predictive policing

I. Overview Surus is the NetFlix Open source UDFs, a data analysis toolbased on pig and hive . Solve the problemSurus functions can solve a variety of problems, such as fractional prediction model , anomaly detection and pattern matching, and so on,Surus can also be used as an assistant tool to improve the ability of big data analysis. Second, the system architecturethe current open source The UDF feature consists of two main features, including scorepmml and robust Anomaly Detection (RAD). Scor

x264 Code Profiling NotesThe basic framework of x264 is still a hybrid coding framework based on predictive + transform, as shown, including: Intra- frame prediction , inter-frame prediction , transformation and quantization , entropy coding , filtering , etc.The following is a series of articles x264 code anatomy:"X264 Code Anatomy (A): Graphic detailed x264 on the Windows platform of the building"x264 Code Anatomy (ii): How to compile the basic fram

) Scale space search for predictive depth information : Most visual inspection methods, such as hog, use the search in the image scale space to discover the target. In the Hod method, you can use the depth information to guide this search process . With in-depth information for predictive assistance, search is more efficient and accurate .Our idea of improving the search process is to propose a method for q

are always connected to a TCP connection when Scoket is ready to send data, so there is a chance to have a valid rating for the request ( prioritization), for example, may reach a higher-priority request during the socket connection. You can also have a better throughput rate (throughput), for example, to reuse a socket that is just available when the connection is opened, and to use a fully available TCP connection. In fact, the traditional TCP pre-connect (pre-connected) and a lot of other op

Business Intelligence product Data mining focuses on solving four types of problems: classification, clustering, correlation, prediction (which will be explained in detail after the four types of questions), while conventional data analysis focuses on solving other data analysis problems, such as descriptive statistics, cross-reporting, hypothesis testing, etc. Data mining is a very clear definition of the kinds of problems it can solve. This is a high degree of induction, the application of dat

the variables of the Yin machine. Prior probability, posterior probability and conditional probability (cancer yin-yang type) A probability of occurring at various times based on historical data or subjective judgments. Posterior probability: Through Bayesian formula, combined with the investigation and other methods to obtain new additional information, the prior probability correction to obtain more realistic probability Conditional probability: The

Several novice programmers won the Kaggle Predictive modeling contest after enrolling for a few days of "machine learning" courses on Coursera for free. The big data talent scare that the industry has made in it--McKinsey is the initiator--has raised expectations and demands for big data and advanced analytics talent, and data scientists have become the sexiest career of the night, with its halo chasing sports stars. Data scientists are portrayed as G

rather a part of a. That is, the above is not "object-oriented", let us change:Classa{privatefinalbb;publica (finalbb) {this.b= b;} Publicvoidask (finalstringquestion) {this.b.answer (this,question);} Publicvoidprocessresult (finalstringanswer) {system.out.println (answer);}} Classb{publicvoidanswer (finalaa,finalstringquestion) {if (Question.equals ("Whatistheanswertolife,theuniverse andeverything? ")) {a.processresult ("42");}} /*** object-oriented mutual invocation * @author [emailprotected]

of the first and two exponential smoothing is the average of the earliest three data, i.e.the tendency of the actual data series is more obvious, the weight coefficient (smoothing coefficient) a Should not take too small, so take a = 0.3. The exponential smoothing value is calculated by calculating the formula once, two times, three times the exponential smoothing value:Calculate the coefficients of nonlinear predictive models at, BT,CT. The current

be particularly useful for situations where the server is in a different logical network. The Equalizer records the response time for each server and selects the fastest one. This is very straightforward, but can cause congestion, because the current response time is not necessarily 1s or 2s.minimum number of connections (Least Connections )--The Least connection equalization algorithm has a data record for each server in the internal load, records the number of connections currently being proc

(heterogenous), such as trading, the node involved in the people, goods, shops and so on; more generally, for example, there are different types of nodes and edges in the knowledge map, and most of the work described above is in homogeneous networks (homogenous) , so understanding the embedding of heterogeneous networks can be helpful in real-world applications.PTE(Predictive text embedding through large-scale heterogeneous text Networks.)The main in

to fail to classify. The common weak classifier can adopt the error rate less than 0.5, such as logistic regression, SVM, neural network.1.4. Generation of multiple classifiersThe classifier can be trained by randomly selecting the data, and a new classifier can be generated by the weights of the training data which is constantly adjusting the error classification.1.5. How to combine multiple weakly classified areasThe integration of basic classifiers generally has a simple majority vote, weigh

F) TRAIN_DFMODEL_AD.DF[S,]TEST_DFS,]#get rid of cust_id.n )])#formulas for generating logistic regressionF 'Defect ~', Paste (n[!n%inch%'defect'],collapse =' + ')))#ModelingModel_full binomial) Summary (model_full)#The model test direction has three kinds of parameters Both,backword,forward#Backword Each test reduces one factor, and ForWord increments one factor at a time#the smaller the value of the AIC, the better the model.Step 'both') Summary (step)5. Test model# using test sets to predict

task to these underlying extensions in C or FORTRAN. Among them, NumPy and scipy are the representatives.NumPy provides a number of effective data structures, such as arrays, and SCIPY provides many algorithms to handle these arrays. Whether it's matrix manipulation, linear algebra, optimization problems, clustering, or even fast Fourier transforms, the Toolbox can meet the requirements.Read-In Data operationsHere we take the page click Data for example, the first dimension attribute is the hou

Review 2 1 0 0 1 We don ' t want to catch every single word this appears in at least one review, because very rare words would increase the SI Ze of the DTM while has little predictive power. So we'll only have keep in our DTM words that appear on least a certain percentage of all reviews, say 1%. This was controlled by the ' sparsity ' parameter in the ' following code, with sparsity = 1-0.01 = 0.99 .The

humans as a priori distribution of the position of the puppy. ） Therefore, we can use the recursive method to get the position distribution of K moment according to the position distribution of the puppy in k-1 time and the GPS observation of k time, which is the core of BF. The calculation of the location of K-time puppies is estimated to consist of two steps, as follows: 1-step: PredictingThis step is to use P (xk−1|y1:k−1) to get P (xk|y1:k−1), note that there is no use of K-time observation

=rand (1,768);[M,n]=sort (k); % Input Output dataInput=data (:, 1:8);Output =data (:, 9); % randomly extracted 500 samples as training samples and 268 samples as forecast samplesInput_train=input (n (1:500),:) ';Output_train=output (n (1:500),:) ';Input_test=input (n (501:768),:) ';Output_test=output (n (501:768),:) '; % of input data normalized[Inputn,inputps]=mapminmax (Input_train); % BP Network training%% Initialize network structureNET=NEWFF (inputn,output_train,10); net.trainparam.epochs=1

The following code is my summary of the predictive Results analysis tool function for two classification problems. The Code has a detailed documentation description. So you can look at the code directly. #-*-Coding:utf-8-*-from __future__ import print_function from __future__ Import Division import NumPy as N P Import pandas as PD import Matplotlib.pyplot as Plt from sklearn.metrics import Roc_curve, AUC from SKL Earn.metrics import Confusion_matr

example to understand this algorithm again. Suppose your friend asked you to solve a riddle. There will only be two results: you untie it or you don't untie it. Imagine you have to answer a lot of questions to find out what you are good at. The results of this study will look like this: Suppose the topic is a trigonometric function in the 10 grade, and you have a 70% chance to solve the problem. However, if the topic is a five-year history question, you are only 30% likely to answer correctly.