xtest

, Outlayer) # Add the Links to Neural network fnn.addconnection (in_to_hidden0) fnn.addconnection (hidden0_to_out) # Make Neural network come into effect fnn.sortmodules () Building Data sets We choose 2 input 1 output, 80% for training, 20% for forecasting # definite the dataset as two input, one output DS = Superviseddataset (2,1) # Add data element to the dataset for I in Np.arange (199): ds.addsample ([u[i],y[i]],[y[i+1]]) # You can get your input/output this way X = ds[' Inp

Words:word_freqs[word] + = 1 Num_recs + = 1print (' Max_len ', maxlen) print (' Nb_words ', Len (WORD_FREQS) # # Preparation Data Max_features = 2000max_sentence_length = 40vocab_size = Min (max_features, Len (word_freqs)) + 2wor D2index = {X[0]: i+2 for I, X in Enumerate (Word_freqs.most_common (max_features))}word2index["PAD"] = 0word2index["UNK"] = 1index2word = {v:k for k, V in word2index.items ()}x = Np.empty (num_recs,dtype=list) y = Np.zeros (num_recs) I=0with open (' tr Ain_data.txt ',

: http://blog.csdn.net/a1015553840/article/details/510851291.sign functionfunction S = sign (x) % calculation Sign[m,n] = size (x); For i = 1:m, for j = 1:n; If X (i,j) 2. Calculation of regularization linear regression function lgwithregularizationfunction Wreg = lgwithregularization (X,Y,LAMBDA) [m,n] = size (X); WREG = INV (x ' * x + lambda * Eye (n)) * x ' * y;% regularization linear regression solution end3. Error calculation function Error01 (Note that 0/1 error is used here)fun

summary: http://blog.csdn.net/a1015553840/article/details/510851291.sign functionfunction S = sign (x) % calculation Sign[m,n] = size (x); For i = 1:m, for j = 1:n; If X (i,j) 2. Calculation of regularization linear regression function lgwithregularizationfunction Wreg = lgwithregularization (X,Y,LAMBDA) [m,n] = size (X); WREG = INV (x ' * x + lambda * Eye (n)) * x ' * y;% regularization linear regression solution end3. Error calculation function Error01 (note. 0/1 error is used here

all weights and intercepts "' Self.nnforward" (t heta,x,y) i= self.numlayers-1 grad = np.empty (0) while i>0: #get the Gradi ent of one Layer gwb=self.alllayers[i].layergradient () #backpropa Gate the error Terms self.alllayers[i-1].delta=self.alllayers[i].backpropagate () GRA D=np.hstack ((Gwb.ravel (), grad)) I=i-1 #get The gradient of the Firs T hidden layer gwb=self.alllayers[0].layergradient () Grad=np.hstack ((Gwb.ravel (), grad)) return grad def costfunc (self

In some cases, we need to use CB or VC to write a DLL and call VB. Here we particularly emphasize the use of standard data types, that is to say, to write in C ++, the best way to export a function is to use standard int, Char, long, double... standard Type and Their pointers. Many people have stressed this issue. However, I recently encountered a DLL that requires VB to pass the structure to CB, and then the data is returned to the structure. Typedef struct

code:Code:#! /Usr/bin/Python#-*-Coding: UTF-8 -*- # Name: smartmouse. py# Useage:#1) record the mouse track# Python smartmouse. py-r #2) replay the mouse track# Python smartmouse. py-P ## Coded by xiooli #2009.10.17 Import xlib. display as DSImport xlib. X as XImport xlib. Ext. xtest as xtest Class mouse ():'''Mouse class which contains couple of mouse Methods '''Def _ init _ (Self ):Self. Display = Ds. Di

In a stored procedure, if the total number of statistics records will seriously affect the efficiency of the same query conditions the total number of records must be the same, so if the first page to save the total number of records, click on the other page when the command storedThe process no longer counts the total number of records will greatly improve system performance. The test results show that each time the total number of records used Row_number () and not using the row_number () diff

, color by classPlt.scatter (X[y = = 1, 0], X[y = = 1, 1], color= "#EE3D34", marker= "X")Plt.scatter (X[y = = 0, 0], X[y = = 0, 1], color= "#4458A7", marker= "O") "' ?? Next, let's divide the data into training sets and test sets . The training set is used to develop and optimize the model. The test set is completely detached until the finished model is finally run on this. Having a test set allows you to run a good estimate of the model outside of the data that yo

Table structure: Copy Code code as follows: CREATE TABLE [dbo]. [Xtest] ( [ID] [bigint] IDENTITY (1,1) not NULL, [XName] [nvarchar] (m) NULL, [Xdemo] [nvarchar] () NULL, CONSTRAINT [pk_xtest] PRIMARY KEY CLUSTERED ( [ID] ASC With (Pad_index = off, Statistics_norecompute = off, Ignore_dup_key = off, Allow_row_locks = on, allow_page_locks = ON) O N [PRIMARY] ) on [PRIMARY] Insert 500,000 data: Copy Code cod

Table Structure:Copy codeThe Code is as follows:Create table [dbo]. [Xtest] ([ID] [bigint] IDENTITY (1, 1) not null,[XName] [nvarchar] (50) NULL,[XDemo] [nvarchar] (500) NULL,CONSTRAINT [PK_xtest] PRIMARY KEY CLUSTERED([ID] ASC) WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]) ON [PRIMARY] Insert 0.5 million data records:Copy codeThe Code is as follows:Declare @ I int

on the sig variable); thenBoxRotate; # rotating elif (sigThis = sigLeft); thenBoxLeft; # moving the left column of elif (sigThis = sigRight); thenBoxRight; # shift a column to the right of elif (sigThis = sigDown); thenBoxDown; # Drop a row of elif (sigThis = sigAllDown); thenBoxAllDown; # Drop to the end of fidone # kill-$ sigDown $ BoxDown # Drop a row of done} # BoxMove (y, x) to test whether to move the square in the movement to the position (x, y). If 0 is returned, yes. 1 cannot functionB

Some time ago, I tried the xpage function in domino8.5. However, browsing xsp files may not be accessible by the browser for a long time, so there is very little information about xpage on the network, the answer was lost. Today, we accidentally discovered a solution to this problem. The following describes the complete example. 1. Create a test form in the test database and fill in several fields at will. For example: 2. Create a document based on this form to display data later. 3.

; #旋转 Elif ((Sigthis = = Sigleft)); Then Boxleft; #左移一列 Elif ((Sigthis = = sigright)); Then Boxright; #右移一列 Elif ((Sigthis = = Sigdown)); Then Boxdown; #下落一行 Elif ((Sigthis = = Sigalldown)); Then Boxalldown; #下落到底 Fi Done #kill-$sigDown $$ Boxdown #下落一行 Done } #BoxMove (y, x), test whether the moving block can be moved to the position (x, y), return 0, 1 can not function Boxmove () { Local j I x y

;" $ sigExit While (1 )) Do # Set the number of cycles based on the current speed level iLevel For (I = 0; I Do Sleep 0.02 SigThis = $ sig Sig = 0 # Determine whether the signal is received based on the sig variable If (sigThis = sigRotate); then BoxRotate; # rotate Elif (sigThis = sigLeft); then BoxLeft; # Move a column left Elif (sigThis = sigRight); then BoxRight; # shift one column to the right Elif (sigThi

Look at the code first:Code One:#!/usr/bin/python#coding:utf8x = def test ():p rint ' x= ', xtest ()Code One execution Result:x = 20We have a slight modification on the basis of code one.Code two:x = def test ():p rint ' x= ', xx = 2 print ' Change X to ', Xtest ()Code Two execution results:X=traceback (most recent): file "D:\Demo\testGlobal.py", line to Parsing the problem: we tried to reassign X to 2 in

Processing Display and game ProcessesFunction RunAsDisplayer (){Local sigThisInitDraw# Mount various signal processing functionsTrap "sig = $ sigRotate;" $ sigRotateTrap "sig = $ sigLeft;" $ sigLeftTrap "sig = $ sigRight;" $ sigRightTrap "sig = $ sigDown;" $ sigDownTrap "sig = $ sigAllDown;" $ sigAllDownTrap "ShowExit;" $ sigExitWhile (1 ))Do# Set the number of cycles based on the current speed level iLevelFor (I = 0; I DoSleep 0.02SigThis = $ sigSig = 0# Determine whether the signal is receive

input sample x values in the range of 0 to 1, then, the noise items are superimposed like the training set to generate the target output value T. We fit 11 training set samples with polynomials from level 1 to level 10, and then use the fitting model to test on the test set. In this way, we can get the error size of the model in the training set and test set. We use mean square error to define the error size, which is the sum of squares of the residual and divided by the number of samples. In t

) : Print("it's converging.") Print("####################") BreakLasterrormax=Errormax#test a well-trained network forIinchRange (5): XTest, Yreal=getsinset () layertmp=sigmoid (Np.dot (xtest,synapselist[0])) forJinchRange (1, Len (synapselist), 1): Layertmp=sigmoid (Np.dot (layertmp,synapselist[j)) Ytest=layertmpPrint("x:") Print(

approximate error of learning will increase.If you choose a smaller k value, which is equivalent to using a training instance in a smaller field to predict, the "learning" approximation error will decrease, and only training instances that are closer to or similar to the input instance will work on the prediction results, while the problem is that the "learning" estimate error will increase, in other words, The decrease of K value means that the whole model becomes complex and easy to fit;Here