncresult object that Beginwork gets, and after the Endwork method gets the Workerasyncresult object, Call the WorkerAsyncResult.AsyncWaitHandle.WaitO

I encountered a problem on Friday: There are three widgets on QT. W1 is at the bottom layer, W2 is at the middle layer, and W3 is at the top layer. The W2 handle is handed over to other interfaces for Video Stream playing. We expect to overlay the transparent layer W3 on W2, and draw some non-filled rectangles to mark some content of

indicates vertical merge *@returnreturns the merged BufferedImage object *@throwsIOException*/ Public StaticBufferedImage Mergeimage (Booleanishorizontal, BufferedImage img1, BufferedImage img2)throwsIOException {intW1 =img1.getwidth (); intH1 =img1.getheight (); intW2 =img2.getwidth (); intH2 =img2.getheight (); //reading RGB from a picture int[] Imagearrayone =New int[W1 *H1]; Imagearrayone= Img1.getrgb (0, 0, W1, H1, Imagearrayone, 0, W1);//scan the RGB of each pixel in the image

is very long. In this regard, a similar simplification has the Naïve Bayes hypothesis, which assumes that all features are independent of each other.We cite a natural language example to illustrate the simplification of computational complexity, assuming that s represents a meaningful sentence, consisting of a sequence of words w1,w2,..., WN, n is the length of the sentence. Now, we want to know the likelihood of S appearing in the text, which is mat

, and the probability of the whole sentence is the product of the probability of each word appearing. These probabilities can be obtained by the number of simultaneous occurrences of n words directly from the expected statistics. Commonly used is two yuan of Bi-gram and ternary tri-gram.Detailed Introduction--N-gram thoughtBefore introducing the N-gram model, let's start with a Shannon game (Shannon). We give a word and guess what the next word is. What do you think of the next word when I say t

DWORD.Ask 2014-12-05 11:22:23OhA. 2014-12-05 11:22:32struct A {Double D;char c;DWORD DW;Longlong ll;WORD W1;WORD W2;};A. 2014-12-05 11:22:56You think this is 48 bytes.A. 2014-12-05 11:23:05It's still 32.Ask 2014-12-05 11:23:21Ah. Why, then?2014-12-05 11:23:47Ask 2014-12-05 11:23:47I've got the wrong idea.A. 2014-12-05 11:24:10struct A {Double D;Char C1;BYTE B1;DWORD DW;Longlong ll;WORD W1;WORD W2;};A. 2014

cyclic neural networks (recurrent neural networks,rnn) using acoustic eigenvector context, and their special case-length memory networks (Long short-term memory,lstm). Language model A language model can represent the probability of a word sequence occurring. The common language model in speech recognition is n-ary Grammar (N-gram), that is, the probability of the occurrence of N-word before and after statistics. The N-ary Grammar assumes that the probability of a word appearing is only related

> 0 Then x = P1 Else x = (0-P1) + A End If Euler = X Exit Function Error2: Euler = 0 End Function Private Function mult (ByVal x as Long, ByVal p as long, ByVal m as Lon g) as Long y = 1 On Error GoTo Error1 Do While P > 0 Do while (P/2) = (P \ 2) x = (x * x) Mod m p = P/2 Loop y = (x * y) Mod m p = p-1 Loop mult = y Exit Function Error1: y = 0 End Function Private Function IsPrime (Lngnumber as Long) as Boolean Dim Lngcount as Long Dim Lngsqr as Long Dim x as Long LNGSQR = SQR (lngnumber)

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